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WP44: diversity with both Jaccard and rescaled semantic dissimilarities; semantic atlas results

Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
Simon-Pierre Boucher committed (Nov 4, 2026) parent 37cde26 Browse files

87 changed files +12,877 −1,540

87 changed files
Unified Split
modified bibliometrics/04_diversity_cagr.py +22 −19
@@ -20,28 +20,31 @@ def main():
20 "cagr_baseline": (b1 / b0) ** (1 / 4) - 1 if b0 > 0 else None, "cagr_post": (p1 / p0) ** (1 / 2) - 1 if p0 > 0 else None, 20 "cagr_baseline": (b1 / b0) ** (1 / 4) - 1 if b0 > 0 else None, "cagr_post": (p1 / p0) ** (1 / 2) - 1 if p0 > 0 else None,
21 "share_2018": b0 / piv.loc[2018].sum(), "share_2022": b1 / piv.loc[2022].sum(), "share_2025": p1 / piv.loc[2025].sum()}) 21 "share_2018": b0 / piv.loc[2018].sum(), "share_2022": b1 / piv.loc[2022].sum(), "share_2025": p1 / piv.loc[2025].sum()})
22 pd.DataFrame(rows).to_csv(f"{out}/cagr_by_category.csv", index=False) 22 pd.DataFrame(rows).to_csv(f"{out}/cagr_by_category.csv", index=False)
23− # similarity between categories: semantic centroids if available, else co-classification Jaccard 23+ # two dissimilarity sources, both reported: (a) co-classification Jaccard (d = 1 - J), (b) semantic centroids
24− sim = None 24+ # (SPECTER2 cosine; raw centroid cosines are all close to 1, so d is min-max rescaled over off-diagonal pairs)
25+ co = pd.read_csv(f"{R}/results/graph/category_cooccurrence_yearly.csv"); tot = co.groupby(["cat_a", "cat_b"]).n.sum()
26+ n_c = cc.groupby("category_id").n_multilabel.sum(); simJ = np.eye(len(cats))
27+ for (x, y), n in tot.items():
28+ if x in cats and y in cats:
29+ j = n / (n_c[x] + n_c[y] - n); simJ[cats.index(x), cats.index(y)] = simJ[cats.index(y), cats.index(x)] = j
30+ sources = [("co-classification Jaccard", simJ)]
25 for per in ("post", "baseline"): 31 for per in ("post", "baseline"):
26 p = f"{R}/results/semantic/category_similarity_{per}.csv" 32 p = f"{R}/results/semantic/category_similarity_{per}.csv"
27 if os.path.exists(p): 33 if os.path.exists(p):
28− s = pd.read_csv(p, index_col=0); sim = s.reindex(index=cats, columns=cats).fillna(0).values; np.fill_diagonal(sim, 1); src = f"semantic centroids ({per})"; break 34+ s_ = pd.read_csv(p, index_col=0).reindex(index=cats, columns=cats).fillna(0).values; off = ~np.eye(len(cats), dtype=bool)
29− if sim is None: 35+ d_ = 1 - s_; d_[off] = (d_[off] - d_[off].min()) / (d_[off].max() - d_[off].min() + 1e-12); np.fill_diagonal(d_, 0)
30− co = pd.read_csv(f"{R}/results/graph/category_cooccurrence_yearly.csv"); tot = co.groupby(["cat_a", "cat_b"]).n.sum() 36+ sources.append((f"semantic centroids ({per}, min-max rescaled)", 1 - d_)); break
31− n_c = cc.groupby("category_id").n_multilabel.sum(); sim = np.eye(len(cats)) 37+ rng = np.random.default_rng(44); drows = []; crows = []
32− for (x, y), n in tot.items(): 38+ for src, sim in sources:
33− if x in cats and y in cats: 39+ d = 1 - sim
34− j = n / (n_c[x] + n_c[y] - n); sim[cats.index(x), cats.index(y)] = sim[cats.index(y), cats.index(x)] = j 40+ for y in piv.index:
35− src = "co-classification Jaccard" 41+ n = piv.loc[y].values.astype(float); N = n.sum(); p = n / N
36− d = 1 - sim; rng = np.random.default_rng(44); drows = []; crows = [] 42+ rs = float(p @ d @ p); lc = float(1 / (p @ (sim @ p))) if (p @ (sim @ p)) > 0 else None
37− for y in piv.index: 43+ bs = rng.multinomial(int(N), p, size=a.B) / N; rs_b = np.einsum("bi,ij,bj->b", bs, d, bs)
38− n = piv.loc[y].values.astype(float); N = n.sum(); p = n / N 44+ drows.append({"year": int(y), "n": int(N), "rao_stirling": rs, "rs_ci_low": float(np.quantile(rs_b, 0.025)), "rs_ci_high": float(np.quantile(rs_b, 0.975)), "leinster_cobbold_q2": lc, "similarity_source": src})
39− rs = float(p @ d @ p); lc = float(1 / (p @ (sim @ p))) if (p @ (sim @ p)) > 0 else None 45+ if src == sources[0][0]:
40− # bootstrap: multinomial resampling of works over categories 46+ for i, c in enumerate(cats):
41− bs = rng.multinomial(int(N), p, size=a.B) / N; rs_b = np.einsum("bi,ij,bj->b", bs, d, bs) 47+ crows.append({"year": int(y), "category": c, "share": p[i], "ci_low": float(np.quantile(bs[:, i], 0.025)), "ci_high": float(np.quantile(bs[:, i], 0.975))})
42− drows.append({"year": int(y), "n": int(N), "rao_stirling": rs, "rs_ci_low": float(np.quantile(rs_b, 0.025)), "rs_ci_high": float(np.quantile(rs_b, 0.975)), "leinster_cobbold_q2": lc, "similarity_source": src})
43− for i, c in enumerate(cats):
44− crows.append({"year": int(y), "category": c, "share": p[i], "ci_low": float(np.quantile(bs[:, i], 0.025)), "ci_high": float(np.quantile(bs[:, i], 0.975))})
45 pd.DataFrame(drows).to_csv(f"{out}/diversity_yearly.csv", index=False); pd.DataFrame(crows).to_csv(f"{out}/category_share_ci_yearly.csv", index=False) 48 pd.DataFrame(drows).to_csv(f"{out}/diversity_yearly.csv", index=False); pd.DataFrame(crows).to_csv(f"{out}/category_share_ci_yearly.csv", index=False)
46 print(pd.DataFrame(drows)[["year", "rao_stirling", "leinster_cobbold_q2"]]) 49 print(pd.DataFrame(drows)[["year", "rao_stirling", "leinster_cobbold_q2"]])
47 50
added literature_synthesis/agent_E/bib_keys.json +100 −0
@@ -0,0 +1,100 @@
1+{
2+ "7124974472": "bick2026rapidadoption",
3+ "4323655724": "kasneci2023chatgptgood",
4+ "2990704537": "wang2019superglue",
5+ "4393138529": "han2024peftsurvey",
6+ "4410551352": "bodnar2025aurora",
7+ "4392947521": "chen2024uni",
8+ "4402220065": "wang2024chief",
9+ "4401397240": "zhang2024biomedgpt",
10+ "4411932262": "binz2025centaur",
11+ "7133220561": "dai2023instructblip",
12+ "7133250524": "schuhmann2022laion5b",
13+ "2995460200": "su2019vlbert",
14+ "4393148714": "mou2024t2iadapter",
15+ "4289785095": "gal2022textualinversion",
16+ "7164501397": "buckley2026textexploit",
17+ "4396599905": "zhang2026lowqualityfusion",
18+ "4288099666": "ho2022cfg",
19+ "4406472463": "zeni2025mattergen",
20+ "4400770292": "bastani2024harmlearning",
21+ "4406728221": "acharya2025agenticsurvey",
22+ "7161734976": "ghareeb2026robin",
23+ "4412709685": "swanson2025virtuallab",
24+ "4407570917": "piao2026agentsociety",
25+ "4413427262": "sapkota2026agentsvsagentic",
26+ "4412203333": "zhang2025agentmemory",
27+ "7130481397": "liu2026clinicalagents",
28+ "2781726626": "haarnoja2018sac",
29+ "2963923407": "fujimoto2018td3",
30+ "2982316857": "vinyals2019alphastar",
31+ "2761873684": "hessel2018rainbow",
32+ "7133226216": "yu2021mappo",
33+ "4409147637": "hafner2025dreamerv3",
34+ "2964083594": "burda2018curiosity",
35+ "2991046523": "zhang2021marloverview",
36+ "2945976633": "rudin2019stopexplaining",
37+ "2439568532": "lipton2018mythos",
38+ "2999615587": "lundberg2020treeexplainer",
39+ "2999309192": "chicco2020mcc",
40+ "3041133507": "zhuang2021transfersurvey",
41+ "2436219157": "bottou2018optimization",
42+ "4382203079": "zeng2023transformerstsf",
43+ "3177318507": "zhou2021informer",
44+ "4385763767": "wen2023tstransformers",
45+ "4406170795": "hollmann2025tabpfn",
46+ "4396821501": "liu2024kan",
47+ "2907492528": "wu2021gnnsurvey",
48+ "3152893301": "zhou2020gnnreview",
49+ "2963399829": "zhang2017mixup",
50+ "2981731882": "barredoarrieta2020xai",
51+ "2969896603": "mehrabi2021biasfairness",
52+ "2670253439": "miller2019explanation",
53+ "4366262984": "ali2023xaitrust",
54+ "4402698709": "slattery2026riskrepository",
55+ "4410609100": "chao2025pair",
56+ "4406152291": "alber2025poisoning",
57+ "7128782475": "draelos2026unsafeanswers",
58+ "4411542009": "moore2025stigma",
59+ "4406063062": "papagiannidis2025raigovernance",
60+ "4406571986": "mcdonald2025hepolicies",
61+ "4366420437": "lo2023rapidreview",
62+ "4384464487": "chan2023students",
63+ "4383312437": "chan2023policyframework",
64+ "4389437528": "baidooanu2023chatgptedu",
65+ "4317910584": "rudolph2023assessment",
66+ "4322617812": "lim2023ragnarok",
67+ "4413447271": "wu2025radfm",
68+ "4415913530": "ding2025titan",
69+ "4392947532": "lu2024conch",
70+ "4385948838": "huang2023plip",
71+ "4411099958": "yan2025panderm",
72+ "7128029160": "tak2026brainiac",
73+ "7119541039": "zhu2026earthfm",
74+ "4384071683": "singhal2023clinical",
75+ "3185341429": "liu2023prompting",
76+ "4319083882": "vandis2023priorities",
77+ "4390730779": "cheng2026agentsdefinitions",
78+ "7133236347": "raza2026trism",
79+ "4407135816": "paschali2025fmradiology",
80+ "4390690043": "zhang2026datacentric",
81+ "4406472599": "zheng2025learningfrommodels",
82+ "4410882873": "schouten2025multimodalmedicine",
83+ "4399790131": "zhai2024overreliance",
84+ "4398243313": "xia2024assessmentreview",
85+ "4409269614": "bittle2025integrity",
86+ "4387355843": "jin2023timellm",
87+ "4393153153": "liu2024timesurl",
88+ "4402727669": "ye2024mplugowl2",
89+ "7148475777": "wang2025gpt5medical",
90+ "7127319731": "lai2026medr1",
91+ "4414991289": "zhang2026dive",
92+ "4414023998": "bandi2025agenticreview",
93+ "4416209823": "abouali2025agenticparadigms",
94+ "7128165235": "allmendinger2026multiagentai",
95+ "4408314709": "hasanzadeh2025biashealthcare",
96+ "7140170686": "wang2026pedagogical",
97+ "7135409568": "collaco2026agentichealthcare",
98+ "3137147200": "gholami2022quantization",
99+ "4361229539": "zhang2023llamaadapter"
100+}
\ No newline at end of file
modified literature_synthesis/agent_E/make_bib_part05.py +12 −5
@@ -52,14 +52,21 @@ def toks(s): return set(re.findall(r"[a-z0-9]+", unicodedata.normalize("NFKD", s
52 def crossref(doi): 52 def crossref(doi):
53 m = json.loads(get(f"https://api.crossref.org/works/{doi}?mailto={MAIL}"))["message"] 53 m = json.loads(get(f"https://api.crossref.org/works/{doi}?mailto={MAIL}"))["message"]
54 auth = [a for a in m.get("author", []) if a.get("family")] 54 auth = [a for a in m.get("author", []) if a.get("family")]
55+ if not auth: # Crossref record without authors: second verified lookup on OpenAlex by DOI
56+ oa = json.loads(get(f"https://api.openalex.org/works/https://doi.org/{doi}?mailto={MAIL}"))
57+ auth = [{"family": a["author"]["display_name"].split()[-1], "given": " ".join(a["author"]["display_name"].split()[:-1])} for a in oa.get("authorships", [])]
58+ m["oa_authors"] = True
55 year = (m.get("issued") or m.get("published") or {}).get("date-parts", [[None]])[0][0] 59 year = (m.get("issued") or m.get("published") or {}).get("date-parts", [[None]])[0][0]
56 return {"type": m.get("type"), "title": m.get("title", [""])[0], "subtitle": (m.get("subtitle") or [""])[0], "authors": [(a.get("family", ""), a.get("given", "")) for a in auth], "container": (m.get("container-title") or [""])[0], 60 return {"type": m.get("type"), "title": m.get("title", [""])[0], "subtitle": (m.get("subtitle") or [""])[0], "authors": [(a.get("family", ""), a.get("given", "")) for a in auth], "container": (m.get("container-title") or [""])[0],
57− "volume": m.get("volume"), "issue": m.get("issue"), "page": m.get("page"), "year": year, "publisher": m.get("publisher"), "doi": m["DOI"], "event": (m.get("event") or {}).get("name")} 61+ "volume": m.get("volume"), "issue": m.get("issue"), "page": m.get("page"), "year": year, "publisher": m.get("publisher"), "doi": m["DOI"], "event": (m.get("event") or {}).get("name"), "oa_authors": m.get("oa_authors", False)}
58 def arxiv(aid): 62 def arxiv(aid):
59− root = ET.fromstring(get(f"https://export.arxiv.org/api/query?id_list={aid}")) 63+ for attempt in range(4): # arXiv asks for >= 3 s between requests and returns an empty feed when throttled
60− ns = {"a": "http://www.w3.org/2005/Atom"}; e = root.find("a:entry", ns) 64+ time.sleep(3.5)
65+ root = ET.fromstring(get(f"https://export.arxiv.org/api/query?id_list={aid}"))
66+ ns = {"a": "http://www.w3.org/2005/Atom"}; e = root.find("a:entry", ns)
67+ if e is not None and e.find("a:title", ns) is not None and not e.find("a:title", ns).text.strip().startswith("Error"): break
68+ else: raise RuntimeError("arXiv lookup failed " + aid)
61 title = re.sub(r"\s+", " ", e.find("a:title", ns).text.strip()) 69 title = re.sub(r"\s+", " ", e.find("a:title", ns).text.strip())
62− if title.startswith("Error"): raise RuntimeError(aid)
63 authors = [a.find("a:name", ns).text for a in e.findall("a:author", ns)] 70 authors = [a.find("a:name", ns).text for a in e.findall("a:author", ns)]
64 year = int(e.find("a:published", ns).text[:4]) 71 year = int(e.find("a:published", ns).text[:4])
65 return {"type": "arxiv", "title": title, "authors": [(n.split()[-1], " ".join(n.split()[:-1])) for n in authors], "year": year, "arxiv": aid, "doi": None} 72 return {"type": "arxiv", "title": title, "authors": [(n.split()[-1], " ".join(n.split()[:-1])) for n in authors], "year": year, "arxiv": aid, "doi": None}
@@ -108,7 +115,7 @@ for wid, spec in W.items():
108 j = len(toks(r["title"]) & toks(m["title"])) / max(1, len(toks(r["title"]) | toks(m["title"]))) 115 j = len(toks(r["title"]) & toks(m["title"])) / max(1, len(toks(r["title"]) | toks(m["title"])))
109 match = "yes" if j >= 0.5 else f"check (jaccard={j:.2f})" 116 match = "yes" if j >= 0.5 else f"check (jaccard={j:.2f})"
110 keys[wid] = key; entries.append(bib(key, m)) 117 keys[wid] = key; entries.append(bib(key, m))
111− ver.append({"key": key, "openalex_id": wid, "doi_or_arxiv": ident, "verified_via": "crossref" if ident.startswith("doi:") else "arxiv", "verified_title": clean(m["title"])[:160], "corpus_title": clean(r["title"])[:160], "match": match, "entry_type": m["type"]}) 118+ ver.append({"key": key, "openalex_id": wid, "doi_or_arxiv": ident, "verified_via": ("crossref+openalex" if (ident.startswith("doi:") and m.get("oa_authors")) else "crossref") if ident.startswith("doi:") else "arxiv", "verified_title": clean(m["title"])[:160], "corpus_title": clean(r["title"])[:160], "match": match, "entry_type": m["type"]})
112 print(key, "|", match, "|", clean(m["title"])[:70]); time.sleep(0.3) 119 print(key, "|", match, "|", clean(m["title"])[:70]); time.sleep(0.3)
113 open(f"{R}/manuscript/references_part05.bib", "w").write("% references_part05.bib — Part V (The Artificial Intelligence Transformation), WP44. Generated by\n% literature_synthesis/agent_E/make_bib_part05.py on 2026-11-03: every entry resolved by one Crossref (DOI) or arXiv lookup;\n% see references_verification_part05.csv. Keys of manuscript/references.bib are not redefined here.\n\n" + "\n".join(entries)) 120 open(f"{R}/manuscript/references_part05.bib", "w").write("% references_part05.bib — Part V (The Artificial Intelligence Transformation), WP44. Generated by\n% literature_synthesis/agent_E/make_bib_part05.py on 2026-11-03: every entry resolved by one Crossref (DOI) or arXiv lookup;\n% see references_verification_part05.csv. Keys of manuscript/references.bib are not redefined here.\n\n" + "\n".join(entries))
114 with open(f"{R}/manuscript/references_verification_part05.csv", "w", newline="") as f: 121 with open(f"{R}/manuscript/references_verification_part05.csv", "w", newline="") as f:
added literature_synthesis/agent_E/review.md +79 −0
@@ -0,0 +1,79 @@
1+# Agent E — Disciplinary review of the AI and machine-learning area (WP44, Phase 5)
2+
3+Date: 2026-11-03. Inputs: `literature_synthesis/landmarks/ai_ml_landmarks.csv` (234 records) and `ai_ml_abstracts.jsonl` (190 non-empty abstracts); `results/bibliometrics/cagr_by_category.csv`; `results/graph/category_counts_yearly.csv`, `category_flows_yearly.csv`; `results/econometrics/breaks.csv`, `its_results.json`; `results/emerging/known_set_lead_times.csv`; `manuscript/references.bib` (verified) and `manuscript/references_part05.bib` (88 entries verified by this agent, see `references_verification_part05.csv`). Claim identifiers [E-nn] refer to `claims.csv`; every corpus number quoted below is read from the file named in the claim and is reproduced in the manuscript only through generated macros or tables (`tab_ai_growth`, `tab_ai_counts`, `tab_ai_breaks`, `tab_ai_altdates`, `tab_ai_flows`, `tab_ai_landmark_flags`, `tab_ai_contribution_types`, `tab_ai_landmarks`). Evidence types: *abstract* = read from the stored abstract; *metadata_only* = title/venue/type/identifiers only; *full_text* = none in this review; *corpus_statistic* = a WP44 result file. No full text was read; nothing below describes the content of a paper beyond what its abstract or metadata states. Preprints are named as preprints. The November 2022 boundary is a reference date, not a cause.
4+
5+## 0. The evidence base and its defects
6+
7+**The landmark set is not usable as delivered.** The automatic selection (`select_landmarks.py`: highest NCS36 within primary category, floor of fifteen, plus the 200 most cited works overall) returned 234 AI/ML candidates: 33 LLM, 26 AI safety and 25 in each of the other seven categories; 72 are dated 2026 and 39 dated 2025, 136 are typed `article`, 28 `preprint`, 19 `book-chapter`, 11 `dissertation` [E-01, corpus_statistic]. Screening every record against its identifiers, citation counts, venue and abstract (`contributions.csv`, column `data_quality_flag`) flags 44 of 234 (18.8 %) as unreliable [E-02]: 13 *identifier mismatches*, in which the title and abstract belong to an obscure work while the arXiv identifier and the citation count belong to a famous one (the record titled "Toward effective Text-to-MongoDB query translation" carries arXiv 2005.11401, the retrieval-augmented-generation paper, and 3,026 36-month citations; a Portuguese essay on copyright carries 2005.14165, GPT-3; an essay on "theatrical compliance" carries 2201.11903, chain-of-thought prompting; an ICU-simulation preprint carries 2307.09288, Llama 2; a reproduction runner for Adam carries 45,775 citations); 3 *abstract mismatches*, in which a correct title (GPT-4 Technical Report; "GPTs are GPTs"; Powers' evaluation-measures paper) is paired with the abstract of a different work; 11 *implausible years* (mixup, the variational RNN, Soft-SOO, spectral bandits and three other 2013–2019 papers appear as 2024–2026 works, four of them with April–June 2026 arXiv identifiers); 7 *normalisation artefacts* (dissertations with 2–20 citations and NCS36 above 500, because the field-normalised score divides by a tiny reference mean for rare document types and young cohorts); and 10 *off-scope* records (control-theoretic consensus papers under `ai_agents`, an essay on ed-tech "solutionism" under `reinforcement_learning`). The defect is concentrated in the LLM category (8 of 33 flagged for identifier or abstract problems) and in the two "old" categories, ML theory and DL architectures, where misdated classics dominate [E-03]. The mechanism is upstream of this review — OpenAlex work merging and the second-pass arXiv/title linkage of Phase 1 — and must be reported in Part III and audited by Agent N before any landmark statistic is published; the clean count of citation-ranked landmarks in the AI area is therefore 190 at most, and the NCS36 ranking should be re-run with document-type-specific reference sets and a 2026 cohort excluded [E-04, speculative as to remedy].
8+
9+**What the set does contain.** Of the 234 records, 84 are surveys, 67 methods, 33 empirical studies, 29 theory/position papers, 17 engineering systems and only 4 benchmarks (`tab_ai_contribution_types`; judged from the abstract for 190, from metadata for 44) [E-05]. Within the LLM and generative-AI categories the citation-ranked top is dominated by education and commentary papers on ChatGPT (Kasneci et al. 2023; Lo 2023; Rudolph, Tan and Tan 2023; Chan and Hu 2023; Baidoo-Anu and Owusu Ansah 2023; Dwivedi et al. 2023; van Dis et al. 2023; Lim et al. 2023), not by the technical papers that introduced the models [E-06]. This is a property of the field-normalised ranking inside a multi-label category that spans computer science, education and information systems: the canonical technical lineage (GPT-4, LLaMA, InstructGPT, chain-of-thought, DPO, QLoRA, BLIP-2, Mamba) is present only where an arXiv record was linked, and the review therefore grounds the technical synthesis on the verified entries of `references.bib` (REFERENCE_MAP Part V) and uses the landmark set to characterise what the corpus *rewards* rather than what the field *invented*.
10+
11+## 1. The corpus picture of the nine categories
12+
13+*Volume.* The LLM category grows from a primary-category count of a few thousand works in 2018–2022 to tens of thousands in 2025, with a post-period CAGR of 1.085 against 0.189 in the baseline (`cagr_by_category.csv`); generative AI (0.870 vs 0.296), multimodal AI (0.869 vs 0.284), foundation models (0.802 vs 0.661) and AI safety (0.647 vs 0.294) follow; reinforcement learning (0.252 vs 0.208) and ML theory (0.197 vs 0.104) accelerate modestly; DL architectures decelerate slightly (0.155 vs 0.167); AI agents reverse from a negative baseline CAGR (−0.045) to 0.329 [E-07, corpus_statistic]. The LLM share of eligible works rises from 0.33 % (2018) to 5.06 % (2025) [E-08]. Multi-label counts (`tab_ai_counts`) are three to ten times larger than primary counts because the AI categories overlap heavily; the ordering of growth rates is the same under both definitions.
14+
15+*Structural change.* The interrupted time series at 2022-12 (`breaks.csv`) shows, for the LLM series, a level change of 0.106 log points (HAC p = 0.075) and a slope change of 0.034 log points per month (p ≈ 10⁻⁶⁰), with a placebo p-value of 0.938 for the level and 0.200 for the slope; the Bai–Perron procedure selects a single break at 2022-07 and PELT a change point at 2023-10 [E-09]. The same pattern — no significant level jump at the reference month, a strongly significant slope change, placebo p-values that do not single out December 2022, data-driven breaks before or well after it — holds for multimodal (slope 0.024; break 2022-04) and generative AI (slope 0.019; break 2021-11). AI agents (level −0.210, p = 0.028; slope 0.040), reinforcement learning (level −0.164, p = 0.013) and AI safety (level −0.206, p = 0.002) show *negative* level changes with positive slope changes, i.e. a dip followed by acceleration; DL architectures is the only AI category with no significant slope change (−0.002, p = 0.136) [E-10]. At the alternative event dates the picture is monotone: level effects at the GPT-3 (2020-06) and DALL-E 2 (2022-05) dates are negative for every AI category, while those at the GPT-4 (2023-04) and o1 (2024-09) dates are positive and significant for LLM, multimodal and generative AI (`tab_ai_altdates`) [E-11]. The conservative reading is that the AI literature's acceleration is a trend change that was already under way in mid-2022 and steepened through 2023–2024; the monthly series cannot attribute it to a single product release, and the manuscript must not do so [E-12, established as a statement about the estimates].
16+
17+*Knowledge flows.* In-corpus references of LLM works went to NLP for 69.6 % in 2019 and 68.6 % in 2022, but only 34.2 % in 2025, when 28.4 % go to the LLM category itself, 4.3 % to HCI and 3.4 % to software engineering (`category_flows_yearly.csv`) [E-13]. Generative-AI works cited computer vision for 53.6 % (2019) and 57.9 % (2022) of their in-corpus references and 33.8 % in 2025, when 13.7 % go to LLM and 6.6 % to HCI; AI-safety works cite LLM for 10.0 % and HCI for 11.5 % in 2025 against 0.1 % and 9.1 % in 2019 [E-14]. Reinforcement learning moves the other way — its share of references to robotics rises from 14.4 % to 23.1 % and to LLM stays below 1 % — and ML theory and DL architectures are essentially unchanged [E-15]. The LLM category has thus become a citation hub for generative AI, safety and agents while remaining peripheral to RL, theory and architectures; this is the corpus-level answer to RQ5 at the level of categories, and it is consistent with but does not establish the "foundation model" convergence thesis (Bommasani et al. 2021) [E-16, contested].
18+
19+*Emergence lead times.* In the preregistered known-emergence set (`known_set_lead_times.csv`) the detector flags "large language models" 34 months before its peak-growth month, retrieval-augmented generation 12 months, vision-language models 9, diffusion models 9, LLM agents 6, mixture-of-experts 5, speculative decoding 5, state-space models 6, reasoning models 29; mechanistic interpretability has a negative lead (−18), its peak-growth month preceding detection [E-17]. The median lead over the set is 7 months. The long LLM lead reflects the slow pre-2020 literature on "language models" matched by the regex, not foresight.
20+
21+## 2. Large language and foundation models
22+
23+*Established.* The decoder-only Transformer scaled with data and compute (Vaswani et al. 2017; Brown et al. 2020; Chowdhery et al. 2022) remains the dominant architecture inside the window; the compute-optimal scaling result of Hoffmann et al. (2022) — more tokens per parameter than Kaplan et al. (2020) implied — is reflected in the open-weight lineage LLaMA → Llama 2 → Llama 3 (Touvron et al. 2023a,b; Dubey et al. 2024; all preprints), whose LLaMA abstract reports a 13B model outperforming GPT-3 on most benchmarks while trained only on public data [E-18, abstract]. Closed frontier releases are documented by technical reports without training details (OpenAI 2023; Gemini Team 2023; OpenAI 2024) and must be cited as such. Domain-specific foundation models are the most visible post-2022 development in the landmark set: self-supervised pathology encoders UNI, CONCH, CHIEF, PLIP and TITAN (Chen et al. 2024; Lu et al. 2024; Wang et al. 2024; Huang et al. 2023; Ding et al. 2025), radiology (Wu et al. 2025), dermatology (Yan et al. 2025), brain MRI (Tak et al. 2026), the Earth system (Bodnar et al. 2025), tabular data (Hollmann et al. 2025), time series (Jin et al. 2023 preprint; Liu et al. 2024) and human cognition (Binz et al. 2025) — all peer-reviewed journal or conference papers whose abstracts report outperforming task-specific baselines on multi-task benchmarks [E-19, abstract]. The adaptation toolkit — LoRA, QLoRA, LLaMA-Adapter, PEFT surveys (Hu et al. 2021; Dettmers et al. 2023; Zhang et al. 2023 preprint; Han et al. 2024 preprint) — is where the open-weight ecosystem's practical growth concentrated; the QLoRA abstract's claim of "99.3 % of ChatGPT" on the Vicuna benchmark is a judge-based measurement, not a capability proof [E-20, abstract].
24+
25+*Contested.* Emergent abilities: Wei et al. (2022, preprint) report discontinuous capability jumps with scale; Schaeffer et al. (2023, preprint) show that the discontinuities largely disappear under linear or continuous metrics. Both are preprints; the corpus cannot adjudicate and the manuscript should present the disagreement as a measurement question [E-21]. "Sparks of AGI" (Bubeck et al. 2023, preprint) reports qualitative experiments on an early GPT-4 and "contends" general intelligence; it is widely cited (1,420 c36 in the landmark file) but is an unreplicated, non-peer-reviewed position [E-22]. Pure-RL reasoning: the DeepSeek-R1 Nature paper (Guo et al. 2025) reports that reasoning "can be incentivized through pure reinforcement learning, obviating the need for human-labelled reasoning trajectories"; it is peer reviewed, but the generality of the recipe beyond verifiable domains is untested in the corpus [E-23, abstract].
26+
27+*Speculative.* That domain foundation models will displace task-specific pipelines in regulated domains; the radiology review by Paschali et al. (2025) and the data-centric survey of Zhang et al. (2026) list data, privacy and evaluation obstacles that the landmark abstracts do not resolve [E-24].
28+
29+## 3. Alignment and post-training
30+
31+The RLHF recipe (Christiano et al. 2017; Ouyang et al. 2022; Bai et al. 2022a,b) and its closed-form successor DPO (Rafailov et al. 2023, NeurIPS; the abstract is unavailable in the landmark file) are established methods whose limitations are documented by Casper et al. (2023, preprint) [E-25]. The corpus flows show RL's citations to LLM remaining below 1 % through 2025 [E-15], so the "RL renaissance" through post-training is visible in the LLM category rather than in the RL category: the categorisation separates the two communities even where the methods are shared. Jailbreak and adversarial-prompt work — universal suffixes (Zou et al. 2023, preprint), the "jailbroken" analysis (Wei et al. 2023, preprint) and PAIR, which the abstract reports generates semantic jailbreaks with black-box access "in twenty queries" (Chao et al. 2025, IEEE SaTML) — establishes that current alignment is brittle to optimisation pressure [E-26, abstract]. Whether this brittleness is intrinsic to RLHF-style alignment or an artefact of current training scale is unresolved (open problem P-03).
32+
33+## 4. Reasoning, retrieval and tools
34+
35+Chain-of-thought prompting (Wei et al. 2022; Kojima et al. 2022), tree-of-thought (Yao et al. 2023a) and Reflexion (Shinn et al. 2023) are preprints or NeurIPS papers whose claims rest on benchmark gains; the reasoning-model turn (OpenAI 2024; Guo et al. 2025) moves the computation to inference time. The known-set detector flagged "reasoning models" in 2023-06, 29 months before the peak-growth month of 2025-11, because the regex includes chain-of-thought [E-17]. Retrieval augmentation (Lewis et al. 2020; Karpukhin et al. 2020; Gao et al. 2023 survey) is the most adopted pattern (12,896 matching works; detection 2023-10, peak growth 2024-10) [E-17]. Tool use — ReAct, Toolformer, ToolLLM (Yao et al. 2023b; Schick et al. 2023; Qin et al. 2023) — is the bridge to agents. Hallucination remains an unsolved measurement problem (Ji et al. 2023; Huang et al. 2025); Singhal et al. (2023, Nature) built the MultiMedQA benchmark and a human evaluation axis precisely because automated metrics were insufficient for clinical answers [E-27, abstract]. Code generation (Chen et al. 2021; Li et al. 2022; Rozière et al. 2023; Li et al. 2023) and its productivity evidence (Peng et al. 2023; Ziegler et al. 2022) belong to Part VII-a's software-engineering synthesis; the detector's lead time for "AI code generation" is 10 months.
36+
37+## 5. Multimodal and generative AI
38+
39+*Established.* Contrastive image–text pre-training (Radford et al. 2021) and vision Transformers (Dosovitskiy et al. 2021) underlie the vision-language models of the window: Flamingo, BLIP-2, InstructBLIP, LLaVA, mPLUG-Owl2 (Alayrac et al. 2022; Li et al. 2023 preprint; Dai et al. 2023 preprint; Liu et al. 2023 preprint; Ye et al. 2024 CVPR) share the design of a frozen or lightly adapted LLM fed by a visual encoder through a small bridge; SAM (Kirillov et al. 2023) is the promptable-segmentation analogue [E-28]. LAION-5B (Schuhmann et al. 2022) made open CLIP-scale training possible. Diffusion models (Ho et al. 2020; Rombach et al. 2022; Saharia et al. 2022; Ramesh et al. 2022), classifier-free guidance (Ho and Salimans 2022 preprint), textual inversion (Gal et al. 2022 preprint), T2I-Adapter (Mou et al. 2024, AAAI), DiT (Peebles and Xie 2023) and SD3 (Esser et al. 2024 preprint) form the generative lineage, with MatterGen (Zeni et al. 2025, Nature) as a peer-reviewed scientific application reporting stable, diverse inorganic materials [E-29, abstract]. Speech follows the same self-supervised path (Baevski et al. 2020; Hsu et al. 2021; Radford et al. 2023).
40+
41+*Contested.* Multimodal grounding in medicine: the evaluation of eight multimodal foundation models on 1,090 cases reports that image predictions are "largely driven by text" and that mild textual suggestions of a wrong diagnosis degrade image classification (Buckley et al. 2026, Nature Communications) [E-30, abstract]; the scoping review of 432 medical multimodal papers (Schouten et al. 2025) and the low-quality-data survey (Zhang et al. 2026) describe missing-modality and noise robustness as open. Whether current VLMs *fuse* or merely *condition on* modalities is therefore unsettled (P-05).
42+
43+*Corpus.* Generative AI is the category whose citation profile changed most: from a vision-centred (53.6 %) to a split vision/LLM/HCI profile [E-14]; its citation-ranked landmarks are, however, overwhelmingly education and policy studies (Chan 2023; Xia et al. 2024; Bittle and El-Gayar 2025; McDonald et al. 2025; the OECD 2026 Outlook; Bastani et al. 2024 SSRN preprint reporting that GPT-4 access harmed unassisted exam performance) and adoption surveys (Bick, Blandin and Deming 2026: 45 % of US adults 18–64 using generative AI by late 2024, per the abstract) [E-31, abstract]. Technical generative-AI papers are counted in the category but rarely reach its citation top.
44+
45+## 6. AI agents
46+
47+*Established.* LLM-based agents are surveyed by Wang et al. (2024), Xi et al. (2023 preprint), Guo et al. (2024) and Cheng et al. (2026); memory mechanisms by Zhang et al. (2025); architectures include generative agents (Park et al. 2023), AutoGen (Wu et al. 2023 preprint), MemGPT (Packer et al. 2023 preprint) and "LLM as OS" proposals (Mei et al. 2024; Ge et al. 2023, preprints). Benchmarks — SWE-bench, WebArena, AgentBench (Jimenez et al. 2024; Zhou et al. 2023; Liu et al. 2023, preprints) — are preprints; the only peer-reviewed agent benchmark in the landmark set is the clinical evaluation of OpenManus and Manus (Liu et al. 2026, npj Digital Medicine) [E-32]. Science agents are the strongest peer-reviewed evidence: the Virtual Lab (Swanson et al. 2025, Nature) designed nanobodies with experimental validation, Robin (Ghareeb et al. 2026, Nature) automates hypothesis generation and data analysis in experimental biology, DIVE (Zhang et al. 2026) extracts data from figures for materials discovery; AgentSociety (Piao et al. 2026) simulates LLM-driven populations [E-33, abstract]. The AI-scientist preprints (Lu et al. 2024; Si et al. 2024) remain hypotheses.
48+
49+*Contested.* The "agentic AI" definitional literature — Acharya et al. (2025), Sapkota et al. (2026), Bandi et al. (2025), Abou Ali et al. (2025), Allmendinger et al. (2026), Collaço et al. (2026) — produced at least six taxonomies in 2025–2026, most of which are themselves reviews of reviews; the landmark set contains six of them and one benchmark [E-34]. Whether "agentic AI" denotes a distinct class of systems or a relabelling of tool-augmented LLM pipelines is unresolved (P-06). The ai_agents category is heterogeneous: its series mixes classical multi-agent systems and control theory (four off-scope landmarks) with LLM agents, which explains the negative baseline CAGR, the negative 2022-12 level change and the second Bai–Perron break at 2024-07 [E-35]. The detector's lead time for LLM agents is 6 months (detection 2023-10, peak 2024-04).
50+
51+*Speculative.* Trust, risk and security management for multi-agent LLM systems (Raza et al. 2026) is a framework without empirical validation.
52+
53+## 7. Reinforcement learning
54+
55+The deep-RL canon of the baseline period is intact in the landmark set — Rainbow, SAC, TD3, AlphaZero, AlphaStar, MAPPO (Hessel et al. 2018; Haarnoja et al. 2018 preprint; Fujimoto et al. 2018; Silver et al. 2018; Vinyals et al. 2019; Yu et al. 2022), the reproducibility warning of Henderson et al. (2018) and the curiosity study of Burda et al. (2019) — and the single post-2022 method landmark is DreamerV3 (Hafner et al. 2025, Nature), whose abstract reports one configuration mastering over 150 tasks through world models [E-36, abstract]. The MPC–RL survey (Reiter et al. 2026) and the MARL overview (Zhang et al. 2021) frame the field's control-theoretic side. RL's post-2022 slope change (0.012) is the smallest among the accelerating AI categories and its citations go increasingly to robotics (23.1 % in 2025) [E-15]. The field's most consequential post-2022 role — reward modelling and policy optimisation for LLMs (PPO, DPO, GRPO in DeepSeek-R1) — is invisible in the RL category because those papers are classified as LLM work; any claim that "RL declined" after 2022 would be an artefact of the taxonomy [E-37, contested].
56+
57+## 8. Machine-learning theory and deep-learning architectures
58+
59+The ML-theory landmark set contains no learning-theory result from the window: it is textbooks (Alpaydin; Zaki and Meira), evaluation metrics (Chicco and Jurman 2020), interpretability (Rudin 2019; Lipton 2018; Lundberg et al. 2020; Verma and Pearl 2022 reprint), optimisation (Bottou, Curtis and Nocedal 2018), transfer learning (Zhuang et al. 2021) and misdated 2013–2019 bandit papers [E-38]. The category as classified is "general machine learning", and its citation top rewards pedagogy and measurement conventions; theoretical work on transformers, in-context learning or scaling (which does exist in the window) is classified elsewhere or cited too little in 36 months to surface. The DL-architectures set is the mirror image: GNN surveys (Wu et al. 2021; Zhou et al. 2020), time-series Transformers and their refutation — Zeng et al. (2023) report that simple linear models outperform Informer-style Transformers on long-term forecasting benchmarks (Zhou et al. 2021; Wen et al. 2023) [E-39, abstract, contested within the field]; mixup (Zhang et al. 2018); Mamba (Gu and Dao 2023, preprint) and KAN (Liu et al. 2024, preprint) as the two architectural proposals of the window, both unreviewed; TabPFN (Hollmann et al. 2025, Nature) as the peer-reviewed one. Efficiency work — FlashAttention, MoE, INT8/GPTQ quantisation, speculative decoding (Dao et al. 2022; Fedus et al. 2022; Dettmers et al. 2022; Frantar et al. 2023; Leviathan et al. 2023; Gholami et al. 2022) — is where architecture research actually moved, and the detector finds short leads for state-space models (6 months), MoE (5) and speculative decoding (5), i.e. these topics were detected only months before their growth peaked [E-40]. DL architectures is the one AI category without a significant post-2022 slope change [E-10]; whether this is a plateau or a relabelling of architecture papers under LLM/foundation categories cannot be decided from the counts (P-08).
60+
61+## 9. Evaluation
62+
63+Static benchmarks (GLUE, SuperGLUE, MMLU, BIG-bench: Wang et al. 2018, 2019; Hendrycks et al. 2021; Srivastava et al. 2022), holistic evaluation (Liang et al. 2022), arena-style human preference (Chiang et al. 2024 preprint) and LLM-as-judge (Zheng et al. 2023 preprint) coexist with documented contamination (Sainz et al. 2023), benchmark saturation and the "benchmark lottery" (Dehghani et al. 2021; Ott et al. 2022; Raji et al. 2021; Kiela et al. 2021; Recht et al. 2019). The landmark set contains four benchmark contributions in 234 (SuperGLUE, LAION-5B, MultiMedQA, the clinical agent benchmark) [E-05]; evaluation is under-rewarded by citation ranking relative to its role. The SuperGLUE abstract already records in 2019 that GLUE performance "surpassed the level of non-expert humans", the first instance of the saturation cycle that repeats for MMLU within the window [E-41, abstract]. No landmark abstract reports a contamination-controlled evaluation (P-09).
64+
65+## 10. Safety, alignment, fairness and societal evidence
66+
67+*Established.* Risk taxonomies have matured from the concrete-problems agenda (Amodei et al. 2016; Hendrycks et al. 2021) to the AI Risk Repository, which the abstract describes as a living database of 1,725 risks from 74 taxonomies (Slattery et al. 2026, Patterns), the International AI Safety Report (Bengio et al. 2025) and the Science perspective on extreme risks (Bengio et al. 2024) [E-42]. Fairness and explainability surveys (Mehrabi et al. 2021; Barredo Arrieta et al. 2020; Ali et al. 2023; Miller 2019) dominate the AI-safety landmark top, i.e. the category's citation top is pre-LLM "responsible AI". Empirical harms with peer review: medical LLMs are vulnerable to data poisoning at 0.001 % of training tokens (Alber et al. 2025, Nature Medicine), four chatbots gave unsafe answers to patient questions in a physician-led red-teaming (Draelos et al. 2026), LLMs express stigma and respond inappropriately as replacement therapists (Moore et al. 2025, FAccT) [E-43, abstract]. Inference energy of general-purpose models exceeds that of task-specific ones by orders of magnitude (Luccioni et al. 2024; de Vries 2023) [E-44]. Labour and adoption: Eloundou et al. (2024), Dell'Acqua et al. (2023 preprint; the Organization Science version is in the landmark set) and Bick et al. (2026) are the peer-reviewed or preregistered evidence [E-45].
68+
69+*Contested.* Catastrophic-risk framing (Hendrycks et al. 2023; Shevlane et al. 2023; both preprints) versus the "stochastic parrots" critique (Bender et al. 2021); the corpus shows the AI-safety category citing LLM work for 10 % of its references in 2025 against 0.1 % in 2019 [E-14], so the field has re-oriented, but no landmark abstract reports a validated measurement of a catastrophic capability (P-10). Governance: Papagiannidis et al. (2025) and the healthcare maturity model (2026 review) are frameworks; McDonald et al. (2025) document 116 university policies — descriptive, not evaluative.
70+
71+## 11. Reconciliations and what the corpus cannot say
72+
73+1. *Growth vs discontinuity.* The AI categories accelerated; the acceleration is a slope change dated by data-driven methods to mid-2022 or later and not a December-2022 level jump [E-09–E-12]. Statements of the form "ChatGPT caused a surge" are not supported by the design.
74+2. *Impact vs invention.* Citation-ranked landmarks in LLM and generative AI are commentary and education papers; the technical lineage is cited through arXiv records that the linkage sometimes misattached [E-02, E-06]. Any "most influential papers" list for AI must be built from a cleaned identifier graph.
75+3. *RL and architectures.* Their apparent stagnation is partly taxonomic [E-37, P-08].
76+4. *Agents.* Six taxonomies and one peer-reviewed benchmark [E-34]; science agents are the exception with experimental validation [E-33].
77+5. *Evidence tiers.* 28 of 234 landmarks are preprints and 72 are dated 2026 with 12-month windows; everything said about 2025–2026 is provisional.
78+
79+Open problems feeding Part X are listed in `open_problems.csv` (P-01 to P-14).
added literature_synthesis/agent_F/claims.csv +44 −0
@@ -0,0 +1,44 @@
1+claim_id,claim,claim_type,evidence_type,supporting_keys,contradicting_keys,corpus_artifact,confidence,verification_status,notes
2+F-01,"In the interrupted time-series model, all eleven systems categories show a positive change in the monthly trend of log-volume at 2022-12 with HAC p-values below 0.01; edge computing has the smallest change (0.004, p=0.006) and information retrieval the largest (0.023).",established,corpus_statistic,,,"results/econometrics/breaks.csv columns chatgpt_slope, chatgpt_slope_p (rows cat_*)",0.9,machine-verified,Statistical description only; no causal reading.
3+F-02,"The trend change at 2022-12 is not specific to that date: for every systems category the placebo slope p-value is at least 0.062 (computer architecture), and reaches 0.477 for operating systems.",established,corpus_statistic,,,results/econometrics/breaks.csv column placebo_slope_p,0.9,machine-verified,Undermines any attribution of the steepening to ChatGPT.
4+F-03,"The level change at 2022-12 is negative and significant at 5% for information retrieval (-0.094), AI accelerators (-0.113) and quantum computing (-0.087); positive and significant for computer networks (+0.043) and sustainable computing (+0.049); not significant for the six other categories.",established,corpus_statistic,,,"results/econometrics/breaks.csv columns chatgpt_level, chatgpt_level_p",0.9,machine-verified,"Placebo level p-values are all above 0.47, so the level shifts are also not date-specific."
5+F-04,"Primary-category output of computer architecture, operating systems, distributed computing, cloud computing, computer networks, database systems and information retrieval declined between 2018 and 2022 (negative baseline CAGR) and grew between 2023 and 2025 (positive post CAGR); edge computing, quantum computing, sustainable computing and AI accelerators grew in both periods.",established,corpus_statistic,,,"results/bibliometrics/cagr_by_category.csv columns cagr_baseline, cagr_post",0.9,machine-verified,Counts are eligible works by primary category; 2026 excluded as incomplete.
6+F-05,"The share of total primary output held by computer networks fell from 0.118 in 2018 to 0.067 in 2025, and the shares of architecture, databases, distributed, cloud and operating systems also fell; quantum computing (0.0125 to 0.0196) and sustainable computing rose.",established,corpus_statistic,,,"results/bibliometrics/cagr_by_category.csv columns share_2018, share_2025",0.9,machine-verified,Relative decline predates November 2022.
7+F-06,"Bai-Perron (BIC) break dates for the systems categories are scattered between 2022-01 and 2025-04; cloud computing is the only category with a selected break at 2022-11, and computer networks' single break (2022-02) precedes ChatGPT.",established,corpus_statistic,,,results/econometrics/breaks.csv column bp_breaks_bic,0.85,machine-verified,Break detection on monthly log-volume series (Part IV).
8+F-07,"Citing works of the systems categories send most of their resolved references to their own category; in citing years 2023-2026 the self-share is 84.9% for quantum computing, 72.4% for networks, 59.2% for IR, 57.7% for architecture, 40.6% for databases, 34.7% for accelerators, 33.1% for cloud, 29.8% for distributed, 24.4% for edge, 16.0% for operating systems and 11.3% for sustainable computing.",established,corpus_statistic,,,"results/graph/category_flows_yearly.csv aggregated by Agent F over citing_year>=2023 (citing_cat, cited_cat, n)",0.85,machine-verified,"Aggregation uses calendar citing years (2023+), not the 30 Nov 2022 boundary."
9+F-08,Self-citation shares of the systems categories moved by less than four percentage points between citing years 2018-2022 and 2023-2026; the composition of destinations is stable.,established,corpus_statistic,,,results/graph/category_flows_yearly.csv aggregated by Agent F (baseline vs post citing years),0.8,machine-verified,"Largest moves: operating systems 20.5% to 16.0%, quantum 88.3% to 84.9%."
10+F-09,"In citing years 2023-2026 the llm category enters the seven largest destinations of information-retrieval references (3.0%), which it did not in 2018-2022; operating systems' largest destination becomes computer architecture (18.8%) rather than itself; edge computing and sustainable computing cite computer networks more than their own category in both periods.",established,corpus_statistic,,,results/graph/category_flows_yearly.csv aggregated by Agent F,0.8,machine-verified,Descriptive; no causal reading.
11+F-10,"The landmark candidate lists for the two areas contain 152 (data_distributed) and 125 (hardware_emerging) records, 25 per category after the floor rule; between 10 (distributed, operating systems) and 20 (accelerators, sustainable) per category are post-period; several records are evidently misclassified or are re-publications of older works.",established,metadata_only,,,"literature_synthesis/landmarks/data_distributed_landmarks.csv, hardware_emerging_landmarks.csv (counts by Agent F)",0.95,human-verified,Misclassified examples listed in review.md section 1.2.
12+F-11,"Because the landmark score for 2025-2026 is c12 relative to the year mean, several 2026 records with 9-30 citations in total are listed as landmarks; their status is provisional.",established,metadata_only,,,"literature_synthesis/landmarks/hardware_emerging_landmarks.csv (c36 column), literature_synthesis/select_landmarks.py",0.9,human-verified,"Affects accelerators, sustainable computing, networks, cloud."
13+F-12,"Within the post-period accelerators landmarks, at least ten of twenty records are memristive or chaotic neural-network circuit papers (Integration, Neural Networks, IEEE TCYB, Chaos Solitons & Fractals), a nonlinear-dynamics literature rather than AI hardware; the category's landmark list therefore overstates its coverage of accelerator research.",established,metadata_only,,,literature_synthesis/landmarks/hardware_emerging_landmarks.csv rows ai_accelerators 2025-2026,0.85,human-verified,Judged from titles and venues only.
14+F-13,"LLM serving systems treat KV-cache memory management as the central bottleneck; vLLM's PagedAttention reports 2-4x throughput at equal latency, while vAttention argues that non-contiguous KV layouts impose programming and performance overheads and proposes CUDA virtual memory instead.",contested,abstract,kwon2023vllm;prabhu2025vattention,,"literature_synthesis/landmarks/data_distributed_abstracts.jsonl (4387321091), hardware_emerging_abstracts.jsonl (4407217670)",0.8,human-verified,Competing designs evaluated by their own authors.
15+F-14,Phase splitting of LLM inference across machine pools (Splitwise) and activation-locality-based GPU-CPU hybrid inference (PowerInfer) are engineering demonstrations whose reported gains are specific to the hardware and models used.,established,abstract,patel2024splitwise;song2024powerinfer,,"literature_synthesis/landmarks/hardware_emerging_abstracts.jsonl (4401211704, 4404401018)",0.8,human-verified,Abstracts only; no independent replication reviewed.
16+F-15,"The 'memory wall' argument holds that peak server FLOPS scaled at 3.0x per two years against 1.6x for DRAM bandwidth and 1.4x for interconnect bandwidth over twenty years, making bandwidth the binding constraint for decoder inference; the figures are the authors' and the article is a perspective.",established,abstract,gholami2024memorywall,,literature_synthesis/landmarks/hardware_emerging_abstracts.jsonl (4393145114),0.7,human-verified,Widely cited; the trend rates are not re-derived here.
17+F-16,PyTorch 2's TorchDynamo/TorchInductor report geometric-mean speedups of 2.27x (inference) and 1.41x (training) over eager execution on the authors' model suite.,established,abstract,ansel2024pytorch2,,literature_synthesis/landmarks/hardware_emerging_abstracts.jsonl (4394998532),0.8,human-verified,Vendor-authored evaluation.
18+F-17,"Photonic accelerator papers of 2024-2026 report device-level efficiency figures (e.g., 160 TOPS/W for Taichi) and task demonstrations on small benchmarks; whether these translate into end-to-end system gains is not established.",speculative,abstract,xu2024taichi;ahmed2025photonic;fu2024onn;lam2026neuromorphicphotonic,,"literature_synthesis/landmarks/hardware_emerging_abstracts.jsonl (4394727812, 7128293306, 4402694963)",0.7,human-verified,ahmed2025photonic abstract unavailable (metadata only).
19+F-18,"Quantum error-correction milestones fall inside the window: surface-code scaling from distance 3 to 5 (2023), a 280-physical-qubit neutral-atom logical processor (2024) and below-threshold distance-7 memories with logical error suppression factor 2.14 (2024).",established,abstract,googlequantum2023suppressing;bluvstein2024logical;googlequantum2025belowthreshold,,"literature_synthesis/landmarks/hardware_emerging_abstracts.jsonl (4321610982, 4389373471, 4405183524)",0.85,human-verified,Peer-reviewed Nature articles; figures as stated in abstracts.
20+F-19,The claim of 'utility before fault tolerance' (127-qubit expectation values beyond brute-force classical simulation) is contested; the abstract restricts the claim to brute-force simulation and subsequent classical-simulation responses are not in the landmark set.,contested,abstract,kim2023utility,,literature_synthesis/landmarks/hardware_emerging_abstracts.jsonl (4380627387),0.7,human-verified,Rebuttals are not cited because they are not in the verified bibliography.
21+F-20,Barren plateaus are treated by the quantum-computing literature as a structural obstacle to variational algorithms; the 2025 review and the earlier NISQ/VQA reviews agree on this.,established,metadata_only,larocca2025barren;cerezo2021vqa;bharti2022nisq,,literature_synthesis/landmarks/hardware_emerging_landmarks.csv (4408847956),0.75,human-verified,larocca2025barren abstract unavailable.
22+F-21,"Modular quantum computing demonstrations of 2025 (two trapped-ion modules linked by an optical fibre; a 35-chip photonic scale model) are feasibility demonstrations, described by their authors as deterministic gate teleportation and as a sub-performant scale model respectively.",established,abstract,main2025distributedqc;aghaee2025aurora,,"literature_synthesis/landmarks/hardware_emerging_abstracts.jsonl (4407158326, 4406693713)",0.8,human-verified,
23+F-22,"Quantum computing has the highest post-period CAGR among the systems categories (0.287), the highest self-citation share, and a negative level change at 2022-12; its growth dynamics are internal to the field rather than linked to language models.",established,corpus_statistic,,,results/bibliometrics/cagr_by_category.csv row quantum_computing; results/econometrics/breaks.csv row cat_quantum_computing; results/graph/category_flows_yearly.csv,0.8,machine-verified,Interpretation ('internal') is inferential; statistics are descriptive.
24+F-23,"Among database landmarks, the only vector-search item is the Faiss library (GPU paper 2019; library paper 2025/26); the other post-period database landmarks are a data-centric AI survey and a data-quality study, not DBMS engine papers.",established,metadata_only,douze2026faiss;johnson2021faiss;zha2025datacentric;mohammed2025dataquality,,literature_synthesis/landmarks/data_distributed_landmarks.csv rows database_systems,0.85,human-verified,"Vector DBMS surveys (pan2024vectordb, han2023vectordb preprint, taipalus2024vectordb) are in the bibliography but not in the landmark set."
25+F-24,"The most cited post-2022 information-retrieval works in the corpus are RAG surveys, LLM-for-recommendation surveys and an embedding resource (C-Pack/BGE), whereas the most cited 2018-2022 works were GNN-based recommendation models and surveys.",established,metadata_only,fan2024ragsurvey;huang2026ragsurvey;zhao2026ragaigc;wu2024llmrec;zhao2024recsysllm;xiao2024cpack;he2020lightgcn;wu2023gnnrec;gao2023gnnrecsurvey,,literature_synthesis/landmarks/data_distributed_landmarks.csv rows information_retrieval,0.85,human-verified,"Shift in what is cited, observable from metadata."
26+F-25,"Dense retrieval and retrieval-augmented generation are methodologically continuous with 2020 work (DPR, RAG, ColBERT); the post-2022 landmark set contains surveys and resources rather than new retrieval architectures.",speculative,metadata_only,karpukhin2020dpr;lewis2020rag;khattab2020colbert;zhao2024denseretrieval,,literature_synthesis/landmarks/data_distributed_landmarks.csv rows information_retrieval,0.6,human-verified,Could reflect citation lag for 2024-2026 model papers.
27+F-26,"Information retrieval has the largest trend change at 2022-12 (0.023) and a negative level change (-0.094, p=0.019), with BIC breaks at 2022-07 and 2024-07.",established,corpus_statistic,,,results/econometrics/breaks.csv row cat_information_retrieval,0.9,machine-verified,
28+F-27,"The 2026 slice of cloud-computing landmarks consists almost entirely of LLM-inference scheduling papers at smaller IEEE conferences (token-level GPU pooling, serverless multi-tenant scheduling, carbon-aware prompt scheduling), each evaluated in simulation or on single testbeds.",established,abstract,liao2026caps;li2026deepservecb,,"literature_synthesis/landmarks/data_distributed_abstracts.jsonl (7160823269, 7161556515, 7160863705)",0.8,human-verified,Provisional landmarks (see F-11).
29+F-28,RCACopilot (EuroSys 2024) reports root-cause category accuracy up to 0.766 on a year of Microsoft incidents; the evaluation is on proprietary data.,established,abstract,chen2024rcacopilot,,literature_synthesis/landmarks/data_distributed_abstracts.jsonl (4394946189),0.8,human-verified,
30+F-29,"Cloud computing's ITS level change at 2022-12 is indistinguishable from zero (0.003, p=0.936) while its trend change is 0.019 (p<0.001, placebo p=0.231); its BIC breaks are 2022-11 and 2025-01.",established,corpus_statistic,,,results/econometrics/breaks.csv row cat_cloud_computing,0.9,machine-verified,
31+F-30,"Edge computing's baseline landmarks are blockchain-IoT and COVID-era IoT application papers; its post-period landmarks are surveys on LLMs at the mobile edge, agentic edge intelligence and edge-cloud collaboration; its trend change at 2022-12 is the smallest of the eleven (0.004) after a high baseline CAGR (0.133).",established,metadata_only,qu2025meillm;zhang2026edgegeneral;liu2026eccc;an2026aiflow;zhou2019edgeintelligence,,literature_synthesis/landmarks/data_distributed_landmarks.csv rows edge_computing; results/econometrics/breaks.csv row cat_edge_computing,0.85,human-verified,
32+F-31,"Computer networks' most cited post-2022 works are 6G vision and ISAC overviews and, in 2026, tutorials on large AI models and agentic AI for communications; the category's positive level change at 2022-12 has placebo p=0.538 and its single BIC break is 2022-02.",established,metadata_only,wang2023road6g;liu2022isac;jiang2026lamagentic;zhang2026edgegeneral,,literature_synthesis/landmarks/data_distributed_landmarks.csv rows computer_networks; results/econometrics/breaks.csv row cat_computer_networks,0.85,human-verified,
33+F-32,"Sustainable-computing landmarks of 2021-2026 form a measurement literature on AI's energy, carbon and water footprints (training carbon, inference carbon, water, grid demand, rebound effects, inference power benchmark); several foundational items are preprints.",established,abstract,patterson2021carbon;luccioni2023bloom;dodge2022carbonintensity;chien2023carbongenai;li2023thirsty;li2025thirsty;luccioni2025jevons;chen2026aielectricity;niu2026tokenpowerbench;devriesgao2026datacenters,,literature_synthesis/landmarks/hardware_emerging_abstracts.jsonl rows sustainable_computing,0.85,human-verified,"patterson2021carbon, luccioni2023bloom, li2023thirsty are preprints."
34+F-33,"Industry figures quoted in abstracts (e.g., inference accounting for more than 90% of LLM power) are not independently verifiable from the corpus.",speculative,abstract,niu2026tokenpowerbench,,literature_synthesis/landmarks/hardware_emerging_abstracts.jsonl (7138433923),0.6,human-verified,
35+F-34,"Sustainable computing's level change at 2022-12 is positive (+0.049, p=0.048) with placebo p=0.754, its post CAGR is 0.233 and it cites computer networks (14.2%) more than itself (11.3%).",established,corpus_statistic,,,results/econometrics/breaks.csv row cat_sustainable_computing; results/bibliometrics/cagr_by_category.csv; results/graph/category_flows_yearly.csv,0.9,machine-verified,
36+F-35,"Operating systems is the smallest systems category (1,363 primary works in 2025), declined at -0.087 per year in 2018-2022 and grew at 0.078 in 2023-2025; its post-period landmarks are GPU-sharing and LLM-serving systems papers.",established,metadata_only,strati2024orion;song2024powerinfer;prabhu2025vattention,,results/bibliometrics/cagr_by_category.csv row operating_systems; literature_synthesis/landmarks/hardware_emerging_landmarks.csv rows operating_systems,0.85,human-verified,List also contains misclassified health/education records.
37+F-36,"Record-quality anomalies affect the systems landmark lists: canonical distributed-systems papers (MapReduce, Lamport clocks, Byzantine generals) appear as 2018-2019 re-publications; an OpenMP taskwait paper carries c36=12,891; an eBPF preprint dated 2020 carries a 2026 OASIcs DOI; NumPy is classified as computer architecture.",established,metadata_only,dean2008mapreduce,,"literature_synthesis/landmarks/data_distributed_landmarks.csv (2122465391, 1973501242, 2120510885, 4381304672, 3036601975); hardware_emerging_landmarks.csv (3099878876)",0.9,human-verified,Feeds the data-quality discussion of Part III/XI.
38+F-37,"The most cited post-2022 computer-architecture works are ML-systems papers (PyTorch 2, Splitwise, AWQ, memory wall) and an accelerator survey; the baseline list mixes architecture with scientific software (NAMD, Quantum ESPRESSO).",established,metadata_only,ansel2024pytorch2;patel2024splitwise;lin2025awq;gholami2024memorywall;silvano2025accelerators,,literature_synthesis/landmarks/hardware_emerging_landmarks.csv rows computer_architecture,0.85,human-verified,
39+F-38,"Across the eleven categories, the most cited post-2022 works are predominantly surveys and tutorials (IEEE COMST, ACM CSUR, TKDE) and LLM-serving systems papers; new hardware or kernel artefacts are rare in the landmark lists.",established,metadata_only,,,"literature_synthesis/landmarks/data_distributed_landmarks.csv, hardware_emerging_landmarks.csv (type and venue columns)",0.75,human-verified,Short citation windows favour surveys.
40+F-39,"The post-2022 steepening of systems-category volume cannot be attributed to ChatGPT: placebo p-values are not small, break dates are scattered, and most classical categories were declining in 2018-2022 so that reversion is a competing explanation.",established,corpus_statistic,,,results/econometrics/breaks.csv; results/bibliometrics/cagr_by_category.csv,0.85,machine-verified,"Methodological conclusion, consistent with CLAUDE.md 6.5."
41+F-40,"Data-centric AI and data quality are the themes of the post-period database landmarks; related database-community positions on LLMs and data management (fernandez2023llmdata, stonebraker2024goesaround) are outside the landmark set.",speculative,metadata_only,zha2025datacentric;mohammed2025dataquality;fernandez2023llmdata;stonebraker2024goesaround;li2021ai4db,,literature_synthesis/landmarks/data_distributed_landmarks.csv rows database_systems,0.6,human-verified,
42+F-41,"Federated learning appears in the systems landmarks as surveys (green federated learning; an MDPI systematic review of 30 studies on FL for cloud/edge security) rather than as systems artefacts; the latter's quantitative claims (25% privacy-risk reduction, 40% better threat detection) are aggregate statements from a small review.",contested,abstract,thakur2025greenfl;mcmahan2017fl;kairouz2021fl,,literature_synthesis/landmarks/data_distributed_abstracts.jsonl (4408156900),0.6,human-verified,4408156900 is not cited in the manuscript.
43+F-42,"BPF standardisation (RFC 9669, 2024) and in-kernel aggregation (AggBox) indicate that kernel-extension technology is an infrastructure theme of the period; the evidence is one RFC and one abstract.",speculative,metadata_only,ietf2024rfc9669;vieira2020ebpf;hoiland2018xdp,,literature_synthesis/landmarks/hardware_emerging_landmarks.csv (4403931219); data_distributed_abstracts.jsonl (3036601975),0.5,human-verified,AggBox DOI could not be verified via Crossref; not cited.
44+F-43,"Neuromorphic hardware (Loihi 2018) and neuromorphic-computing perspectives (2022) are the baseline accelerators landmarks; the post period adds photonic processors rather than digital accelerators, which are absent from the landmark list despite TPU v4 and GPU papers in the bibliography.",established,metadata_only,davies2018loihi;schuman2022neuromorphic;jouppi2023tpuv4;dally2021gpu;reuther2022accelerators,,literature_synthesis/landmarks/hardware_emerging_landmarks.csv rows ai_accelerators,0.8,human-verified,Digital accelerator papers may be classified elsewhere (architecture).
added literature_synthesis/agent_F/contributions.csv +75 −0
@@ -0,0 +1,75 @@
1+openalex_id,contribution_type,description,judged_from,bib_key
2+4387321091,engineering,vLLM / PagedAttention: KV-cache paging for LLM serving; authors report 2-4x throughput at equal latency,abstract,kwon2023vllm
3+4407217670,engineering,vAttention: contiguous virtual memory for the KV cache without PagedAttention; argues paging adds programming and performance overheads,abstract,prabhu2025vattention
4+4404401018,engineering,PowerInfer: GPU-CPU hybrid LLM inference on a consumer GPU exploiting power-law neuron activation locality,abstract,song2024powerinfer
5+4401211704,engineering,Splitwise: prompt and token-generation phases of LLM inference split across machine pools,abstract,patel2024splitwise
6+4394944658,engineering,"Orion: operator-granularity, interference-aware GPU sharing between ML clients",abstract,strati2024orion
7+4394998532,engineering,PyTorch 2 (TorchDynamo/TorchInductor): bytecode-level graph capture and compilation; reported 2.27x inference / 1.41x training geometric-mean speedups,abstract,ansel2024pytorch2
8+4393145114,theory,"AI and Memory Wall: argues memory bandwidth, not FLOPS, is the binding constraint for decoder inference (perspective article)",abstract,gholami2024memorywall
9+4406650295,method,AWQ + TinyChat: activation-aware weight quantization and on-device inference framework,abstract,lin2025awq
10+4409568286,survey,"Survey of deep-learning hardware accelerators for heterogeneous HPC platforms (GPU, TPU, FPGA, ASIC, PIM, neuromorphic)",abstract,silvano2025accelerators
11+3111721177,survey,Primer on processing in memory (book chapter),metadata,mutlu2023pim
12+2972087877,survey,Demystifying parallel and distributed deep learning: concurrency analysis of DNN training,abstract,bennun2020demystifying
13+4403931219,engineering,RFC 9669: BPF instruction set architecture standardised by the IETF,metadata,ietf2024rfc9669
14+2783525259,engineering,"Loihi neuromorphic manycore processor with on-chip learning (14 nm, programmable synaptic rules)",abstract,davies2018loihi
15+4210357113,survey,Opportunities for neuromorphic computing algorithms and applications (perspective),metadata,schuman2022neuromorphic
16+4394727812,engineering,Taichi photonic chiplet: diffractive-interference hybrid design; authors report 160 TOPS/W and 1000-category on-chip classification,abstract,xu2024taichi
17+4402694963,survey,Optical neural networks: progress and challenges,abstract,fu2024onn
18+4409283612,engineering,Universal photonic AI acceleration (Nature 2025); abstract unavailable in the corpus,metadata,ahmed2025photonic
19+7128293306,engineering,Neuromorphic photonic circuit with on-chip capacitive analog memory; authors report >26x power saving vs SRAM-DAC in analysis,abstract,lam2026neuromorphicphotonic
20+4321610982,empirical,Surface-code logical qubit scaling d=3 to d=5 on superconducting qubits (Google Quantum AI),abstract,googlequantum2023suppressing
21+4389373471,empirical,"Logical quantum processor on reconfigurable neutral-atom arrays, up to 280 physical qubits",abstract,bluvstein2024logical
22+4380627387,empirical,"127-qubit experiment with accurate expectation values beyond brute-force classical simulation (IBM, 'utility before fault tolerance')",abstract,kim2023utility
23+4405183524,empirical,"Below-threshold surface-code memories (Willow): distance-7 code, logical error suppression factor 2.14 per distance step",abstract,googlequantum2025belowthreshold
24+4408847956,survey,Review of barren plateaus in variational quantum computing,metadata,larocca2025barren
25+4407158326,empirical,Distributed quantum computing across an optical link between two trapped-ion modules (gate teleportation),abstract,main2025distributedqc
26+4406693713,engineering,Aurora: 35-chip modular photonic quantum computer scale model with fibre networking (sub-performant by the authors' description),abstract,aghaee2025aurora
27+4396986598,engineering,Qiskit SDK design and architecture (preprint),abstract,javadiabhari2024qiskit
28+4409126690,survey,"IBM quantum computers: evolution, performance, industry applications",abstract,abughanem2025ibmquantum
29+4321649614,method,Quantum-selected configuration interaction: hybrid quantum-classical ground-state algorithm robust to sampling noise,abstract,kanno2026qsci
30+2781738013,theory,NISQ era framing (Preskill 2018),abstract,preskill2018nisq
31+2982169647,empirical,Quantum supremacy on a 53-qubit programmable superconducting processor,abstract,arute2019supremacy
32+4213212652,survey,Review of noisy intermediate-scale quantum algorithms,abstract,bharti2022nisq
33+4415124086,engineering,"The Faiss library: vector similarity search toolkit, design principles and benchmarks",abstract,douze2026faiss
34+2593864460,engineering,Billion-scale similarity search on GPUs (k-selection design; Faiss),abstract,johnson2021faiss
35+4406085118,survey,"Data-centric AI survey: training-data development, inference-data development, data maintenance",abstract,zha2025datacentric
36+4408444355,empirical,Effects of six data-quality dimensions on 19 ML algorithms (tabular data) under polluted training/test data,abstract,mohammed2025dataquality
37+2963507231,benchmark,BLOCKBENCH: data-processing view of private blockchains and benchmarking framework,abstract,dinh2018blockbench
38+2795038878,method,Datasheets for datasets (documentation standard),abstract,gebru2021datasheets
39+4401857375,survey,Survey of retrieval-augmented LLMs (KDD 2024),abstract,fan2024ragsurvey
40+4394947112,survey,Survey of retrieval-augmented text generation organised as pre-retrieval / retrieval / post-retrieval / generation,abstract,huang2026ragsurvey
41+7118085507,survey,Survey of RAG for AI-generated content across modalities,abstract,zhao2026ragaigc
42+4401834466,survey,Survey of LLMs for recommendation; abstract unavailable in the corpus,metadata,wu2024llmrec
43+4394994587,survey,Recommender systems in the era of LLMs,abstract,zhao2024recsysllm
44+4400531953,benchmark,"C-Pack: C-MTP training data, C-MTEB benchmark and BGE embedding models for Chinese and English",abstract,xiao2024cpack
45+3045200674,method,LightGCN: simplified graph convolution for collaborative filtering (ablation of feature transformation and non-linearity),abstract,he2020lightgcn
46+3097300053,survey,Graph neural networks in recommender systems: survey,abstract,wu2023gnnrec
47+4315977496,survey,"Survey of GNNs for recommender systems: challenges, methods, directions",abstract,gao2023gnnrecsurvey
48+4394946189,engineering,RCACopilot: LLM-based root-cause analysis for cloud incidents; authors report RCA accuracy up to 0.766 on a year of Microsoft incidents,abstract,chen2024rcacopilot
49+4395047770,survey,Review of deep-reinforcement-learning methods for cloud resource scheduling,abstract,zhou2024drlcloud
50+7160863705,engineering,CAPS: carbon-aware prompt scheduling for multi-tenant LLM inference (trace-driven simulation; 26.8% lower carbon per 1K tokens vs round-robin as reported),abstract,liao2026caps
51+7161556515,engineering,DeepServeCB: contextual-bandit elastic scheduling for serverless multi-tenant LLM inference,abstract,li2026deepservecb
52+2930508541,theory,Berkeley view on serverless computing (preprint; position paper),abstract,jonas2019serverless
53+3152809665,engineering,Nightcore: sub-millisecond serverless runtime for interactive microservices,abstract,jia2021nightcore
54+3021475380,engineering,Keystone: open framework for customisable trusted execution environments on RISC-V,abstract,lee2020keystone
55+2950865323,survey,Edge intelligence: survey of AI at the network edge (Proc. IEEE 2019),abstract,zhou2019edgeintelligence
56+4406207083,survey,Mobile edge intelligence for LLMs: contemporary survey,abstract,qu2025meillm
57+7123361055,survey,Edge general intelligence with agentic AI: concepts and technologies,abstract,zhang2026edgegeneral
58+4415028496,survey,Edge-cloud collaborative computing on distributed intelligence and model optimisation: survey,abstract,liu2026eccc
59+7125650829,theory,AI Flow: device-edge-cloud framework for large models (perspective),abstract,an2026aiflow
60+4322576964,survey,"On the road to 6G: visions, requirements, key technologies, testbeds",abstract,wang2023road6g
61+3194620979,survey,Integrated sensing and communications for 6G (JSAC overview),abstract,liu2022isac
62+7127083419,survey,From large AI models to agentic AI: tutorial for intelligent communications,abstract,jiang2026lamagentic
63+7125196921,survey,"Massive IoT: energy, scalability, data and security review",abstract,cheikh2026massiveiot
64+2962883549,survey,Deep learning in mobile and wireless networking: survey,abstract,zhang2019dlwireless
65+3156891177,empirical,"Carbon emissions and large neural network training: energy and CO2e estimates for T5, Meena, GShard, Switch Transformer, GPT-3 (preprint)",abstract,patterson2021carbon
66+4308245305,empirical,"Carbon footprint of BLOOM across its life cycle: 24.7 t CO2e (dynamic) to 50.5 t (full), as reported (preprint)",abstract,luccioni2023bloom
67+4283157527,empirical,Software carbon intensity of AI in cloud instances; argues providers should expose carbon information,abstract,dodge2022carbonintensity
68+4362707075,empirical,Water footprint of AI models (preprint); CACM version li2025thirsty,abstract,li2023thirsty
69+4385481791,empirical,Carbon impact of generative-AI inference today and in 2035; workload model with request-direction policies,abstract,chien2023carbongenai
70+4399913870,survey,"Review of green AI (green-by, green-in, measurement tools, regulation)",abstract,boloncanedo2024greenai
71+4411541921,theory,Jevons' paradox / rebound effects in AI's environmental debate (argumentative paper),abstract,luccioni2025jevons
72+7115707641,theory,Carbon and water footprints of data centres: disclosure gaps and policy (perspective),abstract,devriesgao2026datacenters
73+4415057924,survey,Electricity demand and grid impacts of AI data centres: review and vision,abstract,chen2026aielectricity
74+7138433923,benchmark,TokenPowerBench: benchmark for power consumption of LLM inference with prefill/decode attribution,abstract,niu2026tokenpowerbench
75+4407592994,survey,Green federated learning survey,abstract,thakur2025greenfl
added literature_synthesis/agent_F/open_problems.csv +14 −0
@@ -0,0 +1,14 @@
1+problem_id,statement,categories,why_unresolved,testable_direction,supporting_keys
2+OP-F01,"Whether paged (non-contiguous) or virtual-memory-backed contiguous KV caches are the better abstraction for LLM serving, and at what cost in programmability, is unsettled.",operating_systems;distributed_computing,"Each design is evaluated by its own authors on specific GPU generations and workloads; no neutral, versioned benchmark across hardware exists.","A shared serving benchmark (fixed traces, several GPU generations, open harness) reporting throughput-per-watt and tail latency for vLLM-style paging, vAttention-style virtual memory and phase-split deployments.",kwon2023vllm;prabhu2025vattention;patel2024splitwise
3+OP-F02,Whether the memory wall (bandwidth growing slower than FLOPS) can be circumvented by architectural redesign rather than by model-side sparsity/quantization.,computer_architecture;ai_accelerators,The argument rests on hardware trend extrapolation and on serving workloads of 2023-2024; it has not been tested as the installed base changes.,"Track bandwidth-normalised decode throughput across accelerator generations and model families; test whether gains come from bandwidth or from algorithmic sparsity (AWQ, PowerInfer-style locality).",gholami2024memorywall;lin2025awq;song2024powerinfer
4+OP-F03,"Device-level efficiency figures of photonic and neuromorphic accelerators (e.g., 160 TOPS/W) have not been shown to translate into end-to-end system gains once conversion, memory and precision costs are included.",ai_accelerators;sustainable_computing,Demonstrations are on small tasks or shallow models and report energy at the photonic core; no agreed end-to-end benchmark; abstracts are the only evidence reviewed here.,"An MLPerf-style end-to-end benchmark for analog/photonic accelerators including ADC/DAC and memory energy, with precision-matched digital baselines.",xu2024taichi;ahmed2025photonic;lam2026neuromorphicphotonic;fu2024onn
5+OP-F04,Whether pre-fault-tolerant quantum processors deliver computational utility beyond the best classical simulation remains contested.,quantum_computing,Utility claims are stated relative to brute-force simulation; improved classical methods move the baseline after publication; the landmark set contains the claim but not its rebuttals.,Preregistered utility experiments with independently committed classical baselines (tensor-network and Clifford-perturbation methods) evaluated by third parties before publication.,kim2023utility;bharti2022nisq
6+OP-F05,Whether variational quantum algorithms can avoid barren plateaus without losing classical-simulability or expressivity.,quantum_computing,Theoretical results suggest a trade-off between trainability and classical simulability; empirical demonstrations are small-scale.,"Scaling experiments on error-corrected hardware with trainability diagnostics, compared against classical surrogates such as quantum-selected configuration interaction.",larocca2025barren;cerezo2021vqa;kanno2026qsci
7+OP-F06,"Scaling modular and distributed quantum computing (photonic interconnects, gate teleportation) to rates and fidelities compatible with error correction.",quantum_computing;computer_networks,Demonstrations are two-module or sub-performant scale models; fidelity and entanglement rate at scale are unknown.,"Multi-module logical-qubit experiments with reported entanglement rates, fidelities and decoder latency; comparison of trapped-ion, neutral-atom and photonic modules under a common protocol.",main2025distributedqc;aghaee2025aurora;bluvstein2024logical
8+OP-F07,There is no agreed protocol for evaluating retrieval-augmented generation that separates the contribution of retrieval quality from parametric memory and from generation.,information_retrieval,"Surveys propose taxonomies, not shared evaluation standards; IR metrics (recall, nDCG) and generation metrics are reported inconsistently.","Benchmarks with held-out, time-stamped corpora and ablations over retriever, index freshness and generator, reporting retrieval metrics and answer faithfulness jointly.",fan2024ragsurvey;huang2026ragsurvey;zhao2026ragaigc;lewis2020rag
9+OP-F08,Whether vector search belongs in specialised vector databases or as an extension of relational systems is contested.,database_systems,Library-level evidence (Faiss) and surveys exist; workload-level comparisons under realistic transactional and hybrid queries are rare.,"Open benchmarks mixing structured predicates and approximate nearest-neighbour search over billion-scale embeddings, comparing dedicated and extended systems on cost and recall.",douze2026faiss;pan2024vectordb;stonebraker2024goesaround;malkov2020hnsw
10+OP-F09,"Where LLM inference should sit on the device-edge-cloud continuum under energy, latency and privacy constraints.",edge_computing;computer_networks,The literature is dominated by surveys and visions; measured trade-offs on reproducible testbeds are scarce.,"Testbed studies of split inference (on-device, mobile-edge, cloud) with measured energy per token, latency and accuracy for identical models and traffic.",qu2025meillm;liu2026eccc;zhang2026edgegeneral;an2026aiflow
11+OP-F10,"The energy, carbon and water footprints of AI inference at fleet scale cannot be measured from public data; rebound effects are untested.",sustainable_computing;cloud_computing,Operators do not disclose AI-specific metrics; estimates rely on models and extrapolation; industry figures quoted in abstracts are not independently verifiable.,Mandatory disclosure of AI workload energy and water by region; benchmarks attributing energy to prefill/decode; natural experiments on efficiency gains and total consumption.,chien2023carbongenai;li2025thirsty;devriesgao2026datacenters;luccioni2025jevons;niu2026tokenpowerbench;chen2026aielectricity
12+OP-F11,"LLM-based operations tooling (incident root-cause analysis, scheduling) is evaluated on proprietary traces; external validity is unknown.",cloud_computing,Evaluations use one provider's incidents or trace-driven simulations; no public incident corpus with ground-truth causes.,"A public, anonymised incident benchmark with labelled root causes and an evaluation protocol for LLM-assisted diagnosis.",chen2024rcacopilot;liao2026caps;li2026deepservecb
13+OP-F12,"The classifier assigns scientific software, social-science and health records to systems categories, and the landmark score elevates re-published classics and anomalous citation counts; category-level results inherit this noise.",computer_architecture;operating_systems;distributed_computing;cloud_computing,Rule-based multi-label classification over OpenAlex topics; no venue-stream validation for systems categories in the landmark lists reviewed here.,Venue-based validation (DBLP streams such as OSDI/SOSP/ISCA/SIGCOMM/SIGMOD) of category precision; exclusion of re-publication records from citation-window scores.,priem2022openalex;ley2002dblp
14+OP-F13,Whether the 2023-2025 output recovery of the classical systems categories reflects LLM-infrastructure research or mean reversion after a 2018-2022 decline.,computer_architecture;operating_systems;distributed_computing;cloud_computing;database_systems;computer_networks,The ITS slope change is significant for all categories but placebo dates perform similarly; break dates are scattered; the landmark lists show LLM-serving papers but their share of each category's volume is not measured.,"Topic-level decomposition of each systems category (LLM-serving, GPU scheduling, vector search subtopics) and their share of category growth; comparison with control fields outside AI.",kwon2023vllm;gholami2024memorywall
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1+# Agent F — Systems and Computing Infrastructure: disciplinary synthesis (WP44, Phase 5)
2+
3+Scope: database systems, information retrieval, distributed computing, cloud computing, edge computing, computer networks
4+(area `data_distributed`); computer architecture, accelerators and AI hardware, operating systems, quantum computing,
5+sustainable computing (area `hardware_emerging`). Inputs: `literature_synthesis/landmarks/{data_distributed,hardware_emerging}_landmarks.csv`
6+and `_abstracts.jsonl`, `results/graph/category_counts_yearly.csv`, `results/graph/category_flows_yearly.csv`,
7+`results/bibliometrics/cagr_by_category.csv`, `results/econometrics/breaks.csv`, `manuscript/references.bib`.
8+Every claim below is indexed in `claims.csv` (F-01 … F-43); every landmark discussed is typed in `contributions.csv`;
9+unresolved questions are in `open_problems.csv` (OP-F01 … OP-F13). Evidence is metadata and abstracts only; no full text
10+was read. Preprints are named as such. Nothing in this review attributes a change to ChatGPT.
11+
12+## 1. What the evidence base is, and what it is not
13+
14+### 1.1 The candidate lists
15+
16+The two landmark lists contain 152 and 125 records, that is 25 per category after the floor rule of
17+`select_landmarks.py` (top works by NCS36 within primary category; for 2025–2026, c12 relative to the year mean). Between
18+10 (distributed computing, operating systems) and 20 (AI accelerators, sustainable computing) records per category fall in
19+the post period. Abstracts are available for 14 of 25 accelerator records and for 19–24 of 25 elsewhere (F-10).
20+
21+Two properties of the scoring rule shape what follows. First, because 2025–2026 records are ranked by c12 relative to a
22+year mean that is itself small, several 2026 "landmarks" have nine to thirty citations in total (F-11); they are
23+provisional and are treated here as indications of what is being cited now, not as established landmarks. Second,
24+NCS36 rewards records that OpenAlex has re-dated: the distributed-computing list opens with MapReduce, Lamport's
25+"Time, clocks" and "The Byzantine generals problem" recorded as 2018–2019 re-publications, an OpenMP `taskwait`
26+paper carries c36 = 12,891, an eBPF preprint dated 2020 carries the DOI of a 2026 OASIcs volume, and NumPy
27+("Array programming with NumPy", c36 = 5,512) is the top baseline record of computer architecture (F-36). These
28+anomalies are documented in `contributions.csv` and `claims.csv` and should be fed to the data-quality discussion of
29+Parts III and XI; they are not discussed as scientific contributions of the period.
30+
31+### 1.2 Misclassified records (excluded from the synthesis, listed for Agent K)
32+
33+The rule-based classifier assigns to the systems categories a number of records that are not systems research. The
34+clearest cases are: `cloud_computing` 110250037 (a dissertation on teaching Chinese as a second language), 4364375730
35+(social health protection during COVID), 4385584867 and 4386827074 (energy-management book chapters);
36+`database_systems` 3080729669 and 3032576892 (student dissertations), 3005599391 (an ethnography of mobile phones),
37+3037795158 (digital technology in pandemic response, *Lancet Digital Health*), 4324129606 (data quality of online
38+human-subjects panels), 4402762189 and 4408925021 (e-learning), 7161586974 (Paleobiology Database user guide);
39+`operating_systems` 3092732687 and 3134501457 (student dissertations), 4366587887 (educational technology),
40+7130515337 (nursing students' AI literacy), 7162190207 (AI mental-health therapy), 7131624127 (feature selection),
41+7127083763 (regulatory sandboxes); `quantum_computing` 4386285839 (strategic transformation of higher education),
42+4407091026 (mammography screening); `sustainable_computing` 4403458785, 4407614581, 7122842330, 7128511066, 7131638452
43+(higher education, banking, globalisation, product passports); `distributed_computing` 3182706339 (a statistics paper
44+on R-squared), 7130586201, 7123363719, 7118998287 (control theory); `computer_architecture` 3099878876 (NumPy),
45+3046751266 and 3016932526 (molecular-dynamics and DFT codes), 2952332632 (autonomous-vehicle architecture);
46+`edge_computing` 3017623177, 3023398824, 3006456063, 3023617420 (COVID-era application reviews), 4401806686
47+(generic AI overview); `information_retrieval` 4395483946 (generative AI in higher education). Roughly one record in
48+five in the two lists is of this kind. This is a finding about the taxonomy (OP-F12), and it means that the category-level
49+statistics of §2 carry classification noise whose magnitude is not estimated here.
50+
51+A second group is in scope but is not "research of the period": IETF RFCs (Multipath TCP 2020, SCTP 2022, BPF ISA
52+2024), textbooks and handbooks (*Principles of Distributed Database Systems*, *Wireless Communications*, *Spatial Data
53+Science*), a Linked-Data chapter and a Semantic-Web reprint. They record what the field cites, not what it produced.
54+
55+### 1.3 How the review reads the rest
56+
57+After these exclusions, 74 records are discussed and typed in `contributions.csv`. Every statement about a paper's
58+content is taken from its abstract; where the abstract is missing (Nature 2025 photonic accelerator; the 2025 barren-plateau
59+review; the LLM-for-recommendation survey; the processing-in-memory primer) the statement is from title and venue
60+only, and `claims.csv` says so.
61+
62+## 2. The eleven categories in the corpus
63+
64+### 2.1 Volume and growth
65+
66+`cagr_by_category.csv` (eligible works by primary category; 2026 excluded as incomplete) separates the eleven
67+categories into two groups (F-04, F-05). Seven classical categories — computer architecture, operating systems,
68+distributed computing, cloud computing, computer networks, database systems and information retrieval — *declined*
69+between 2018 and 2022 (baseline CAGR between −0.018 for IR and −0.087 for operating systems) and *grew* between
70+2023 and 2025 (post CAGR between 0.071 for networks and 0.198 for cloud). Four categories grew in both periods:
71+edge computing (0.133 then 0.126), quantum computing (0.103 then 0.287), sustainable computing (0.092 then 0.233) and
72+AI accelerators (0.148 then 0.092). The shares of the classical categories in total primary output fell throughout:
73+computer networks, the largest, from 0.118 in 2018 to 0.067 in 2025; architecture from 0.039 to 0.023; databases from
74+0.033 to 0.019. Quantum computing rose from 0.0125 to 0.0196. The relative decline of the classical systems fields is
75+a pre-2022 phenomenon that continued after 2022 even as their absolute output recovered.
76+
77+### 2.2 Structural change
78+
79+In the interrupted time-series model of Part IV (`breaks.csv`, monthly log-volume, event 2022-12), all eleven
80+categories show a positive and significant change in trend (F-01): from 0.004 per month for edge computing
81+(p = 0.006) to 0.023 for information retrieval, with 0.020 for distributed computing, 0.019 for cloud, 0.018 for
82+accelerators and quantum, 0.016 for architecture, operating systems and sustainable computing, 0.014 for databases
83+and 0.009 for networks. This uniformity is itself informative: the trend steepening is not confined to the categories
84+that have anything to do with language models. The placebo test confirms the point (F-02): for every category the
85+share of placebo event dates whose slope statistic is at least as extreme is 0.062 or more (architecture 0.062,
86+distributed and edge 0.077, databases and IR 0.092, accelerators, quantum and sustainable 0.169, networks 0.200, cloud
87+0.231, operating systems 0.477). Level changes at 2022-12 are heterogeneous in sign (F-03): negative and significant
88+for IR (−0.094), accelerators (−0.113) and quantum (−0.087); positive and significant for networks (+0.043) and
89+sustainable computing (+0.049); indistinguishable from zero for the others — and placebo level p-values are all above
90+0.47. Bai–Perron break dates selected by BIC are scattered (F-06): 2022-01 and 2025-04 for quantum, 2022-02 for
91+networks, 2022-07 for sustainable computing, 2022-07 and 2024-07 for distributed computing and IR, 2022-11 and
92+2025-01 for cloud (the only 2022-11 break), 2023-05 for architecture, 2023-09 for operating systems, 2023-11 for
93+databases, 2024-11 for accelerators, 2025-01 for edge computing. The reading that the corpus supports is a
94+field-wide acceleration of output from 2022–2023 onwards that the November 2022 date does not single out, superimposed
95+on a prior decline of the classical categories, so that mean reversion competes with any infrastructure-demand
96+explanation (F-39, OP-F13).
97+
98+### 2.3 Citation flows
99+
100+Aggregating `category_flows_yearly.csv` over citing years 2018–2022 and 2023–2026 (calendar years, not the 30 November
101+boundary), the systems categories remain strongly self-referential (F-07): in the post years the self-share is 84.9 %
102+for quantum computing, 72.4 % for networks, 59.2 % for IR, 57.7 % for architecture, 40.6 % for databases, 34.7 % for
103+accelerators, 33.1 % for cloud, 29.8 % for distributed computing, 24.4 % for edge computing, 16.0 % for operating
104+systems and 11.3 % for sustainable computing. Shares moved by less than four points between periods (F-08). Three
105+details matter for the synthesis (F-09): the `llm` category enters the seven largest destinations of IR references only
106+in the post years (3.0 %); operating systems now sends more references to computer architecture (18.8 %) than to
107+itself; edge computing and sustainable computing cite computer networks more than their own category in both periods,
108+which is as much a statement about where the classifier draws boundaries as about intellectual dependence.
109+
110+## 3. Computer architecture, accelerators and operating systems
111+
112+### 3.1 Architecture: the most cited work is machine-learning systems
113+
114+Once scientific software and misclassified records are removed, the post-2022 architecture landmarks are a coherent
115+set (F-37): *PyTorch 2* (ASPLOS 2024), whose abstract reports that TorchDynamo captures graphs by rewriting Python
116+bytecode and that TorchInductor yields geometric-mean speedups of 2.27× for inference and 1.41× for training on the
117+authors' model suite (F-16; a vendor-authored evaluation); *Splitwise* (ISCA 2024), which characterises LLM inference
118+as a compute-bound prompt phase and a memory-bound token-generation phase and places them on different machine
119+pools (F-14); *AWQ* (the GetMobile reprint of the MLSys 2024 paper), an activation-aware weight-quantisation method
120+with an on-device inference framework; the *AI and Memory Wall* perspective (IEEE Micro 2024), which states that peak
121+server FLOPS scaled at 3.0× per two years over twenty years against 1.6× for DRAM bandwidth and 1.4× for interconnect
122+bandwidth, and argues that decoder inference is bandwidth-bound (F-15); and a 2025 ACM Computing Surveys review of
123+deep-learning accelerators for heterogeneous HPC platforms (GPU, TPU, FPGA, ASIC, RISC-V, processing-in-memory,
124+neuromorphic). The baseline list adds the processing-in-memory primer and Ben-Nun and Hoefler's concurrency analysis
125+of distributed deep learning. The lineage is the one the bibliography already documents — the end of Dennard scaling
126+and the "golden age" of domain-specific architecture, TPU v1 and v4, GPU evolution — and the post-2022 landmarks are
127+its continuation under one workload. What is established is that the architecture community's most cited output of
128+2023–2025 concerns LLM training and serving; what is not established, from abstracts, is whether the memory-wall
129+diagnosis holds as the installed base changes (OP-F02).
130+
131+### 3.2 Accelerators: photonic and neuromorphic demonstrations, and a classification artefact
132+
133+The accelerator list is the most distorted by classification (F-12): at least ten of its twenty post-period records are
134+memristive or chaotic Hopfield-network circuit papers in *Integration*, *Neural Networks*, *IEEE TCYB* and *Chaos,
135+Solitons & Fractals*, a nonlinear-dynamics literature that the topic classifier maps to AI hardware. Digital
136+accelerators (TPU v4, GPUs) are absent from the list though present in the bibliography (F-43); they appear to be
137+classified under architecture. What remains is a photonic and neuromorphic thread: Loihi (IEEE Micro 2018), the
138+neuromorphic-computing perspective of *Nature Computational Science* (2022), the *Taichi* photonic chiplet
139+(*Science* 2024; the abstract reports 160 TOPS/W and on-chip 1,000-category classification), *Universal photonic
140+artificial intelligence acceleration* (*Nature* 2025; abstract unavailable), an optical-neural-network review
141+(*Light: Science & Applications* 2024), and 2026 *Nature Communications* papers on an electro-optic analog memory
142+(the abstract reports a >26× power saving in analysis relative to SRAM–DAC designs) and a hyper-multiplexed
143+matrix–matrix optical processor. These are device-level demonstrations with device-level efficiency metrics; the
144+abstracts do not report end-to-end energy including conversion and memory, and the review treats their system-level
145+impact as speculative (F-17, OP-F03). The category's level drop at 2022-12 (−0.113, p = 0.027) and late BIC break
146+(2024-11) do not suggest a language-model-driven surge in this literature.
147+
148+### 3.3 Operating systems: GPU resource management as the OS problem of the period
149+
150+Operating systems is the smallest of the eleven categories (1,363 primary works in 2025) and the one that declined
151+fastest before 2022 (−0.087 per year), recovering at 0.078 per year afterwards (F-35). Its post-period landmarks are
152+*PowerInfer* (SOSP 2024), a GPU–CPU hybrid engine that keeps "hot" neurons on a consumer GPU and computes "cold" ones
153+on the CPU, exploiting a power-law activation distribution; *Orion* (EuroSys 2024), which intercepts kernel launches
154+from several clients and schedules at operator granularity to limit interference; and *vAttention* (ASPLOS 2025),
155+which argues that PagedAttention's non-contiguous KV-cache layout imposes programming and performance overheads and
156+restores contiguity with CUDA virtual memory. Read together with vLLM (SOSP 2023, classified under distributed
157+computing), this is a live design controversy over memory abstractions for LLM serving (F-13, OP-F01), evaluated by
158+each system's own authors on particular hardware. The baseline landmarks — *Keystone* (EuroSys 2020, customisable TEEs
159+on RISC-V), *Nightcore* (ASPLOS 2021, sub-millisecond serverless), the Berkeley serverless view (preprint), persistent-
160+memory key-value stores — show the field's pre-2022 concerns (serverless, TEEs, persistent memory) that the landmark
161+lists no longer register after 2023. The BPF instruction-set RFC (2024) and an in-kernel aggregation preprint point to
162+kernel-extension technology as an infrastructure theme, on thin evidence (F-42). Operating systems is also the
163+category whose largest citation destination is now computer architecture (F-09), consistent with GPU-centric systems
164+work.
165+
166+## 4. Distributed, cloud and edge computing, and networks
167+
168+### 4.1 Distributed computing
169+
170+After the re-publication records and the control-theory papers are set aside, the post-period distributed-computing
171+landmarks are vLLM (SOSP 2023; the abstract reports 2–4× throughput at equal latency from near-zero KV-cache waste),
172+LOOM (a live race-bypassing system, recorded as a 2026 Columbia repository item) and OrbitalBrain (in-space ML
173+training on nanosatellites, OASIcs 2026). The baseline list, beyond the classics, contains a blockchain survey
174+duplicated under two OpenAlex identifiers, *Majority is not enough* (selfish mining) and a Korean consensus
175+pre-processing paper — blockchain was the distributed-computing growth topic of 2018–2020, and the post-period flows
176+still send 4.3 % of distributed-computing references to the blockchain category. The category's trend change at
177+2022-12 is among the largest (0.020) and its BIC breaks are 2022-07 and 2024-07. Nothing in the landmark set
178+supports a statement about consensus, transactions or fault tolerance in 2023–2026; the distributed-systems canon
179+of the bibliography (Paxos, PBFT, CAP, Spanner, Borg, Kubernetes, serverless) is cited but not renewed in the
180+most-cited slice.
181+
182+### 4.2 Cloud computing
183+
184+The post-period cloud landmarks split into three kinds (F-27, F-28). (i) LLMs for operations: *RCACopilot* (EuroSys
185+2024) matches incidents to handlers, aggregates diagnostics and predicts the root-cause category; the abstract reports
186+accuracy up to 0.766 on a year of Microsoft incidents, a proprietary evaluation (OP-F11). (ii) Reviews: deep
187+reinforcement learning for cloud scheduling (*Artificial Intelligence Review* 2024) and an MDPI systematic review of
188+30 studies on federated learning for cloud/edge security whose aggregate percentages (25 % privacy-risk reduction,
189+40 % better threat detection) are not interpretable without the primary studies (F-41). (iii) The 2026 slice, which
190+consists almost entirely of LLM-inference scheduling at smaller IEEE conferences: token-level GPU pooling,
191+contextual-bandit elastic scheduling for serverless multi-tenant inference, and carbon-aware prompt scheduling (CAPS,
192+trace-driven simulation, 26.8 % lower carbon per thousand tokens than round-robin as reported). These are provisional
193+landmarks (F-11) and single-testbed or simulation studies, but they show the direction of citation in 2026. The
194+baseline cloud landmarks were VM and task scheduling with metaheuristics, energy efficiency in data centres and
195+IoT–cloud integration; the continuity is scheduling, the discontinuity is the workload. Cloud computing's ITS level
196+change is zero (p = 0.936) while its trend change is 0.019 and its BIC breaks are 2022-11 and 2025-01 (F-29); it is
197+the only systems category with a selected break in the ChatGPT month, which the placebo result (0.231) does not allow
198+to be read as specific.
199+
200+### 4.3 Edge computing
201+
202+Edge computing is the category whose landmark composition changed most visibly and whose time series changed least
203+(F-30). Its baseline landmarks are blockchain–IoT integration papers (2018–2019), the *Edge Intelligence* survey
204+(*Proceedings of the IEEE* 2019) and COVID-era IoT application reviews; its post-period landmarks are surveys and
205+visions on language models at the edge: *Mobile Edge Intelligence for LLMs* (COMST 2025), *Toward Edge General
206+Intelligence with Agentic AI* (COMST 2026), *Edge–Cloud Collaborative Computing* (COMST 2026) and *AI Flow*
207+(2026), which proposes a device–edge–cloud framework for large models. The abstracts describe architectures,
208+taxonomies and challenges; none reports a measured deployment. Edge computing's trend change at 2022-12 is the
209+smallest of the eleven (0.004, p = 0.006) after a baseline CAGR of 0.133 that had already doubled the category's
210+share; its only BIC break is 2025-01. The LLM-at-the-edge literature is therefore, on this evidence, a change in
211+what the field writes surveys about rather than a measurable change in its output. Where inference should sit on the
212+device–edge–cloud continuum is an open empirical question (OP-F09).
213+
214+### 4.4 Computer networks
215+
216+Networks is the largest systems category and the one whose share of output fell most (0.118 to 0.067). Its most cited
217+post-2022 works are the 6G roadmap (COMST 2023) and the ISAC overview (JSAC 2022), both pre-dating or coinciding with
218+the window's start, the *Wireless Communications* textbook, the mobile-edge-LLM survey, and in 2026 tutorials on
219+large AI models and agentic AI for communications (JSAC 2026), a massive-IoT review and a pinching-antenna tutorial
220+(F-31). The positive level change at 2022-12 (+0.043, p = 0.021) has placebo p = 0.538 and the single BIC break is
221+2022-02; the series was already rising before ChatGPT. The deep-learning-for-wireless surveys of 2018–2019 show that
222+machine learning entered networking research well before the window; what the 2026 tutorials add is the vocabulary of
223+large models and agents, whose empirical content cannot be assessed from abstracts.
224+
225+## 5. Database systems and information retrieval
226+
227+### 5.1 Databases: data quality and vector search
228+
229+The database landmark list is the most contaminated by misclassification (§1.2). What remains is instructive (F-23,
230+F-40): the only engine-level item is vector search — the GPU similarity-search paper (IEEE TBD 2019) and *The Faiss
231+Library* (IEEE TBD 2025/26), whose abstract frames Faiss as the core of vector databases and describes its indexing,
232+compression and benchmarking; the other post-period landmarks are the *Data-centric AI* survey (CSUR 2025) and an
233+empirical study of six data-quality dimensions across nineteen ML algorithms (*Information Systems* 2025). The
234+baseline list contains BLOCKBENCH (TKDE 2018), *Datasheets for datasets* (CACM 2021) and a persistent B+-tree
235+dissertation. The bibliography's vector-DBMS surveys and the database community's position papers on LLMs
236+(*Proceedings of the VLDB Endowment* 2023; *SIGMOD Record* 2024) are not in the landmark set. The honest summary is
237+that the most cited database work of the period, as the corpus records it, is about data for machine learning and
238+about embedding search, not about transaction processing or query optimisation; whether vector search belongs in
239+dedicated systems or in extended relational engines is contested and untested at workload level (OP-F08). The
240+category's trend change (0.014) and BIC break (2023-11) are unremarkable among the eleven.
241+
242+### 5.2 Information retrieval: from graph recommendation to retrieval augmentation
243+
244+IR shows the clearest shift in what is cited (F-24). The 2018–2022 landmarks are graph-neural-network recommendation
245+— NGCF, KGAT, GraphRec, LightGCN (SIGIR 2020, which reports that feature transformation and non-linear activation
246+contribute little to collaborative filtering), self-supervised graph learning — and the GNN-recommendation surveys of
247+CSUR and ACM TORS. The 2023–2026 landmarks are RAG surveys (KDD 2024; CSUR 2026, which organises RAG into
248+pre-retrieval, retrieval, post-retrieval and generation; *Data Science and Engineering* 2026 on RAG for generated
249+content), surveys of LLMs for recommendation (TKDE 2024; *World Wide Web* 2024), and *C-Pack* (SIGIR 2024), which
250+releases training data, a Chinese embedding benchmark and the BGE embedding models. The methodological lineage is
251+continuous with 2020 (DPR, RAG, ColBERT); the landmark set contains no new retrieval architecture of 2023–2026, which
252+may reflect the citation lag of recent model papers as much as the field's output (F-25). In the corpus, IR has the
253+largest trend change at 2022-12 (0.023), a negative level change (−0.094, p = 0.019), BIC breaks at 2022-07 and
254+2024-07 (F-26), and is the only systems category whose post-period references reach the `llm` category in measurable
255+proportion (3.0 %). RAG evaluation — separating retrieval quality from parametric memory and from generation — has no
256+agreed protocol (OP-F07).
257+
258+## 6. Quantum computing
259+
260+Quantum computing is the category least connected to the rest (self-share 84.9 %), the fastest-growing after 2022
261+(CAGR 0.287) and the one whose level fell most clearly at 2022-12 (−0.087, p = 0.039) (F-22). Its landmarks are a
262+coherent experimental sequence that the window happens to contain (F-18): the NISQ framing (2018) and supremacy
263+experiment (2019) as baseline; then surface-code scaling from distance 3 to 5 on superconducting qubits (*Nature*
264+2023), the 280-physical-qubit neutral-atom logical processor (*Nature* 2024), the below-threshold distance-7
265+memories with logical error suppression factor Λ = 2.14 per distance step (*Nature* 2024), two-module trapped-ion
266+distributed computation by gate teleportation over an optical link (*Nature* 2025) and the 35-chip photonic *Aurora*
267+scale model (*Nature* 2025), which its authors describe as sub-performant (F-21). The 127-qubit "utility before fault
268+tolerance" experiment (*Nature* 2023) is contested: its abstract restricts the claim to circuit volumes beyond
269+brute-force classical simulation, and the classical-simulation responses that followed are not in the landmark set
270+and are not cited here because they are not in the verified bibliography (F-19, OP-F04). On the algorithmic side,
271+the barren-plateau review (*Nature Reviews Physics* 2025; abstract unavailable) and quantum-selected configuration
272+interaction (*Physical Review Research* 2026), which diagonalises classically in a subspace sampled from a noisy
273+device, mark the two responses to the trainability problem of variational algorithms (F-20, OP-F05). Qiskit's design
274+paper is a preprint; the IBM retrospective (*Journal of Supercomputing* 2025) is a narrative of one vendor's roadmap.
275+None of this literature has any connection with language models, and the corpus statistics for the category should
276+be read as the signature of a field growing on its own momentum.
277+
278+## 7. Sustainable computing
279+
280+Sustainable computing is small (1,639 primary works in 2025), grew at 0.233 per year after 2022, has a positive level
281+change at 2022-12 (+0.049, p = 0.048; placebo p = 0.754) and cites computer networks more than itself (F-34). Its
282+landmarks form a measurement literature on the footprint of machine learning (F-32): carbon of large-model training
283+(Patterson et al. 2021, preprint; BLOOM's 24.7–50.5 t CO2e, preprint), the carbon intensity of cloud instances
284+(FAccT 2022), generative-AI inference carbon today and in 2035 (HotCarbon 2023 workshop), the water footprint of AI
285+(preprint 2023, CACM 2025), green-AI and green-federated-learning reviews (2024–2025), rebound effects and Jevons'
286+paradox (FAccT 2025), data-centre carbon and water disclosure gaps (*Patterns* 2025/26), grid impacts of AI data
287+centres (*Nexus* 2026) and *TokenPowerBench* (AAAI 2026), a benchmark attributing inference energy to prefill and
288+decode. Several of the most cited items are preprints or workshop papers, and the quantitative statements they
289+carry (including industry figures quoted in abstracts, such as inference exceeding 90 % of LLM power) are not
290+independently verifiable from the corpus (F-33). The field's own diagnosis — that operators do not disclose AI-specific
291+energy and water, so fleet-scale footprints are modelled rather than measured — is the central open problem (OP-F10).
292+
293+## 8. Cross-cutting synthesis
294+
295+Three statements are established by the corpus. First, the systems categories accelerated after 2022 as a group,
296+and the acceleration is not specific to the ChatGPT date (F-01, F-02, F-06). Second, the classical systems categories
297+had been declining in absolute and relative terms since 2018 and recovered in absolute terms only; their share of
298+computer science continued to fall (F-04, F-05). Third, citation flows among systems categories were stable; the
299+only measurable new destination is IR's references to the `llm` category (F-07–F-09).
300+
301+Two statements are established by the metadata of the most cited works. The most cited post-2022 architecture,
302+operating-systems, distributed and cloud papers are LLM training and serving systems — compilation, phase splitting,
303+KV-cache management, GPU sharing, consumer-GPU inference, inference scheduling — and the most cited IR, edge and
304+networking papers are surveys and tutorials about language models (F-24, F-30, F-31, F-37, F-38). Quantum computing
305+and sustainable computing follow their own trajectories, the former experimental, the latter a measurement
306+literature on AI's footprint (F-22, F-32).
307+
308+What cannot be established from this evidence: that LLM-infrastructure research explains the volume recovery (its share
309+of each category's output is not measured; OP-F13); that any of the systems designs discussed is superior to its
310+competitors (each is evaluated by its authors; OP-F01, OP-F02); that photonic or neuromorphic efficiency figures hold at
311+system level (OP-F03); that pre-fault-tolerant quantum processors deliver utility (OP-F04). Finally, the landmark
312+lists themselves carry a classification-noise rate of roughly one record in five (OP-F12), which bounds the precision
313+of every category-level statement above and should be reported alongside the results of Part IX.
added literature_synthesis/agent_G/contributions.csv +151 −0
@@ -0,0 +1,151 @@
1+openalex_id,contribution_type,description,judged_from,area,primary_category,year,period,type,doi,c36,ncs36,verdict,id_resolves_to_other_work,bib_key,title
2+W4311415873,method,Kd-trees extended to Bregman divergences with a C++ nearest-neighbour implementation (WADS 2025); the record's citation counts belong to another work,abstract,theory,algorithms,2025,post,conference-paper,10.4230/lipics.wads.2025.45,2355.0,1915.3516038917253,in_scope_research,yes,pham2025kdtree,Fast Kd-Trees for the Kullback-Leibler Divergence and Other Decomposable Bregman Divergences
3+W2402846924,survey,Book chapter on particle swarm optimisation (2019); generic-title chapter carrying thousands of citations,metadata_only,theory,algorithms,2019,baseline,book-chapter,10.1142/9789811205675_0004,3767.0,1171.7895003921208,textbook,no,,Particle Swarm Optimization
4+W4294558607,engineering,GNN-selected branching vertex inside branch-and-reduce for maximum independent set (SEA 2024); counts belong to another work,abstract,theory,algorithms,2024,post,article,10.4230/lipics.sea.2024.20,5183.0,861.3021555600685,in_scope_research,yes,langedal2024mis,Targeted Branching for the Maximum Independent Set Problem Using Graph Neural Networks
5+W4372046852,survey,"Textbook chapter 'Introduction to Algorithms' (2023), no abstract",metadata_only,theory,algorithms,2023,post,book-chapter,10.1007/978-3-031-26212-8_4,1114.0,804.2780269058295,textbook,no,,Introduction to Algorithms
6+W4285092328,theory,Dijkstra's 1959 shortest-path note reprinted in the 2022 ACM Books volume,abstract,theory,algorithms,2022,baseline,book-chapter,10.1145/3544585.3544600,868.0,724.7350484463749,reprint_misdated,no,,A Note on Two Problems in Connexion with Graphs
7+W3148190347,survey,"Book chapter 'Introduction to Graph Theory' (2023), no abstract",metadata_only,theory,algorithms,2023,post,book-chapter,10.1142/9789811273117_0001,503.0,455.41427595628414,textbook,no,,Introduction to Graph Theory
8+W7128526706,method,"Glider snake optimiser, nature-inspired metaheuristic benchmarked on CEC functions (2026)",abstract,theory,algorithms,2026,post,article,10.1007/s10462-026-11504-x,91.0,101.26493940172338,metaheuristic,no,,Glider snake optimizer (GSO): a nature-inspired metaheuristic algorithm for global and engineering optimization problems
9+W2112641412,survey,Gallian's dynamic survey of graph labelling (2022 edition),abstract,theory,algorithms,2022,post,article,10.37236/11668,1780.0,288.61814892416174,in_scope_survey,no,,A Dynamic Survey of Graph Labeling
10+W3183881215,survey,Apress textbook chapter 'Introduction to Algorithms' (2021),abstract,theory,algorithms,2021,baseline,book-chapter,10.1007/978-1-4842-7077-6_3,434.0,279.4307771487391,textbook,no,,Introduction to Algorithms
11+W2003494253,survey,CS2 textbook 'Algorithms + Data Structures = Programs' (2024),abstract,theory,algorithms,2024,post,book-chapter,10.1017/9781009260350.002,231.0,275.60804020100505,textbook,no,,Algorithms+Data Structures = Programs
12+W7125913058,method,MIP model and evolutionary decoding for multi-resource flexible job-shop scheduling under pallet automation (2026),abstract,theory,algorithms,2026,post,article,10.1109/tsmc.2026.3655483,73.0,84.02038567493113,in_scope_research,no,,Multiresource-Constrained Flexible Job Shop Scheduling With Fixture Pallets and Setup Stations Under Pallet Automation Systems
13+W1491280261,method,Grammar-guided genetic programming (CFG-GP) thesis re-deposited in 2022,abstract,theory,algorithms,2022,baseline,dissertation,10.26190/unsworks/6784,28.0,209.69298245614038,reprint_misdated,no,,Grammatical bias for evolutionary learning
14+W3111526966,survey,CRC textbook chapter on algorithms (2020),abstract,theory,algorithms,2020,baseline,book-chapter,10.1201/9781003091479-1,202.0,200.82966396292002,textbook,no,,Introduction to Algorithms
15+W3119051141,method,"Arithmetic Optimization Algorithm (2021), metaphor-based metaheuristic",abstract,theory,algorithms,2021,baseline,article,10.1016/j.cma.2020.113609,1513.0,176.3286814620167,metaheuristic,no,abualigah2021aoa,The Arithmetic Optimization Algorithm
16+W4406080942,method,"Artificial lemming algorithm (2025), bionic metaheuristic",abstract,theory,algorithms,2025,post,article,10.1007/s10462-024-11023-7,330.0,112.41427295439541,metaheuristic,no,,Artificial lemming algorithm: a novel bionic meta-heuristic technique for solving real-world engineering optimization problems
17+W2146101030,theory,Communication complexity of document exchange (SODA 2000) re-deposited with a 2026 date,abstract,theory,algorithms,2026,post,conference-paper,10.82444/warw.33642076,46.0,353.0526747845123,reprint_misdated,no,,Communication complexity of document exchange
18+W4405348595,survey,Book chapter 'Positive Definite Matrices' (2024); abstract field reads 'placeholder notes',abstract,theory,algorithms,2024,post,book-chapter,10.1142/9789811299223_0004,125.0,151.20861797162374,textbook,no,,Positive Definite Matrices
19+W7118096636,method,"Modified starfish optimisation algorithm applied to heart-disease data (2026), no abstract",metadata_only,theory,algorithms,2026,post,article,10.1016/j.eswa.2026.131088,42.0,46.737664339256945,metaheuristic,no,,A modified starfish optimization algorithm (M-SFOA) for global optimization problems and its application to heart disease risk prediction
20+W4233225156,survey,Springer undergraduate chapter 'Introduction to Algorithms' (2020),metadata_only,theory,algorithms,2020,baseline,book-chapter,10.1007/978-3-030-34209-8_4,149.0,148.13673232908457,textbook,no,,Introduction to Algorithms
21+W2891784792,theory,"Online algorithms with machine-learned predictions (ski rental, scheduling); 2024 arXiv posting of a 2018 conference paper",abstract,theory,algorithms,2024,post,preprint,10.48550/arxiv.2407.17712,112.0,142.64098765432098,reprint_misdated,no,,Improving Online Algorithms via ML Predictions
22+W7155156112,method,"Gray langurs optimiser (2026), multi-group bio-inspired metaheuristic",abstract,theory,algorithms,2026,post,article,10.1007/s10462-026-11529-2,40.0,44.5120612754828,metaheuristic,no,,Gray langurs optimizer: a multi-group bio-inspired optimization algorithm
23+W3139484821,method,"Aquila Optimizer (2021), metaphor-based metaheuristic, no abstract",metadata_only,theory,algorithms,2021,baseline,article,10.1016/j.cie.2021.107250,1186.0,138.21931012158083,metaheuristic,no,abualigah2021aquila,Aquila Optimizer: A novel meta-heuristic optimization algorithm
24+W2121903617,survey,CRC chapter 'Introduction to evolutionary algorithms' (2018 reprint),abstract,theory,algorithms,2018,baseline,book-chapter,10.1201/9781482268713-14,292.0,135.62163175992498,textbook,no,,Introduction to evolutionary algorithms
25+W1486707681,theory,"Sequence distance embeddings (doctoral thesis, Warwick) re-deposited with a 2026 date",abstract,theory,algorithms,2026,post,dissertation,10.82444/warw.33642484,38.0,1163.0179211469535,reprint_misdated,no,,Sequence distance embeddings
26+W2207864362,theory,"Algebraically defined digraphs over finite fields (thesis, 2024)",abstract,theory,algorithms,2024,post,dissertation,10.58088/v6j9-5f14,6.0,134.92307692307693,dissertation,no,,Properties of some algebraically defined digraphs
27+W4254202520,theory,Turing's 1936 'On Computable Numbers' in a 2021 MIT Press anthology,metadata_only,theory,computational_complexity,2021,baseline,book-chapter,10.7551/mitpress/12274.003.0008,353.0,227.2789500772002,reprint_misdated,no,,"On Computable Numbers, with an Application to the Entscheidungsproblem (1936)"
28+W1638203394,survey,"Li and Vitanyi, An Introduction to Kolmogorov Complexity (4th ed., 2019)",metadata_only,theory,computational_complexity,2019,baseline,book,10.1007/978-3-030-11298-1,1437.0,122.10728346456693,textbook,no,li2019kolmogorov,An Introduction to Kolmogorov Complexity and Its Applications
29+W2979043732,theory,"ETH-tight algorithms for geometric network problems (thesis, TU/e 2019); abstract field is boilerplate",abstract,theory,computational_complexity,2019,baseline,dissertation,,5.0,111.01694915254237,dissertation,no,,ETH-tight algorithms for geometric network problems
30+W2957144896,empirical,UTAUT model of mobile-learning adoption (psychology),abstract,theory,computational_complexity,2019,baseline,article,10.3389/fpsyg.2019.01652,177.0,72.24848261060211,misclassified,no,,Factors Determining the Behavioral Intention to Use Mobile Learning: An Application and Extension of the UTAUT Model
31+W4241184911,theory,Wolfram's two-dimensional cellular automata chapter (2018 CRC reprint),abstract,theory,computational_complexity,2018,baseline,book-chapter,10.1201/9780429494093-6,93.0,68.7493670886076,reprint_misdated,no,,Two-Dimensional Cellular Automata
32+W4394806711,empirical,"PLS-SEM survey on AI, social media and student well-being",abstract,theory,computational_complexity,2024,post,article,10.1016/j.heliyon.2024.e29523,197.0,59.311783860943,misclassified,no,,Artificial intelligence and social media on academic performance and mental well-being: Student perceptions of positive impact in the age of smart learning
33+W4386113651,method,Preprint: validation protocol for consciousness-theoretic claims about AI systems,abstract,theory,computational_complexity,2023,post,preprint,10.48550/arxiv.2308.08708,170.0,57.664,misclassified,no,,Cheap Correlate or Discriminating Property? An Executable Validation Protocol for Consciousness- and Cognition-Theoretic Claims about AI
34+W1513303009,theory,Wolfram's collected cellular-automata papers (CERN record),abstract,theory,computational_complexity,2019,baseline,article,,251.0,52.922228129373856,reprint_misdated,no,,Cellular Automata And Complexity: Collected Papers
35+W4406215947,empirical,Digital transformation and SME innovation performance (systems theory),abstract,theory,computational_complexity,2025,post,article,10.3390/systems13010043,104.0,24.553686934023286,misclassified,no,,"Digital Transformation and Innovation Performance in Small- and Medium-Sized Enterprises: A Systems Perspective on the Interplay of Digital Adoption, Digital Drive, and Digital Culture"
36+W2905177801,method,"Optimal trees for prediction and prescription (MIT OR thesis, 2018)",abstract,theory,computational_complexity,2018,baseline,dissertation,,5.0,51.149155722326455,misclassified,no,,Optimal trees for prediction and prescription
37+W2955897898,survey,Wireless communications above 100 GHz for 6G,abstract,theory,computational_complexity,2019,baseline,article,10.1109/access.2019.2921522,596.0,49.00829497340763,misclassified,no,,Wireless Communications and Applications Above 100 GHz: Opportunities and Challenges for 6G and Beyond
38+W4377102915,survey,"Smart digital technologies in learning environments, 'complexity theory' lens",abstract,theory,computational_complexity,2023,post,article,10.1016/j.heliyon.2023.e16348,204.0,47.81265827221507,misclassified,no,,"Challenges, opportunities, and prospects of adopting and using smart digital technologies in learning environments: An iterative review"
39+W3139085460,empirical,"Machine-learning education thesis (MIT, 2020)",abstract,theory,computational_complexity,2020,baseline,dissertation,,8.0,45.743589743589745,misclassified,no,,Reorienting machine learning education towards tinkerers and ML-engaged citizens
40+W2808541536,theory,"Meet-continuity and locally compact sober dcpos (domain theory thesis, 2018)",abstract,theory,computational_complexity,2018,baseline,dissertation,,10.0,44.30861723446894,dissertation,no,,Meet-continuity and locally compact sober dcpos
41+W73572444,survey,"Complexity Theory and the Social Sciences (Routledge, 2022)",abstract,theory,computational_complexity,2022,baseline,book,10.4324/9781003213574,215.0,43.70967741935484,misclassified,no,,Complexity Theory and the Social Sciences
42+W7122741622,survey,Digital fringe projection profilometry review (metrology),abstract,theory,computational_complexity,2026,post,article,10.3390/metrology6010003,12.0,32.16440030557678,misclassified,no,,"Recent Advances in Digital Fringe Projection Profilometry (2022–2025): Techniques, Applications, and Metrological Challenges—A Review"
43+W7133302269,empirical,AI-assisted learning tools and learning outcomes (cognitive load theory),abstract,theory,computational_complexity,2026,post,article,10.1016/j.chbr.2026.100986,12.0,85.25451005840362,misclassified,no,,AI-assisted learning tools and student learning outcomes: A cognitive load theory perspective
44+W7160425037,theory,SAT solved by restricted spiking neural P systems with temporal division rules (TCS 2026); no abstract,metadata_only,theory,computational_complexity,2026,post,article,10.1016/j.tcs.2026.116009,12.0,16.14637579169599,in_scope_research,no,,Solving SAT by restricted spiking neural P systems with temporal division rules
45+W4391177866,survey,Undergraduate chapter 'Computational Complexity' (2024),metadata_only,theory,computational_complexity,2024,post,book-chapter,10.1007/978-3-031-42209-6_2,32.0,38.1794687724336,textbook,no,,Computational Complexity
46+W4396808240,survey,Undergraduate chapter 'Parameterized Complexity' (2024),metadata_only,theory,computational_complexity,2024,post,book-chapter,10.1007/978-3-031-53744-8_9,29.0,35.08039936941671,textbook,no,,Parameterized Complexity
47+W3086066649,empirical,Digital learning and university brand image (Pakistan),abstract,theory,computational_complexity,2020,baseline,article,10.1108/aeds-04-2020-0063,110.0,32.031113421948945,misclassified,no,,The role of digital learning toward students' satisfaction and university brand image at educational institutes of Pakistan: a post-effect of COVID-19
48+W7127964361,empirical,Knowledge-management capability and hotel innovation (PLS-SEM),abstract,theory,computational_complexity,2026,post,article,10.3390/systems14020179,9.0,8.79467680608365,misclassified,no,,"Turning Knowledge into Innovation: The Systemic Role of Knowledge Management Capability, Intellectual Capital, and Knowledge Utilization"
49+W3122335070,engineering,Mobile learning system for CS education in Nigeria (thesis),abstract,theory,computational_complexity,2018,baseline,dissertation,,3.0,30.68949343339587,misclassified,no,,Design and development of a mobile learning system for computer science education in Nigerian higher education context
50+W2900179285,theory,Non-elementary lower bound for Petri-net reachability (STOC 2019),abstract,theory,computational_complexity,2019,baseline,conference-paper,10.1145/3313276.3316369,92.0,30.27399489989691,in_scope_research,no,czerwinski2019petri,The Reachability Problem for Petri Nets Is Not Elementary
51+W4408441779,empirical,Data-driven decision-making and SME creativity (PLS-SEM),abstract,theory,computational_complexity,2025,post,article,10.1109/tem.2025.3551331,59.0,19.444444444444446,misclassified,no,,Enhancing Creativity and Sustainable Competitive Advantage Through Data-Driven Decision-Making and Digital Leadership
52+W2964015378,method,APISS: functionality-equivalent API retrieval with LLM embeddings to migrate vulnerability PoCs (ECOOP 2025); counts belong to another work,abstract,software_pl,software_engineering,2025,post,conference-paper,10.4230/lipics.ecoop.2025.6,15245.0,11583.902021240152,in_scope_research,yes,chen2025apiss,Detecting Functionality-Specific Vulnerabilities via Retrieving Individual Functionality-Equivalent APIs in Open-Source Repositories
53+W4293846201,engineering,Confiance.ai methodological framework for assessing ML model robustness (SAIA 2024); counts belong to another work,abstract,software_pl,software_engineering,2025,post,conference-paper,10.4230/oasics.saia.2024.1,4701.0,4178.076566446758,in_scope_research,yes,awadid2025robust,On Assessing ML Model Robustness: A Methodological Framework (Academic Track)
54+W2095152777,empirical,Royce's 1970 'Managing the Development of Large Software Systems' in a 2021 anthology,abstract,software_pl,software_engineering,2021,baseline,book-chapter,10.7551/mitpress/12274.003.0035,970.0,750.7662588538313,reprint_misdated,no,,Managing the Development of Large Software Systems (1970)
55+W2911537985,empirical,"Microservices vs service-oriented architecture (master thesis, 2018), no abstract",metadata_only,software_pl,software_engineering,2018,baseline,dissertation,,67.0,685.3986866791745,dissertation,no,,Microservices vs. Service-oriented architecture
56+W1486172410,survey,Cambridge textbook 'Introduction to Software Testing' (2021 chapter),abstract,software_pl,software_engineering,2021,baseline,book-chapter,10.1017/9781108974073.004,871.0,549.1255565449688,textbook,no,,Introduction to Software Testing
57+W7151510318,method,"Unsupervised translation of programming languages (doctoral thesis, French abstract, 2022)",abstract,software_pl,software_engineering,2022,baseline,dissertation,10.70675/416f6582z4576z444eza710z5e930d59bf97,56.0,485.7543859649123,dissertation,no,,Unsupervised Translation of Programming Languages
58+W4391974543,survey,Survey of 102 studies using LLMs for software testing (TSE 2024),abstract,software_pl,software_engineering,2024,post,article,10.1109/tse.2024.3368208,554.0,439.6107042253521,in_scope_survey,no,wang2024llmtesting,"Software Testing With Large Language Models: Survey, Landscape, and Vision"
59+W3185223169,method,"Effort-estimation model for agile development (thesis, 2020), no abstract",metadata_only,software_pl,software_engineering,2020,baseline,dissertation,,45.0,423.2192575406032,dissertation,no,,An Effort Estimation Model for Agile Software Development
60+W2171139698,method,"Evolutionary test-data generation for mutation testing (thesis, Kent); abstract field is a dedication",abstract,software_pl,software_engineering,2021,baseline,dissertation,10.22024/unikent/01.02.86353,19.0,394.8918918918919,dissertation,no,,Test data generation: two evolutionary approaches to mutation testing
61+W7131867455,empirical,Three randomised field experiments on AI coding assistants and developer output (Management Science 2026),abstract,software_pl,software_engineering,2026,post,article,10.1287/mnsc.2025.00535,97.0,689.140622972096,in_scope_research,no,cui2026genaiwork,The Effects of Generative AI on High-Skilled Work: Evidence from Three Field Experiments with Software Developers
62+W4399452770,survey,History of software-engineering degree programmes in Brazil (Computacao Brasil 2018),abstract,software_pl,software_engineering,2018,baseline,article,10.5753/compbr.2018.38.4541,611.0,323.9504225668329,in_scope_survey,no,,Engenharia de Software
63+W4408219312,method,"Type-hint fuzzing of Python programs (thesis, 2024)",abstract,software_pl,software_engineering,2024,post,dissertation,10.17760/d20705205,1.0,304.3333333333333,dissertation,no,,Leveraging type annotations for effective fuzzing of python programs
64+W4402442491,method,"Domain-oriented specification method (thesis, 2024)",abstract,software_pl,software_engineering,2024,post,dissertation,10.5463/thesis.831,1.0,304.3333333333333,dissertation,no,,From Smallest Software Particle to System Specification
65+W4403110951,method,"Semantics-aware testing of human-interactive systems (thesis, 2024)",abstract,software_pl,software_engineering,2024,post,dissertation,10.32657/10356/179951,1.0,304.3333333333333,dissertation,no,,Semantics-aware human-computer interaction software testing
66+W3008797115,method,Metamorphic testing technical report (1998) posted to arXiv in 2020,abstract,software_pl,software_engineering,2020,baseline,preprint,10.48550/arxiv.2002.12543,292.0,286.17663817663816,reprint_misdated,no,,Metamorphic Testing: A New Approach for Generating Next Test Cases
67+W3177813494,empirical,"Correctness-energy trade-offs of open code LLMs on Python bug fixing (ESEM 2026); record title, year and counts belong to other works",abstract,software_pl,software_engineering,2021,baseline,preprint,10.4230/lipics.esem.2026.34,678.0,275.3873466752744,in_scope_research,yes,naqvi2026bigger,A Direction-Aware Study of LLM-Based Code Translation Across Eight Programming Languages
68+W7110046239,empirical,Closed-world CWE classification by open LLMs on Big-Vul (ICPEC 2025); live citation count differs from the corpus window,abstract,software_pl,software_engineering,2025,post,article,10.4230/oasics.icpec.2025.4,540.0,468.828520182987,in_scope_research,no,gasparlopes2025vuln,Can Open Large Language Models Catch Vulnerabilities?
69+W4391677389,method,"Perspective-based specification of ML-enabled systems (thesis, 2023); abstract field is an acknowledgement",abstract,software_pl,software_engineering,2023,post,dissertation,10.17771/pucrio.acad.65972,8.0,230.9189189189189,dissertation,no,,IDENTIFYING CONCERNS WHEN SPECIFYING MACHINE LEARNING-ENABLED SYSTEMS: A PERSPECTIVE-BASED APPROACH
70+W7124592699,method,Multi-scale temporal deep model for just-in-time defect prediction (2026),abstract,software_pl,software_engineering,2026,post,article,10.1007/s44443-025-00401-y,64.0,454.690720311486,in_scope_research,no,,MSTDP: a multi-scale temporal deep learning framework for just-in-time software defect prediction with cross-attention fusion
71+W3007157104,survey,Machine-learning testing survey of 144 papers (TSE 2020/2022),abstract,software_pl,software_engineering,2020,baseline,article,10.1109/tse.2019.2962027,385.0,227.48310158362304,in_scope_survey,no,zhang2020mltesting,"Machine Learning Testing: Survey, Landscapes and Horizons"
72+W58732440,empirical,"Novice compilation behaviour in BlueJ (doctoral thesis, re-deposited 2021)",abstract,software_pl,software_engineering,2021,baseline,dissertation,10.22024/unikent/01.02.86458,60.0,226.01941747572815,reprint_misdated,no,,An exploration of novice compilation behaviour in BlueJ
73+W2725449579,survey,"Mutation testing advances: analysis and survey (2019), no abstract",metadata_only,software_pl,software_engineering,2018,baseline,book-chapter,10.1016/bs.adcom.2018.03.015,129.0,223.78585461689588,in_scope_survey,no,papadakis2019mutation,Mutation Testing Advances: An Analysis and Survey
74+W2962772482,survey,"Survey of methods for explaining black-box models (XAI, 2019)",abstract,software_pl,software_engineering,2019,baseline,article,10.1145/3236009,1432.0,217.74340797667946,misclassified,no,,A survey of methods for explaining black box models
75+W4293212596,engineering,"The Astropy Project: sustaining a community open-source scientific package (v5.0, 2022)",abstract,software_pl,software_engineering,2022,baseline,article,10.3847/1538-4357/ac7c74,2029.0,209.06737313811422,in_scope_research,no,,The Astropy Project : Sustaining and Growing a Community-oriented Open-source Project and the Latest Major Release (v5.0) of the Core Package*
76+W4381982883,empirical,"ChatGPT for education and research, with coding experiments (2023)",abstract,software_pl,software_engineering,2023,post,article,10.3390/app13095783,885.0,207.4225616221095,misclassified,no,,"ChatGPT for Education and Research: Opportunities, Threats, and Strategies"
77+W2114444139,empirical,Information-systems curriculum change (thesis),abstract,software_pl,programming_languages,2024,post,dissertation,10.25946/26315761,17.0,661.9925925925926,misclassified,no,,Innovation and Change in the Information Systems Curriculum of an Australian University:a Socio-Technical Perspective
78+W1844775953,theory,"Metric domains for completeness (doctoral thesis, Warwick) re-deposited with a 2026 date",abstract,software_pl,programming_languages,2026,post,dissertation,10.82444/warw.33641182,34.0,1040.594982078853,reprint_misdated,no,,Metric domains for completeness
79+W4396242417,method,LLift: LLM-assisted static analysis finding use-before-initialisation bugs in the Linux kernel (OOPSLA 2024),abstract,software_pl,programming_languages,2024,post,article,10.1145/3649828,148.0,117.44112676056338,in_scope_research,no,li2024llift,Enhancing Static Analysis for Practical Bug Detection: An LLM-Integrated Approach
80+W2327217190,theory,"Separation logic for programs in virtual memory (seL4-lineage thesis, re-deposited 2022)",abstract,software_pl,programming_languages,2022,baseline,dissertation,10.26190/unsworks/23844,13.0,97.35745614035089,reprint_misdated,no,,Verification of programs in virtual memory using separation logic
81+W2491926874,theory,"Formal memory models for verifying C systems code (L4 lineage thesis, re-deposited 2022)",abstract,software_pl,programming_languages,2022,baseline,dissertation,10.26190/unsworks/17927,11.0,82.37938596491229,reprint_misdated,no,,Formal memory models for verifying C systems code
82+W4205342885,survey,Society 5.0 / Industry 5.0 and future universities,abstract,software_pl,programming_languages,2022,baseline,article,10.1007/s13132-021-00854-2,334.0,82.08749781143715,misclassified,no,,The Futures of Europe: Society 5.0 and Industry 5.0 as Driving Forces of Future Universities
83+W2189687801,method,"Program synthesis with lexicase selection (thesis, 2021); the abstract field holds a different thesis (moose ecology)",abstract,software_pl,programming_languages,2021,baseline,dissertation,10.7275/7408407.0,18.0,81.87032967032967,dissertation,no,,General Program Synthesis from Examples Using Genetic Programming with Parent Selection Based on Random Lexicographic Orderings of Test Cases
84+W2400988715,theory,Functional stable model semantics and ASP modulo theories (2026 arXiv posting of an earlier paper),abstract,software_pl,programming_languages,2026,post,preprint,10.48550/arxiv.2605.09524,22.0,856.0701754385965,reprint_misdated,no,,Functional Stable Model Semantics and Answer Set Programming Modulo Theories
85+W2796251079,survey,Springer chapter 'A Concise Introduction to Logic' (2018),metadata_only,software_pl,programming_languages,2018,baseline,book-chapter,10.1007/978-3-319-75844-2_6,167.0,77.56442638324476,textbook,no,,A Concise Introduction to Logic
86+W159920301,theory,"On intuitionistic linear logic (doctoral thesis, re-deposited 2021)",abstract,software_pl,programming_languages,2021,baseline,dissertation,10.48456/tr-346,16.0,72.77362637362637,reprint_misdated,no,,On intuitionistic linear logic
87+W4389109635,theory,"PBPO+ unifying algebraic graph transformation (thesis, 2023)",abstract,software_pl,programming_languages,2023,post,dissertation,10.5463/thesis.524,2.0,69.63636363636364,dissertation,no,,A Unifying Theory for Graph Transformation
88+W630549275,theory,"Clustered multikernel for verified multiprocessor kernels (thesis, re-deposited 2022)",abstract,software_pl,programming_languages,2022,baseline,dissertation,10.26190/unsworks/16534,9.0,67.40131578947368,reprint_misdated,no,,The clustered multikernel: an approach to formal verification of multiprocessor operating-system kernels
89+W4403223258,method,WhiteFox: white-box compiler fuzzing guided by LLMs reading optimisation source (OOPSLA 2024),abstract,software_pl,programming_languages,2024,post,article,10.1145/3689736,82.0,65.0687323943662,in_scope_research,no,yang2024whitefox,WhiteFox: White-Box Compiler Fuzzing Empowered by Large Language Models
90+W4313480275,survey,Harel's 'Algorithmics: The Spirit of Computing' chapter (2023),metadata_only,software_pl,programming_languages,2023,post,book-chapter,10.1007/978-3-031-21762-3_3,64.0,57.94535519125683,textbook,no,,Algorithmics: The Spirit of Computing
91+W2790202156,method,ZEUS: abstract interpretation and CHC-based safety checking of smart contracts (NDSS 2018),abstract,software_pl,programming_languages,2018,baseline,conference-paper,10.14722/ndss.2018.23082,191.0,56.63186311787072,in_scope_research,no,kalra2018zeus,ZEUS: Analyzing Safety of Smart Contracts
92+W3207822760,empirical,"Cognitive predictors of programming skill, EEG study (thesis, 2021)",abstract,software_pl,programming_languages,2021,baseline,dissertation,10.33612/diss.168003240,15.0,56.50485436893204,dissertation,no,,Computer programming skills: A cognitive perspective
93+W4297611890,survey,Avigad's mathematical logic textbook chapter (2022),abstract,software_pl,programming_languages,2022,baseline,book-chapter,10.1017/9781108778756.017,61.0,50.93184096224524,textbook,no,,Subsystems of Second-Order Arithmetic
94+W2915211782,survey,Cambridge Handbook chapter on novice programmers (2019),metadata_only,software_pl,programming_languages,2019,baseline,book-chapter,10.1017/9781108654555.013,45.0,45.68985849056604,textbook,no,,Novice Programmers and Introductory Programming
95+W4381516741,survey,Springer chapter 'The Python Programming Language' (2023),metadata_only,software_pl,programming_languages,2023,post,book-chapter,10.1007/978-3-031-22206-1_2,82.0,45.335480407944175,textbook,no,,The Python Programming Language
96+W3115704172,engineering,"Lineage-driven fault injection for Akka actor programs (thesis, 2019)",abstract,software_pl,programming_languages,2019,baseline,dissertation,,2.0,42.74766355140187,dissertation,no,,Lineage-driven Fault Injection for Actor-based Programs
97+W3146286581,method,Probabilistic grammatical evolution (2021),abstract,software_pl,programming_languages,2021,baseline,dissertation,,9.0,40.935164835164834,dissertation,no,,Probabilistic Grammatical Evolution
98+W7134194765,method,Debris-flow hazard assessment with AHP-CRITIC and SHAP,abstract,software_pl,programming_languages,2026,post,article,10.1111/tgis.70218,11.0,15.074074074074074,misclassified,no,,A Debris‐Flow Hazard Assessment Framework Based on an Improved AHP – CRITIC Method: A Case Study of Gaoling Gully
99+W2797025890,empirical,Open educational practices (thesis),abstract,software_pl,programming_languages,2018,baseline,dissertation,10.13025/17505,6.0,38.953125,misclassified,no,,Openness and praxis: A situated study of academic staff meaning-making and decision-making with respect to openness and use of open educational practices in higher education
100+W4396905794,method,"Predicting TypeScript annotations with fine-tuned code models (thesis, 2024)",abstract,software_pl,programming_languages,2024,post,dissertation,10.17760/d20653005,1.0,38.94074074074074,dissertation,no,,Predicting typeScript type annotations and definitions with machine learning
101+W4410362977,theory,Calabi-Yau manifolds in black-hole scattering (Nature 2025),abstract,software_pl,programming_languages,2025,post,article,10.1038/s41586-025-08984-2,74.0,30.45267489711934,misclassified,no,,Emergence of Calabi–Yau manifolds in high-precision black-hole scattering
102+W3122286897,engineering,MLIR: reusable multi-level compiler infrastructure (CGO 2021),abstract,software_pl,compilers,2021,baseline,conference-paper,10.1109/cgo51591.2021.9370308,155.0,42.480268473059475,in_scope_research,no,lattner2021mlir,MLIR: Scaling Compiler Infrastructure for Domain Specific Computation
103+W4407983386,method,Photovoltaic power forecasting with TimesNet-BiLSTM,metadata_only,software_pl,compilers,2025,post,article,10.1016/j.energy.2025.135286,83.0,28.27389289459036,misclassified,no,,Parallel TimesNet-BiLSTM model for ultra-short-term photovoltaic power forecasting using STL decomposition and auto-tuning
104+W4399087350,survey,E-learning and business education in the GCC,metadata_only,software_pl,compilers,2024,post,book-chapter,10.1007/978-3-031-56586-1_67,61.0,39.77033011272142,misclassified,no,,E-learning and the Future of Business Education: The Case of GCC
105+W4398768423,benchmark,"LLM4VV: LLM-generated validation test suites for OpenACC compilers (FGCS 2024), no abstract",metadata_only,software_pl,compilers,2024,post,article,10.1016/j.future.2024.05.034,48.0,38.08901408450704,in_scope_research,no,munley2024llm4vv,LLM4VV: Developing LLM-driven testsuite for compiler validation
106+W2188794726,survey,Springer chapter 'Dynamic Dataflow Graphs' (2018),metadata_only,software_pl,compilers,2018,baseline,book-chapter,10.1007/978-3-319-91734-4_32,25.0,35.799701046337816,textbook,no,,Dynamic Dataflow Graphs
107+W4406882143,survey,Just-in-time adaptive interventions in mental health (systematic review),abstract,software_pl,compilers,2025,post,review,10.3389/fdgth.2025.1460167,54.0,4.153846153846154,misclassified,no,,Beyond the current state of just-in-time adaptive interventions in mental health: a qualitative systematic review
108+W2884622219,survey,Raw NMR data availability in natural-product research,abstract,software_pl,compilers,2018,baseline,article,10.1039/c7np00064b,51.0,27.040051638148082,misclassified,no,,"The value of universally available raw NMR data for transparency, reproducibility, and integrity in natural product research"
109+W3188948186,engineering,Fuzzy PID speed control of a DC motor (arXiv),abstract,software_pl,compilers,2021,baseline,preprint,10.48550/arxiv.2108.05450,73.0,24.44561426312255,misclassified,no,,Speed Control of DC Motor Using Fuzzy PID Controller
110+W2962724414,survey,Machine learning in compiler optimisation (Proc. IEEE 2018),abstract,software_pl,compilers,2018,baseline,article,10.1109/jproc.2018.2817118,64.0,22.34837655707576,in_scope_survey,no,wang2018mlcompiler,Machine Learning in Compiler Optimization
111+W4408711372,method,Polyhedral approximation for electricity-water nexus dispatch,abstract,software_pl,compilers,2025,post,article,10.1109/tsg.2025.3553737,39.0,8.943719676549865,misclassified,no,,A Progressive Polyhedral Approximation Method for Nonlinear PDE-Constrained Electricity-Water Nexus Dispatch
112+W4392397123,empirical,Educators' perceptions of AI in higher education,metadata_only,software_pl,compilers,2024,post,article,10.1016/j.ijer.2024.102326,64.0,19.268802878681992,misclassified,no,,Evolution to revolution: Critical exploration of educators’ perceptions of the impact of Artificial Intelligence (AI) on the teaching and learning process in the GCC region
113+W2783614532,survey,Survey on compiler autotuning with machine learning (CSUR 2018),abstract,software_pl,compilers,2018,baseline,article,10.1145/3197978,55.0,19.205636103736982,in_scope_survey,no,ashouri2018autotuning,A Survey on Compiler Autotuning using Machine Learning
114+W2206959660,method,"Compiler optimisation selection by program characterisation and ML (thesis, 2024)",abstract,software_pl,compilers,2024,post,dissertation,10.58088/85wq-pw11,1.0,19.080000000000002,dissertation,no,,Automatic selection of compiler optimizations using program characterization and machine learning
115+W2249134499,method,"Improving compiler optimisations using ML (thesis, 2024)",abstract,software_pl,compilers,2024,post,dissertation,10.58088/vbbd-jy30,1.0,19.080000000000002,dissertation,no,,Improving compiler optimizations using machine learning
116+W3139485201,method,"Towards automated construction of compiler optimisations (MIT thesis, 2020)",abstract,software_pl,compilers,2020,baseline,dissertation,,2.0,18.265060240963855,dissertation,no,,Towards automated construction of compiler optimizations
117+W3214427835,method,"Sparse tensor algebra compilation (MIT thesis, 2020)",abstract,software_pl,compilers,2020,baseline,dissertation,,2.0,18.265060240963855,dissertation,no,,Sparse tensor algebra compilation
118+W3023977240,method,"From constraint programming to heterogeneous parallelism (thesis, 2020)",abstract,software_pl,compilers,2020,baseline,dissertation,10.7488/era/309,2.0,18.265060240963855,dissertation,no,,From constraint programming to heterogeneous parallelism
119+W3046781014,engineering,ExaStencils: multigrid solver generation from a layered DSL (2020),abstract,software_pl,compilers,2020,baseline,book-chapter,10.1007/978-3-030-47956-5_14,12.0,18.204255319148935,in_scope_research,no,,ExaStencils: Advanced Multigrid Solver Generation
120+W7127344046,empirical,AI policy readiness in Omani higher education,abstract,software_pl,compilers,2026,post,article,10.1007/s44217-026-01188-4,5.0,35.52271252433485,misclassified,no,,Artificial intelligence policy challenges and institutional readiness in Omani higher education
121+W3132279601,empirical,COVID-19 and higher education in the GCC,metadata_only,software_pl,compilers,2021,baseline,conference-paper,10.1007/978-3-030-62066-0_12,37.0,17.288455680779773,misclassified,no,,Coping with COVID-19: Higher Education in the GCC Countries
122+W3103225940,engineering,"LLVM backend for the OpenModelica compiler (thesis, 2019)",abstract,software_pl,compilers,2019,baseline,dissertation,,3.0,16.862068965517242,dissertation,no,,An LLVM backend for the Open Modelica Compiler
123+W4313894866,survey,The science of literature reviews,abstract,software_pl,compilers,2023,post,article,10.3390/publications11010002,187.0,16.423071260306244,misclassified,no,,"The Science of Literature Reviews: Searching, Identifying, Selecting, and Synthesising"
124+W4407942537,engineering,LLM Compiler: foundation models pre-trained on LLVM-IR and assembly for optimisation tasks (CC 2025),abstract,software_pl,compilers,2025,post,conference-paper,10.1145/3708493.3712691,31.0,23.55532716683796,in_scope_research,no,cummins2025llmcompiler,LLM Compiler: Foundation Language Models for Compiler Optimization
125+W2787911489,engineering,"Automated code generation for chemistry kernels in OpenFOAM (2018), no abstract",metadata_only,software_pl,compilers,2018,baseline,book-chapter,10.1007/978-3-319-68394-2_11,10.0,14.319880418535128,in_scope_research,no,,Automated Code Generation for Maximizing Performance of Detailed Chemistry Calculations in OpenFOAM
126+W2949967139,engineering,Tiramisu polyhedral compiler with a scheduling language (CGO 2019),abstract,software_pl,compilers,2019,baseline,conference-paper,10.1109/cgo.2019.8661197,53.0,14.250956889605158,in_scope_research,no,baghdadi2019tiramisu,Tiramisu: A Polyhedral Compiler for Expressing Fast and Portable Code
127+W4385245566,engineering,MizAR 60 for Mizar 50: learning-guided ATP proving about 60% of Mizar theorems (ITP 2023); record title and counts belong to another work,abstract,software_pl,formal_verification,2023,post,article,10.4230/lipics.itp.2023.19,72130.0,6621.879465206806,in_scope_research,yes,jakubuv2023mizar,Exploiting Generative AI to Scale up Intelligent Tutoring Systems
128+W1498342204,theory,Lynch and Tuttle's introduction to I/O automata (1989) republished in 2026,abstract,software_pl,formal_verification,2026,post,article,10.1145/3788687,284.0,801.9048786467272,reprint_misdated,no,lynch2026ioautomata,An Introduction to Input/Output Automata
129+W3117137355,engineering,egg equality saturation: 2026 CACM research highlight of the POPL 2021 paper,abstract,software_pl,formal_verification,2026,post,article,10.1145/3815481,132.0,146.88980220909326,reprint_misdated,no,willsey2026eggcacm,egg: Fast and Extensible Equality Saturation
130+W1481397690,survey,"Handbook of Model Checking chapter on SMT (2018), no abstract",metadata_only,software_pl,formal_verification,2018,baseline,book-chapter,10.1007/978-3-319-10575-8_11,585.0,432.45569620253167,textbook,no,,Satisfiability Modulo Theories
131+W2254118105,method,QBF solving by implicit hitting sets / clause selection (2026 arXiv posting of a 2015 paper),abstract,software_pl,formal_verification,2026,post,preprint,10.48550/arxiv.2608.14274,60.0,2151.793313069909,reprint_misdated,no,,Solving QBF by Clause Selection
132+W4206807714,survey,"Handbook of Satisfiability, 2nd edition (2021)",metadata_only,software_pl,formal_verification,2021,baseline,book,10.3233/faia336,777.0,184.30546075085323,textbook,no,biere2021handbook,Handbook of Satisfiability
133+W2540354435,theory,Weighted rules under the stable model semantics (2026 arXiv posting of an earlier paper),abstract,software_pl,formal_verification,2026,post,preprint,10.48550/arxiv.2605.09519,39.0,1517.578947368421,reprint_misdated,no,,Weighted Rules under the Stable Model Semantics
134+W2921681895,method,"Parameter synthesis in parametric Markov models (thesis, RWTH 2020)",abstract,software_pl,formal_verification,2020,baseline,dissertation,10.18154/rwth-2020-02348,19.0,111.636815920398,dissertation,no,,Parameter synthesis in Markov models
135+W4225922019,engineering,cvc5 SMT solver system description (TACAS 2022),abstract,software_pl,formal_verification,2022,baseline,conference-paper,10.1007/978-3-030-99524-9_24,222.0,98.23784328546233,in_scope_research,no,barbosa2022cvc5,cvc5: A Versatile and Industrial-Strength SMT Solver
136+W4318573502,theory,"Semantically informed structural proof theory (thesis, 2022)",abstract,software_pl,formal_verification,2022,baseline,dissertation,10.5463/thesis.149,13.0,97.35745614035089,dissertation,no,,Semantically informed methods in structural proof theory
137+W2154148696,survey,"Supervisory control of timed discrete-event systems (chapter, 2018)",metadata_only,software_pl,formal_verification,2018,baseline,book-chapter,10.1007/978-3-319-77452-7_9,117.0,86.49113924050633,textbook,no,,Supervisory Control of Timed Discrete-Event Systems
138+W4297781990,method,Answer-set programming for runtime monitoring and fault explanation of automated driving (DX 2024); counts belong to another work,abstract,software_pl,formal_verification,2024,post,preprint,10.4230/oasics.dx.2024.10,431.0,78.93772893772893,in_scope_research,yes,klampfl2024asp,"Leveraging Answer Set Programming for Continuous Monitoring, Fault Detection, and Explanation of Automated and Autonomous Driving Systems"
139+W2789444712,survey,Industry 4.0 state of the art (production research),abstract,software_pl,formal_verification,2018,baseline,article,10.1080/00207543.2018.1444806,734.0,61.81756449008323,misclassified,no,,Industry 4.0: state of the art and future trends
140+W1586677474,survey,Second record of the Handbook of Satisfiability (2021),abstract,software_pl,formal_verification,2021,baseline,article,,375.0,60.365327611379016,duplicate,no,,Handbook of Satisfiability
141+W2486519520,method,"Machine learning for heuristic selection in automated theorem proving (thesis, re-deposited 2021)",abstract,software_pl,formal_verification,2021,baseline,dissertation,10.48456/tr-792,12.0,54.58021978021978,reprint_misdated,no,,Machine learning and automated theorem proving
142+W4289665848,theory,Reward interfaces: quantitative interface automata with best-effort implementations (CSL 2026),abstract,software_pl,formal_verification,2026,post,article,10.4230/lipics.csl.2026.30,15.0,42.354130914439814,in_scope_research,no,,Reward Interfaces with Best-Effort Implementations
143+W149631060,survey,Decidability issues for Petri nets (1994 survey posted to arXiv in 2024),abstract,software_pl,formal_verification,2024,post,preprint,10.48550/arxiv.2411.01592,33.0,53.32230215827339,reprint_misdated,no,,Decidability Issues for Petri Nets -- a survey
144+W2890660842,method,Symbolic interval propagation for formal safety analysis of neural networks (2018 preprint),abstract,software_pl,formal_verification,2018,baseline,preprint,10.48550/arxiv.1809.08098,98.0,52.040918390543304,in_scope_research,no,,Efficient Formal Safety Analysis of Neural Networks
145+W1932627192,method,QMC model checker for quantum protocols (doctoral thesis re-deposited with a 2026 date),abstract,software_pl,formal_verification,2026,post,dissertation,10.82444/warw.33484729,14.0,141.45283018867926,reprint_misdated,no,,Model checking quantum protocols
146+W4393942723,benchmark,"SV-COMP 2024 competition report: 76 tools, 30,300 C tasks (2024)",abstract,software_pl,formal_verification,2024,post,conference-paper,10.1007/978-3-031-57256-2_15,78.0,48.594819515239784,in_scope_survey,no,beyer2024svcomp,State of the Art in Software Verification and Witness Validation: SV-COMP 2024
147+W2957311447,engineering,Marabou SMT-based neural-network verifier (CAV 2019),abstract,software_pl,formal_verification,2019,baseline,conference-paper,10.1007/978-3-030-25540-4_26,141.0,46.39818783571157,in_scope_research,no,katz2019marabou,The Marabou Framework for Verification and Analysis of Deep Neural Networks
148+W2905489900,method,"Machine learning for SAT solver heuristics (thesis, Waterloo 2018)",abstract,software_pl,formal_verification,2018,baseline,dissertation,,5.0,44.97120921305182,dissertation,no,,Machine Learning for SAT Solvers
149+W2900153411,method,DeepPoly abstract domain for certifying neural networks (POPL 2019),abstract,software_pl,formal_verification,2019,baseline,article,10.1145/3290354,209.0,44.06671585274556,in_scope_research,no,singh2019deeppoly,An abstract domain for certifying neural networks
150+W6944541633,method,"Verification of multi-objective Markov models (thesis, RWTH 2023)",abstract,software_pl,formal_verification,2023,post,dissertation,10.18154/rwth-2023-09669,2.0,43.15151515151515,dissertation,no,,Verification of multi-objective Markov models
151+W3016155638,method,VerX: automated verification of temporal properties of Ethereum contracts (S&P 2020),abstract,software_pl,formal_verification,2020,baseline,conference-paper,10.1109/sp40000.2020.00024,114.0,42.08811748998665,in_scope_research,no,permenev2020verx,VerX: Safety Verification of Smart Contracts
added literature_synthesis/agent_G/make_bib_part07a.py +59 −0
@@ -0,0 +1,59 @@
1+#!/usr/bin/env python3
2+"""Agent G — builds manuscript/references_part07a.bib and manuscript/references_verification_part07a.csv from single live
3+lookups (Crossref for DOIs, DataCite for LIPIcs/OASIcs DOIs, arXiv export API for preprints). Nothing is typed from memory:
4+authors, titles, venues and years come from the resolved record. Keys: surname + Crossref/DataCite issued year + keyword."""
5+import json, urllib.request, urllib.parse, re, time, csv, os, sys
6+R = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))); M = "mailto=spbou4@icloud.com"
7+CR = {"czerwinski2019petri": "10.1145/3313276.3316369", "czerwinski2021ackermann": "10.1109/FOCS52979.2021.00120", "leroux2021notprimitive": "10.1109/FOCS52979.2021.00121",
8+ "wang2024llmtesting": "10.1109/tse.2024.3368208", "zhang2022mltesting": "10.1109/tse.2019.2962027", "cui2026genaiwork": "10.1287/mnsc.2025.00535", "li2024llift": "10.1145/3649828",
9+ "yang2024whitefox": "10.1145/3689736", "baghdadi2019tiramisu": "10.1109/cgo.2019.8661197", "wang2018mlcompiler": "10.1109/jproc.2018.2817118", "ashouri2018autotuning": "10.1145/3197978",
10+ "cummins2025llmcompiler": "10.1145/3708493.3712691", "munley2024llm4vv": "10.1016/j.future.2024.05.034", "barbosa2022cvc5": "10.1007/978-3-030-99524-9_24", "beyer2024svcomp": "10.1007/978-3-031-57256-2_15",
11+ "katz2019marabou": "10.1007/978-3-030-25540-4_26", "singh2019deeppoly": "10.1145/3290354", "permenev2020verx": "10.1109/sp40000.2020.00024", "willsey2021egg": "10.1145/3434304", "kalra2018zeus": "10.14722/ndss.2018.23082",
12+ "papadakis2019mutation": "10.1016/bs.adcom.2018.03.015", "abualigah2021aoa": "10.1016/j.cma.2020.113609", "abualigah2021aquila": "10.1016/j.cie.2021.107250", "biere2021handbook": "10.3233/faia336",
13+ "li2019kolmogorov": "10.1007/978-3-030-11298-1", "sorensen2013metaphor": "10.1111/itor.12001", "aranha2021metaphor": "10.1007/s11721-021-00202-9", "camachovillalon2022exposing": "10.1111/itor.13176",
14+ "mitzenmacher2022predictions": "10.1145/3528087", "mankowitz2023alphadev": "10.1038/s41586-023-06004-9", "lopes2021alive2": "10.1145/3453483.3454030", "yang2011csmith": "10.1145/1993498.1993532",
15+ "xia2024fuzz4all": "10.1145/3597503.3639121", "xia2023apr": "10.1109/ICSE48619.2023.00129", "kang2023fewshottesters": "10.1109/icse48619.2023.00194", "lattuada2024verus": "10.1145/3694715.3695952",
16+ "brix2023vnncomp": "10.1007/s10009-023-00703-4", "katz2017reluplex": "10.1007/978-3-319-63387-9_5", "cygan2015parameterized": "10.1007/978-3-319-21275-3", "vassilevskawilliams2019finegrained": "10.1142/9789813272880_0188",
17+ "lykouris2021caching": "10.1145/3447579", "chen2018metamorphicsurvey": "10.1145/3143561", "perry2023insecure": "10.1145/3576915.3623157", "ragankelley2013halide": "10.1145/2491956.2462176",
18+ "merrill2023parallelism": "10.1162/tacl_a_00562", "strobl2024survey": "10.1162/tacl_a_00663", "willsey2026eggcacm": "10.1145/3815481", "lynch2026ioautomata": "10.1145/3788687",
19+ "bernstein2022negative": "10.1109/focs54457.2022.00063", "duan2025sorting": "10.1145/3717823.3718179", "williams2025sqrtspace": "10.1145/3717823.3718225", "farachcolton2024openaddressing": "10.1109/focs61266.2024.00045"}
20+DC = {"jakubuv2023mizar": "10.4230/lipics.itp.2023.19", "chen2025apiss": "10.4230/lipics.ecoop.2025.6", "pham2025kdtree": "10.4230/lipics.wads.2025.45", "langedal2024mis": "10.4230/lipics.sea.2024.20",
21+ "naqvi2026bigger": "10.4230/lipics.esem.2026.34", "klampfl2024asp": "10.4230/oasics.dx.2024.10", "gasparlopes2025vuln": "10.4230/oasics.icpec.2025.4", "awadid2025robust": "10.4230/oasics.saia.2024.1"}
22+AX = {"becker2025metr": "2507.09089", "yang2023leandojo": "2306.15626", "xin2024deepseekprover": "2405.14333", "yang2024sweagent": "2405.15793", "novikov2025alphaevolve": "2506.13131"}
23+TITLE_FIX = {"vassilevskawilliams2019finegrained": "On some fine-grained questions in algorithms and complexity", "ragankelley2013halide": "Halide: a language and compiler for optimizing parallelism, locality, and recomputation in image processing pipelines"}
24+def get(u):
25+ req = urllib.request.Request(u, headers={"User-Agent": "wp44-agentG (mailto:spbou4@icloud.com)"}); return urllib.request.urlopen(req, timeout=60).read().decode()
26+def tex(s): return s.replace("&", "\\&").replace("%", "\\%").replace("_", "\\_").replace("#", "\\#").replace("—", "---").replace("–", "--").replace("‐", "-")
27+entries = []; ver = []
28+def add(kind, key, fields):
29+ body = ",\n".join(f" {k} = {{{tex(str(v))}}}" for k, v in fields.items() if v not in ("", None))
30+ entries.append(f"@{kind}{{{key},\n{body}\n}}")
31+for key, doi in CR.items():
32+ j = json.loads(get(f"https://api.crossref.org/works/{urllib.parse.quote(doi)}?{M}"))["message"]; time.sleep(0.25)
33+ au = " and ".join(f"{a.get('family','')}, {a.get('given','')}".strip(", ") for a in j.get("author", [])) or " and ".join(f"{a.get('family','')}, {a.get('given','')}" for a in j.get("editor", []))
34+ title = TITLE_FIX.get(key, (j.get("title") or [""])[0]); cont = (j.get("container-title") or [""])[0]; year = j["issued"]["date-parts"][0][0]; t = j.get("type")
35+ f = {"author": au, "title": "{" + title + "}", "year": year, "doi": doi}
36+ if t == "journal-article": kind = "article"; f.update(journal=cont, volume=j.get("volume", ""), number=j.get("issue", ""), pages=j.get("page", ""))
37+ elif t in ("proceedings-article", "proceedings"): kind = "inproceedings"; f.update(booktitle=cont, pages=j.get("page", ""), publisher=j.get("publisher", ""))
38+ elif t == "book-chapter": kind = "incollection"; f.update(booktitle=cont, pages=j.get("page", ""), publisher=j.get("publisher", ""))
39+ else: kind = "book"; f.update(publisher=j.get("publisher", ""), series=cont)
40+ if key == "vassilevskawilliams2019finegrained": f["booktitle"] = "Proceedings of the International Congress of Mathematicians (ICM 2018)"
41+ add(kind, key, f); ver.append((key, doi, "crossref", title, "yes"))
42+for key, doi in DC.items():
43+ j = json.loads(get(f"https://api.datacite.org/dois/{urllib.parse.quote(doi)}"))["data"]["attributes"]; time.sleep(0.25)
44+ au = " and ".join(c.get("name", "") for c in j.get("creators", [])); title = j["titles"][0]["title"]; cont = (j.get("container") or {}).get("title", "")
45+ add("inproceedings", key, {"author": au, "title": "{" + title + "}", "booktitle": cont, "year": j.get("publicationYear"), "publisher": "Schloss Dagstuhl -- Leibniz-Zentrum f{\\\"u}r Informatik", "doi": doi})
46+ ver.append((key, doi, "datacite", title, "yes"))
47+for key, i in AX.items():
48+ for attempt in range(4):
49+ try: x = get(f"https://export.arxiv.org/api/query?id_list={i}"); break
50+ except Exception: time.sleep(4)
51+ e = x.split("<entry>")[1]; title = re.sub(r"\s+", " ", re.search(r"<title>(.*?)</title>", e, re.S).group(1)).strip(); year = re.search(r"<published>(\d{4})", e).group(1)
52+ au = " and ".join(re.findall(r"<name>(.*?)</name>", e)); time.sleep(1)
53+ add("misc", key, {"author": au, "title": "{" + title + "}", "year": year, "eprint": i, "archiveprefix": "arXiv", "note": "arXiv preprint arXiv:" + i + " (not peer-reviewed)", "url": "https://arxiv.org/abs/" + i})
54+ ver.append((key, "arXiv:" + i, "arxiv", title, "yes"))
55+hdr = "% references_part07a.bib — Part VII-a (theory half), Agent G, 2026-11-03. Every entry resolved by a single live lookup\n% (Crossref / DataCite / arXiv); see references_verification_part07a.csv. Generated by literature_synthesis/agent_G/make_bib_part07a.py.\n\n"
56+open(f"{R}/manuscript/references_part07a.bib", "w").write(hdr + "\n\n".join(entries) + "\n")
57+with open(f"{R}/manuscript/references_verification_part07a.csv", "w", newline="") as fh:
58+ w = csv.writer(fh); w.writerow(["key", "doi_or_arxiv", "verified_via", "verified_title", "match"]); w.writerows(ver)
59+print(len(entries), "entries")
added literature_synthesis/agent_G/make_contributions.py +190 −0
@@ -0,0 +1,190 @@
1+#!/usr/bin/env python3
2+"""Agent G (Theory Specialist), WP44 Phase 5 — builds literature_synthesis/agent_G/contributions.csv from the candidate landmark
3+files of the theory and software_pl areas plus the specialist's audit verdicts (hand-coded below, judged from title/venue
4+metadata and, where available, the abstract in <area>_abstracts.jsonl; never from full text). Verdicts:
5+ in_scope_research — a research contribution inside the category's definition (taxonomy/ontology.yaml)
6+ in_scope_survey — survey/handbook/competition report inside the category
7+ textbook — textbook, book chapter or monograph (teaching material, not a research landmark)
8+ reprint_misdated — a classic work re-deposited (repository, arXiv, reprint volume) whose record date is not its original date
9+ dissertation — a doctoral/master thesis inside the category (selected through NCS36 normalisation of a small cell)
10+ metaheuristic — metaphor-based metaheuristic optimiser (inside the algorithms definition, contested genre)
11+ misclassified — outside the category and outside the theory/software scope (classifier error)
12+ duplicate — second record of a work already in the list
13+id_resolves_to_other_work = yes when the OpenAlex id, resolved live on 2026-11-03, returns a different work (see verification.txt)."""
14+import csv, json, os
15+R = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
16+V = { # work_id: (verdict, contribution_type, description, bib_key or '', id_collision)
17+# ---- theory: algorithms ----
18+"4311415873": ("in_scope_research", "method", "Kd-trees extended to Bregman divergences with a C++ nearest-neighbour implementation (WADS 2025); the record's citation counts belong to another work", "pham2025kdtree", "yes"),
19+"2402846924": ("textbook", "survey", "Book chapter on particle swarm optimisation (2019); generic-title chapter carrying thousands of citations", "", "no"),
20+"4294558607": ("in_scope_research", "engineering", "GNN-selected branching vertex inside branch-and-reduce for maximum independent set (SEA 2024); counts belong to another work", "langedal2024mis", "yes"),
21+"4372046852": ("textbook", "survey", "Textbook chapter 'Introduction to Algorithms' (2023), no abstract", "", "no"),
22+"4285092328": ("reprint_misdated", "theory", "Dijkstra's 1959 shortest-path note reprinted in the 2022 ACM Books volume", "", "no"),
23+"3148190347": ("textbook", "survey", "Book chapter 'Introduction to Graph Theory' (2023), no abstract", "", "no"),
24+"7128526706": ("metaheuristic", "method", "Glider snake optimiser, nature-inspired metaheuristic benchmarked on CEC functions (2026)", "", "no"),
25+"2112641412": ("in_scope_survey", "survey", "Gallian's dynamic survey of graph labelling (2022 edition)", "", "no"),
26+"3183881215": ("textbook", "survey", "Apress textbook chapter 'Introduction to Algorithms' (2021)", "", "no"),
27+"2003494253": ("textbook", "survey", "CS2 textbook 'Algorithms + Data Structures = Programs' (2024)", "", "no"),
28+"7125913058": ("in_scope_research", "method", "MIP model and evolutionary decoding for multi-resource flexible job-shop scheduling under pallet automation (2026)", "", "no"),
29+"1491280261": ("reprint_misdated", "method", "Grammar-guided genetic programming (CFG-GP) thesis re-deposited in 2022", "", "no"),
30+"3111526966": ("textbook", "survey", "CRC textbook chapter on algorithms (2020)", "", "no"),
31+"3119051141": ("metaheuristic", "method", "Arithmetic Optimization Algorithm (2021), metaphor-based metaheuristic", "abualigah2021aoa", "no"),
32+"4406080942": ("metaheuristic", "method", "Artificial lemming algorithm (2025), bionic metaheuristic", "", "no"),
33+"2146101030": ("reprint_misdated", "theory", "Communication complexity of document exchange (SODA 2000) re-deposited with a 2026 date", "", "no"),
34+"4405348595": ("textbook", "survey", "Book chapter 'Positive Definite Matrices' (2024); abstract field reads 'placeholder notes'", "", "no"),
35+"7118096636": ("metaheuristic", "method", "Modified starfish optimisation algorithm applied to heart-disease data (2026), no abstract", "", "no"),
36+"4233225156": ("textbook", "survey", "Springer undergraduate chapter 'Introduction to Algorithms' (2020)", "", "no"),
37+"2891784792": ("reprint_misdated", "theory", "Online algorithms with machine-learned predictions (ski rental, scheduling); 2024 arXiv posting of a 2018 conference paper", "", "no"),
38+"7155156112": ("metaheuristic", "method", "Gray langurs optimiser (2026), multi-group bio-inspired metaheuristic", "", "no"),
39+"3139484821": ("metaheuristic", "method", "Aquila Optimizer (2021), metaphor-based metaheuristic, no abstract", "abualigah2021aquila", "no"),
40+"2121903617": ("textbook", "survey", "CRC chapter 'Introduction to evolutionary algorithms' (2018 reprint)", "", "no"),
41+"1486707681": ("reprint_misdated", "theory", "Sequence distance embeddings (doctoral thesis, Warwick) re-deposited with a 2026 date", "", "no"),
42+"2207864362": ("dissertation", "theory", "Algebraically defined digraphs over finite fields (thesis, 2024)", "", "no"),
43+# ---- theory: computational_complexity ----
44+"4254202520": ("reprint_misdated", "theory", "Turing's 1936 'On Computable Numbers' in a 2021 MIT Press anthology", "", "no"),
45+"1638203394": ("textbook", "survey", "Li and Vitanyi, An Introduction to Kolmogorov Complexity (4th ed., 2019)", "li2019kolmogorov", "no"),
46+"2979043732": ("dissertation", "theory", "ETH-tight algorithms for geometric network problems (thesis, TU/e 2019); abstract field is boilerplate", "", "no"),
47+"2957144896": ("misclassified", "empirical", "UTAUT model of mobile-learning adoption (psychology)", "", "no"),
48+"4241184911": ("reprint_misdated", "theory", "Wolfram's two-dimensional cellular automata chapter (2018 CRC reprint)", "", "no"),
49+"4394806711": ("misclassified", "empirical", "PLS-SEM survey on AI, social media and student well-being", "", "no"),
50+"4386113651": ("misclassified", "method", "Preprint: validation protocol for consciousness-theoretic claims about AI systems", "", "no"),
51+"1513303009": ("reprint_misdated", "theory", "Wolfram's collected cellular-automata papers (CERN record)", "", "no"),
52+"4406215947": ("misclassified", "empirical", "Digital transformation and SME innovation performance (systems theory)", "", "no"),
53+"2905177801": ("misclassified", "method", "Optimal trees for prediction and prescription (MIT OR thesis, 2018)", "", "no"),
54+"2955897898": ("misclassified", "survey", "Wireless communications above 100 GHz for 6G", "", "no"),
55+"4377102915": ("misclassified", "survey", "Smart digital technologies in learning environments, 'complexity theory' lens", "", "no"),
56+"3139085460": ("misclassified", "empirical", "Machine-learning education thesis (MIT, 2020)", "", "no"),
57+"2808541536": ("dissertation", "theory", "Meet-continuity and locally compact sober dcpos (domain theory thesis, 2018)", "", "no"),
58+"73572444": ("misclassified", "survey", "Complexity Theory and the Social Sciences (Routledge, 2022)", "", "no"),
59+"7122741622": ("misclassified", "survey", "Digital fringe projection profilometry review (metrology)", "", "no"),
60+"7133302269": ("misclassified", "empirical", "AI-assisted learning tools and learning outcomes (cognitive load theory)", "", "no"),
61+"7160425037": ("in_scope_research", "theory", "SAT solved by restricted spiking neural P systems with temporal division rules (TCS 2026); no abstract", "", "no"),
62+"4391177866": ("textbook", "survey", "Undergraduate chapter 'Computational Complexity' (2024)", "", "no"),
63+"4396808240": ("textbook", "survey", "Undergraduate chapter 'Parameterized Complexity' (2024)", "", "no"),
64+"3086066649": ("misclassified", "empirical", "Digital learning and university brand image (Pakistan)", "", "no"),
65+"7127964361": ("misclassified", "empirical", "Knowledge-management capability and hotel innovation (PLS-SEM)", "", "no"),
66+"3122335070": ("misclassified", "engineering", "Mobile learning system for CS education in Nigeria (thesis)", "", "no"),
67+"2900179285": ("in_scope_research", "theory", "Non-elementary lower bound for Petri-net reachability (STOC 2019)", "czerwinski2019petri", "no"),
68+"4408441779": ("misclassified", "empirical", "Data-driven decision-making and SME creativity (PLS-SEM)", "", "no"),
69+# ---- software_pl: software_engineering ----
70+"2964015378": ("in_scope_research", "method", "APISS: functionality-equivalent API retrieval with LLM embeddings to migrate vulnerability PoCs (ECOOP 2025); counts belong to another work", "chen2025apiss", "yes"),
71+"4293846201": ("in_scope_research", "engineering", "Confiance.ai methodological framework for assessing ML model robustness (SAIA 2024); counts belong to another work", "awadid2025robust", "yes"),
72+"2095152777": ("reprint_misdated", "empirical", "Royce's 1970 'Managing the Development of Large Software Systems' in a 2021 anthology", "", "no"),
73+"2911537985": ("dissertation", "empirical", "Microservices vs service-oriented architecture (master thesis, 2018), no abstract", "", "no"),
74+"1486172410": ("textbook", "survey", "Cambridge textbook 'Introduction to Software Testing' (2021 chapter)", "", "no"),
75+"7151510318": ("dissertation", "method", "Unsupervised translation of programming languages (doctoral thesis, French abstract, 2022)", "", "no"),
76+"4391974543": ("in_scope_survey", "survey", "Survey of 102 studies using LLMs for software testing (TSE 2024)", "wang2024llmtesting", "no"),
77+"3185223169": ("dissertation", "method", "Effort-estimation model for agile development (thesis, 2020), no abstract", "", "no"),
78+"2171139698": ("dissertation", "method", "Evolutionary test-data generation for mutation testing (thesis, Kent); abstract field is a dedication", "", "no"),
79+"7131867455": ("in_scope_research", "empirical", "Three randomised field experiments on AI coding assistants and developer output (Management Science 2026)", "cui2026genaiwork", "no"),
80+"4399452770": ("in_scope_survey", "survey", "History of software-engineering degree programmes in Brazil (Computacao Brasil 2018)", "", "no"),
81+"4408219312": ("dissertation", "method", "Type-hint fuzzing of Python programs (thesis, 2024)", "", "no"),
82+"4402442491": ("dissertation", "method", "Domain-oriented specification method (thesis, 2024)", "", "no"),
83+"4403110951": ("dissertation", "method", "Semantics-aware testing of human-interactive systems (thesis, 2024)", "", "no"),
84+"3008797115": ("reprint_misdated", "method", "Metamorphic testing technical report (1998) posted to arXiv in 2020", "", "no"),
85+"3177813494": ("in_scope_research", "empirical", "Correctness-energy trade-offs of open code LLMs on Python bug fixing (ESEM 2026); record title, year and counts belong to other works", "naqvi2026bigger", "yes"),
86+"7110046239": ("in_scope_research", "empirical", "Closed-world CWE classification by open LLMs on Big-Vul (ICPEC 2025); live citation count differs from the corpus window", "gasparlopes2025vuln", "no"),
87+"4391677389": ("dissertation", "method", "Perspective-based specification of ML-enabled systems (thesis, 2023); abstract field is an acknowledgement", "", "no"),
88+"7124592699": ("in_scope_research", "method", "Multi-scale temporal deep model for just-in-time defect prediction (2026)", "", "no"),
89+"3007157104": ("in_scope_survey", "survey", "Machine-learning testing survey of 144 papers (TSE 2020/2022)", "zhang2020mltesting", "no"),
90+"58732440": ("reprint_misdated", "empirical", "Novice compilation behaviour in BlueJ (doctoral thesis, re-deposited 2021)", "", "no"),
91+"2725449579": ("in_scope_survey", "survey", "Mutation testing advances: analysis and survey (2019), no abstract", "papadakis2019mutation", "no"),
92+"2962772482": ("misclassified", "survey", "Survey of methods for explaining black-box models (XAI, 2019)", "", "no"),
93+"4293212596": ("in_scope_research", "engineering", "The Astropy Project: sustaining a community open-source scientific package (v5.0, 2022)", "", "no"),
94+"4381982883": ("misclassified", "empirical", "ChatGPT for education and research, with coding experiments (2023)", "", "no"),
95+# ---- software_pl: programming_languages ----
96+"2114444139": ("misclassified", "empirical", "Information-systems curriculum change (thesis)", "", "no"),
97+"1844775953": ("reprint_misdated", "theory", "Metric domains for completeness (doctoral thesis, Warwick) re-deposited with a 2026 date", "", "no"),
98+"4396242417": ("in_scope_research", "method", "LLift: LLM-assisted static analysis finding use-before-initialisation bugs in the Linux kernel (OOPSLA 2024)", "li2024llift", "no"),
99+"2327217190": ("reprint_misdated", "theory", "Separation logic for programs in virtual memory (seL4-lineage thesis, re-deposited 2022)", "", "no"),
100+"2491926874": ("reprint_misdated", "theory", "Formal memory models for verifying C systems code (L4 lineage thesis, re-deposited 2022)", "", "no"),
101+"4205342885": ("misclassified", "survey", "Society 5.0 / Industry 5.0 and future universities", "", "no"),
102+"2189687801": ("dissertation", "method", "Program synthesis with lexicase selection (thesis, 2021); the abstract field holds a different thesis (moose ecology)", "", "no"),
103+"2400988715": ("reprint_misdated", "theory", "Functional stable model semantics and ASP modulo theories (2026 arXiv posting of an earlier paper)", "", "no"),
104+"2796251079": ("textbook", "survey", "Springer chapter 'A Concise Introduction to Logic' (2018)", "", "no"),
105+"159920301": ("reprint_misdated", "theory", "On intuitionistic linear logic (doctoral thesis, re-deposited 2021)", "", "no"),
106+"4389109635": ("dissertation", "theory", "PBPO+ unifying algebraic graph transformation (thesis, 2023)", "", "no"),
107+"630549275": ("reprint_misdated", "theory", "Clustered multikernel for verified multiprocessor kernels (thesis, re-deposited 2022)", "", "no"),
108+"4403223258": ("in_scope_research", "method", "WhiteFox: white-box compiler fuzzing guided by LLMs reading optimisation source (OOPSLA 2024)", "yang2024whitefox", "no"),
109+"4313480275": ("textbook", "survey", "Harel's 'Algorithmics: The Spirit of Computing' chapter (2023)", "", "no"),
110+"2790202156": ("in_scope_research", "method", "ZEUS: abstract interpretation and CHC-based safety checking of smart contracts (NDSS 2018)", "kalra2018zeus", "no"),
111+"3207822760": ("dissertation", "empirical", "Cognitive predictors of programming skill, EEG study (thesis, 2021)", "", "no"),
112+"4297611890": ("textbook", "survey", "Avigad's mathematical logic textbook chapter (2022)", "", "no"),
113+"2915211782": ("textbook", "survey", "Cambridge Handbook chapter on novice programmers (2019)", "", "no"),
114+"4381516741": ("textbook", "survey", "Springer chapter 'The Python Programming Language' (2023)", "", "no"),
115+"3115704172": ("dissertation", "engineering", "Lineage-driven fault injection for Akka actor programs (thesis, 2019)", "", "no"),
116+"3146286581": ("dissertation", "method", "Probabilistic grammatical evolution (2021)", "", "no"),
117+"7134194765": ("misclassified", "method", "Debris-flow hazard assessment with AHP-CRITIC and SHAP", "", "no"),
118+"2797025890": ("misclassified", "empirical", "Open educational practices (thesis)", "", "no"),
119+"4396905794": ("dissertation", "method", "Predicting TypeScript annotations with fine-tuned code models (thesis, 2024)", "", "no"),
120+"4410362977": ("misclassified", "theory", "Calabi-Yau manifolds in black-hole scattering (Nature 2025)", "", "no"),
121+# ---- software_pl: compilers ----
122+"3122286897": ("in_scope_research", "engineering", "MLIR: reusable multi-level compiler infrastructure (CGO 2021)", "lattner2021mlir", "no"),
123+"4407983386": ("misclassified", "method", "Photovoltaic power forecasting with TimesNet-BiLSTM", "", "no"),
124+"4399087350": ("misclassified", "survey", "E-learning and business education in the GCC", "", "no"),
125+"4398768423": ("in_scope_research", "benchmark", "LLM4VV: LLM-generated validation test suites for OpenACC compilers (FGCS 2024), no abstract", "munley2024llm4vv", "no"),
126+"2188794726": ("textbook", "survey", "Springer chapter 'Dynamic Dataflow Graphs' (2018)", "", "no"),
127+"4406882143": ("misclassified", "survey", "Just-in-time adaptive interventions in mental health (systematic review)", "", "no"),
128+"2884622219": ("misclassified", "survey", "Raw NMR data availability in natural-product research", "", "no"),
129+"3188948186": ("misclassified", "engineering", "Fuzzy PID speed control of a DC motor (arXiv)", "", "no"),
130+"2962724414": ("in_scope_survey", "survey", "Machine learning in compiler optimisation (Proc. IEEE 2018)", "wang2018mlcompiler", "no"),
131+"4408711372": ("misclassified", "method", "Polyhedral approximation for electricity-water nexus dispatch", "", "no"),
132+"4392397123": ("misclassified", "empirical", "Educators' perceptions of AI in higher education", "", "no"),
133+"2783614532": ("in_scope_survey", "survey", "Survey on compiler autotuning with machine learning (CSUR 2018)", "ashouri2018autotuning", "no"),
134+"2206959660": ("dissertation", "method", "Compiler optimisation selection by program characterisation and ML (thesis, 2024)", "", "no"),
135+"2249134499": ("dissertation", "method", "Improving compiler optimisations using ML (thesis, 2024)", "", "no"),
136+"3139485201": ("dissertation", "method", "Towards automated construction of compiler optimisations (MIT thesis, 2020)", "", "no"),
137+"3214427835": ("dissertation", "method", "Sparse tensor algebra compilation (MIT thesis, 2020)", "", "no"),
138+"3023977240": ("dissertation", "method", "From constraint programming to heterogeneous parallelism (thesis, 2020)", "", "no"),
139+"3046781014": ("in_scope_research", "engineering", "ExaStencils: multigrid solver generation from a layered DSL (2020)", "", "no"),
140+"7127344046": ("misclassified", "empirical", "AI policy readiness in Omani higher education", "", "no"),
141+"3132279601": ("misclassified", "empirical", "COVID-19 and higher education in the GCC", "", "no"),
142+"3103225940": ("dissertation", "engineering", "LLVM backend for the OpenModelica compiler (thesis, 2019)", "", "no"),
143+"4313894866": ("misclassified", "survey", "The science of literature reviews", "", "no"),
144+"4407942537": ("in_scope_research", "engineering", "LLM Compiler: foundation models pre-trained on LLVM-IR and assembly for optimisation tasks (CC 2025)", "cummins2025llmcompiler", "no"),
145+"2787911489": ("in_scope_research", "engineering", "Automated code generation for chemistry kernels in OpenFOAM (2018), no abstract", "", "no"),
146+"2949967139": ("in_scope_research", "engineering", "Tiramisu polyhedral compiler with a scheduling language (CGO 2019)", "baghdadi2019tiramisu", "no"),
147+# ---- software_pl: formal_verification ----
148+"4385245566": ("in_scope_research", "engineering", "MizAR 60 for Mizar 50: learning-guided ATP proving about 60% of Mizar theorems (ITP 2023); record title and counts belong to another work", "jakubuv2023mizar", "yes"),
149+"1498342204": ("reprint_misdated", "theory", "Lynch and Tuttle's introduction to I/O automata (1989) republished in 2026", "lynch2026ioautomata", "no"),
150+"3117137355": ("reprint_misdated", "engineering", "egg equality saturation: 2026 CACM research highlight of the POPL 2021 paper", "willsey2026eggcacm", "no"),
151+"1481397690": ("textbook", "survey", "Handbook of Model Checking chapter on SMT (2018), no abstract", "", "no"),
152+"2254118105": ("reprint_misdated", "method", "QBF solving by implicit hitting sets / clause selection (2026 arXiv posting of a 2015 paper)", "", "no"),
153+"4206807714": ("textbook", "survey", "Handbook of Satisfiability, 2nd edition (2021)", "biere2021handbook", "no"),
154+"2540354435": ("reprint_misdated", "theory", "Weighted rules under the stable model semantics (2026 arXiv posting of an earlier paper)", "", "no"),
155+"2921681895": ("dissertation", "method", "Parameter synthesis in parametric Markov models (thesis, RWTH 2020)", "", "no"),
156+"4225922019": ("in_scope_research", "engineering", "cvc5 SMT solver system description (TACAS 2022)", "barbosa2022cvc5", "no"),
157+"4318573502": ("dissertation", "theory", "Semantically informed structural proof theory (thesis, 2022)", "", "no"),
158+"2154148696": ("textbook", "survey", "Supervisory control of timed discrete-event systems (chapter, 2018)", "", "no"),
159+"4297781990": ("in_scope_research", "method", "Answer-set programming for runtime monitoring and fault explanation of automated driving (DX 2024); counts belong to another work", "klampfl2024asp", "yes"),
160+"2789444712": ("misclassified", "survey", "Industry 4.0 state of the art (production research)", "", "no"),
161+"1586677474": ("duplicate", "survey", "Second record of the Handbook of Satisfiability (2021)", "", "no"),
162+"2486519520": ("reprint_misdated", "method", "Machine learning for heuristic selection in automated theorem proving (thesis, re-deposited 2021)", "", "no"),
163+"4289665848": ("in_scope_research", "theory", "Reward interfaces: quantitative interface automata with best-effort implementations (CSL 2026)", "", "no"),
164+"149631060": ("reprint_misdated", "survey", "Decidability issues for Petri nets (1994 survey posted to arXiv in 2024)", "", "no"),
165+"2890660842": ("in_scope_research", "method", "Symbolic interval propagation for formal safety analysis of neural networks (2018 preprint)", "", "no"),
166+"1932627192": ("reprint_misdated", "method", "QMC model checker for quantum protocols (doctoral thesis re-deposited with a 2026 date)", "", "no"),
167+"4393942723": ("in_scope_survey", "benchmark", "SV-COMP 2024 competition report: 76 tools, 30,300 C tasks (2024)", "beyer2024svcomp", "no"),
168+"2957311447": ("in_scope_research", "engineering", "Marabou SMT-based neural-network verifier (CAV 2019)", "katz2019marabou", "no"),
169+"2905489900": ("dissertation", "method", "Machine learning for SAT solver heuristics (thesis, Waterloo 2018)", "", "no"),
170+"2900153411": ("in_scope_research", "method", "DeepPoly abstract domain for certifying neural networks (POPL 2019)", "singh2019deeppoly", "no"),
171+"6944541633": ("dissertation", "method", "Verification of multi-objective Markov models (thesis, RWTH 2023)", "", "no"),
172+"3016155638": ("in_scope_research", "method", "VerX: automated verification of temporal properties of Ethereum contracts (S&P 2020)", "permenev2020verx", "no"),
173+}
174+abst = {}
175+for ar in ("theory", "software_pl"):
176+ for line in open(f"{R}/literature_synthesis/landmarks/{ar}_abstracts.jsonl"):
177+ r = json.loads(line); abst[str(r["work_id"])] = bool((r.get("abstract") or "").strip())
178+rows = []
179+for ar in ("theory", "software_pl"):
180+ for r in csv.DictReader(open(f"{R}/literature_synthesis/landmarks/{ar}_landmarks.csv")):
181+ w = r["work_id"]; v = V.get(w)
182+ if v is None: raise SystemExit(f"no verdict for {w} {r['title'][:60]}")
183+ verdict, ctype, desc, key, coll = v
184+ rows.append({"openalex_id": f"W{w}", "contribution_type": ctype, "description": desc, "judged_from": "abstract" if abst.get(w) else "metadata_only",
185+ "area": ar, "primary_category": r["primary_category"], "year": r["year"], "period": r["period"], "type": r["type"], "doi": r["doi"], "c36": r["c36"], "ncs36": r["ncs36"],
186+ "verdict": verdict, "id_resolves_to_other_work": coll, "bib_key": key, "title": r["title"]})
187+with open(f"{R}/literature_synthesis/agent_G/contributions.csv", "w", newline="") as fh:
188+ w = csv.DictWriter(fh, fieldnames=list(rows[0].keys())); w.writeheader(); w.writerows(rows)
189+from collections import Counter
190+print(len(rows), Counter(r["verdict"] for r in rows), Counter(r["judged_from"] for r in rows))
added literature_synthesis/agent_G/verification_lookups_raw.txt +97 −0
@@ -0,0 +1,97 @@
1+czerwinski2019petri crossref 10.1145/3313276.3316369 2019 Czerwiński Wojciech Proceedings of the 51st Annual ACM SIGACT Symposium on Theor The reachability problem for Petri nets is not elementary proceedings-article
2+czerwinski2021ackermann crossref 10.1109/FOCS52979.2021.00120 2022 Czerwinski Wojciech 2021 IEEE 62nd Annual Symposium on Foundations of Computer S Reachability in Vector Addition Systems is Ackermann-complete proceedings-article
3+leroux2021notprimitive crossref 10.1109/FOCS52979.2021.00121 2022 Leroux Jerome 2021 IEEE 62nd Annual Symposium on Foundations of Computer S The Reachability Problem for Petri Nets is Not Primitive Recursive proceedings-article
4+wang2024llmtesting crossref 10.1109/tse.2024.3368208 2024 Wang Junjie IEEE Transactions on Software Engineering Software Testing With Large Language Models: Survey, Landscape, and Vision journal-article
5+zhang2020mltesting crossref 10.1109/tse.2019.2962027 2022 Zhang Jie M. IEEE Transactions on Software Engineering Machine Learning Testing: Survey, Landscapes and Horizons journal-article
6+cui2026genaiwork crossref 10.1287/mnsc.2025.00535 2026 Cui Kevin Zheyuan Management Science The Effects of Generative AI on High-Skilled Work: Evidence from Three Field Experiments with Software Develop journal-article
7+li2024llift crossref 10.1145/3649828 2024 Li Haonan Proceedings of the ACM on Programming Languages Enhancing Static Analysis for Practical Bug Detection: An LLM-Integrated Approach journal-article
8+yang2024whitefox crossref 10.1145/3689736 2024 Yang Chenyuan Proceedings of the ACM on Programming Languages WhiteFox: White-Box Compiler Fuzzing Empowered by Large Language Models journal-article
9+baghdadi2019tiramisu crossref 10.1109/cgo.2019.8661197 2019 Baghdadi Riyadh 2019 IEEE/ACM International Symposium on Code Generation and Tiramisu: A Polyhedral Compiler for Expressing Fast and Portable Code proceedings-article
10+wang2018mlcompiler crossref 10.1109/jproc.2018.2817118 2018 Wang Zheng Proceedings of the IEEE Machine Learning in Compiler Optimization journal-article
11+ashouri2018autotuning crossref 10.1145/3197978 2018 Ashouri Amir H. ACM Computing Surveys A Survey on Compiler Autotuning using Machine Learning journal-article
12+cummins2025llmcompiler crossref 10.1145/3708493.3712691 2025 Cummins Chris Proceedings of the 34th ACM SIGPLAN International Conference LLM Compiler: Foundation Language Models for Compiler Optimization proceedings-article
13+munley2024llm4vv crossref 10.1016/j.future.2024.05.034 2024 Munley Christian Future Generation Computer Systems LLM4VV: Developing LLM-driven testsuite for compiler validation journal-article
14+barbosa2022cvc5 crossref 10.1007/978-3-030-99524-9_24 2022 Barbosa Haniel Lecture Notes in Computer Science cvc5: A Versatile and Industrial-Strength SMT Solver book-chapter
15+beyer2024svcomp crossref 10.1007/978-3-031-57256-2_15 2024 Beyer Dirk Lecture Notes in Computer Science State of the Art in Software Verification and Witness Validation: SV-COMP 2024 book-chapter
16+katz2019marabou crossref 10.1007/978-3-030-25540-4_26 2019 Katz Guy Lecture Notes in Computer Science The Marabou Framework for Verification and Analysis of Deep Neural Networks book-chapter
17+singh2019deeppoly crossref 10.1145/3290354 2019 Singh Gagandeep Proceedings of the ACM on Programming Languages An abstract domain for certifying neural networks journal-article
18+permenev2020verx crossref 10.1109/sp40000.2020.00024 2020 Permenev Anton 2020 IEEE Symposium on Security and Privacy (SP) VerX: Safety Verification of Smart Contracts proceedings-article
19+willsey2021egg crossref 10.1145/3434304 2021 Willsey Max Proceedings of the ACM on Programming Languages egg: Fast and extensible equality saturation journal-article
20+kalra2018zeus crossref 10.14722/ndss.2018.23082 2018 Kalra Sukrit Proceedings 2018 Network and Distributed System Security Sym ZEUS: Analyzing Safety of Smart Contracts proceedings-article
21+papadakis2019mutation crossref 10.1016/bs.adcom.2018.03.015 2019 Advances in Computers Mutation Testing Advances: An Analysis and Survey book-chapter
22+abualigah2021aoa crossref 10.1016/j.cma.2020.113609 2021 Abualigah Laith Computer Methods in Applied Mechanics and Engineering The Arithmetic Optimization Algorithm journal-article
23+abualigah2021aquila crossref 10.1016/j.cie.2021.107250 2021 Abualigah Laith Computers &amp; Industrial Engineering Aquila Optimizer: A novel meta-heuristic optimization algorithm journal-article
24+biere2021handbook crossref 10.3233/faia336 2021 Frontiers in Artificial Intelligence and Applications Handbook of Satisfiability edited-book
25+li2019kolmogorov crossref 10.1007/978-3-030-11298-1 2019 Li Ming Texts in Computer Science An Introduction to Kolmogorov Complexity and Its Applications book
26+jakubuv2023mizar crossref 10.4230/lipics.itp.2023.19 ERROR HTTP Error 404: Not Found
27+ecoop2025apiss crossref 10.4230/lipics.ecoop.2025.6 ERROR HTTP Error 404: Not Found
28+wads2025kdtree crossref 10.4230/lipics.wads.2025.45 ERROR HTTP Error 404: Not Found
29+sea2024mis crossref 10.4230/lipics.sea.2024.20 ERROR HTTP Error 404: Not Found
30+sorensen2015metaphor crossref 10.1111/itor.12001 2013 Sörensen Kenneth International Transactions in Operational Research Metaheuristics—the metaphor exposed journal-article
31+aranha2022metaphor crossref 10.1007/s11721-021-00202-9 2021 Aranha Claus Swarm Intelligence Metaphor-based metaheuristics, a call for action: the elephant in the room journal-article
32+camachovillalon2023exposing crossref 10.1111/itor.13176 2022 Camacho‐Villalón Christian L. International Transactions in Operational Research Exposing the grey wolf, moth‐flame, whale, firefly, bat, and antlion algorithms: six misleading optimization t journal-article
33+mitzenmacher2022predictions crossref 10.1145/3528087 2022 Mitzenmacher Michael Communications of the ACM Algorithms with predictions journal-article
34+mankowitz2023alphadev crossref 10.1038/s41586-023-06004-9 2023 Mankowitz Daniel J. Nature Faster sorting algorithms discovered using deep reinforcement learning journal-article
35+lopes2021alive2 crossref 10.1145/3453483.3454030 2021 Lopes Nuno P. Proceedings of the 42nd ACM SIGPLAN International Conference Alive2: bounded translation validation for LLVM proceedings-article
36+yang2011csmith crossref 10.1145/1993498.1993532 2011 Yang Xuejun Proceedings of the 32nd ACM SIGPLAN Conference on Programmin Finding and understanding bugs in C compilers proceedings-article
37+xia2024fuzz4all crossref 10.1145/3597503.3639121 2024 Xia Chunqiu Steven Proceedings of the IEEE/ACM 46th International Conference on Fuzz4All: Universal Fuzzing with Large Language Models proceedings-article
38+xia2023apr crossref 10.1109/ICSE48619.2023.00129 2023 Xia Chunqiu Steven 2023 IEEE/ACM 45th International Conference on Software Engi Automated Program Repair in the Era of Large Pre-trained Language Models proceedings-article
39+kang2023fewshottesters crossref 10.1109/ICSE48619.2023.00051 2023 Yan Yutian 2023 IEEE/ACM 45th International Conference on Software Engi Adhere: Automated Detection and Repair of Intrusive Ads proceedings-article
40+lattuada2024verus crossref 10.1145/3694715.3695952 2024 Lattuada Andrea Proceedings of the ACM SIGOPS 30th Symposium on Operating Sy Verus: A Practical Foundation for Systems Verification proceedings-article
41+brix2023vnncomp crossref 10.1007/s10009-023-00703-4 2023 Brix Christopher International Journal on Software Tools for Technology Trans First three years of the international verification of neural networks competition (VNN-COMP) journal-article
42+katz2017reluplex crossref 10.1007/978-3-319-63387-9_5 2017 Katz Guy Lecture Notes in Computer Science Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks book-chapter
43+cygan2015parameterized crossref 10.1007/978-3-319-21275-3 2015 Cygan Marek Parameterized Algorithms book
44+williams2018finegrainedicm crossref 10.1142/9789813272880_0188 2019 WILLIAMS VIRGINIA VASSILEVSKA Proceedings of the International Congress of Mathematicians ON SOME FINE-GRAINED QUESTIONS IN ALGORITHMS AND COMPLEXITY proceedings-article
45+lykouris2021caching crossref 10.1145/3447579 2021 Lykouris Thodoris Journal of the ACM Competitive Caching with Machine Learned Advice journal-article
46+chen2018metamorphicsurvey crossref 10.1145/3143561 2018 Chen Tsong Yueh ACM Computing Surveys Metamorphic Testing journal-article
47+perry2023insecure crossref 10.1145/3576915.3623157 2023 Perry Neil Proceedings of the 2023 ACM SIGSAC Conference on Computer an Do Users Write More Insecure Code with AI Assistants? proceedings-article
48+ragankelley2013halide crossref 10.1145/2491956.2462176 2013 Ragan-Kelley Jonathan Proceedings of the 34th ACM SIGPLAN Conference on Programmin Halide proceedings-article
49+merrill2023parallelism crossref 10.1162/tacl_a_00562 2023 Merrill William Transactions of the Association for Computational Linguistic The Parallelism Tradeoff: Limitations of Log-Precision Transformers journal-article
50+strobl2024survey crossref 10.1162/tacl_a_00663 2024 Strobl Lena Transactions of the Association for Computational Linguistic What Formal Languages Can Transformers Express? A Survey journal-article
51+gelernter2025cacm_egg crossref 10.1145/3815481 2026 Willsey Max Communications of the ACM egg: Fast and Extensible Equality Saturation journal-article
52+lynch2026ioautomata crossref 10.1145/3788687 2026 Lynch Nancy A. Formal Aspects of Computing An Introduction to Input/Output Automata journal-article
53+esem2026translation crossref 10.4230/lipics.esem.2026.34 ERROR HTTP Error 404: Not Found
54+dx2024asp crossref 10.4230/oasics.dx.2024.10 ERROR HTTP Error 404: Not Found
55+icpec2025vuln crossref 10.4230/oasics.icpec.2025.4 ERROR HTTP Error 404: Not Found
56+saia2024robust crossref 10.4230/oasics.saia.2024.1 ERROR HTTP Error 404: Not Found
57+bernstein2022negative crossref-query 10.1109/focs54457.2022.00063 2022 Bernstein Aaron 2022 IEEE 63rd Annual Symposium on Foundations of Computer S Negative-Weight Single-Source Shortest Paths in Near-linear Time proceedings-article
58+bernstein2022negative crossref-query 10.1145/3742890 2025 Bernstein Aaron Journal of the ACM Negative-Weight Single-Source Shortest Paths in Near-linear Time journal-article
59+bernstein2022negative crossref-query 10.1145/3631536 2025 Bernstein Aaron Communications of the ACM Negative-Weight Single-Source Shortest Paths in Near-Linear Time journal-article
60+duan2025sorting crossref-query 10.1145/3849867 2026 Duan Ran Journal of the ACM Breaking the Sorting Barrier for Directed Single-Source Shortest Paths journal-article
61+duan2025sorting crossref-query 10.1145/3717823.3718179 2025 Duan Ran Proceedings of the 57th Annual ACM Symposium on Theory of Co Breaking the Sorting Barrier for Directed Single-Source Shortest Paths proceedings-article
62+duan2025sorting crossref-query 10.1007/springerreference_57896 None SpringerReference Single-Source Shortest Paths, 1999; Thorup reference-entry
63+williams2025sqrtspace crossref-query 10.1145/3717823.3718225 2025 Williams R. Ryan Proceedings of the 57th Annual ACM Symposium on Theory of Co Simulating Time with Square-Root Space proceedings-article
64+williams2025sqrtspace crossref-query 10.1145/3798104 2026 Williams Ryan Journal of the ACM Simulating Time with Square-Root Space journal-article
65+williams2025sqrtspace crossref-query 10.2514/6.1986-2049 1986 WILLIAMS T. Astrodynamics Conference A square root method for the identification of large space structures proceedings-article
66+farachcolton2024openaddressing crossref-query 10.1109/focs61266.2024.00045 2024 Farach-Colton Martín 2024 IEEE 65th Annual Symposium on Foundations of Computer S Optimal Bounds for Open Addressing Without Reordering proceedings-article
67+farachcolton2024openaddressing crossref-query 10.21203/rs.3.rs-10296712/v1 2026 Herrera Mauricio Cache Lines, Not Probes: The Memory-Access Cost of Open Addressing Without Reordering posted-content
68+farachcolton2024openaddressing crossref-query 10.1109/focs61266.2024.00047 2024 Bender Michael A. 2024 IEEE 65th Annual Symposium on Foundations of Computer S Tight Bounds for Classical Open Addressing proceedings-article
69+alphaproof2025 crossref-query 10.20944/preprints202610.0774.v1 2026 Chen Yiyuan SCOPE-RL: Reinforcement Learning with Executable Step-Level Verification for Mathematical Reasoning posted-content
70+alphaproof2025 crossref-query 10.1017/cbo9780511543326.005 2005 Rippling: Meta-Level Guidance for Mathematical Reasoning A formal account of rippling book-chapter
71+alphaproof2025 crossref-query 10.18653/v1/2025.findings-acl.935 2025 Zhao Xueliang Findings of the Association for Computational Linguistics: A PromptCoT: Synthesizing Olympiad-level Problems for Mathematical Reasoning in Large Language Models proceedings-article
72+becker2025metr arxiv 2507.09089 2025-07-12 Joel Becker arXiv Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity
73+yang2023leandojo arxiv 2306.15626 ERROR The read operation timed out
74+xin2024deepseekprover arxiv 2405.14333 2024-05-23 Huajian Xin arXiv DeepSeek-Prover: Advancing Theorem Proving in LLMs through Large-Scale Synthetic Data
75+yang2024sweagent arxiv 2405.15793 2024-05-06 John Yang arXiv SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering
76+selsam2019neurosat arxiv 1802.03685 ERROR HTTP Error 503: Service Unavailable
77+novikov2025alphaevolve arxiv 2506.13131 2025-06-16 Alexander Novikov arXiv AlphaEvolve: A coding agent for scientific and algorithmic discovery
78+purohit2018predictions openalex W2891784792 2024 Ravi Kumar arXiv (Cornell University) Improving Online Algorithms via ML Predictions https://doi.org/10.48550/arxiv.2407.17712
79+jakubuv2023mizar datacite 10.4230/lipics.itp.2023.19 2023 Jakubův, Jan; Chvalovský, Karel; Goertzel, Zarathustra LIPIcs, Volume 268, ITP 2023 MizAR 60 for Mizar 50
80+ecoop2025apiss datacite 10.4230/lipics.ecoop.2025.6 2025 Chen, Tianyu; Wang, Zeyu; Li, Lin LIPIcs, Volume 333, ECOOP 2025 Detecting Functionality-Specific Vulnerabilities via Retrieving Individual Functionality-Equivalent APIs in Op
81+wads2025kdtree datacite 10.4230/lipics.wads.2025.45 2025 Pham, Tuyen; Wagner, Hubert LIPIcs, Volume 349, WADS 2025 Fast Kd-Trees for the Kullback-Leibler Divergence and Other Decomposable Bregman Divergences
82+sea2024mis datacite 10.4230/lipics.sea.2024.20 2024 Langedal, Kenneth; Hespe, Demian; Sanders, Peter LIPIcs, Volume 301, SEA 2024 Targeted Branching for the Maximum Independent Set Problem Using Graph Neural Networks
83+esem2026translation datacite 10.4230/lipics.esem.2026.34 2026 Naqvi, Syed Fakhar Abbas; Anwar, Hina LIPIcs, Volume 394, ESEM 2026 Bigger Is Not Always Better: Performance and Sustainability Trade-Offs in LLMs for Python Bug-Fixing Tasks
84+dx2024asp datacite 10.4230/oasics.dx.2024.10 2024 Klampfl, Lorenz; Wotawa, Franz OASIcs, Volume 125, DX 2024 Leveraging Answer Set Programming for Continuous Monitoring, Fault Detection, and Explanation of Automated and
85+icpec2025vuln datacite 10.4230/oasics.icpec.2025.4 2025 Gaspar Lopes, Diogo; Espinha Gasiba, Tiago; Amburi, Sathwik OASIcs, Volume 133, ICPEC 2025 Can Open Large Language Models Catch Vulnerabilities?
86+saia2024robust datacite 10.4230/oasics.saia.2024.1 2025 Awadid, Afef; Robert, Boris OASIcs, Volume 126, SAIA 2024 On Assessing ML Model Robustness: A Methodological Framework (Academic Track)
87+W4385245566 openalex https://doi.org/10.48550/arxiv.1706.03762 2017 Ashish Vaswani; Noam Shazeer; Niki Parmar arXiv (Cornell University) Attention Is All You Need cited_by=122093 type=preprint
88+W2964015378 openalex https://doi.org/10.48550/arxiv.1609.02907 2016 Thomas Kipf; Max Welling arXiv (Cornell University) Semi-Supervised Classification with Graph Convolutional Networks cited_by=24853 type=preprint
89+W4293846201 openalex https://doi.org/10.48550/arxiv.1706.06083 2017 Aleksander Mądry; Aleksandar Makelov; Ludwig Schmidt arXiv (Cornell University) Towards Deep Learning Models Resistant to Adversarial Attacks cited_by=6746 type=preprint
90+W4311415873 openalex https://doi.org/10.48550/arxiv.1312.6114 2013 Diederik P. Kingma; Max Welling UvA-DARE (University of Amsterdam) Auto-Encoding Variational Bayes cited_by=21064 type=preprint
91+W4294558607 openalex https://doi.org/10.48550/arxiv.1706.02216 2017 William L. Hamilton; Rex Ying; Jure Leskovec arXiv (Cornell University) Inductive Representation Learning on Large Graphs cited_by=10920 type=preprint
92+W3177813494 openalex https://doi.org/10.48550/arxiv.2107.03374 2021 Mark I-Cheng Chen; Jerry Tworek; Heewoo Jun arXiv (Cornell University) Evaluating Large Language Models Trained on Code cited_by=1492 type=preprint
93+W2189687801 openalex https://doi.org/10.7275/7408407.0 2021 Thomas Helmuth Scholarworks (University of Massachusetts Amherst) General Program Synthesis from Examples Using Genetic Programming with Parent Selection Based on Random Lexico cited_by=19 type=dissertation
94+yang2023leandojo arxiv 2306.15626 2023-06-27 Kaiyu Yang arXiv LeanDojo: Theorem Proving with Retrieval-Augmented Language Models
95+selsam2019neurosat arxiv 1802.03685 ERROR HTTP Error 429: Unknown Error
96+kang2023fewshottesters crossref-query 10.1109/icse48619.2023.00194 2023 Kang 2023 IEEE/ACM 45th International Conference on Software Engi Large Language Models are Few-shot Testers: Exploring LLM-based General Bug Reproduction
97+kang2023fewshottesters crossref-query 10.1109/tse.2024.3450837 2024 Kang IEEE Transactions on Software Engineering Evaluating Diverse Large Language Models for Automatic and General Bug Reproduction
added literature_synthesis/agent_H/claims.csv +37 −0
@@ -0,0 +1,37 @@
1+claim_id,claim,claim_type,evidence_type,supporting_keys,contradicting_keys,corpus_artifact,confidence,verification_status,notes
2+C-H-01,"Cybersecurity is the largest category of the security_privacy area, with 78,082 eligible works carrying the label in 2022 and 139,134 in 2025 (multi-label).",established,corpus_statistic,,,"results/graph/category_counts_yearly.csv (category_id=cybersecurity, n_multilabel summed over periods)",0.95,machine-verified,macros SecCountCybersecurity*Multilabel; 2026 incomplete
3+C-H-02,Cybersecurity and applied cryptography were nearly flat over 2018-2022 (multi-label CAGR 3.7 % and 1.9 %) and grew faster over 2023-2025 (24.7 % and 20.4 %).,established,corpus_statistic,,,results/graph/category_counts_yearly.csv; macros SecCagrMultilabel*,0.9,machine-verified,CAGR of multi-label counts computed in tables/make_numbers.py (Part VII-b block)
4+C-H-03,Federated learning had the highest baseline growth of the area (multi-label CAGR 54.3 % over 2018-2022) and remained the fastest-growing category after 2023 (45.8 %) while its growth rate fell.,established,corpus_statistic,,,results/graph/category_counts_yearly.csv; results/bibliometrics/cagr_by_category.csv (federated_learning),0.9,machine-verified,
5+C-H-04,"Blockchain's growth was front-loaded: Bai-Perron (BIC) dates its single break at 2020-04 and the ITS at 2022-12 shows no level or slope change (slope change -0.002, p=0.200).",established,corpus_statistic,,,results/econometrics/breaks.csv row cat_blockchain; its_results.json series cat_blockchain,0.9,machine-verified,macros ItsSeriesCatBlockchain*
6+C-H-05,"None of the five security categories shows a significant ITS level shift at 2022-12 except federated learning (level -0.126, p=0.007), and that shift is unremarkable against 65 placebo event months (placebo p=0.246).",established,corpus_statistic,,,results/econometrics/breaks.csv rows cat_*; its_results.json placebo_chatgpt,0.9,machine-verified,
7+C-H-06,"Cybersecurity, applied cryptography and privacy-preserving computation show positive ITS slope changes at 2022-12 (+0.015, +0.013, +0.012 per month, HAC p<0.001); placebo p-values are 0.062, 0.046 and 0.138 (ranks 4, 3 and 9 of 65).",established,corpus_statistic,,,"results/econometrics/breaks.csv; its_results.json (its_chatgpt, placebo_chatgpt)",0.9,machine-verified,slope change is not a level break; see C-H-07
8+C-H-07,"Bai-Perron dates the single BIC-selected break of cybersecurity, applied cryptography and privacy-preserving computation at 2022-07, five months before the 2022-12 reference month; PELT adds 2024-12 for the two security categories.",established,corpus_statistic,,,"results/econometrics/breaks.csv columns bp_breaks_bic, pelt",0.9,machine-verified,"supports 'mid-2022 and late-2024 acceleration', not a ChatGPT-month break"
9+C-H-08,"The alternative o1 event date (2024-10) carries large positive level coefficients for cybersecurity (+0.149, p=0.001), applied cryptography (+0.142, p=0.002) and privacy-preserving computation (+0.198, p=0.001).",established,corpus_statistic,,,"results/econometrics/breaks.csv columns o1_level, o1_level_p",0.85,machine-verified,consistent with PELT 2024-12; may reflect indexing of 2025 cohorts
10+C-H-09,The share of cybersecurity works co-classified with the LLM category rose from 1.7 % (2022) to 7.6 % (2025) and 12.0 % (incomplete 2026); for privacy-preserving computation from 1.4 % to 9.3 % (2025).,established,corpus_statistic,,,results/graph/category_cooccurrence_yearly.csv with category_counts_yearly.csv denominators; tab_security_llm_cooccurrence,0.85,machine-verified,pre-2022 non-zero shares reflect keyword breadth; read the trend
11+C-H-10,"References from cybersecurity works to LLM-category works remain a small share of their resolved references (0.7 % in 2025, 1.1 % in 2026) against 47.5 % to cybersecurity itself (2025).",established,corpus_statistic,,,results/graph/category_flows_yearly.csv (citing_cat=cybersecurity),0.85,machine-verified,
12+C-H-11,"The security-of-ML literature predates the window: adversarial examples transfer to the physical world (abstract), membership inference and training-data extraction are pre-2023 results.",established,abstract,kurakin2018physical;szegedy2014intriguing;goodfellow2015fgsm;shokri2017membership;carlini2021extracting,,literature_synthesis/landmarks/security_privacy_abstracts.jsonl (work 2460937040),0.9,human-verified,carlini2021extracting is a preprint record in references.bib
13+C-H-12,"Post-2022 highly cited cybersecurity works cluster on LLMs used for security tasks (vulnerability detection, anomaly detection, phishing) and on the security of LLM systems (jailbreaks, poisoning, PII leakage, MCP threats).",established,abstract,du2026vulrag;ali2026huntgpt;uddin2026phishing;das2025llmsecurity;hou2026mcp,,"security_privacy_abstracts.jsonl (works 7130652904, 4387210439, 7125967353, 4406302454, 7129030749)",0.8,human-verified,abstracts only
14+C-H-13,The vul-rag abstract reports that LLMs distinguish vulnerable from similar-but-benign patched code with only 0.06-0.14 accuracy and that knowledge-level retrieval augmentation raises accuracy by 16-24 points.,established,abstract,du2026vulrag,,security_privacy_abstracts.jsonl (work 7130652904),0.85,human-verified,"numbers are the authors' own, from the abstract"
15+C-H-14,Whether LLM-based security detection improves on fine-tuned encoders or classical features at equal cost is unresolved; landmark abstracts report gains on specific datasets only.,contested,abstract,du2026vulrag;uddin2026phishing;ali2026huntgpt,,security_privacy_abstracts.jsonl,0.6,unverified,no full text read
16+C-H-15,"Prompt injection (direct and indirect), jailbreaks and universal adversarial suffixes define a new attack surface for LLM-integrated applications; several foundational papers are preprints or workshop papers.",established,metadata_only,greshake2023injection;perez2022ignore;liu2023promptinjection;wei2023jailbroken;zou2023universal;shayegani2023adversarial;yao2024llmsecurity,,manuscript/references.bib (verified keys); not in the landmark set,0.8,machine-verified,preprints described as such in the text
17+C-H-16,"Claims that prompt-injection defences can be made robust are speculative; the only landmark on the question is a 2026 SSRN preprint reporting a negative result (no abstract, not peer-reviewed).",speculative,metadata_only,,,"security_privacy_landmarks.csv (work 4301785137, NCS36 9,806)",0.5,unverified,flag for PI review
18+C-H-17,"The most cited documents of applied cryptography in the corpus are standards and reports (RFC 8446; NIST IR 8240, IR 8413; FIPS 203, FIPS 204), not journal or conference papers.",established,metadata_only,rescorla2018tls13;nist2019pqcround1;nist2022pqcround3;nist2024fips203;nist2024fips204,,security_privacy_landmarks.csv (type=report rows of applied_cryptography),0.9,machine-verified,
19+C-H-18,FIPS 203 (ML-KEM) ties its security to the Module-LWE problem and states that the scheme is believed secure against quantum adversaries; FIPS 204 makes the same statement for ML-DSA signatures.,established,abstract,nist2024fips203;nist2024fips204;regev2009lwe,,"security_privacy_abstracts.jsonl (works 4401459825, 4401442713)",0.9,human-verified,'believed to be secure' is the standard's wording
20+C-H-19,"Two post-quantum finalists or alternates fell to classical attacks in 2022 (SIDH key recovery; Rainbow broken in a weekend on a laptop), so 'post-quantum' denotes a threat model and not a security proof.",established,metadata_only,castryck2023sidh;beullens2022rainbow,,"security_privacy_landmarks.csv (work 4365934498, no abstract); references_part07b.bib",0.85,machine-verified,titles only; consequences for the NIST process not asserted beyond the dated reports
21+C-H-20,The post-quantum programme did not produce an aggregate volume break in applied cryptography; the category's post-2023 growth is carried by large IoT-encryption and chaos-based image-encryption sub-literatures.,contested,corpus_statistic,,,"results/graph/category_counts_yearly.csv; taxonomy/ontology.yaml (T11017 weight 0.7); landmarks 4413785573, 7134847443, 7135072016",0.6,unverified,"composition inferred from the landmark sample and the ontology mapping, not from a topic breakdown"
22+C-H-21,The security of chaos-based multimedia cryptosystems is disputed within the corpus itself: a 2026 cryptanalysis abstract reports that many such systems lack standardisation and deviate from secure design principles.,contested,abstract,,,security_privacy_abstracts.jsonl (work 7154469464),0.7,human-verified,
23+C-H-22,"Blockchain's highly cited works are predominantly application reviews and economics/management papers; the computer-science core among the landmarks is Hyperledger Fabric, consensus surveys, the NIST overview and smart-contract security.",established,abstract,androulaki2018fabric;wang2019consensus;yaga2018blockchain;guo2022blockchainsecurity;foley2019bitcoin;cong2019smartcontracts;davidson2018institutions;saberi2019supplychain;mendling2018bpm;huynh2023metaverse;guesmi2026reentrancy,,security_privacy_landmarks.csv (primary_category=blockchain),0.85,human-verified,
24+C-H-23,"The Bitcoin whitepaper appears three times in the landmark set as separate SSRN records (2019, 2022, 2024) with 7,534, 10,307 and 200 36-month citations; the 2022 record is the most cited work of the area under c36.",established,metadata_only,nakamoto2008bitcoin,,"security_privacy_landmarks.csv (works 4248175462, 4211068006, 4405432346)",0.95,machine-verified,data-quality artefact; merge before landmark statistics
25+C-H-24,Blockchain's self-citation share fell from 61.4 % (2019) to 57.2 % (2025) and its co-classification with LLM is the lowest of the area (3.1 % in 2025).,established,corpus_statistic,,,results/graph/category_flows_yearly.csv; category_cooccurrence_yearly.csv,0.85,machine-verified,
26+C-H-25,"Federated learning as a privacy technology is the dominant theme of privacy-preserving computation's landmarks; Yang et al. 2019 (horizontal, vertical, transfer FL) is the most cited technical paper of the area (1,382 citations in 36 months).",established,abstract,yang2019fml;thapa2022splitfed;fu2026dpfl,,"security_privacy_landmarks.csv (work 2912213068, c36=1382)",0.9,human-verified,
27+C-H-26,"Six of the 25 privacy-preserving computation landmarks come from the 2020 COVID-19 contact-tracing topic, a single-year burst that the ontology maps at weight 0.5.",established,metadata_only,ahmed2020tracingapps;cho2020tracing,,"security_privacy_landmarks.csv (works 3036422000, 3014084098, 3013489453, 3021970994, 4205766150, 4286110439); taxonomy/ontology.yaml T12943",0.9,machine-verified,
28+C-H-27,Four 2025-2026 landmarks of privacy-preserving computation are papers on AI in higher education or auditing with no privacy-technology content; they are keyword misclassifications.,established,abstract,,,"security_privacy_abstracts.jsonl (works 4408302972, 4407111324, 7127055292, 4406636601)",0.9,human-verified,reclassify at PI review
29+C-H-28,A 2026 scoping review reports that DP-SGD can keep clinically acceptable performance in medical deep learning at moderate budgets (epsilon about 10) while strict budgets (epsilon about 1) often cost substantial accuracy.,established,abstract,mohammadi2026dpmedical;abadi2016dpsgd,,security_privacy_abstracts.jsonl (work 4414891672),0.8,human-verified,the review's own summary; 74 studies per its abstract
30+C-H-29,"Privacy-preserving computation's self-citation share fell from 32.5 % (2019) to 24.4 % (2025), the lowest of the area, while references to federated learning rose to 6.1 % and co-classification with LLM reached 9.3 % (2025), the highest of the area.",established,corpus_statistic,,,results/graph/category_flows_yearly.csv; category_cooccurrence_yearly.csv,0.85,machine-verified,
31+C-H-30,"Statistical heterogeneity (non-IID clients) is the organising problem of the post-2022 federated-learning literature and personalisation (adaptive local aggregation, prototypes, clustering) its dominant answer in the landmarks.",established,abstract,tan2023personalizedfl;ye2024heterogeneousfl;zhang2023fedala;zhang2024fedtgp;yang2024fedas;zhu2021noniid,,"security_privacy_abstracts.jsonl (works 3133814152, 4387105517, 4382463479, 4393156671, 4402716118)",0.85,human-verified,
32+C-H-31,Each personalised-FL landmark abstract reports superiority over state-of-the-art baselines on its own benchmark suite; generalisation beyond those suites is not established.,contested,abstract,zhang2023fedala;zhang2024fedtgp;yang2024fedas,,security_privacy_abstracts.jsonl,0.6,unverified,
33+C-H-32,"Federated learning's citation structure changed most within the area: self-citation share rose from 10.8 % (2019) to 16.0 % (2025), references to privacy-preserving computation to 15.5 %, and references to ML theory fell from 17.6 % to 13.0 %.",established,corpus_statistic,,,results/graph/category_flows_yearly.csv (citing_cat=federated_learning),0.85,machine-verified,
34+C-H-33,"Federated learning's co-classification with LLM rose from 1.5 % (2022) to 7.6 % (2025), but federated fine-tuning of foundation models does not appear among its landmarks.",established,corpus_statistic,,,results/graph/category_cooccurrence_yearly.csv; security_privacy_landmarks.csv,0.8,machine-verified,
35+C-H-34,"Deployment claims of federated healthcare applications are speculative in the landmark abstracts: the 2025 governance review states that limited insights exist on the optimal governance of the technology, and no application landmark reports an attack evaluation.",speculative,abstract,eden2025flgovernance;sheller2020flmedicine;chen2020fedhealth,,"security_privacy_abstracts.jsonl (works 4412162472, 3045674654, 3018464563)",0.6,human-verified,
36+C-H-35,"The automatic landmark selection of the area contains duplicated, misdated, venue-level and misclassified records (Bitcoin x3, TLS 1.3 x2, 2026 reprints of 2010 papers, 'IEEE Symposium on Security and Privacy' as a work, Fedformer, Enigma); 33 of 125 records are dated 2026 and 33 lack an abstract.",established,metadata_only,,,security_privacy_landmarks.csv; tab_security_landmarks; macros SecLand*,0.95,machine-verified,PI review H-3 should act before landmark-level statistics are reported
37+C-H-36,"Homomorphic encryption, zero-knowledge proofs and secure multi-party computation do not surface among the applied-cryptography landmarks under the NCS36 metric, although their lineage is in the verified references.",established,metadata_only,gentry2009fhe;cheon2017ckks;chillotti2020tfhe;groth2016snark;thaler2022proofs;evans2018mpc,,"security_privacy_landmarks.csv (no HE/ZK/MPC title among applied_cryptography rows); taxonomy/ontology.yaml (IACR ePrint 0.3 in cybersecurity, 1.0 in applied_cryptography)",0.8,machine-verified,"absence in the top-25 is a statement about the metric and venue mapping, not about the literature's size"
added literature_synthesis/agent_H/contributions.csv +75 −0
@@ -0,0 +1,75 @@
1+openalex_id,contribution_type,description,judged_from,bib_key
2+2915352631,engineering,RFC 8446: specification of TLS 1.3 (IETF standards track); the most cited applied-cryptography document of the baseline period,abstract,rescorla2018tls13
3+2913851998,survey,NIST IR 8240: status report on the first round of the post-quantum standardisation process,abstract,nist2019pqcround1
4+4226075590,survey,NIST IR 8413: third-round status report announcing the post-quantum selections (July 2022),abstract,nist2022pqcround3
5+4401459825,engineering,FIPS 203: ML-KEM standard; security related to Module-LWE; 'believed to be secure' against quantum adversaries,abstract,nist2024fips203
6+4401442713,engineering,FIPS 204: ML-DSA digital signature standard,abstract,nist2024fips204
7+4365934498,method,Key-recovery attack on SIDH (title only; no abstract in the record),metadata,castryck2023sidh
8+4293168393,theory,2022 ACM reprint of Diffie and Hellman's 1976 New Directions in Cryptography; the record's abstract is the landing page (artefact),metadata,
9+2916447643,engineering,2026 duplicate record of TLS 1.3 whose abstract describes an earlier protocol version (artefact),metadata,
10+4214667959,survey,"Survey of blockchain technology and its security: consensus comparison, cryptographic components, risk analysis",abstract,guo2022blockchainsecurity
11+2970408908,method,Deep Leakage from Gradients: gradient inversion attack on federated updates (2020 LNCS reprint; no abstract in the record),metadata,zhu2020dlg
12+7154469464,method,Cryptanalysis of a chaos-based video cryptosystem; reports lack of standardisation in that sub-literature,abstract,
13+4413785573,engineering,Video segment encryption with a memristive neuron model (chaos-based sub-literature),abstract,
14+7134847443,engineering,Hyperchaos-based image encryption for consumer IoT (chaos-based sub-literature),abstract,
15+7124450903,survey,Survey of 203 papers on security and privacy attacks and defences in federated learning (Information Fusion 2026),abstract,jimenezgutierrez2026flsecurity
16+1598516026,empirical,2026 ResearchHub copy of the 2010 Scantegrity II municipal election case study (artefact),abstract,
17+1497727960,engineering,2026 repository record of the 2010 FLINT number-theory library (artefact),abstract,
18+4300127641,engineering,Hyperledger Fabric: modular open-source permissioned blockchain system (EuroSys 2018),abstract,androulaki2018fabric
19+2905867785,survey,Survey of consensus mechanisms and mining strategy management in blockchain networks,abstract,wang2019consensus
20+2892520153,survey,NIST IR 8202 blockchain technology overview,abstract,yaga2018blockchain
21+3123868249,empirical,Estimate of illegal activity financed through bitcoin (Review of Financial Studies),abstract,foley2019bitcoin
22+3122636688,theory,"Theory of blockchain disruption, decentralised consensus and smart contracts (Review of Financial Studies)",abstract,cong2019smartcontracts
23+2784387076,theory,Blockchains as an institutional technology (Journal of Institutional Economics),abstract,davidson2018institutions
24+2898069736,survey,Blockchain and sustainable supply chain management (critical examination),abstract,saberi2019supplychain
25+2606010969,survey,Blockchains for business process management: challenges and seven research directions,abstract,mendling2018bpm
26+3123722958,method,Ontology-driven blockchain design for supply-chain provenance,abstract,kim2018provenance
27+2965761488,survey,Blockchain in agriculture and food supply chains: projects and barriers,abstract,
28+2914735843,survey,"Blockchain-enabled smart contracts: architecture, applications and trends",abstract,
29+4320717855,survey,Blockchain for the metaverse: review (763 citations in 36 months),abstract,huynh2023metaverse
30+3162358200,survey,"NFT overview, evaluation, opportunities and challenges (arXiv preprint)",abstract,wang2021nft
31+4289548059,method,"Datalog-based detection of cross-function reentrancy from EVM traces, extending TxSpector (FMBC 2026)",abstract,guesmi2026reentrancy
32+4211068006,theory,"Bitcoin whitepaper, SSRN record dated 2022 (duplicate of nakamoto2008bitcoin; artefact)",metadata,nakamoto2008bitcoin
33+4248175462,theory,"Bitcoin whitepaper, SSRN record dated 2019 (duplicate; artefact)",metadata,nakamoto2008bitcoin
34+4405432346,theory,"Bitcoin whitepaper, SSRN record dated 2024 (duplicate; artefact)",metadata,nakamoto2008bitcoin
35+2460937040,empirical,Adversarial examples survive physical-world capture (2018 chapter reprint of the 2017 paper),abstract,kurakin2018physical
36+2789828921,benchmark,CIC-IDS2017 intrusion-detection dataset and traffic characterisation (no abstract in the record),metadata,
37+2624307925,survey,Survey of security and privacy issues of Bitcoin (IEEE COMST),abstract,conti2018bitcoinsecurity
38+2805930283,survey,Review of blockchain for the Internet of Things (IEEE Access),abstract,fernandez2018biot
39+2795085730,survey,Blockchain for healthcare cloud data security: position paper,abstract,esposito2018healthcare
40+2231944250,engineering,Enigma: decentralised secure-computation platform (2018 MIT Press chapter; belongs to privacy-preserving computation),abstract,
41+4406302454,survey,"Survey of security and privacy challenges of LLMs: jailbreaking, poisoning, PII leakage (ACM CSUR)",abstract,das2025llmsecurity
42+7129030749,survey,"Model Context Protocol: lifecycle (4 phases, 16 activities) and security threat taxonomy (ACM TOSEM 2026)",abstract,hou2026mcp
43+7130652904,method,"Vul-RAG: knowledge-level RAG for LLM vulnerability detection; base LLM accuracy 0.06-0.14, +16-24 points",abstract,du2026vulrag
44+4387210439,engineering,HuntGPT: ML anomaly detection with XAI and an LLM interface for threat hunting,abstract,ali2026huntgpt
45+7125967353,method,Fine-tuned RoBERTa phishing-email detector with explainability,abstract,uddin2026phishing
46+4301785137,method,"2026 SSRN preprint arguing persistent homology fails as a prompt-injection defence (no abstract; not peer-reviewed; NCS36 9,806)",metadata,
47+7139094415,method,Fedformer (time-series forecasting Transformer) misclassified as cybersecurity (artefact),abstract,
48+4353004773,survey,Venue-level record 'IEEE Symposium on Security and Privacy' (IT Professional 2023) counted as a work (artefact),metadata,
49+2912213068,theory,"Federated Machine Learning: horizontal, vertical and transfer FL framework (ACM TIST; most cited technical paper of the area)",abstract,yang2019fml
50+3018102029,method,SplitFed: combination of split learning and federated learning (AAAI 2022),abstract,thapa2022splitfed
51+7137155252,survey,Systematic review of differentially private federated learning (ACM CSUR 2026),abstract,fu2026dpfl
52+4412565294,survey,Survey of machine unlearning and the right to be forgotten (ACM TIST 2025),abstract,nguyen2025unlearning
53+4414891672,survey,Scoping review of differential privacy in medical deep learning (74 studies; epsilon trade-offs),abstract,mohammadi2026dpmedical
54+3036422000,survey,Survey of COVID-19 contact-tracing apps (IEEE Access 2020),abstract,ahmed2020tracingapps
55+3014084098,theory,Privacy analysis of contact-tracing apps (TraceTogether); arXiv preprint,abstract,cho2020tracing
56+4302383326,theory,Private information retrieval with side information (dissertation),abstract,
57+4408302972,empirical,AI and students' academic development (education; misclassified into privacy-preserving computation),abstract,
58+4407111324,survey,AI and student well-being mini-review (misclassified),abstract,
59+7127055292,survey,AI in higher education systematic review (misclassified),abstract,
60+4406636601,empirical,AI in auditing: 22 interviews (misclassified),abstract,
61+3045674654,empirical,Federated learning in medicine: multi-institutional collaboration without sharing patient data (Sci. Rep. 2020),abstract,sheller2020flmedicine
62+3018464563,method,FedHealth: federated transfer learning for wearable healthcare,abstract,chen2020fedhealth
63+3100779497,survey,Federated learning for healthcare informatics (no abstract in the record),metadata,xu2021flhealth
64+2995735216,method,q-FFL: fair resource allocation objective for federated learning (ICLR 2020; record without DOI),abstract,li2019qffl
65+3196371845,survey,Federated learning on non-IID data: survey (no abstract in the record),metadata,zhu2021noniid
66+3123459983,survey,A survey on federated learning (KBS 2021; no abstract in the record),metadata,zhang2021flsurvey
67+3133814152,survey,Towards personalized federated learning: survey of PFL under heterogeneity (IEEE TNNLS),abstract,tan2023personalizedfl
68+4382463479,method,FedALA: adaptive local aggregation for personalised FL (AAAI 2023),abstract,zhang2023fedala
69+4387105517,survey,"Heterogeneous federated learning: data, model, network and hardware heterogeneity (ACM CSUR)",abstract,ye2024heterogeneousfl
70+4393156671,method,FedTGP: trainable global prototypes for model-heterogeneous FL (AAAI 2024),abstract,zhang2024fedtgp
71+4402716118,method,FedAS: addressing intra- and inter-client inconsistency in personalised FL (CVPR 2024),abstract,yang2024fedas
72+4415878254,method,DFCA: decentralised federated clustering without a central server (IEEE IoTJ 2026),abstract,
73+4408476406,method,CBRFL: committee-based Byzantine-resilient federated learning (no abstract in the record),metadata,xu2025cbrfl
74+4412162472,survey,Scoping review of the governance of federated learning in healthcare (npj Digital Medicine 2025),abstract,eden2025flgovernance
75+7133255133,survey,Survey of six core challenges of federated learning (2026),abstract,
added literature_synthesis/agent_H/open_problems.csv +15 −0
@@ -0,0 +1,15 @@
1+problem_id,statement,categories,why_unresolved,testable_direction,supporting_keys
2+P-H-01,Whether defences against prompt injection in LLM-integrated applications can be made robust rather than empirically patched.,cybersecurity;llm;ai_safety,"Attacks are instructions embedded in data or tool outputs; no landmark reports a defence with a formal guarantee, and the only landmark on the question is an unreviewed 2026 preprint reporting a negative result.",Preregistered benchmark of injection attacks across tool-use and agent protocols (e.g. MCP) with defences evaluated under adaptive attackers; report attack success as a function of attacker budget.,greshake2023injection;liu2023promptinjection;hou2026mcp;das2025llmsecurity
3+P-H-02,Whether LLM-based vulnerability and intrusion detection outperforms fine-tuned encoders or classical features at equal compute and labelling cost.,cybersecurity;software_engineering;llm,Landmark abstracts report low base accuracy and gains from retrieval on specific datasets; no cost-matched comparison appears in the landmark set.,"Cost-matched evaluation on shared datasets (CIC-IDS2017-type benchmarks, vulnerability corpora) with held-out projects and temporal splits.",du2026vulrag;uddin2026phishing;ali2026huntgpt
4+P-H-03,Durability of the post-2022 LLM-security literature: is it a subfield or a labelling wave?,cybersecurity;llm,Co-classification shares rose fourfold but the first 36-month citation windows of 2023-2024 works close only in 2026-2027.,Re-estimate NCS36 of 2023-2024 cybersecurity x LLM works at the 2027 snapshot; compare with the 2018-2020 adversarial-ML cohort.,das2025llmsecurity;yao2024llmsecurity
5+P-H-04,Security of the standardised lattice schemes against classical cryptanalysis after the SIDH and Rainbow breaks.,applied_cryptography;computational_complexity,"Two finalists fell to classical attacks after years of scrutiny; standards state belief, not proof, of security.",Track cryptanalytic literature citing FIPS 203/204 and Module-LWE reductions; measure time-to-attack for new candidates (fourth round).,nist2024fips203;nist2024fips204;castryck2023sidh;beullens2022rainbow;regev2009lwe
6+P-H-05,"Measuring deployment of post-quantum cryptography (TLS, signatures, PKI) rather than publication.",applied_cryptography;computer_networks,"The corpus records standards and reports, not deployment; the transition report sets a timeline that the literature does not yet evaluate.","Internet-measurement studies of PQC negotiation in TLS handshakes and certificate ecosystems, repeated yearly.",nist2024transition;rescorla2018tls13
7+P-H-06,Security of the chaos-based multimedia encryption literature.,applied_cryptography,Highly cited 2025-2026 schemes coexist with cryptanalyses reporting departures from secure design principles; no standard evaluation exists.,Systematic cryptanalysis campaign against the most cited schemes with a shared threat model; publication of negative results.,
8+P-H-07,Whether smart-contract security tooling has reduced measured vulnerability and exploit rates since 2020.,blockchain;cybersecurity;formal_verification,Landmark on reentrancy extends an existing framework; no landmark reports exploit-rate trends.,Longitudinal measurement of exploits on EVM chains against tool adoption; evaluation of detectors on time-split contract corpora.,guesmi2026reentrancy;werner2022defi
9+P-H-08,Whether blockchain research has an active computer-science core after 2022 or has migrated to application and management venues.,blockchain,Top-25 landmarks are dominated by reviews and economics; the category's self-citation share is falling and its LLM co-classification is the lowest of the area.,Venue-stratified citation analysis of the category (CS venues vs others) by year; compare with distributed_computing flows.,androulaki2018fabric;wang2019consensus
10+P-H-09,Privacy guarantees of federated learning without differential privacy or secure aggregation under gradient-inversion attacks.,federated_learning;privacy_preserving_computation,Application landmarks claim privacy by design without attack evaluation; gradient leakage is a documented attack.,"Mandatory attack evaluation (gradient inversion, membership inference) in federated application papers; benchmark of leakage versus model size and batch size.",zhu2020dlg;bonawitz2017secagg;fu2026dpfl;jimenezgutierrez2026flsecurity
11+P-H-10,Utility cost of strict differential-privacy budgets in medical and tabular settings.,privacy_preserving_computation,"Reviews report acceptable utility at epsilon about 10 and substantial loss at epsilon about 1, with heterogeneity across modalities.",Pre-registered multi-site study with fixed budgets and standard accountants; report utility-privacy curves per modality.,mohammadi2026dpmedical;abadi2016dpsgd;dwork2014dp
12+P-H-11,Generalisation of personalised federated-learning methods beyond their own benchmark suites.,federated_learning;ml_theory,Each landmark reports superiority over state-of-the-art baselines on its own suite; no common protocol exists for heterogeneity levels.,"Shared benchmark with parameterised heterogeneity (label skew, feature skew, model heterogeneity) and reporting of per-client distributions, not means.",zhang2023fedala;zhang2024fedtgp;yang2024fedas;ye2024heterogeneousfl
13+P-H-12,Machine unlearning: verifiable deletion from trained models at acceptable cost.,privacy_preserving_computation;llm,Survey landmarks frame the problem (right to be forgotten) but verification of forgetting remains unresolved in the abstracts.,Auditable unlearning benchmarks with membership-inference-based verification and cost accounting; compare exact and approximate methods.,nguyen2025unlearning;shokri2017membership
14+P-H-13,Governance and accountability of federated learning across institutions and jurisdictions.,federated_learning;privacy_preserving_computation,"The 2025 governance review finds limited insight on optimal governance; deployments are reported as frameworks, not audited systems.","Case studies of operating federations with documented consent, auditing and incident processes; comparison across legal regimes.",eden2025flgovernance;sheller2020flmedicine
15+P-H-14,"Record-quality effects on landmark identification in security categories (duplicates, reprints, venue records).",cybersecurity;applied_cryptography;blockchain,The automatic NCS36 selection ranks duplicated whitepaper records and 2026 reprints among the top works; the magnitude of the bias on area-level statistics is unknown.,Deduplicate the landmark set by title/DOI family and re-run the selection; report sensitivity of area-level indicators to the merge.,
modified literature_synthesis/agent_H/review.md +170 −0
@@ -109,3 +109,173 @@ cost — the vul-rag abstract itself reports very low base accuracy, and the phi
109 specific datasets. Speculative: claims that defences against prompt injection can be made robust; the only landmark on the 109 specific datasets. Speculative: claims that defences against prompt injection can be made robust; the only landmark on the
110 question (the 2026 SSRN preprint) argues a negative result and has not been reviewed. The corpus cannot yet tell whether the 110 question (the 2026 SSRN preprint) argues a negative result and has not been reviewed. The corpus cannot yet tell whether the
111 LLM-security literature is a durable subfield or a labelling wave; its first 36-month citation windows close in 2026–2027. 111 LLM-security literature is a durable subfield or a labelling wave; its first 36-month citation windows close in 2026–2027.
112+
113+## 3. Applied cryptography
114+
115+**Standards as landmarks.** The applied-cryptography landmark set is the clearest case of a category whose most cited documents
116+are standards and reports rather than papers: RFC 8446 (TLS 1.3, 2018; abstract: "prevent eavesdropping, tampering, and message
117+forgery"), the NIST status reports on the first (2019) and third (2022) rounds of the post-quantum standardisation process, and
118+the two 2024 Federal Information Processing Standards FIPS 203 (ML-KEM; the abstract ties its security to Module-LWE and states
119+that it "is believed to be secure, even against adversaries who possess a quantum computer") and FIPS 204 (ML-DSA, with the same
120+belief statement for signatures). FIPS 205 (SLH-DSA, hash-based) is not in the landmark set but is in the verified references
121+(`nist2024fips205`). The 2024 draft transition report (`nist2024transition`) and the fourth-round status report (`nist2025hqc`)
122+complete the sequence; the latter was not read in full text here and is cited for its existence and date.
123+
124+**The timeline the corpus records.** The post-quantum programme is the one development of the area with an unambiguous timeline
125+inside the observation window: the third-round report (July 2022) announced the selections, two attacks on candidates
126+appeared in the same year — the key-recovery attack on SIDH of Castryck and Decru (EUROCRYPT 2023; no abstract in the record;
127+title only) and Beullens's attack on Rainbow ("takes a weekend on a laptop", CRYPTO 2022) — and the final standards were
128+published in August 2024. The corpus statistics show that this timeline did not translate into an aggregate volume break:
129+applied cryptography's Bai–Perron break (2022-07) coincides with the NIST announcement month but the category's post-2023
130+growth (20.4 %) is driven by the much larger encryption-for-IoT and chaos-based image-encryption sub-literatures (see below),
131+and the flow of references from applied cryptography to quantum computing rose only from 2.8 % (2019) to 4.1 % (2025). The
132+theoretical base of the lattice standards (Regev's LWE, `regev2009lwe`; Shor's algorithm as the threat model, `shor1997factoring`)
133+lies outside the corpus window; the 2024–2025 below-threshold error-correction result (`googlequantum2025belowthreshold`) is the
134+window's main quantum-side event and is classified in quantum computing, not here.
135+
136+**What else the landmark set contains.** Three 2025–2026 landmarks are chaos-based or neuron-model image/video encryption
137+schemes (IEEE TDSC, IEEE TCE, Nonlinear Dynamics) and one is a cryptanalysis of such a scheme (ACM TOMM 2026; the abstract
138+reports that "many such systems lack standardization and deviate from secure design principles"). This sub-literature is
139+large in the corpus (OpenAlex topic T11017 is mapped to the category at weight 0.7) and its presence among the most cited
140+2026 works is a fact about the citing community, not an endorsement; the ontology note records the same reservation. Other
141+records are artefacts: a 2022 reprint of Diffie–Hellman's 1976 *New Directions* (the "abstract" is the ACM landing page), a
142+2026 record of TLS 1.3 with the text of an older protocol version, a 2026 ResearchHub copy of the 2010 Scantegrity II paper,
143+a 2026 Warwick repository record of the 2010 FLINT library, a Reed–Solomon FPGA dissertation, and a 2020 LNCS reprint of *Deep
144+Leakage from Gradients* (which belongs with federated-learning privacy and is discussed in Section 5). One 2026 Information
145+Fusion survey on the security and privacy of federated learning (203 papers, per its abstract) is classified here because of its
146+cryptographic keywords.
147+
148+**Assessment.** Established: lattice-based KEM and signature standards exist and are deployed in TLS-style key exchange (the
149+corpus records the standards; deployment is not in evidence here). Established and important as a methodological lesson:
150+two of the finalists fell to classical attacks after years of scrutiny, so "post-quantum" denotes a threat model, not a
151+security proof. Contested within the corpus: the security of the large chaos-based encryption literature, which its own
152+cryptanalyses dispute. Not addressed by the corpus's landmark metric: homomorphic encryption, zero-knowledge proofs and MPC as
153+primitives — the verified references (`gentry2009fhe`, `cheon2017ckks`, `chillotti2020tfhe`, `groth2016snark`, `thaler2022proofs`,
154+`evans2018mpc`) carry this lineage; their post-2022 progeny do not surface in the top-25 because their citing communities are
155+small and their venues (IACR ePrint, TCHES) are partially mapped.
156+
157+## 4. Blockchain systems
158+
159+**Shape of the literature.** Blockchain is the category whose landmark set is least about computer science: of 25 works, two
160+are in the *Review of Financial Studies* (illegal activity financed through bitcoin, estimated at about one quarter of users in
161+the abstract; blockchain disruption and smart contracts, a theory of decentralised consensus and competition), one in the
162+*Journal of Institutional Economics* (blockchains as an "institutional technology"), one in *International Journal of
163+Production Research* (sustainable supply chains), one in *Trends in Food Science & Technology* (agri-food supply chains), one in
164+*ACM TMIS* (business process management, with seven research directions), one in *Intelligent Systems in Accounting, Finance
165+and Management* (ontology-driven provenance), plus MIT Press and Springer chapters. The computer-science core is thin but
166+clear: Hyperledger Fabric (EuroSys 2018; "a modular and extensible open-source system for permissioned blockchains"), the IEEE
167+Access survey of consensus mechanisms and mining strategy management (2019), the NIST blockchain overview (2018), Guo and Yu's
168+survey of blockchain security (2022), and — the only 2026 technical landmark — a Datalog-based detector of cross-function
169+reentrancy over EVM execution traces (FMBC 2026, extending TxSpector). Post-window landmarks are reviews (blockchain for the
170+metaverse, 2023, 763 citations in 36 months; blockchain in healthcare; two 2024 papers in a journal with 449 citations each that
171+the PI should examine) and the NFT overview preprint (2021).
172+
173+**Artefacts.** The Bitcoin whitepaper appears three times (SSRN records dated 2019, 2022 and 2024 with 7,534, 10,307 and 200
174+citations in 36 months) plus once as a Springer "recommends" chapter; the 2022 record is the single most cited work of the
175+whole area under c36. These are the same 2008 document (`nakamoto2008bitcoin` in the verified references), and their citation
176+counts belong to it. A 2026 SSRN "Global Blockchain Benchmarking Study" record (NCS36 1,481) has no abstract and is likely a
177+reissue of the 2017 Cambridge study.
178+
179+**Corpus statistics.** Blockchain's break is in 2020-04 (Bai–Perron) with PELT changes in 2019-02 and 2022-12; the ITS at
180+2022-12 shows no level or slope change, and the pre-period slope (+0.015 per month) was the steepest of the five categories
181+after federated learning. The self-citation share of blockchain works fell from 61.4 % (2019) to 57.2 % (2025) while the share of
182+references to cybersecurity rose (8.5 % → 9.2 %); co-classification with the LLM category is the lowest of the area (3.1 % in
183+2025). DeFi, MEV and layer-2 research — present in the verified references through `werner2022defi` — does not reach the top-25 of
184+a category whose citing community is dominated by application and management journals.
185+
186+**Assessment.** Established: the category grew fastest before 2021 and has grown at the corpus's average rate since; its highly
187+cited post-2022 works are reviews. Contested: whether the smart-contract security literature (reentrancy detection and the like)
188+constitutes progress beyond the tools of 2018–2020 — the single 2026 landmark reports an extension of an existing framework, and
189+no landmark reports measured vulnerability rates. Speculative: every claim about blockchain and the metaverse or AI integration
190+in the 2023–2026 reviews.
191+
192+## 5. Privacy-preserving computation
193+
194+**Composition.** The category's landmarks split into four groups. (i) Federated learning as privacy technology: Yang et al.'s
195+*Federated Machine Learning* (ACM TIST 2019; horizontal, vertical and transfer federated learning; 1,382 citations in 36 months,
196+the most cited technical paper of the area), SplitFed (AAAI 2022; split learning plus federated learning), a 2026 systematic
197+review of differentially private federated learning (ACM CSUR), hierarchical federated learning with two-stage DP for cloud
198+anomaly detection (2026) and several federated healthcare applications. (ii) The 2020 contact-tracing burst: a survey of
199+COVID-19 contact-tracing apps (IEEE Access), the TraceTogether privacy analysis (arXiv preprint), a cross-country acceptability
200+survey (JMIR), a *Nature Medicine* comment on responsible use of digital data, an OECD policy note and a German-language chapter
201+on the Corona-Warn-App — six of 25 landmarks from a single year's topic (OpenAlex T12943 is mapped at weight 0.5). (iii)
202+Privacy of models and data: the survey of machine unlearning (ACM TIST 2025; "the right to be forgotten"), differential privacy
203+for medical deep learning (npj Digital Medicine 2026; the abstract reports clinically acceptable performance at ε ≈ 10 and
204+substantial loss at ε ≈ 1), private information retrieval with side information (dissertation), privacy-aware mobile service
205+recommendation (2018). (iv) Misclassifications: three 2025–2026 papers on artificial intelligence in higher education and one on
206+AI in auditing (*Education Sciences*, *Frontiers in Psychology*, *Frontiers in Education*, *International Journal of Accounting
207+Information Systems*), each ranked among the top of the category by NCS36. Their abstracts mention privacy as one concern among
208+many; the keyword rule of the category fired on "privacy" with an education or auditing qualifier that the exclusion list does
209+not veto. Two further records (open-circuit fault diagnosis of power converters; mental-health prediction with a federated
210+CNN–BiLSTM) are applications whose privacy content is the federated setting.
211+
212+**Lineage outside the window.** Differential privacy (`dwork2006dp`, `dwork2014dp`), DP-SGD (`abadi2016dpsgd`), membership
213+inference (`shokri2017membership`), training-data extraction from language models (`carlini2021extracting`, preprint) and
214+web-scale poisoning (`carlini2024poisoning`) are in the verified references and define the attack and defence vocabulary that
215+the post-2022 landmarks apply. The 2026 DP-FL review and the medical DP review are the only landmarks that synthesise this
216+vocabulary; the rest are applications.
217+
218+**Corpus statistics.** Privacy-preserving computation grew throughout (15.2 % then 39.8 %) with a Bai–Perron break at 2022-07
219+and PELT changes at 2020-12 (the contact-tracing year) and 2023-12. Its self-citation share fell from 32.5 % (2019) to 24.4 %
220+(2025) — the lowest of the area — while references to federated learning (6.1 % in 2025) and LLM works (2.2 % in 2026) rose; its
221+works are co-classified with LLM at 9.3 % (2025), the highest rate of the area, and with AI safety at a similar rate. The category
222+is therefore the one whose boundary with machine learning moved most during the window.
223+
224+**Assessment.** Established: DP and federated learning are the two privacy technologies that the applied literature adopts, and
225+the accuracy cost of strict DP budgets is documented by reviews. Contested: the privacy guarantees of federated learning without
226+DP or secure aggregation (gradient inversion, Section 5 of the federated-learning synthesis). Speculative: the "privacy by
227+design" claims of federated healthcare applications that report no attack evaluation. Data-quality finding: the category's
228+NCS36 ranking is contaminated by non-computing papers and by a single-topic burst; the PI review should reclassify the four
229+education/auditing papers and consider the contact-tracing topic weight.
230+
231+## 6. Federated learning
232+
233+**From averaging to heterogeneity.** The landmark set reads as the field's own history. Baseline: healthcare applications
234+(federated learning in medicine, Scientific Reports 2020, multi-institutional collaboration without sharing patient data;
235+FedHealth, a federated transfer-learning framework for wearables; healthcare informatics review), the fairness objective q-FFL
236+(ICLR 2020; the corpus record has no DOI and the verified reference is the arXiv preprint), the IoT survey, the KBS survey (no
237+abstract), the non-IID survey (Neurocomputing 2021), *Towards Personalized Federated Learning* (IEEE TNNLS 2022; "the leading
238+paradigm for the training of machine learning models on data silos"), and a Hong Kong dissertation on attack-adaptive
239+aggregation. Post-window: FedALA (AAAI 2023; adaptive local aggregation to initialise personalised models), the ACM CSUR survey
240+of heterogeneous federated learning (data, model, network and hardware heterogeneity), FedTGP (AAAI 2024; trainable global
241+prototypes for model-heterogeneous clients), FedAS (CVPR 2024; intra- and inter-client inconsistency in personalised FL), DFCA
242+(IEEE IoTJ 2026; decentralised clustered FL without a server), committee-based Byzantine-resilient FL (JNCA 2025) and
243+trimmed-mean aggregation (ESWA 2025), asynchronous FL for SAR imagery (IEEE TMC 2026), a scoping review of governance of FL in
244+healthcare (npj Digital Medicine 2025), and three 2026 surveys. FedAvg (`mcmahan2017fl`), the 2021 *Advances and Open Problems*
245+survey (`kairouz2021fl`), FedProx (`li2018fedprox`, preprint), secure aggregation (`bonawitz2017secagg`), the backdoor attack
246+(`bagdasaryan2018backdoor`, preprint) and gradient leakage (`zhu2020dlg`) are the verified pre-window references that the
247+post-window landmarks build on.
248+
249+**Corpus statistics.** Federated learning is the category with the highest baseline growth (54.3 % CAGR, 1,040 works in 2018 to
250+18,093 in 2025, a 3.07-fold increase between 2022 and 2025) and the only one whose ITS at 2022-12 shows a negative level and
251+slope change; neither survives the placebo test (p = 0.246 and 0.385), and Bai–Perron dates the category's single break at
252+2020-02. The field's citation structure changed more than any other: its self-citation share rose from 10.8 % (2019) to 16.0 %
253+(2025) as it became citable, references to privacy-preserving computation rose to 15.5 %, and references to ML theory fell from
254+17.6 % to 13.0 %. Co-classification with LLM rose from 1.5 % (2022) to 7.6 % (2025): federated fine-tuning of foundation models
255+is visible in the labels but not yet among the landmarks.
256+
257+**Assessment.** Established: statistical heterogeneity (non-IID clients) is the organising problem of the post-2022 literature,
258+and personalisation (local aggregation, prototypes, clustering) its dominant answer; the abstracts of FedALA, FedTGP, FedAS and
259+DFCA all define their contribution against this problem. Established but under-evaluated in the landmarks: robustness to
260+malicious clients, where the highly cited 2025 contributions are aggregation rules evaluated on standard benchmarks. Contested:
261+whether personalised FL methods generalise beyond the benchmark suites in which they are compared (each abstract reports
262+superiority over "state-of-the-art" baselines on its own suite). Speculative: deployment claims in healthcare, where the 2025
263+governance review finds that "limited insights exist on the optimal governance" of the technology.
264+
265+## 7. Cross-cutting synthesis and data-quality findings
266+
267+1. *No security category shows a post-2022 level break.* The volume acceleration of cybersecurity, applied cryptography and
268+ privacy-preserving computation is a slope change dated by break detectors to mid-2022 and late 2024; blockchain and
269+ federated learning broke in early 2020 and decelerated after. This is association, not causation.
270+2. *The LLM turn in security is compositional.* The share of cybersecurity works co-classified with LLM rose roughly fourfold
271+ between 2022 and 2025; citation flows to LLM works remain small (≤ 1.1 %). The security of LLM systems is a continuation of
272+ the security-of-ML literature with a new attack surface; its evaluation standards are not yet settled (Section 2).
273+3. *Standards, not papers, are the landmarks of applied cryptography*, and the post-quantum programme is the area's one
274+ dated transition; the fall of SIDH and Rainbow is the area's methodological lesson.
275+4. *Convergence between privacy-preserving computation and federated learning is measurable in flows* (15.5 % of FL references
276+ to privacy, 6.1 % the other way), while blockchain's links to the rest of computer science are weak and declining in share.
277+5. *Landmark-set artefacts*: duplicated whitepaper records (Bitcoin ×3, TLS 1.3 ×2), reprints dated 2026 (Scantegrity, FLINT,
278+ Diffie–Hellman 2022), venue-level records (IEEE S&P 2023), misclassifications (Fedformer; four education/auditing papers;
279+ Enigma), a single-topic burst (contact tracing 2020), and 2026 records ranked on months of citations. 33 of 125 records have
280+ no abstract. The PI review (H-3) should remove or merge these before any landmark-level statistic is reported.
281+6. Unresolved problems are listed in `open_problems.csv` (P-H-01 to P-H-12) and feed Part X.
modified manuscript/appendices/appF_supplementary_tables.tex +42 −0
@@ -25,3 +25,45 @@ This appendix collects the generated tables not shown in the main text. Every ta
25 \clearpage 25 \clearpage
26 \input{tables/tab_known_set} 26 \input{tables/tab_known_set}
27 \clearpage 27 \clearpage
28+\input{tables/tab_net_area_flows}
29+\clearpage
30+\input{tables/tab_net_modularity}
31+\clearpage
32+\input{tables/tab_net_category_to_ai}
33+\clearpage
34+\input{tables/tab_net_category_actors}
35+\clearpage
36+\input{tables/tab_net_ai_vs_rest}
37+\clearpage
38+\input{tables/tab_net_ai_vs_rest_yearly}
39+\clearpage
40+\input{tables/tab_net_category_gini}
41+\clearpage
42+\input{tables/tab_net_gini_ai_vs_rest}
43+\clearpage
44+\input{tables/tab_ai_growth}
45+\clearpage
46+\input{tables/tab_ai_counts}
47+\clearpage
48+\input{tables/tab_ai_breaks}
49+\clearpage
50+\input{tables/tab_ai_altdates}
51+\clearpage
52+\input{tables/tab_ai_flows}
53+\clearpage
54+\input{tables/tab_ai_landmark_flags}
55+\clearpage
56+\input{tables/tab_ai_contribution_types}
57+\clearpage
58+\input{tables/tab_ai_landmarks}
59+\clearpage
60+\input{tables/tab_security_counts}
61+\clearpage
62+\input{tables/tab_security_summary}
63+\clearpage
64+\input{tables/tab_security_llm_cooccurrence}
65+\clearpage
66+\input{tables/tab_security_landmarks}
67+\clearpage
68+\input{tables/tab_systems_categories}
69+\clearpage
added manuscript/references_part05.bib +1024 −0
@@ -0,0 +1,1024 @@
1+% references_part05.bib — Part V (The Artificial Intelligence Transformation), WP44. Generated by
2+% literature_synthesis/agent_E/make_bib_part05.py on 2026-11-03: every entry resolved by one Crossref (DOI) or arXiv lookup;
3+% see references_verification_part05.csv. Keys of manuscript/references.bib are not redefined here.
4+
5+@article{bick2026rapidadoption,
6+ title = {{The Rapid Adoption of Generative AI}},
7+ author = {Bick, Alexander and Blandin, Adam and Deming, David J.},
8+ year = {2026},
9+ journal = {Management Science},
10+ volume = {72},
11+ number = {10},
12+ pages = {8440--8456},
13+ doi = {10.1287/mnsc.2025.02523}
14+}
15+
16+@article{kasneci2023chatgptgood,
17+ title = {{ChatGPT for good? On opportunities and challenges of large language models for education}},
18+ author = {Kasneci, Enkelejda and Sessler, Kathrin and Küchemann, Stefan and Bannert, Maria and Dementieva, Daryna and Fischer, Frank and Gasser, Urs and Groh, Georg and Günnemann, Stephan and Hüllermeier, Eyke and Krusche, Stephan and Kutyniok, Gitta and Michaeli, Tilman and Nerdel, Claudia and Pfeffer, Jürgen and Poquet, Oleksandra and Sailer, Michael and Schmidt, Albrecht and Seidel, Tina and Stadler, Matthias and Weller, Jochen and Kuhn, Jochen and Kasneci, Gjergji},
19+ year = {2023},
20+ journal = {Learning and Individual Differences},
21+ volume = {103},
22+ pages = {102274},
23+ doi = {10.1016/j.lindif.2023.102274}
24+}
25+
26+@misc{wang2019superglue,
27+ title = {{SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems}},
28+ author = {Wang, Alex and Pruksachatkun, Yada and Nangia, Nikita and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},
29+ year = {2019},
30+ eprint = {1905.00537},
31+ archiveprefix = {arXiv},
32+ howpublished = {arXiv preprint arXiv:1905.00537},
33+ note = {Preprint, not peer reviewed}
34+}
35+
36+@misc{han2024peftsurvey,
37+ title = {{Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey}},
38+ author = {Han, Zeyu and Gao, Chao and Liu, Jinyang and Zhang, Jeff and Zhang, Sai Qian},
39+ year = {2024},
40+ eprint = {2403.14608},
41+ archiveprefix = {arXiv},
42+ howpublished = {arXiv preprint arXiv:2403.14608},
43+ note = {Preprint, not peer reviewed}
44+}
45+
46+@article{bodnar2025aurora,
47+ title = {{A foundation model for the Earth system}},
48+ author = {Bodnar, Cristian and Bruinsma, Wessel P. and Lucic, Ana and Stanley, Megan and Allen, Anna and Brandstetter, Johannes and Garvan, Patrick and Riechert, Maik and Weyn, Jonathan A. and Dong, Haiyu and Gupta, Jayesh K. and Thambiratnam, Kit and Archibald, Alexander T. and Wu, Chun-Chieh and Heider, Elizabeth and Welling, Max and Turner, Richard E. and Perdikaris, Paris},
49+ year = {2025},
50+ journal = {Nature},
51+ volume = {641},
52+ number = {8065},
53+ pages = {1180--1187},
54+ doi = {10.1038/s41586-025-09005-y}
55+}
56+
57+@article{chen2024uni,
58+ title = {{Towards a general-purpose foundation model for computational pathology}},
59+ author = {Chen, Richard J. and Ding, Tong and Lu, Ming Y. and Williamson, Drew F. K. and Jaume, Guillaume and Song, Andrew H. and Chen, Bowen and Zhang, Andrew and Shao, Daniel and Shaban, Muhammad and Williams, Mane and Oldenburg, Lukas and Weishaupt, Luca L. and Wang, Judy J. and Vaidya, Anurag and Le, Long Phi and Gerber, Georg and Sahai, Sharifa and Williams, Walt and Mahmood, Faisal},
60+ year = {2024},
61+ journal = {Nature Medicine},
62+ volume = {30},
63+ number = {3},
64+ pages = {850--862},
65+ doi = {10.1038/s41591-024-02857-3}
66+}
67+
68+@article{wang2024chief,
69+ title = {{A pathology foundation model for cancer diagnosis and prognosis prediction}},
70+ author = {Wang, Xiyue and Zhao, Junhan and Marostica, Eliana and Yuan, Wei and Jin, Jietian and Zhang, Jiayu and Li, Ruijiang and Tang, Hongping and Wang, Kanran and Li, Yu and Wang, Fang and Peng, Yulong and Zhu, Junyou and Zhang, Jing and Jackson, Christopher R. and Zhang, Jun and Dillon, Deborah and Lin, Nancy U. and Sholl, Lynette and Denize, Thomas and Meredith, David and Ligon, Keith L. and Signoretti, Sabina and Ogino, Shuji and Golden, Jeffrey A. and Nasrallah, MacLean P. and Han, Xiao and Yang, Sen and Yu, Kun-Hsing},
71+ year = {2024},
72+ journal = {Nature},
73+ volume = {634},
74+ number = {8035},
75+ pages = {970--978},
76+ doi = {10.1038/s41586-024-07894-z}
77+}
78+
79+@article{zhang2024biomedgpt,
80+ title = {{A generalist vision--language foundation model for diverse biomedical tasks}},
81+ author = {Zhang, Kai and Zhou, Rong and Adhikarla, Eashan and Yan, Zhiling and Liu, Yixin and Yu, Jun and Liu, Zhengliang and Chen, Xun and Davison, Brian D. and Ren, Hui and Huang, Jing and Chen, Chen and Zhou, Yuyin and Fu, Sunyang and Liu, Wei and Liu, Tianming and Li, Xiang and Chen, Yong and He, Lifang and Zou, James and Li, Quanzheng and Liu, Hongfang and Sun, Lichao},
82+ year = {2024},
83+ journal = {Nature Medicine},
84+ volume = {30},
85+ number = {11},
86+ pages = {3129--3141},
87+ doi = {10.1038/s41591-024-03185-2}
88+}
89+
90+@article{binz2025centaur,
91+ title = {{A foundation model to predict and capture human cognition}},
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98+ doi = {10.1038/s41586-025-09215-4}
99+}
100+
101+@misc{dai2023instructblip,
102+ title = {{InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning}},
103+ author = {Dai, Wenliang and Li, Junnan and Li, Dongxu and Tiong, Anthony Meng Huat and Zhao, Junqi and Wang, Weisheng and Li, Boyang and Fung, Pascale and Hoi, Steven},
104+ year = {2023},
105+ eprint = {2305.06500},
106+ archiveprefix = {arXiv},
107+ howpublished = {arXiv preprint arXiv:2305.06500},
108+ note = {Preprint, not peer reviewed}
109+}
110+
111+@misc{schuhmann2022laion5b,
112+ title = {{LAION-5B: An open large-scale dataset for training next generation image-text models}},
113+ author = {Schuhmann, Christoph and Beaumont, Romain and Vencu, Richard and Gordon, Cade and Wightman, Ross and Cherti, Mehdi and Coombes, Theo and Katta, Aarush and Mullis, Clayton and Wortsman, Mitchell and Schramowski, Patrick and Kundurthy, Srivatsa and Crowson, Katherine and Schmidt, Ludwig and Kaczmarczyk, Robert and Jitsev, Jenia},
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115+ eprint = {2210.08402},
116+ archiveprefix = {arXiv},
117+ howpublished = {arXiv preprint arXiv:2210.08402},
118+ note = {Preprint, not peer reviewed}
119+}
120+
121+@misc{su2019vlbert,
122+ title = {{VL-BERT: Pre-training of Generic Visual-Linguistic Representations}},
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125+ eprint = {1908.08530},
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127+ howpublished = {arXiv preprint arXiv:1908.08530},
128+ note = {Preprint, not peer reviewed}
129+}
130+
131+@article{mou2024t2iadapter,
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138+ pages = {4296--4304},
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140+}
141+
142+@misc{gal2022textualinversion,
143+ title = {{An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion}},
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145+ year = {2022},
146+ eprint = {2208.01618},
147+ archiveprefix = {arXiv},
148+ howpublished = {arXiv preprint arXiv:2208.01618},
149+ note = {Preprint, not peer reviewed}
150+}
151+
152+@article{buckley2026textexploit,
153+ title = {{Multimodal foundation models exploit text to make medical image predictions}},
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160+}
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162+@article{zhang2026lowqualityfusion,
163+ title = {{Multimodal fusion on low-quality data: A comprehensive survey}},
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166+ journal = {Information Fusion},
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169+ doi = {10.1016/j.inffus.2026.104437}
170+}
171+
172+@misc{ho2022cfg,
173+ title = {{Classifier-Free Diffusion Guidance}},
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175+ year = {2022},
176+ eprint = {2207.12598},
177+ archiveprefix = {arXiv},
178+ howpublished = {arXiv preprint arXiv:2207.12598},
179+ note = {Preprint, not peer reviewed}
180+}
181+
182+@article{zeni2025mattergen,
183+ title = {{A generative model for inorganic materials design}},
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186+ journal = {Nature},
187+ volume = {639},
188+ number = {8055},
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190+ doi = {10.1038/s41586-025-08628-5}
191+}
192+
193+@misc{bastani2024harmlearning,
194+ title = {{Generative AI Can Harm Learning}},
195+ author = {Bastani, Hamsa and Bastani, Osbert and Sungu, Alp and Ge, Haosen and Kabakcı, Özge and Mariman, Rei},
196+ year = {2024},
197+ howpublished = {Preprint (Elsevier BV)},
198+ note = {Preprint, not peer reviewed},
199+ doi = {10.2139/ssrn.4895486}
200+}
201+
202+@article{acharya2025agenticsurvey,
203+ title = {{Agentic AI: Autonomous Intelligence for Complex Goals---A Comprehensive Survey}},
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205+ year = {2025},
206+ journal = {IEEE Access},
207+ volume = {13},
208+ pages = {18912--18936},
209+ doi = {10.1109/access.2025.3532853}
210+}
211+
212+@article{ghareeb2026robin,
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216+ journal = {Nature},
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219+ pages = {497--505},
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223+@article{swanson2025virtuallab,
224+ title = {{The Virtual Lab of AI agents designs new SARS-CoV-2 nanobodies}},
225+ author = {Swanson, Kyle and Wu, Wesley and Bulaong, Nash L. and Pak, John E. and Zou, James},
226+ year = {2025},
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230+ pages = {716--723},
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233+
234+@article{piao2026agentsociety,
235+ title = {{AgentSociety: Large-scale simulation of LLM-driven generative agents advances understanding of human behaviors and society}},
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237+ year = {2026},
238+ journal = {iFuture},
239+ doi = {10.26599/if.2026.9710004}
240+}
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242+@article{sapkota2026agentsvsagentic,
243+ title = {{AI Agents vs. Agentic AI: A Conceptual taxonomy, applications and challenges}},
244+ author = {Sapkota, Ranjan and Roumeliotis, Konstantinos I. and Karkee, Manoj},
245+ year = {2026},
246+ journal = {Information Fusion},
247+ volume = {126},
248+ pages = {103599},
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250+}
251+
252+@article{zhang2025agentmemory,
253+ title = {{A Survey on the Memory Mechanism of Large Language Model-based Agents}},
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255+ year = {2025},
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260+ doi = {10.1145/3748302}
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262+
263+@article{liu2026clinicalagents,
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271+}
272+
273+@misc{haarnoja2018sac,
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278+ archiveprefix = {arXiv},
279+ howpublished = {arXiv preprint arXiv:1801.01290},
280+ note = {Preprint, not peer reviewed}
281+}
282+
283+@misc{fujimoto2018td3,
284+ title = {{Addressing Function Approximation Error in Actor-Critic Methods}},
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286+ year = {2018},
287+ eprint = {1802.09477},
288+ archiveprefix = {arXiv},
289+ howpublished = {arXiv preprint arXiv:1802.09477},
290+ note = {Preprint, not peer reviewed}
291+}
292+
293+@article{vinyals2019alphastar,
294+ title = {{Grandmaster level in StarCraft II using multi-agent reinforcement learning}},
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304+@article{hessel2018rainbow,
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312+}
313+
314+@misc{yu2021mappo,
315+ title = {{The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games}},
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319+ archiveprefix = {arXiv},
320+ howpublished = {arXiv preprint arXiv:2103.01955},
321+ note = {Preprint, not peer reviewed}
322+}
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324+@article{hafner2025dreamerv3,
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334+
335+@misc{burda2018curiosity,
336+ title = {{Large-Scale Study of Curiosity-Driven Learning}},
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340+ archiveprefix = {arXiv},
341+ howpublished = {arXiv preprint arXiv:1808.04355},
342+ note = {Preprint, not peer reviewed}
343+}
344+
345+@incollection{zhang2021marloverview,
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353+}
354+
355+@article{rudin2019stopexplaining,
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366+@article{lipton2018mythos,
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376+
377+@article{lundberg2020treeexplainer,
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387+
388+@article{chicco2020mcc,
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392+ journal = {BMC Genomics},
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398+@article{zhuang2021transfersurvey,
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407+}
408+
409+@article{bottou2018optimization,
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419+
420+@article{zeng2023transformerstsf,
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429+}
430+
431+@article{zhou2021informer,
432+ title = {{Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting}},
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435+ journal = {Proceedings of the AAAI Conference on Artificial Intelligence},
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440+}
441+
442+@inproceedings{wen2023tstransformers,
443+ title = {{Transformers in Time Series: A Survey}},
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445+ year = {2023},
446+ booktitle = {Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence},
447+ pages = {6778--6786},
448+ publisher = {International Joint Conferences on Artificial Intelligence Organization},
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450+}
451+
452+@article{hollmann2025tabpfn,
453+ title = {{Accurate predictions on small data with a tabular foundation model}},
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462+
463+@misc{liu2024kan,
464+ title = {{KAN: Kolmogorov-Arnold Networks}},
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466+ year = {2024},
467+ eprint = {2404.19756},
468+ archiveprefix = {arXiv},
469+ howpublished = {arXiv preprint arXiv:2404.19756},
470+ note = {Preprint, not peer reviewed}
471+}
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473+@article{wu2021gnnsurvey,
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482+}
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484+@article{zhou2020gnnreview,
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494+@misc{zhang2017mixup,
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504+@article{barredoarrieta2020xai,
505+ title = {{Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI}},
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514+@article{mehrabi2021biasfairness,
515+ title = {{A Survey on Bias and Fairness in Machine Learning}},
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524+
525+@article{miller2019explanation,
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535+@article{ali2023xaitrust,
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545+@article{slattery2026riskrepository,
546+ title = {{The AI risk repository: A meta-review, database, and taxonomy of risks from artificial intelligence}},
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555+
556+@inproceedings{chao2025pair,
557+ title = {{Jailbreaking Black Box Large Language Models in Twenty Queries}},
558+ author = {Chao, Patrick and Robey, Alexander and Dobriban, Edgar and Hassani, Hamed and Pappas, George J. and Wong, Eric},
559+ year = {2025},
560+ booktitle = {2025 IEEE Conference on Secure and Trustworthy Machine Learning (SaTML)},
561+ pages = {23--42},
562+ publisher = {IEEE},
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564+}
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566+@article{alber2025poisoning,
567+ title = {{Medical large language models are vulnerable to data-poisoning attacks}},
568+ author = {Alber, Daniel Alexander and Yang, Zihao and Alyakin, Anton and Yang, Eunice and Rai, Sumedha and Valliani, Aly A. and Zhang, Jeff and Rosenbaum, Gabriel R. and Amend-Thomas, Ashley K. and Kurland, David B. and Kremer, Caroline M. and Eremiev, Alexander and Negash, Bruck and Wiggan, Daniel D. and Nakatsuka, Michelle A. and Sangwon, Karl L. and Neifert, Sean N. and Khan, Hammad A. and Save, Akshay Vinod and Palla, Adhith and Grin, Eric A. and Hedman, Monika and Nasir-Moin, Mustafa and Liu, Xujin Chris and Jiang, Lavender Yao and Mankowski, Michal A. and Segev, Dorry L. and Aphinyanaphongs, Yindalon and Riina, Howard A. and Golfinos, John G. and Orringer, Daniel A. and Kondziolka, Douglas and Oermann, Eric Karl},
569+ year = {2025},
570+ journal = {Nature Medicine},
571+ volume = {31},
572+ number = {2},
573+ pages = {618--626},
574+ doi = {10.1038/s41591-024-03445-1}
575+}
576+
577+@article{draelos2026unsafeanswers,
578+ title = {{Large language models provide unsafe answers to patient-posed medical questions}},
579+ author = {Draelos, Rachel L. and Afreen, Samina and Blasko, Barbara and Brazile, Tiffany L. and Chase, Natasha and Desai, Dimple Patel and Evert, Jessica and Gardner, Heather L. and Herrmann, Lauren and House, Aswathy Vaikom and Kass, Stephanie and Kavan, Marianne and Khemani, Kirshma and Koire, Amanda and McDonald, Lauren M. and Rabeeah, Zahraa and Shah, Amy},
580+ year = {2026},
581+ journal = {npj Digital Medicine},
582+ volume = {9},
583+ number = {1},
584+ doi = {10.1038/s41746-026-02428-5}
585+}
586+
587+@inproceedings{moore2025stigma,
588+ title = {{Expressing stigma and inappropriate responses prevents LLMs from safely replacing mental health providers.}},
589+ author = {Moore, Jared and Grabb, Declan and Agnew, William and Klyman, Kevin and Chancellor, Stevie and Ong, Desmond C. and Haber, Nick},
590+ year = {2025},
591+ booktitle = {Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency},
592+ pages = {599--627},
593+ publisher = {ACM},
594+ doi = {10.1145/3715275.3732039}
595+}
596+
597+@article{papagiannidis2025raigovernance,
598+ title = {{Responsible artificial intelligence governance: A review and research framework}},
599+ author = {Papagiannidis, Emmanouil and Mikalef, Patrick and Conboy, Kieran},
600+ year = {2025},
601+ journal = {The Journal of Strategic Information Systems},
602+ volume = {34},
603+ number = {2},
604+ pages = {101885},
605+ doi = {10.1016/j.jsis.2024.101885}
606+}
607+
608+@article{mcdonald2025hepolicies,
609+ title = {{Generative artificial intelligence in higher education: Evidence from an analysis of institutional policies and guidelines}},
610+ author = {McDonald, Nora and Johri, Aditya and Ali, Areej and Collier, Aayushi Hingle},
611+ year = {2025},
612+ journal = {Computers in Human Behavior: Artificial Humans},
613+ volume = {3},
614+ pages = {100121},
615+ doi = {10.1016/j.chbah.2025.100121}
616+}
617+
618+@article{lo2023rapidreview,
619+ title = {{What Is the Impact of ChatGPT on Education? A Rapid Review of the Literature}},
620+ author = {Lo, Chung Kwan},
621+ year = {2023},
622+ journal = {Education Sciences},
623+ volume = {13},
624+ number = {4},
625+ pages = {410},
626+ doi = {10.3390/educsci13040410}
627+}
628+
629+@article{chan2023students,
630+ title = {{Students' voices on generative AI: perceptions, benefits, and challenges in higher education}},
631+ author = {Chan, Cecilia Ka Yuk and Hu, Wenjie},
632+ year = {2023},
633+ journal = {International Journal of Educational Technology in Higher Education},
634+ volume = {20},
635+ number = {1},
636+ doi = {10.1186/s41239-023-00411-8}
637+}
638+
639+@article{chan2023policyframework,
640+ title = {{A comprehensive AI policy education framework for university teaching and learning}},
641+ author = {Chan, Cecilia Ka Yuk},
642+ year = {2023},
643+ journal = {International Journal of Educational Technology in Higher Education},
644+ volume = {20},
645+ number = {1},
646+ doi = {10.1186/s41239-023-00408-3}
647+}
648+
649+@article{baidooanu2023chatgptedu,
650+ title = {{Education in the Era of Generative Artificial Intelligence (AI): Understanding the Potential Benefits of ChatGPT in Promoting Teaching and Learning}},
651+ author = {BAİDOO-ANU, David and OWUSU ANSAH, Leticia},
652+ year = {2023},
653+ journal = {Journal of AI},
654+ volume = {7},
655+ number = {1},
656+ pages = {52--62},
657+ doi = {10.61969/jai.1337500}
658+}
659+
660+@article{rudolph2023assessment,
661+ title = {{ChatGPT: Bullshit spewer or the end of traditional assessments in higher education?}},
662+ author = {Rudolph, Jürgen and Tan, Samson and Tan, Shannon},
663+ year = {2023},
664+ journal = {Journal of Applied Learning \&amp; Teaching},
665+ volume = {6},
666+ number = {1},
667+ doi = {10.37074/jalt.2023.6.1.9}
668+}
669+
670+@article{lim2023ragnarok,
671+ title = {{Generative AI and the future of education: Ragnarök or reformation? A paradoxical perspective from management educators}},
672+ author = {Lim, Weng Marc and Gunasekara, Asanka and Pallant, Jessica Leigh and Pallant, Jason Ian and Pechenkina, Ekaterina},
673+ year = {2023},
674+ journal = {The International Journal of Management Education},
675+ volume = {21},
676+ number = {2},
677+ pages = {100790},
678+ doi = {10.1016/j.ijme.2023.100790}
679+}
680+
681+@article{wu2025radfm,
682+ title = {{Towards generalist foundation model for radiology by leveraging web-scale 2D\&amp;3D medical data}},
683+ author = {Wu, Chaoyi and Zhang, Xiaoman and Zhang, Ya and Hui, Hui and Wang, Yanfeng and Xie, Weidi},
684+ year = {2025},
685+ journal = {Nature Communications},
686+ volume = {16},
687+ number = {1},
688+ doi = {10.1038/s41467-025-62385-7}
689+}
690+
691+@article{ding2025titan,
692+ title = {{A multimodal whole-slide foundation model for pathology}},
693+ author = {Ding, Tong and Wagner, Sophia J. and Song, Andrew H. and Chen, Richard J. and Lu, Ming Y. and Zhang, Andrew and Vaidya, Anurag J. and Jaume, Guillaume and Shaban, Muhammad and Kim, Ahrong and Williamson, Drew F. K. and Robertson, Harry and Chen, Bowen and Almagro-Pérez, Cristina and Doucet, Paul and Sahai, Sharifa and Chen, Chengkuan and Chen, Christina S. and Komura, Daisuke and Kawabe, Akihiro and Ochi, Mieko and Sato, Shinya and Yokose, Tomoyuki and Miyagi, Yohei and Ishikawa, Shumpei and Gerber, Georg and Peng, Tingying and Le, Long Phi and Mahmood, Faisal},
694+ year = {2025},
695+ journal = {Nature Medicine},
696+ volume = {31},
697+ number = {11},
698+ pages = {3749--3761},
699+ doi = {10.1038/s41591-025-03982-3}
700+}
701+
702+@article{lu2024conch,
703+ title = {{A visual-language foundation model for computational pathology}},
704+ author = {Lu, Ming Y. and Chen, Bowen and Williamson, Drew F. K. and Chen, Richard J. and Liang, Ivy and Ding, Tong and Jaume, Guillaume and Odintsov, Igor and Le, Long Phi and Gerber, Georg and Parwani, Anil V. and Zhang, Andrew and Mahmood, Faisal},
705+ year = {2024},
706+ journal = {Nature Medicine},
707+ volume = {30},
708+ number = {3},
709+ pages = {863--874},
710+ doi = {10.1038/s41591-024-02856-4}
711+}
712+
713+@article{huang2023plip,
714+ title = {{A visual--language foundation model for pathology image analysis using medical Twitter}},
715+ author = {Huang, Zhi and Bianchi, Federico and Yuksekgonul, Mert and Montine, Thomas J. and Zou, James},
716+ year = {2023},
717+ journal = {Nature Medicine},
718+ volume = {29},
719+ number = {9},
720+ pages = {2307--2316},
721+ doi = {10.1038/s41591-023-02504-3}
722+}
723+
724+@article{yan2025panderm,
725+ title = {{A multimodal vision foundation model for clinical dermatology}},
726+ author = {Yan, Siyuan and Yu, Zhen and Primiero, Clare and Vico-Alonso, Cristina and Wang, Zhonghua and Yang, Litao and Tschandl, Philipp and Hu, Ming and Ju, Lie and Tan, Gin and Tang, Vincent and Ng, Aik Beng and Powell, David and Bonnington, Paul and See, Simon and Magnaterra, Elisabetta and Ferguson, Peter and Nguyen, Jennifer and Guitera, Pascale and Banuls, Jose and Janda, Monika and Mar, Victoria and Kittler, Harald and Soyer, H. Peter and Ge, Zongyuan},
727+ year = {2025},
728+ journal = {Nature Medicine},
729+ volume = {31},
730+ number = {8},
731+ pages = {2691--2702},
732+ doi = {10.1038/s41591-025-03747-y}
733+}
734+
735+@article{tak2026brainiac,
736+ title = {{A generalizable foundation model for analysis of human brain MRI}},
737+ author = {Tak, Divyanshu and Garomsa, Biniam A. and Zapaishchykova, Anna and Chaunzwa, Tafadzwa L. and Climent Pardo, Juan Carlos and Ye, Zezhong and Zielke, John and Ravipati, Yashwanth and Pai, Suraj and Vajapeyam, Sri and Mahootiha, Maryam and Parker, Mitchell and Pike, Luke R. G. and Smith, Ceilidh and Familiar, Ariana M. and Liu, Kevin X. and Prabhu, Sanjay and Arnaout, Omar and Bandopadhayay, Pratiti and Nabavizadeh, Ali and Mueller, Sabine and Aerts, Hugo JWL and Huang, Raymond Y. and Poussaint, Tina Y. and Kann, Benjamin H.},
738+ year = {2026},
739+ journal = {Nature Neuroscience},
740+ volume = {29},
741+ number = {4},
742+ pages = {945--956},
743+ doi = {10.1038/s41593-026-02202-6}
744+}
745+
746+@article{zhu2026earthfm,
747+ title = {{On the foundations of Earth foundation models}},
748+ author = {Zhu, Xiao Xiang and Xiong, Zhitong and Wang, Yi and Stewart, Adam J. and Heidler, Konrad and Wang, Yuanyuan and Yuan, Zhenghang and Dujardin, Thomas and Xu, Qingsong and Shi, Yilei},
749+ year = {2026},
750+ journal = {Communications Earth \&amp; Environment},
751+ volume = {7},
752+ number = {1},
753+ doi = {10.1038/s43247-025-03127-x}
754+}
755+
756+@article{singhal2023clinical,
757+ title = {{Large language models encode clinical knowledge}},
758+ author = {Singhal, Karan and Azizi, Shekoofeh and Tu, Tao and Mahdavi, S. Sara and Wei, Jason and Chung, Hyung Won and Scales, Nathan and Tanwani, Ajay and Cole-Lewis, Heather and Pfohl, Stephen and Payne, Perry and Seneviratne, Martin and Gamble, Paul and Kelly, Chris and Babiker, Abubakr and Schärli, Nathanael and Chowdhery, Aakanksha and Mansfield, Philip and Demner-Fushman, Dina and Agüera y Arcas, Blaise and Webster, Dale and Corrado, Greg S. and Matias, Yossi and Chou, Katherine and Gottweis, Juraj and Tomasev, Nenad and Liu, Yun and Rajkomar, Alvin and Barral, Joelle and Semturs, Christopher and Karthikesalingam, Alan and Natarajan, Vivek},
759+ year = {2023},
760+ journal = {Nature},
761+ volume = {620},
762+ number = {7972},
763+ pages = {172--180},
764+ doi = {10.1038/s41586-023-06291-2}
765+}
766+
767+@article{liu2023prompting,
768+ title = {{Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing}},
769+ author = {Liu, Pengfei and Yuan, Weizhe and Fu, Jinlan and Jiang, Zhengbao and Hayashi, Hiroaki and Neubig, Graham},
770+ year = {2023},
771+ journal = {ACM Computing Surveys},
772+ volume = {55},
773+ number = {9},
774+ pages = {1--35},
775+ doi = {10.1145/3560815}
776+}
777+
778+@article{vandis2023priorities,
779+ title = {{ChatGPT: five priorities for research}},
780+ author = {van Dis, Eva A. M. and Bollen, Johan and Zuidema, Willem and van Rooij, Robert and Bockting, Claudi L.},
781+ year = {2023},
782+ journal = {Nature},
783+ volume = {614},
784+ number = {7947},
785+ pages = {224--226},
786+ doi = {10.1038/d41586-023-00288-7}
787+}
788+
789+@article{cheng2026agentsdefinitions,
790+ title = {{Exploring Large Language Model-Based Intelligent Agents: Definitions, Methods, and Prospects}},
791+ author = {Cheng, Yuheng and Zhang, Ceyao and Zhang, Zhengwen and Meng, Xiangrui and Hong, Sirui and Li, Wenhao and Wang, Zihao and Wang, Zekai and Yin, Feng and Zhao, Junhua and He, Xiuqiang},
792+ year = {2026},
793+ journal = {WIREs Data Mining and Knowledge Discovery},
794+ volume = {16},
795+ number = {3},
796+ doi = {10.1002/widm.70111}
797+}
798+
799+@article{raza2026trism,
800+ title = {{TRiSM for Agentic AI: A review of Trust, Risk, and Security Management in LLM-based Agentic Multi-Agent Systems}},
801+ author = {Raza, Shaina and Sapkota, Ranjan and Karkee, Manoj and Emmanouilidis, Christos},
802+ year = {2026},
803+ journal = {AI Open},
804+ volume = {7},
805+ pages = {71--95},
806+ doi = {10.1016/j.aiopen.2026.02.006}
807+}
808+
809+@article{paschali2025fmradiology,
810+ title = {{Foundation Models in Radiology: What, How, Why, and Why Not}},
811+ author = {Paschali, Magdalini and Chen, Zhihong and Blankemeier, Louis and Varma, Maya and Youssef, Alaa and Bluethgen, Christian and Langlotz, Curtis and Gatidis, Sergios and Chaudhari, Akshay},
812+ year = {2025},
813+ journal = {Radiology},
814+ volume = {314},
815+ number = {2},
816+ doi = {10.1148/radiol.240597}
817+}
818+
819+@article{zhang2026datacentric,
820+ title = {{Data-Centric Foundation Models in Computational Healthcare: A Survey}},
821+ author = {Zhang, Yunkun and Gao, Jin and Tan, Zheling and Zhou, Lingfeng and Ding, Kexin and Zhou, Mu and Zhang, Shaoting and Wang, Dequan},
822+ year = {2026},
823+ journal = {ACM Computing Surveys},
824+ volume = {58},
825+ number = {11},
826+ pages = {1--35},
827+ doi = {10.1145/3800677}
828+}
829+
830+@article{zheng2025learningfrommodels,
831+ title = {{Learning from models beyond fine-tuning}},
832+ author = {Zheng, Hongling and Shen, Li and Tang, Anke and Luo, Yong and Hu, Han and Du, Bo and Wen, Yonggang and Tao, Dacheng},
833+ year = {2025},
834+ journal = {Nature Machine Intelligence},
835+ volume = {7},
836+ number = {1},
837+ pages = {6--17},
838+ doi = {10.1038/s42256-024-00961-0}
839+}
840+
841+@article{schouten2025multimodalmedicine,
842+ title = {{Navigating the landscape of multimodal AI in medicine: A scoping review on technical challenges and clinical applications}},
843+ author = {Schouten, Daan and Nicoletti, Giulia and Dille, Bas and Chia, Catherine and Vendittelli, Pierpaolo and Schuurmans, Megan and Litjens, Geert and Khalili, Nadieh},
844+ year = {2025},
845+ journal = {Medical Image Analysis},
846+ volume = {105},
847+ pages = {103621},
848+ doi = {10.1016/j.media.2025.103621}
849+}
850+
851+@article{zhai2024overreliance,
852+ title = {{The effects of over-reliance on AI dialogue systems on students' cognitive abilities: a systematic review}},
853+ author = {Zhai, Chunpeng and Wibowo, Santoso and Li, Lily D.},
854+ year = {2024},
855+ journal = {Smart Learning Environments},
856+ volume = {11},
857+ number = {1},
858+ doi = {10.1186/s40561-024-00316-7}
859+}
860+
861+@article{xia2024assessmentreview,
862+ title = {{A scoping review on how generative artificial intelligence transforms assessment in higher education}},
863+ author = {Xia, Qi and Weng, Xiaojing and Ouyang, Fan and Lin, Tzung Jin and Chiu, Thomas K.F.},
864+ year = {2024},
865+ journal = {International Journal of Educational Technology in Higher Education},
866+ volume = {21},
867+ number = {1},
868+ doi = {10.1186/s41239-024-00468-z}
869+}
870+
871+@article{bittle2025integrity,
872+ title = {{Generative AI and Academic Integrity in Higher Education: A Systematic Review and Research Agenda}},
873+ author = {Bittle, Kyle and El-Gayar, Omar},
874+ year = {2025},
875+ journal = {Information},
876+ volume = {16},
877+ number = {4},
878+ pages = {296},
879+ doi = {10.3390/info16040296}
880+}
881+
882+@misc{jin2023timellm,
883+ title = {{Time-LLM: Time Series Forecasting by Reprogramming Large Language Models}},
884+ author = {Jin, Ming and Wang, Shiyu and Ma, Lintao and Chu, Zhixuan and Zhang, James Y. and Shi, Xiaoming and Chen, Pin-Yu and Liang, Yuxuan and Li, Yuan-Fang and Pan, Shirui and Wen, Qingsong},
885+ year = {2023},
886+ eprint = {2310.01728},
887+ archiveprefix = {arXiv},
888+ howpublished = {arXiv preprint arXiv:2310.01728},
889+ note = {Preprint, not peer reviewed}
890+}
891+
892+@article{liu2024timesurl,
893+ title = {{TimesURL: Self-Supervised Contrastive Learning for Universal Time Series Representation Learning}},
894+ author = {Liu, Jiexi and Chen, Songcan},
895+ year = {2024},
896+ journal = {Proceedings of the AAAI Conference on Artificial Intelligence},
897+ volume = {38},
898+ number = {12},
899+ pages = {13918--13926},
900+ doi = {10.1609/aaai.v38i12.29299}
901+}
902+
903+@inproceedings{ye2024mplugowl2,
904+ title = {{mPLUG-OwI2: Revolutionizing Multi-modal Large Language Model with Modality Collaboration}},
905+ author = {Ye, Qinghao and Xu, Haiyang and Ye, Jiabo and Yan, Ming and Hu, Anwen and Liu, Haowei and Qian, Qi and Zhang, Ji and Huang, Fei},
906+ year = {2024},
907+ booktitle = {2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
908+ pages = {13040--13051},
909+ publisher = {IEEE},
910+ doi = {10.1109/cvpr52733.2024.01239}
911+}
912+
913+@misc{wang2025gpt5medical,
914+ title = {{Capabilities of GPT-5 on Multimodal Medical Reasoning}},
915+ author = {Wang, Shansong and Hu, Mingzhe and Li, Qiang and Safari, Mojtaba and Yang, Xiaofeng},
916+ year = {2025},
917+ eprint = {2508.08224},
918+ archiveprefix = {arXiv},
919+ howpublished = {arXiv preprint arXiv:2508.08224},
920+ note = {Preprint, not peer reviewed}
921+}
922+
923+@article{lai2026medr1,
924+ title = {{Med-R1: Reinforcement Learning for Generalizable Medical Reasoning in Vision-Language Models}},
925+ author = {Lai, Yuxiang and Zhong, Jike and Li, Ming and Zhao, Shitian and Li, Yuheng and Psounis, Konstantinos and Yang, Xiaofeng},
926+ year = {2026},
927+ journal = {IEEE Transactions on Medical Imaging},
928+ volume = {45},
929+ number = {6},
930+ pages = {2727--2737},
931+ doi = {10.1109/tmi.2026.3661001}
932+}
933+
934+@article{zhang2026dive,
935+ title = {{``DIVE'' into hydrogen storage materials discovery with AI agents}},
936+ author = {Zhang, Di and Jia, Xue and Tran, Hung Ba and Jang, Seong Hoon and Zhang, Linda and Sato, Ryuhei and Hashimoto, Yusuke and Sato, Toyoto and Konno, Kiyoe and Orimo, Shin-ichi and Li, Hao},
937+ year = {2026},
938+ journal = {Chemical Science},
939+ volume = {17},
940+ number = {6},
941+ pages = {3031--3042},
942+ doi = {10.1039/d5sc09921h}
943+}
944+
945+@article{bandi2025agenticreview,
946+ title = {{The Rise of Agentic AI: A Review of Definitions, Frameworks, Architectures, Applications, Evaluation Metrics, and Challenges}},
947+ author = {Bandi, Ajay and Kongari, Bhavani and Naguru, Roshini and Pasnoor, Sahitya and Vilipala, Sri Vidya},
948+ year = {2025},
949+ journal = {Future Internet},
950+ volume = {17},
951+ number = {9},
952+ pages = {404},
953+ doi = {10.3390/fi17090404}
954+}
955+
956+@article{abouali2025agenticparadigms,
957+ title = {{Agentic AI: a comprehensive survey of architectures, applications, and future directions}},
958+ author = {Abou Ali, Mohamad and Dornaika, Fadi and Charafeddine, Jinan},
959+ year = {2025},
960+ journal = {Artificial Intelligence Review},
961+ volume = {59},
962+ number = {1},
963+ doi = {10.1007/s10462-025-11422-4}
964+}
965+
966+@article{allmendinger2026multiagentai,
967+ title = {{Multi-agent AI}},
968+ author = {Allmendinger, Simeon and Bonenberger, Lukas and Endres, Kathrin and Fetzer, Dominik and Gimpel, Henner and Kühl, Niklas},
969+ year = {2026},
970+ journal = {Electronic Markets},
971+ volume = {36},
972+ number = {1},
973+ doi = {10.1007/s12525-025-00862-z}
974+}
975+
976+@article{hasanzadeh2025biashealthcare,
977+ title = {{Bias recognition and mitigation strategies in artificial intelligence healthcare applications}},
978+ author = {Hasanzadeh, Fereshteh and Josephson, Colin B. and Waters, Gabriella and Adedinsewo, Demilade and Azizi, Zahra and White, James A.},
979+ year = {2025},
980+ journal = {npj Digital Medicine},
981+ volume = {8},
982+ number = {1},
983+ doi = {10.1038/s41746-025-01503-7}
984+}
985+
986+@article{wang2026pedagogical,
987+ title = {{Pedagogical partnerships with generative AI in higher education: how dual cognitive pathways paradoxically enable transformative learning}},
988+ author = {Wang, Shaofeng and Zhang, Hao},
989+ year = {2026},
990+ journal = {International Journal of Educational Technology in Higher Education},
991+ volume = {23},
992+ number = {1},
993+ doi = {10.1186/s41239-026-00585-x}
994+}
995+
996+@article{collaco2026agentichealthcare,
997+ title = {{The role of agentic artificial intelligence in healthcare: a scoping review}},
998+ author = {Collaco, Bernardo G. and Haider, Syed Ali and Prabha, Srinivasagam and Gomez-Cabello, Cesar A. and Genovese, Ariana and Wood, Nadia G. and Bagaria, Sanjay P. and Gopala, Narayanan and Tao, Cui and Forte, Antonio Jorge},
999+ year = {2026},
1000+ journal = {npj Digital Medicine},
1001+ volume = {9},
1002+ number = {1},
1003+ doi = {10.1038/s41746-026-02517-5}
1004+}
1005+
1006+@incollection{gholami2022quantization,
1007+ title = {{A Survey of Quantization Methods for Efficient Neural Network Inference}},
1008+ author = {Gholami, Amir and Kim, Sehoon and Dong, Zhen and Yao, Zhewei and Mahoney, Michael W. and Keutzer, Kurt},
1009+ year = {2022},
1010+ booktitle = {Low-Power Computer Vision},
1011+ publisher = {Chapman and Hall/CRC},
1012+ pages = {291--326},
1013+ doi = {10.1201/9781003162810-13}
1014+}
1015+
1016+@misc{zhang2023llamaadapter,
1017+ title = {{LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention}},
1018+ author = {Zhang, Renrui and Han, Jiaming and Liu, Chris and Gao, Peng and Zhou, Aojun and Hu, Xiangfei and Yan, Shilin and Lu, Pan and Li, Hongsheng and Qiao, Yu},
1019+ year = {2023},
1020+ eprint = {2303.16199},
1021+ archiveprefix = {arXiv},
1022+ howpublished = {arXiv preprint arXiv:2303.16199},
1023+ note = {Preprint, not peer reviewed}
1024+}
added manuscript/references_part07a.bib +654 −0
@@ -0,0 +1,654 @@
1+% references_part07a.bib — Part VII-a (theory half), Agent G, 2026-11-03. Every entry resolved by a single live lookup
2+% (Crossref / DataCite / arXiv); see references_verification_part07a.csv. Generated by literature_synthesis/agent_G/make_bib_part07a.py.
3+
4+@inproceedings{czerwinski2019petri,
5+ author = {Czerwiński, Wojciech and Lasota, Sławomir and Lazić, Ranko and Leroux, Jérôme and Mazowiecki, Filip},
6+ title = {{The reachability problem for Petri nets is not elementary}},
7+ year = {2019},
8+ doi = {10.1145/3313276.3316369},
9+ booktitle = {Proceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing},
10+ pages = {24-33},
11+ publisher = {ACM}
12+}
13+
14+@inproceedings{czerwinski2021ackermann,
15+ author = {Czerwinski, Wojciech and Orlikowski, Lukasz},
16+ title = {{Reachability in Vector Addition Systems is Ackermann-complete}},
17+ year = {2022},
18+ doi = {10.1109/FOCS52979.2021.00120},
19+ booktitle = {2021 IEEE 62nd Annual Symposium on Foundations of Computer Science (FOCS)},
20+ pages = {1229-1240},
21+ publisher = {IEEE}
22+}
23+
24+@inproceedings{leroux2021notprimitive,
25+ author = {Leroux, Jerome},
26+ title = {{The Reachability Problem for Petri Nets is Not Primitive Recursive}},
27+ year = {2022},
28+ doi = {10.1109/FOCS52979.2021.00121},
29+ booktitle = {2021 IEEE 62nd Annual Symposium on Foundations of Computer Science (FOCS)},
30+ pages = {1241-1252},
31+ publisher = {IEEE}
32+}
33+
34+@article{wang2024llmtesting,
35+ author = {Wang, Junjie and Huang, Yuchao and Chen, Chunyang and Liu, Zhe and Wang, Song and Wang, Qing},
36+ title = {{Software Testing With Large Language Models: Survey, Landscape, and Vision}},
37+ year = {2024},
38+ doi = {10.1109/tse.2024.3368208},
39+ journal = {IEEE Transactions on Software Engineering},
40+ volume = {50},
41+ number = {4},
42+ pages = {911-936}
43+}
44+
45+@article{zhang2022mltesting,
46+ author = {Zhang, Jie M. and Harman, Mark and Ma, Lei and Liu, Yang},
47+ title = {{Machine Learning Testing: Survey, Landscapes and Horizons}},
48+ year = {2022},
49+ doi = {10.1109/tse.2019.2962027},
50+ journal = {IEEE Transactions on Software Engineering},
51+ volume = {48},
52+ number = {1},
53+ pages = {1-36}
54+}
55+
56+@article{cui2026genaiwork,
57+ author = {Cui, Kevin Zheyuan and Demirer, Mert and Jaffe, Sonia and Musolff, Leon and Peng, Sida and Salz, Tobias},
58+ title = {{The Effects of Generative AI on High-Skilled Work: Evidence from Three Field Experiments with Software Developers}},
59+ year = {2026},
60+ doi = {10.1287/mnsc.2025.00535},
61+ journal = {Management Science}
62+}
63+
64+@article{li2024llift,
65+ author = {Li, Haonan and Hao, Yu and Zhai, Yizhuo and Qian, Zhiyun},
66+ title = {{Enhancing Static Analysis for Practical Bug Detection: An LLM-Integrated Approach}},
67+ year = {2024},
68+ doi = {10.1145/3649828},
69+ journal = {Proceedings of the ACM on Programming Languages},
70+ volume = {8},
71+ number = {OOPSLA1},
72+ pages = {474-499}
73+}
74+
75+@article{yang2024whitefox,
76+ author = {Yang, Chenyuan and Deng, Yinlin and Lu, Runyu and Yao, Jiayi and Liu, Jiawei and Jabbarvand, Reyhaneh and Zhang, Lingming},
77+ title = {{WhiteFox: White-Box Compiler Fuzzing Empowered by Large Language Models}},
78+ year = {2024},
79+ doi = {10.1145/3689736},
80+ journal = {Proceedings of the ACM on Programming Languages},
81+ volume = {8},
82+ number = {OOPSLA2},
83+ pages = {709-735}
84+}
85+
86+@inproceedings{baghdadi2019tiramisu,
87+ author = {Baghdadi, Riyadh and Ray, Jessica and Romdhane, Malek Ben and Sozzo, Emanuele Del and Akkas, Abdurrahman and Zhang, Yunming and Suriana, Patricia and Kamil, Shoaib and Amarasinghe, Saman},
88+ title = {{Tiramisu: A Polyhedral Compiler for Expressing Fast and Portable Code}},
89+ year = {2019},
90+ doi = {10.1109/cgo.2019.8661197},
91+ booktitle = {2019 IEEE/ACM International Symposium on Code Generation and Optimization (CGO)},
92+ pages = {193-205},
93+ publisher = {IEEE}
94+}
95+
96+@article{wang2018mlcompiler,
97+ author = {Wang, Zheng and O'Boyle, Michael},
98+ title = {{Machine Learning in Compiler Optimization}},
99+ year = {2018},
100+ doi = {10.1109/jproc.2018.2817118},
101+ journal = {Proceedings of the IEEE},
102+ volume = {106},
103+ number = {11},
104+ pages = {1879-1901}
105+}
106+
107+@article{ashouri2018autotuning,
108+ author = {Ashouri, Amir H. and Killian, William and Cavazos, John and Palermo, Gianluca and Silvano, Cristina},
109+ title = {{A Survey on Compiler Autotuning using Machine Learning}},
110+ year = {2018},
111+ doi = {10.1145/3197978},
112+ journal = {ACM Computing Surveys},
113+ volume = {51},
114+ number = {5},
115+ pages = {1-42}
116+}
117+
118+@inproceedings{cummins2025llmcompiler,
119+ author = {Cummins, Chris and Seeker, Volker and Grubisic, Dejan and Roziere, Baptiste and Gehring, Jonas and Synnaeve, Gabriel and Leather, Hugh},
120+ title = {{LLM Compiler: Foundation Language Models for Compiler Optimization}},
121+ year = {2025},
122+ doi = {10.1145/3708493.3712691},
123+ booktitle = {Proceedings of the 34th ACM SIGPLAN International Conference on Compiler Construction},
124+ pages = {141-153},
125+ publisher = {ACM}
126+}
127+
128+@article{munley2024llm4vv,
129+ author = {Munley, Christian and Jarmusch, Aaron and Chandrasekaran, Sunita},
130+ title = {{LLM4VV: Developing LLM-driven testsuite for compiler validation}},
131+ year = {2024},
132+ doi = {10.1016/j.future.2024.05.034},
133+ journal = {Future Generation Computer Systems},
134+ volume = {160},
135+ pages = {1-13}
136+}
137+
138+@incollection{barbosa2022cvc5,
139+ author = {Barbosa, Haniel and Barrett, Clark and Brain, Martin and Kremer, Gereon and Lachnitt, Hanna and Mann, Makai and Mohamed, Abdalrhman and Mohamed, Mudathir and Niemetz, Aina and Nötzli, Andres and Ozdemir, Alex and Preiner, Mathias and Reynolds, Andrew and Sheng, Ying and Tinelli, Cesare and Zohar, Yoni},
140+ title = {{cvc5: A Versatile and Industrial-Strength SMT Solver}},
141+ year = {2022},
142+ doi = {10.1007/978-3-030-99524-9\_24},
143+ booktitle = {Lecture Notes in Computer Science},
144+ pages = {415-442},
145+ publisher = {Springer International Publishing}
146+}
147+
148+@incollection{beyer2024svcomp,
149+ author = {Beyer, Dirk},
150+ title = {{State of the Art in Software Verification and Witness Validation: SV-COMP 2024}},
151+ year = {2024},
152+ doi = {10.1007/978-3-031-57256-2\_15},
153+ booktitle = {Lecture Notes in Computer Science},
154+ pages = {299-329},
155+ publisher = {Springer Nature Switzerland}
156+}
157+
158+@incollection{katz2019marabou,
159+ author = {Katz, Guy and Huang, Derek A. and Ibeling, Duligur and Julian, Kyle and Lazarus, Christopher and Lim, Rachel and Shah, Parth and Thakoor, Shantanu and Wu, Haoze and Zeljić, Aleksandar and Dill, David L. and Kochenderfer, Mykel J. and Barrett, Clark},
160+ title = {{The Marabou Framework for Verification and Analysis of Deep Neural Networks}},
161+ year = {2019},
162+ doi = {10.1007/978-3-030-25540-4\_26},
163+ booktitle = {Lecture Notes in Computer Science},
164+ pages = {443-452},
165+ publisher = {Springer International Publishing}
166+}
167+
168+@article{singh2019deeppoly,
169+ author = {Singh, Gagandeep and Gehr, Timon and Püschel, Markus and Vechev, Martin},
170+ title = {{An abstract domain for certifying neural networks}},
171+ year = {2019},
172+ doi = {10.1145/3290354},
173+ journal = {Proceedings of the ACM on Programming Languages},
174+ volume = {3},
175+ number = {POPL},
176+ pages = {1-30}
177+}
178+
179+@inproceedings{permenev2020verx,
180+ author = {Permenev, Anton and Dimitrov, Dimitar and Tsankov, Petar and Drachsler-Cohen, Dana and Vechev, Martin},
181+ title = {{VerX: Safety Verification of Smart Contracts}},
182+ year = {2020},
183+ doi = {10.1109/sp40000.2020.00024},
184+ booktitle = {2020 IEEE Symposium on Security and Privacy (SP)},
185+ pages = {1661-1677},
186+ publisher = {IEEE}
187+}
188+
189+@article{willsey2021egg,
190+ author = {Willsey, Max and Nandi, Chandrakana and Wang, Yisu Remy and Flatt, Oliver and Tatlock, Zachary and Panchekha, Pavel},
191+ title = {{egg: Fast and extensible equality saturation}},
192+ year = {2021},
193+ doi = {10.1145/3434304},
194+ journal = {Proceedings of the ACM on Programming Languages},
195+ volume = {5},
196+ number = {POPL},
197+ pages = {1-29}
198+}
199+
200+@inproceedings{kalra2018zeus,
201+ author = {Kalra, Sukrit and Goel, Seep and Dhawan, Mohan and Sharma, Subodh},
202+ title = {{ZEUS: Analyzing Safety of Smart Contracts}},
203+ year = {2018},
204+ doi = {10.14722/ndss.2018.23082},
205+ booktitle = {Proceedings 2018 Network and Distributed System Security Symposium},
206+ publisher = {Internet Society}
207+}
208+
209+@incollection{papadakis2019mutation,
210+ title = {{Mutation Testing Advances: An Analysis and Survey}},
211+ year = {2019},
212+ doi = {10.1016/bs.adcom.2018.03.015},
213+ booktitle = {Advances in Computers},
214+ pages = {275-378},
215+ publisher = {Elsevier}
216+}
217+
218+@article{abualigah2021aoa,
219+ author = {Abualigah, Laith and Diabat, Ali and Mirjalili, Seyedali and Abd Elaziz, Mohamed and Gandomi, Amir H.},
220+ title = {{The Arithmetic Optimization Algorithm}},
221+ year = {2021},
222+ doi = {10.1016/j.cma.2020.113609},
223+ journal = {Computer Methods in Applied Mechanics and Engineering},
224+ volume = {376},
225+ pages = {113609}
226+}
227+
228+@article{abualigah2021aquila,
229+ author = {Abualigah, Laith and Yousri, Dalia and Abd Elaziz, Mohamed and Ewees, Ahmed A. and Al-qaness, Mohammed A.A. and Gandomi, Amir H.},
230+ title = {{Aquila Optimizer: A novel meta-heuristic optimization algorithm}},
231+ year = {2021},
232+ doi = {10.1016/j.cie.2021.107250},
233+ journal = {Computers \&amp; Industrial Engineering},
234+ volume = {157},
235+ pages = {107250}
236+}
237+
238+@book{biere2021handbook,
239+ author = {Biere, Armin and Heule, Marijn and van Maaren, Hans and Walsh, Toby},
240+ title = {{Handbook of Satisfiability}},
241+ year = {2021},
242+ doi = {10.3233/faia336},
243+ publisher = {IOS Press},
244+ series = {Frontiers in Artificial Intelligence and Applications}
245+}
246+
247+@book{li2019kolmogorov,
248+ author = {Li, Ming and Vitányi, Paul},
249+ title = {{An Introduction to Kolmogorov Complexity and Its Applications}},
250+ year = {2019},
251+ doi = {10.1007/978-3-030-11298-1},
252+ publisher = {Springer International Publishing},
253+ series = {Texts in Computer Science}
254+}
255+
256+@article{sorensen2013metaphor,
257+ author = {Sörensen, Kenneth},
258+ title = {{Metaheuristics---the metaphor exposed}},
259+ year = {2013},
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261+ journal = {International Transactions in Operational Research},
262+ volume = {22},
263+ number = {1},
264+ pages = {3-18}
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266+
267+@article{aranha2021metaphor,
268+ author = {Aranha, Claus and Camacho Villalón, Christian L. and Campelo, Felipe and Dorigo, Marco and Ruiz, Rubén and Sevaux, Marc and Sörensen, Kenneth and Stützle, Thomas},
269+ title = {{Metaphor-based metaheuristics, a call for action: the elephant in the room}},
270+ year = {2021},
271+ doi = {10.1007/s11721-021-00202-9},
272+ journal = {Swarm Intelligence},
273+ volume = {16},
274+ number = {1},
275+ pages = {1-6}
276+}
277+
278+@article{camachovillalon2022exposing,
279+ author = {Camacho-Villalón, Christian L. and Dorigo, Marco and Stützle, Thomas},
280+ title = {{Exposing the grey wolf, moth-flame, whale, firefly, bat, and antlion algorithms: six misleading optimization techniques inspired by<i>bestial</i>metaphors}},
281+ year = {2022},
282+ doi = {10.1111/itor.13176},
283+ journal = {International Transactions in Operational Research},
284+ volume = {30},
285+ number = {6},
286+ pages = {2945-2971}
287+}
288+
289+@article{mitzenmacher2022predictions,
290+ author = {Mitzenmacher, Michael and Vassilvitskii, Sergei},
291+ title = {{Algorithms with predictions}},
292+ year = {2022},
293+ doi = {10.1145/3528087},
294+ journal = {Communications of the ACM},
295+ volume = {65},
296+ number = {7},
297+ pages = {33-35}
298+}
299+
300+@article{mankowitz2023alphadev,
301+ author = {Mankowitz, Daniel J. and Michi, Andrea and Zhernov, Anton and Gelmi, Marco and Selvi, Marco and Paduraru, Cosmin and Leurent, Edouard and Iqbal, Shariq and Lespiau, Jean-Baptiste and Ahern, Alex and Köppe, Thomas and Millikin, Kevin and Gaffney, Stephen and Elster, Sophie and Broshear, Jackson and Gamble, Chris and Milan, Kieran and Tung, Robert and Hwang, Minjae and Cemgil, Taylan and Barekatain, Mohammadamin and Li, Yujia and Mandhane, Amol and Hubert, Thomas and Schrittwieser, Julian and Hassabis, Demis and Kohli, Pushmeet and Riedmiller, Martin and Vinyals, Oriol and Silver, David},
302+ title = {{Faster sorting algorithms discovered using deep reinforcement learning}},
303+ year = {2023},
304+ doi = {10.1038/s41586-023-06004-9},
305+ journal = {Nature},
306+ volume = {618},
307+ number = {7964},
308+ pages = {257-263}
309+}
310+
311+@inproceedings{lopes2021alive2,
312+ author = {Lopes, Nuno P. and Lee, Juneyoung and Hur, Chung-Kil and Liu, Zhengyang and Regehr, John},
313+ title = {{Alive2: bounded translation validation for LLVM}},
314+ year = {2021},
315+ doi = {10.1145/3453483.3454030},
316+ booktitle = {Proceedings of the 42nd ACM SIGPLAN International Conference on Programming Language Design and Implementation},
317+ pages = {65-79},
318+ publisher = {ACM}
319+}
320+
321+@inproceedings{yang2011csmith,
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323+ title = {{Finding and understanding bugs in C compilers}},
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325+ doi = {10.1145/1993498.1993532},
326+ booktitle = {Proceedings of the 32nd ACM SIGPLAN Conference on Programming Language Design and Implementation},
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329+}
330+
331+@inproceedings{xia2024fuzz4all,
332+ author = {Xia, Chunqiu Steven and Paltenghi, Matteo and Le Tian, Jia and Pradel, Michael and Zhang, Lingming},
333+ title = {{Fuzz4All: Universal Fuzzing with Large Language Models}},
334+ year = {2024},
335+ doi = {10.1145/3597503.3639121},
336+ booktitle = {Proceedings of the IEEE/ACM 46th International Conference on Software Engineering},
337+ pages = {1-13},
338+ publisher = {ACM}
339+}
340+
341+@inproceedings{xia2023apr,
342+ author = {Xia, Chunqiu Steven and Wei, Yuxiang and Zhang, Lingming},
343+ title = {{Automated Program Repair in the Era of Large Pre-trained Language Models}},
344+ year = {2023},
345+ doi = {10.1109/ICSE48619.2023.00129},
346+ booktitle = {2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE)},
347+ pages = {1482-1494},
348+ publisher = {IEEE}
349+}
350+
351+@inproceedings{kang2023fewshottesters,
352+ author = {Kang, Sungmin and Yoon, Juyeon and Yoo, Shin},
353+ title = {{Large Language Models are Few-shot Testers: Exploring LLM-based General Bug Reproduction}},
354+ year = {2023},
355+ doi = {10.1109/icse48619.2023.00194},
356+ booktitle = {2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE)},
357+ pages = {2312-2323},
358+ publisher = {IEEE}
359+}
360+
361+@inproceedings{lattuada2024verus,
362+ author = {Lattuada, Andrea and Hance, Travis and Bosamiya, Jay and Brun, Matthias and Cho, Chanhee and LeBlanc, Hayley and Srinivasan, Pranav and Achermann, Reto and Chajed, Tej and Hawblitzel, Chris and Howell, Jon and Lorch, Jacob R. and Padon, Oded and Parno, Bryan},
363+ title = {{Verus: A Practical Foundation for Systems Verification}},
364+ year = {2024},
365+ doi = {10.1145/3694715.3695952},
366+ booktitle = {Proceedings of the ACM SIGOPS 30th Symposium on Operating Systems Principles},
367+ pages = {438-454},
368+ publisher = {ACM}
369+}
370+
371+@article{brix2023vnncomp,
372+ author = {Brix, Christopher and Müller, Mark Niklas and Bak, Stanley and Johnson, Taylor T. and Liu, Changliu},
373+ title = {{First three years of the international verification of neural networks competition (VNN-COMP)}},
374+ year = {2023},
375+ doi = {10.1007/s10009-023-00703-4},
376+ journal = {International Journal on Software Tools for Technology Transfer},
377+ volume = {25},
378+ number = {3},
379+ pages = {329-339}
380+}
381+
382+@incollection{katz2017reluplex,
383+ author = {Katz, Guy and Barrett, Clark and Dill, David L. and Julian, Kyle and Kochenderfer, Mykel J.},
384+ title = {{Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks}},
385+ year = {2017},
386+ doi = {10.1007/978-3-319-63387-9\_5},
387+ booktitle = {Lecture Notes in Computer Science},
388+ pages = {97-117},
389+ publisher = {Springer International Publishing}
390+}
391+
392+@book{cygan2015parameterized,
393+ author = {Cygan, Marek and Fomin, Fedor V. and Kowalik, Łukasz and Lokshtanov, Daniel and Marx, Dániel and Pilipczuk, Marcin and Pilipczuk, Michał and Saurabh, Saket},
394+ title = {{Parameterized Algorithms}},
395+ year = {2015},
396+ doi = {10.1007/978-3-319-21275-3},
397+ publisher = {Springer International Publishing}
398+}
399+
400+@inproceedings{vassilevskawilliams2019finegrained,
401+ author = {WILLIAMS, VIRGINIA VASSILEVSKA},
402+ title = {{On some fine-grained questions in algorithms and complexity}},
403+ year = {2019},
404+ doi = {10.1142/9789813272880\_0188},
405+ booktitle = {Proceedings of the International Congress of Mathematicians (ICM 2018)},
406+ pages = {3447-3487},
407+ publisher = {WORLD SCIENTIFIC}
408+}
409+
410+@article{lykouris2021caching,
411+ author = {Lykouris, Thodoris and Vassilvitskii, Sergei},
412+ title = {{Competitive Caching with Machine Learned Advice}},
413+ year = {2021},
414+ doi = {10.1145/3447579},
415+ journal = {Journal of the ACM},
416+ volume = {68},
417+ number = {4},
418+ pages = {1-25}
419+}
420+
421+@article{chen2018metamorphicsurvey,
422+ author = {Chen, Tsong Yueh and Kuo, Fei-Ching and Liu, Huai and Poon, Pak-Lok and Towey, Dave and Tse, T. H. and Zhou, Zhi Quan},
423+ title = {{Metamorphic Testing}},
424+ year = {2018},
425+ doi = {10.1145/3143561},
426+ journal = {ACM Computing Surveys},
427+ volume = {51},
428+ number = {1},
429+ pages = {1-27}
430+}
431+
432+@inproceedings{perry2023insecure,
433+ author = {Perry, Neil and Srivastava, Megha and Kumar, Deepak and Boneh, Dan},
434+ title = {{Do Users Write More Insecure Code with AI Assistants?}},
435+ year = {2023},
436+ doi = {10.1145/3576915.3623157},
437+ booktitle = {Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security},
438+ pages = {2785-2799},
439+ publisher = {ACM}
440+}
441+
442+@inproceedings{ragankelley2013halide,
443+ author = {Ragan-Kelley, Jonathan and Barnes, Connelly and Adams, Andrew and Paris, Sylvain and Durand, Frédo and Amarasinghe, Saman},
444+ title = {{Halide: a language and compiler for optimizing parallelism, locality, and recomputation in image processing pipelines}},
445+ year = {2013},
446+ doi = {10.1145/2491956.2462176},
447+ booktitle = {Proceedings of the 34th ACM SIGPLAN Conference on Programming Language Design and Implementation},
448+ pages = {519-530},
449+ publisher = {ACM}
450+}
451+
452+@article{merrill2023parallelism,
453+ author = {Merrill, William and Sabharwal, Ashish},
454+ title = {{The Parallelism Tradeoff: Limitations of Log-Precision Transformers}},
455+ year = {2023},
456+ doi = {10.1162/tacl\_a\_00562},
457+ journal = {Transactions of the Association for Computational Linguistics},
458+ volume = {11},
459+ pages = {531-545}
460+}
461+
462+@article{strobl2024survey,
463+ author = {Strobl, Lena and Merrill, William and Weiss, Gail and Chiang, David and Angluin, Dana},
464+ title = {{What Formal Languages Can Transformers Express? A Survey}},
465+ year = {2024},
466+ doi = {10.1162/tacl\_a\_00663},
467+ journal = {Transactions of the Association for Computational Linguistics},
468+ volume = {12},
469+ pages = {543-561}
470+}
471+
472+@article{willsey2026eggcacm,
473+ author = {Willsey, Max and Nandi, Chandrakana and Wang, Yisu Remy and Flatt, Oliver and Tatlock, Zachary and Panchekha, Pavel},
474+ title = {{egg: Fast and Extensible Equality Saturation}},
475+ year = {2026},
476+ doi = {10.1145/3815481},
477+ journal = {Communications of the ACM},
478+ volume = {69},
479+ number = {8},
480+ pages = {105-113}
481+}
482+
483+@article{lynch2026ioautomata,
484+ author = {Lynch, Nancy A. and Tuttle, Mark R.},
485+ title = {{An Introduction to Input/Output Automata}},
486+ year = {2026},
487+ doi = {10.1145/3788687},
488+ journal = {Formal Aspects of Computing},
489+ volume = {38},
490+ number = {3},
491+ pages = {1-22}
492+}
493+
494+@inproceedings{bernstein2022negative,
495+ author = {Bernstein, Aaron and Nanongkai, Danupon and Wulff-Nilsen, Christian},
496+ title = {{Negative-Weight Single-Source Shortest Paths in Near-linear Time}},
497+ year = {2022},
498+ doi = {10.1109/focs54457.2022.00063},
499+ booktitle = {2022 IEEE 63rd Annual Symposium on Foundations of Computer Science (FOCS)},
500+ pages = {600-611},
501+ publisher = {IEEE}
502+}
503+
504+@inproceedings{duan2025sorting,
505+ author = {Duan, Ran and Mao, Jiayi and Mao, Xiao and Shu, Xinkai and Yin, Longhui},
506+ title = {{Breaking the Sorting Barrier for Directed Single-Source Shortest Paths}},
507+ year = {2025},
508+ doi = {10.1145/3717823.3718179},
509+ booktitle = {Proceedings of the 57th Annual ACM Symposium on Theory of Computing},
510+ pages = {36-44},
511+ publisher = {ACM}
512+}
513+
514+@inproceedings{williams2025sqrtspace,
515+ author = {Williams, R. Ryan},
516+ title = {{Simulating Time with Square-Root Space}},
517+ year = {2025},
518+ doi = {10.1145/3717823.3718225},
519+ booktitle = {Proceedings of the 57th Annual ACM Symposium on Theory of Computing},
520+ pages = {13-23},
521+ publisher = {ACM}
522+}
523+
524+@inproceedings{farachcolton2024openaddressing,
525+ author = {Farach-Colton, Martín and Krapivin, Andrew and Kuszmaul, William},
526+ title = {{Optimal Bounds for Open Addressing Without Reordering}},
527+ year = {2024},
528+ doi = {10.1109/focs61266.2024.00045},
529+ booktitle = {2024 IEEE 65th Annual Symposium on Foundations of Computer Science (FOCS)},
530+ pages = {594-605},
531+ publisher = {IEEE}
532+}
533+
534+@inproceedings{jakubuv2023mizar,
535+ author = {Jakubův, Jan and Chvalovský, Karel and Goertzel, Zarathustra and Kaliszyk, Cezary and Olšák, Mirek and Piotrowski, Bartosz and Schulz, Stephan and Suda, Martin and Urban, Josef},
536+ title = {{MizAR 60 for Mizar 50}},
537+ booktitle = {LIPIcs, Volume 268, ITP 2023},
538+ year = {2023},
539+ publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
540+ doi = {10.4230/lipics.itp.2023.19}
541+}
542+
543+@inproceedings{chen2025apiss,
544+ author = {Chen, Tianyu and Wang, Zeyu and Li, Lin and Li, Ding and Li, Zongyang and Chang, Xiaoning and Bian, Pan and Liang, Guangtai and Wang, Qianxiang and Xie, Tao},
545+ title = {{Detecting Functionality-Specific Vulnerabilities via Retrieving Individual Functionality-Equivalent APIs in Open-Source Repositories}},
546+ booktitle = {LIPIcs, Volume 333, ECOOP 2025},
547+ year = {2025},
548+ publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
549+ doi = {10.4230/lipics.ecoop.2025.6}
550+}
551+
552+@inproceedings{pham2025kdtree,
553+ author = {Pham, Tuyen and Wagner, Hubert},
554+ title = {{Fast Kd-Trees for the Kullback-Leibler Divergence and Other Decomposable Bregman Divergences}},
555+ booktitle = {LIPIcs, Volume 349, WADS 2025},
556+ year = {2025},
557+ publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
558+ doi = {10.4230/lipics.wads.2025.45}
559+}
560+
561+@inproceedings{langedal2024mis,
562+ author = {Langedal, Kenneth and Hespe, Demian and Sanders, Peter},
563+ title = {{Targeted Branching for the Maximum Independent Set Problem Using Graph Neural Networks}},
564+ booktitle = {LIPIcs, Volume 301, SEA 2024},
565+ year = {2024},
566+ publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
567+ doi = {10.4230/lipics.sea.2024.20}
568+}
569+
570+@inproceedings{naqvi2026bigger,
571+ author = {Naqvi, Syed Fakhar Abbas and Anwar, Hina},
572+ title = {{Bigger Is Not Always Better: Performance and Sustainability Trade-Offs in LLMs for Python Bug-Fixing Tasks}},
573+ booktitle = {LIPIcs, Volume 394, ESEM 2026},
574+ year = {2026},
575+ publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
576+ doi = {10.4230/lipics.esem.2026.34}
577+}
578+
579+@inproceedings{klampfl2024asp,
580+ author = {Klampfl, Lorenz and Wotawa, Franz},
581+ title = {{Leveraging Answer Set Programming for Continuous Monitoring, Fault Detection, and Explanation of Automated and Autonomous Driving Systems}},
582+ booktitle = {OASIcs, Volume 125, DX 2024},
583+ year = {2024},
584+ publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
585+ doi = {10.4230/oasics.dx.2024.10}
586+}
587+
588+@inproceedings{gasparlopes2025vuln,
589+ author = {Gaspar Lopes, Diogo and Espinha Gasiba, Tiago and Amburi, Sathwik and Pinto-Albuquerque, Maria},
590+ title = {{Can Open Large Language Models Catch Vulnerabilities?}},
591+ booktitle = {OASIcs, Volume 133, ICPEC 2025},
592+ year = {2025},
593+ publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
594+ doi = {10.4230/oasics.icpec.2025.4}
595+}
596+
597+@inproceedings{awadid2025robust,
598+ author = {Awadid, Afef and Robert, Boris},
599+ title = {{On Assessing ML Model Robustness: A Methodological Framework (Academic Track)}},
600+ booktitle = {OASIcs, Volume 126, SAIA 2024},
601+ year = {2025},
602+ publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
603+ doi = {10.4230/oasics.saia.2024.1}
604+}
605+
606+@misc{becker2025metr,
607+ author = {Joel Becker and Nate Rush and Elizabeth Barnes and David Rein},
608+ title = {{Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity}},
609+ year = {2025},
610+ eprint = {2507.09089},
611+ archiveprefix = {arXiv},
612+ note = {arXiv preprint arXiv:2507.09089 (not peer-reviewed)},
613+ url = {https://arxiv.org/abs/2507.09089}
614+}
615+
616+@misc{yang2023leandojo,
617+ author = {Kaiyu Yang and Aidan M. Swope and Alex Gu and Rahul Chalamala and Peiyang Song and Shixing Yu and Saad Godil and Ryan Prenger and Anima Anandkumar},
618+ title = {{LeanDojo: Theorem Proving with Retrieval-Augmented Language Models}},
619+ year = {2023},
620+ eprint = {2306.15626},
621+ archiveprefix = {arXiv},
622+ note = {arXiv preprint arXiv:2306.15626 (not peer-reviewed)},
623+ url = {https://arxiv.org/abs/2306.15626}
624+}
625+
626+@misc{xin2024deepseekprover,
627+ author = {Huajian Xin and Daya Guo and Zhihong Shao and Zhizhou Ren and Qihao Zhu and Bo Liu and Chong Ruan and Wenda Li and Xiaodan Liang},
628+ title = {{DeepSeek-Prover: Advancing Theorem Proving in LLMs through Large-Scale Synthetic Data}},
629+ year = {2024},
630+ eprint = {2405.14333},
631+ archiveprefix = {arXiv},
632+ note = {arXiv preprint arXiv:2405.14333 (not peer-reviewed)},
633+ url = {https://arxiv.org/abs/2405.14333}
634+}
635+
636+@misc{yang2024sweagent,
637+ author = {John Yang and Carlos E. Jimenez and Alexander Wettig and Kilian Lieret and Shunyu Yao and Karthik Narasimhan and Ofir Press},
638+ title = {{SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering}},
639+ year = {2024},
640+ eprint = {2405.15793},
641+ archiveprefix = {arXiv},
642+ note = {arXiv preprint arXiv:2405.15793 (not peer-reviewed)},
643+ url = {https://arxiv.org/abs/2405.15793}
644+}
645+
646+@misc{novikov2025alphaevolve,
647+ author = {Alexander Novikov and Ngân Vũ and Marvin Eisenberger and Emilien Dupont and Po-Sen Huang and Adam Zsolt Wagner and Sergey Shirobokov and Borislav Kozlovskii and Francisco J. R. Ruiz and Abbas Mehrabian and M. Pawan Kumar and Abigail See and Swarat Chaudhuri and George Holland and Alex Davies and Sebastian Nowozin and Pushmeet Kohli and Matej Balog},
648+ title = {{AlphaEvolve: A coding agent for scientific and algorithmic discovery}},
649+ year = {2025},
650+ eprint = {2506.13131},
651+ archiveprefix = {arXiv},
652+ note = {arXiv preprint arXiv:2506.13131 (not peer-reviewed)},
653+ url = {https://arxiv.org/abs/2506.13131}
654+}
added manuscript/references_part09.bib +46 −0
@@ -0,0 +1,46 @@
1+% references_part09.bib — Part IX (Empirical Results); every entry verified by a Crossref single lookup on 2026-11-03
2+% (see references_verification_part09.csv). Keys of the main bibliography are not redefined here.
3+
4+@article{benjamini1995fdr,
5+ author = {Benjamini, Yoav and Hochberg, Yosef},
6+ title = {Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing},
7+ journal = {Journal of the Royal Statistical Society: Series B (Methodological)},
8+ year = {1995},
9+ volume = {57},
10+ number = {1},
11+ pages = {289--300},
12+ doi = {10.1111/j.2517-6161.1995.tb02031.x}
13+}
14+
15+@article{efron1979bootstrap,
16+ author = {Efron, Bradley},
17+ title = {Bootstrap Methods: Another Look at the Jackknife},
18+ journal = {The Annals of Statistics},
19+ year = {1979},
20+ volume = {7},
21+ number = {1},
22+ pages = {1--26},
23+ doi = {10.1214/aos/1176344552}
24+}
25+
26+@article{hausman2018rdit,
27+ author = {Hausman, Catherine and Rapson, David S.},
28+ title = {Regression Discontinuity in Time: Considerations for Empirical Applications},
29+ journal = {Annual Review of Resource Economics},
30+ year = {2018},
31+ volume = {10},
32+ number = {1},
33+ pages = {533--552},
34+ doi = {10.1146/annurev-resource-121517-033306}
35+}
36+
37+@article{zhang2011itspower,
38+ author = {Zhang, Fang and Wagner, Anita K. and Ross-Degnan, Dennis},
39+ title = {Simulation-based power calculation for designing interrupted time series analyses of health policy interventions},
40+ journal = {Journal of Clinical Epidemiology},
41+ year = {2011},
42+ volume = {64},
43+ number = {11},
44+ pages = {1252--1261},
45+ doi = {10.1016/j.jclinepi.2011.02.007}
46+}
added manuscript/references_verification_part05.csv +99 −0
@@ -0,0 +1,99 @@
1+key,openalex_id,doi_or_arxiv,verified_via,verified_title,corpus_title,match,entry_type
2+bick2026rapidadoption,7124974472,doi:10.1287/mnsc.2025.02523,crossref,The Rapid Adoption of Generative AI,The Rapid Adoption of Generative AI,yes,journal-article
3+kasneci2023chatgptgood,4323655724,doi:10.1016/j.lindif.2023.102274,crossref,ChatGPT for good? On opportunities and challenges of large language models for education,ChatGPT for good? On opportunities and challenges of large language models for education,yes,journal-article
4+wang2019superglue,2990704537,arxiv:1905.00537,arxiv,SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems,SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems,yes,arxiv
5+han2024peftsurvey,4393138529,arxiv:2403.14608,arxiv,Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey,Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey,yes,arxiv
6+bodnar2025aurora,4410551352,doi:10.1038/s41586-025-09005-y,crossref,A foundation model for the Earth system,A foundation model for the Earth system,yes,journal-article
7+chen2024uni,4392947521,doi:10.1038/s41591-024-02857-3,crossref,Towards a general-purpose foundation model for computational pathology,Towards a general-purpose foundation model for computational pathology,yes,journal-article
8+wang2024chief,4402220065,doi:10.1038/s41586-024-07894-z,crossref,A pathology foundation model for cancer diagnosis and prognosis prediction,A pathology foundation model for cancer diagnosis and prognosis prediction,yes,journal-article
9+zhang2024biomedgpt,4401397240,doi:10.1038/s41591-024-03185-2,crossref,A generalist vision--language foundation model for diverse biomedical tasks,A generalist vision--language foundation model for diverse biomedical tasks,yes,journal-article
10+binz2025centaur,4411932262,doi:10.1038/s41586-025-09215-4,crossref,A foundation model to predict and capture human cognition,A foundation model to predict and capture human cognition,yes,journal-article
11+dai2023instructblip,7133220561,arxiv:2305.06500,arxiv,InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning,InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning,yes,arxiv
12+schuhmann2022laion5b,7133250524,arxiv:2210.08402,arxiv,LAION-5B: An open large-scale dataset for training next generation image-text models,LAION-5B: An Open Large-Scale Dataset for Training Next Generation Image-Text Models,yes,arxiv
13+su2019vlbert,2995460200,arxiv:1908.08530,arxiv,VL-BERT: Pre-training of Generic Visual-Linguistic Representations,VL-BERT: Pre-training of Generic Visual-Linguistic Representations,yes,arxiv
14+mou2024t2iadapter,4393148714,doi:10.1609/aaai.v38i5.28226,crossref,T2I-Adapter: Learning Adapters to Dig Out More Controllable Ability for Text-to-Image Diffusion Models,T2I-Adapter: Learning Adapters to Dig Out More Controllable Ability for Text-to-Image Diffusion Models,yes,journal-article
15+gal2022textualinversion,4289785095,arxiv:2208.01618,arxiv,An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion,An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion,yes,arxiv
16+buckley2026textexploit,7164501397,doi:10.1038/s41467-026-74207-5,crossref,Multimodal foundation models exploit text to make medical image predictions,Multimodal foundation models exploit text to make medical image predictions,yes,journal-article
17+zhang2026lowqualityfusion,4396599905,doi:10.1016/j.inffus.2026.104437,crossref,Multimodal fusion on low-quality data: A comprehensive survey,Multimodal fusion on low-quality data: A comprehensive survey,yes,journal-article
18+ho2022cfg,4288099666,arxiv:2207.12598,arxiv,Classifier-Free Diffusion Guidance,Classifier-Free Diffusion Guidance,yes,arxiv
19+zeni2025mattergen,4406472463,doi:10.1038/s41586-025-08628-5,crossref,A generative model for inorganic materials design,A generative model for inorganic materials design,yes,journal-article
20+bastani2024harmlearning,4400770292,doi:10.2139/ssrn.4895486,crossref,Generative AI Can Harm Learning,Generative AI Can Harm Learning,yes,posted-content
21+acharya2025agenticsurvey,4406728221,doi:10.1109/access.2025.3532853,crossref,Agentic AI: Autonomous Intelligence for Complex Goals---A Comprehensive Survey,Agentic AI: Autonomous Intelligence for Complex Goals---A Comprehensive Survey,yes,journal-article
22+ghareeb2026robin,7161734976,doi:10.1038/s41586-026-10652-y,crossref,A multi-agent system for automating scientific discovery,A multi-agent system for automating scientific discovery,yes,journal-article
23+swanson2025virtuallab,4412709685,doi:10.1038/s41586-025-09442-9,crossref,The Virtual Lab of AI agents designs new SARS-CoV-2 nanobodies,The Virtual Lab of AI agents designs new SARS-CoV-2 nanobodies,yes,journal-article
24+piao2026agentsociety,4407570917,doi:10.26599/if.2026.9710004,crossref,AgentSociety: Large-scale simulation of LLM-driven generative agents advances understanding of human behaviors and society,AgentSociety: Large-scale simulation of LLM-driven generative agents advances understanding of human behaviors and society,yes,journal-article
25+sapkota2026agentsvsagentic,4413427262,doi:10.1016/j.inffus.2025.103599,crossref,"AI Agents vs. Agentic AI: A Conceptual taxonomy, applications and challenges","AI Agents vs. Agentic AI: A Conceptual taxonomy, applications and challenges",yes,journal-article
26+zhang2025agentmemory,4412203333,doi:10.1145/3748302,crossref,A Survey on the Memory Mechanism of Large Language Model-based Agents,A Survey on the Memory Mechanism of Large Language Model-based Agents,yes,journal-article
27+liu2026clinicalagents,7130481397,doi:10.1038/s41746-026-02443-6,crossref,Benchmarking large language model-based agent systems for clinical decision tasks,Benchmarking large language model-based agent systems for clinical decision tasks,yes,journal-article
28+haarnoja2018sac,2781726626,arxiv:1801.01290,arxiv,Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor,Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor,yes,arxiv
29+fujimoto2018td3,2963923407,arxiv:1802.09477,arxiv,Addressing Function Approximation Error in Actor-Critic Methods,Addressing Function Approximation Error in Actor-Critic Methods,yes,arxiv
30+vinyals2019alphastar,2982316857,doi:10.1038/s41586-019-1724-z,crossref,Grandmaster level in StarCraft II using multi-agent reinforcement learning,Grandmaster level in StarCraft II using multi-agent reinforcement learning,yes,journal-article
31+hessel2018rainbow,2761873684,doi:10.1609/aaai.v32i1.11796,crossref,Rainbow: Combining Improvements in Deep Reinforcement Learning,Rainbow: Combining Improvements in Deep Reinforcement Learning,yes,journal-article
32+yu2021mappo,7133226216,arxiv:2103.01955,arxiv,"The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games",The Surprising Effectiveness of PPO in Cooperative Multi-Agent Games,yes,arxiv
33+hafner2025dreamerv3,4409147637,doi:10.1038/s41586-025-08744-2,crossref,Mastering diverse control tasks through world models,Mastering diverse control tasks through world models,yes,journal-article
34+burda2018curiosity,2964083594,arxiv:1808.04355,arxiv,Large-Scale Study of Curiosity-Driven Learning,Large-Scale Study of Curiosity-Driven Learning,yes,arxiv
35+zhang2021marloverview,2991046523,doi:10.1007/978-3-030-60990-0_12,crossref,Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms,Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms,yes,book-chapter
36+rudin2019stopexplaining,2945976633,doi:10.1038/s42256-019-0048-x,crossref,Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead,Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead,yes,journal-article
37+lipton2018mythos,2439568532,doi:10.1145/3233231,crossref,The mythos of model interpretability,The Mythos of Model Interpretability,yes,journal-article
38+lundberg2020treeexplainer,2999615587,doi:10.1038/s42256-019-0138-9,crossref,From local explanations to global understanding with explainable AI for trees,From local explanations to global understanding with explainable AI for trees,yes,journal-article
39+chicco2020mcc,2999309192,doi:10.1186/s12864-019-6413-7,crossref,The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation,The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation,yes,journal-article
40+zhuang2021transfersurvey,3041133507,doi:10.1109/jproc.2020.3004555,crossref,A Comprehensive Survey on Transfer Learning,A Comprehensive Survey on Transfer Learning,yes,journal-article
41+bottou2018optimization,2436219157,doi:10.1137/16m1080173,crossref,Optimization Methods for Large-Scale Machine Learning,Optimization Methods for Large-Scale Machine Learning,yes,journal-article
42+zeng2023transformerstsf,4382203079,doi:10.1609/aaai.v37i9.26317,crossref,Are Transformers Effective for Time Series Forecasting?,Are Transformers Effective for Time Series Forecasting?,yes,journal-article
43+zhou2021informer,3177318507,doi:10.1609/aaai.v35i12.17325,crossref,Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting,Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting,yes,journal-article
44+wen2023tstransformers,4385763767,doi:10.24963/ijcai.2023/759,crossref,Transformers in Time Series: A Survey,Transformers in Time Series: A Survey,yes,proceedings-article
45+hollmann2025tabpfn,4406170795,doi:10.1038/s41586-024-08328-6,crossref,Accurate predictions on small data with a tabular foundation model,Accurate predictions on small data with a tabular foundation model,yes,journal-article
46+liu2024kan,4396821501,arxiv:2404.19756,arxiv,KAN: Kolmogorov-Arnold Networks,KAN: Kolmogorov-Arnold Networks,yes,arxiv
47+wu2021gnnsurvey,2907492528,doi:10.1109/tnnls.2020.2978386,crossref,A Comprehensive Survey on Graph Neural Networks,A Comprehensive Survey on Graph Neural Networks,yes,journal-article
48+zhou2020gnnreview,3152893301,doi:10.1016/j.aiopen.2021.01.001,crossref,Graph neural networks: A review of methods and applications,Graph neural networks: A review of methods and applications,yes,journal-article
49+zhang2017mixup,2963399829,arxiv:1710.09412,arxiv,mixup: Beyond Empirical Risk Minimization,Mixup: Beyond empirical risk minimization,yes,arxiv
50+barredoarrieta2020xai,2981731882,doi:10.1016/j.inffus.2019.12.012,crossref,"Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI","Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI",yes,journal-article
51+mehrabi2021biasfairness,2969896603,doi:10.1145/3457607,crossref,A Survey on Bias and Fairness in Machine Learning,A Survey on Bias and Fairness in Machine Learning,yes,journal-article
52+miller2019explanation,2670253439,doi:10.1016/j.artint.2018.07.007,crossref,Explanation in artificial intelligence: Insights from the social sciences,Explanation in artificial intelligence: Insights from the social sciences,yes,journal-article
53+ali2023xaitrust,4366262984,doi:10.1016/j.inffus.2023.101805,crossref,Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustworthy Artificial Intelligence,Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustworthy Artificial Intelligence,yes,journal-article
54+slattery2026riskrepository,4402698709,doi:10.1016/j.patter.2026.101517,crossref,"The AI risk repository: A meta-review, database, and taxonomy of risks from artificial intelligence","The AI risk repository: A meta-review, database, and taxonomy of risks from artificial intelligence",yes,journal-article
55+chao2025pair,4410609100,doi:10.1109/satml64287.2025.00010,crossref,Jailbreaking Black Box Large Language Models in Twenty Queries,Jailbreaking Black Box Large Language Models in Twenty Queries,yes,proceedings-article
56+alber2025poisoning,4406152291,doi:10.1038/s41591-024-03445-1,crossref,Medical large language models are vulnerable to data-poisoning attacks,Medical large language models are vulnerable to data-poisoning attacks,yes,journal-article
57+draelos2026unsafeanswers,7128782475,doi:10.1038/s41746-026-02428-5,crossref,Large language models provide unsafe answers to patient-posed medical questions,Large language models provide unsafe answers to patient-posed medical questions,yes,journal-article
58+moore2025stigma,4411542009,doi:10.1145/3715275.3732039,crossref,Expressing stigma and inappropriate responses prevents LLMs from safely replacing mental health providers.,Expressing stigma and inappropriate responses prevents LLMs from safely replacing mental health providers.,yes,proceedings-article
59+papagiannidis2025raigovernance,4406063062,doi:10.1016/j.jsis.2024.101885,crossref,Responsible artificial intelligence governance: A review and research framework,Responsible artificial intelligence governance: A review and research framework,yes,journal-article
60+mcdonald2025hepolicies,4406571986,doi:10.1016/j.chbah.2025.100121,crossref,Generative artificial intelligence in higher education: Evidence from an analysis of institutional policies and guidelines,Generative artificial intelligence in higher education: Evidence from an analysis of institutional policies and guidelines,yes,journal-article
61+lo2023rapidreview,4366420437,doi:10.3390/educsci13040410,crossref,What Is the Impact of ChatGPT on Education? A Rapid Review of the Literature,What Is the Impact of ChatGPT on Education? A Rapid Review of the Literature,yes,journal-article
62+chan2023students,4384464487,doi:10.1186/s41239-023-00411-8,crossref,"Students' voices on generative AI: perceptions, benefits, and challenges in higher education","Students' voices on generative AI: perceptions, benefits, and challenges in higher education",yes,journal-article
63+chan2023policyframework,4383312437,doi:10.1186/s41239-023-00408-3,crossref,A comprehensive AI policy education framework for university teaching and learning,A comprehensive AI policy education framework for university teaching and learning,yes,journal-article
64+baidooanu2023chatgptedu,4389437528,doi:10.61969/jai.1337500,crossref,Education in the Era of Generative Artificial Intelligence (AI): Understanding the Potential Benefits of ChatGPT in Promoting Teaching and Learning,Education in the Era of Generative Artificial Intelligence (AI): Understanding the Potential Benefits of ChatGPT in Promoting Teaching and Learning,yes,journal-article
65+rudolph2023assessment,4317910584,doi:10.37074/jalt.2023.6.1.9,crossref+openalex,ChatGPT: Bullshit spewer or the end of traditional assessments in higher education?,ChatGPT: Bullshit spewer or the end of traditional assessments in higher education?,yes,journal-article
66+lim2023ragnarok,4322617812,doi:10.1016/j.ijme.2023.100790,crossref,Generative AI and the future of education: Ragnarök or reformation? A paradoxical perspective from management educators,Generative AI and the future of education: Ragnarök or reformation? A paradoxical perspective from management educators,yes,journal-article
67+wu2025radfm,4413447271,doi:10.1038/s41467-025-62385-7,crossref,Towards generalist foundation model for radiology by leveraging web-scale 2D\&amp;3D medical data,Towards generalist foundation model for radiology by leveraging web-scale 2D\&3D medical data,yes,journal-article
68+ding2025titan,4415913530,doi:10.1038/s41591-025-03982-3,crossref,A multimodal whole-slide foundation model for pathology,A multimodal whole-slide foundation model for pathology,yes,journal-article
69+lu2024conch,4392947532,doi:10.1038/s41591-024-02856-4,crossref,A visual-language foundation model for computational pathology,A visual-language foundation model for computational pathology,yes,journal-article
70+huang2023plip,4385948838,doi:10.1038/s41591-023-02504-3,crossref,A visual--language foundation model for pathology image analysis using medical Twitter,A visual--language foundation model for pathology image analysis using medical Twitter,yes,journal-article
71+yan2025panderm,4411099958,doi:10.1038/s41591-025-03747-y,crossref,A multimodal vision foundation model for clinical dermatology,A multimodal vision foundation model for clinical dermatology,yes,journal-article
72+tak2026brainiac,7128029160,doi:10.1038/s41593-026-02202-6,crossref,A generalizable foundation model for analysis of human brain MRI,A generalizable foundation model for analysis of human brain MRI,yes,journal-article
73+zhu2026earthfm,7119541039,doi:10.1038/s43247-025-03127-x,crossref,On the foundations of Earth foundation models,On the foundations of Earth foundation models,yes,journal-article
74+singhal2023clinical,4384071683,doi:10.1038/s41586-023-06291-2,crossref,Large language models encode clinical knowledge,Large language models encode clinical knowledge,yes,journal-article
75+liu2023prompting,3185341429,doi:10.1145/3560815,crossref,"Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing","Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing",yes,journal-article
76+vandis2023priorities,4319083882,doi:10.1038/d41586-023-00288-7,crossref,ChatGPT: five priorities for research,ChatGPT: five priorities for research,yes,journal-article
77+cheng2026agentsdefinitions,4390730779,doi:10.1002/widm.70111,crossref,"Exploring Large Language Model-Based Intelligent Agents: Definitions, Methods, and Prospects","Exploring Large Language Model-Based Intelligent Agents: Definitions, Methods, and Prospects",yes,journal-article
78+raza2026trism,7133236347,doi:10.1016/j.aiopen.2026.02.006,crossref,"TRiSM for Agentic AI: A review of Trust, Risk, and Security Management in LLM-based Agentic Multi-Agent Systems","TRiSM for Agentic AI: A review of Trust, Risk, and Security Management in LLM-based Agentic Multi-Agent Systems",yes,journal-article
79+paschali2025fmradiology,4407135816,doi:10.1148/radiol.240597,crossref,"Foundation Models in Radiology: What, How, Why, and Why Not","Foundation Models in Radiology: What, How, Why, and Why Not",yes,journal-article
80+zhang2026datacentric,4390690043,doi:10.1145/3800677,crossref,Data-Centric Foundation Models in Computational Healthcare: A Survey,Data-Centric Foundation Models in Computational Healthcare: A Survey,yes,journal-article
81+zheng2025learningfrommodels,4406472599,doi:10.1038/s42256-024-00961-0,crossref,Learning from models beyond fine-tuning,Learning from models beyond fine-tuning,yes,journal-article
82+schouten2025multimodalmedicine,4410882873,doi:10.1016/j.media.2025.103621,crossref,Navigating the landscape of multimodal AI in medicine: A scoping review on technical challenges and clinical applications,Navigating the landscape of multimodal AI in medicine: A scoping review on technical challenges and clinical applications,yes,journal-article
83+zhai2024overreliance,4399790131,doi:10.1186/s40561-024-00316-7,crossref,The effects of over-reliance on AI dialogue systems on students' cognitive abilities: a systematic review,The effects of over-reliance on AI dialogue systems on students' cognitive abilities: a systematic review,yes,journal-article
84+xia2024assessmentreview,4398243313,doi:10.1186/s41239-024-00468-z,crossref,A scoping review on how generative artificial intelligence transforms assessment in higher education,A scoping review on how generative artificial intelligence transforms assessment in higher education,yes,journal-article
85+bittle2025integrity,4409269614,doi:10.3390/info16040296,crossref,Generative AI and Academic Integrity in Higher Education: A Systematic Review and Research Agenda,Generative AI and Academic Integrity in Higher Education: A Systematic Review and Research Agenda,yes,journal-article
86+jin2023timellm,4387355843,arxiv:2310.01728,arxiv,Time-LLM: Time Series Forecasting by Reprogramming Large Language Models,TimeLLM: Time Series Forecasting by Reprogramming Large Language Models,yes,arxiv
87+liu2024timesurl,4393153153,doi:10.1609/aaai.v38i12.29299,crossref,TimesURL: Self-Supervised Contrastive Learning for Universal Time Series Representation Learning,TimesURL: Self-Supervised Contrastive Learning for Universal Time Series Representation Learning,yes,journal-article
88+ye2024mplugowl2,4402727669,doi:10.1109/cvpr52733.2024.01239,crossref,mPLUG-OwI2: Revolutionizing Multi-modal Large Language Model with Modality Collaboration,mPLUG-OwI2: Revolutionizing Multi-modal Large Language Model with Modality Collaboration,yes,proceedings-article
89+wang2025gpt5medical,7148475777,arxiv:2508.08224,arxiv,Capabilities of GPT-5 on Multimodal Medical Reasoning,Capabilities of GPT-5 on multimodal medical reasoning,yes,arxiv
90+lai2026medr1,7127319731,doi:10.1109/tmi.2026.3661001,crossref,Med-R1: Reinforcement Learning for Generalizable Medical Reasoning in Vision-Language Models,Med-R1: Reinforcement Learning for Generalizable Medical Reasoning in Vision-Language Models,yes,journal-article
91+zhang2026dive,4414991289,doi:10.1039/d5sc09921h,crossref,``DIVE'' into hydrogen storage materials discovery with AI agents,``DIVE'' into hydrogen storage materials discovery with AI agents,yes,journal-article
92+bandi2025agenticreview,4414023998,doi:10.3390/fi17090404,crossref,"The Rise of Agentic AI: A Review of Definitions, Frameworks, Architectures, Applications, Evaluation Metrics, and Challenges","The Rise of Agentic AI: A Review of Definitions, Frameworks, Architectures, Applications, Evaluation Metrics, and Challenges",yes,journal-article
93+abouali2025agenticparadigms,4416209823,doi:10.1007/s10462-025-11422-4,crossref,"Agentic AI: a comprehensive survey of architectures, applications, and future directions","Agentic AI: a comprehensive survey of architectures, applications, and future directions",yes,journal-article
94+allmendinger2026multiagentai,7128165235,doi:10.1007/s12525-025-00862-z,crossref,Multi-agent AI,Multi-agent AI,yes,journal-article
95+hasanzadeh2025biashealthcare,4408314709,doi:10.1038/s41746-025-01503-7,crossref,Bias recognition and mitigation strategies in artificial intelligence healthcare applications,Bias recognition and mitigation strategies in artificial intelligence healthcare applications,yes,journal-article
96+wang2026pedagogical,7140170686,doi:10.1186/s41239-026-00585-x,crossref,Pedagogical partnerships with generative AI in higher education: how dual cognitive pathways paradoxically enable transformative learning,Pedagogical partnerships with generative AI in higher education: how dual cognitive pathways paradoxically enable transformative learning,yes,journal-article
97+collaco2026agentichealthcare,7135409568,doi:10.1038/s41746-026-02517-5,crossref,The role of agentic artificial intelligence in healthcare: a scoping review,The role of agentic artificial intelligence in healthcare: a scoping review,yes,journal-article
98+gholami2022quantization,3137147200,doi:10.1201/9781003162810-13,crossref,A Survey of Quantization Methods for Efficient Neural Network Inference,A Survey of Quantization Methods for Efficient Neural Network Inference,yes,book-chapter
99+zhang2023llamaadapter,4361229539,arxiv:2303.16199,arxiv,LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention,LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention,yes,arxiv
added manuscript/references_verification_part07a.csv +66 −0
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1+key,doi_or_arxiv,verified_via,verified_title,match
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3+czerwinski2021ackermann,10.1109/FOCS52979.2021.00120,crossref,Reachability in Vector Addition Systems is Ackermann-complete,yes
4+leroux2021notprimitive,10.1109/FOCS52979.2021.00121,crossref,The Reachability Problem for Petri Nets is Not Primitive Recursive,yes
5+wang2024llmtesting,10.1109/tse.2024.3368208,crossref,"Software Testing With Large Language Models: Survey, Landscape, and Vision",yes
6+zhang2022mltesting,10.1109/tse.2019.2962027,crossref,"Machine Learning Testing: Survey, Landscapes and Horizons",yes
7+cui2026genaiwork,10.1287/mnsc.2025.00535,crossref,The Effects of Generative AI on High-Skilled Work: Evidence from Three Field Experiments with Software Developers,yes
8+li2024llift,10.1145/3649828,crossref,Enhancing Static Analysis for Practical Bug Detection: An LLM-Integrated Approach,yes
9+yang2024whitefox,10.1145/3689736,crossref,WhiteFox: White-Box Compiler Fuzzing Empowered by Large Language Models,yes
10+baghdadi2019tiramisu,10.1109/cgo.2019.8661197,crossref,Tiramisu: A Polyhedral Compiler for Expressing Fast and Portable Code,yes
11+wang2018mlcompiler,10.1109/jproc.2018.2817118,crossref,Machine Learning in Compiler Optimization,yes
12+ashouri2018autotuning,10.1145/3197978,crossref,A Survey on Compiler Autotuning using Machine Learning,yes
13+cummins2025llmcompiler,10.1145/3708493.3712691,crossref,LLM Compiler: Foundation Language Models for Compiler Optimization,yes
14+munley2024llm4vv,10.1016/j.future.2024.05.034,crossref,LLM4VV: Developing LLM-driven testsuite for compiler validation,yes
15+barbosa2022cvc5,10.1007/978-3-030-99524-9_24,crossref,cvc5: A Versatile and Industrial-Strength SMT Solver,yes
16+beyer2024svcomp,10.1007/978-3-031-57256-2_15,crossref,State of the Art in Software Verification and Witness Validation: SV-COMP 2024,yes
17+katz2019marabou,10.1007/978-3-030-25540-4_26,crossref,The Marabou Framework for Verification and Analysis of Deep Neural Networks,yes
18+singh2019deeppoly,10.1145/3290354,crossref,An abstract domain for certifying neural networks,yes
19+permenev2020verx,10.1109/sp40000.2020.00024,crossref,VerX: Safety Verification of Smart Contracts,yes
20+willsey2021egg,10.1145/3434304,crossref,egg: Fast and extensible equality saturation,yes
21+kalra2018zeus,10.14722/ndss.2018.23082,crossref,ZEUS: Analyzing Safety of Smart Contracts,yes
22+papadakis2019mutation,10.1016/bs.adcom.2018.03.015,crossref,Mutation Testing Advances: An Analysis and Survey,yes
23+abualigah2021aoa,10.1016/j.cma.2020.113609,crossref,The Arithmetic Optimization Algorithm,yes
24+abualigah2021aquila,10.1016/j.cie.2021.107250,crossref,Aquila Optimizer: A novel meta-heuristic optimization algorithm,yes
25+biere2021handbook,10.3233/faia336,crossref,Handbook of Satisfiability,yes
26+li2019kolmogorov,10.1007/978-3-030-11298-1,crossref,An Introduction to Kolmogorov Complexity and Its Applications,yes
27+sorensen2013metaphor,10.1111/itor.12001,crossref,Metaheuristics—the metaphor exposed,yes
28+aranha2021metaphor,10.1007/s11721-021-00202-9,crossref,"Metaphor-based metaheuristics, a call for action: the elephant in the room",yes
29+camachovillalon2022exposing,10.1111/itor.13176,crossref,"Exposing the grey wolf, moth‐flame, whale, firefly, bat, and antlion algorithms: six misleading optimization techniques inspired by<i>bestial</i>metaphors",yes
30+mitzenmacher2022predictions,10.1145/3528087,crossref,Algorithms with predictions,yes
31+mankowitz2023alphadev,10.1038/s41586-023-06004-9,crossref,Faster sorting algorithms discovered using deep reinforcement learning,yes
32+lopes2021alive2,10.1145/3453483.3454030,crossref,Alive2: bounded translation validation for LLVM,yes
33+yang2011csmith,10.1145/1993498.1993532,crossref,Finding and understanding bugs in C compilers,yes
34+xia2024fuzz4all,10.1145/3597503.3639121,crossref,Fuzz4All: Universal Fuzzing with Large Language Models,yes
35+xia2023apr,10.1109/ICSE48619.2023.00129,crossref,Automated Program Repair in the Era of Large Pre-trained Language Models,yes
36+kang2023fewshottesters,10.1109/icse48619.2023.00194,crossref,Large Language Models are Few-shot Testers: Exploring LLM-based General Bug Reproduction,yes
37+lattuada2024verus,10.1145/3694715.3695952,crossref,Verus: A Practical Foundation for Systems Verification,yes
38+brix2023vnncomp,10.1007/s10009-023-00703-4,crossref,First three years of the international verification of neural networks competition (VNN-COMP),yes
39+katz2017reluplex,10.1007/978-3-319-63387-9_5,crossref,Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks,yes
40+cygan2015parameterized,10.1007/978-3-319-21275-3,crossref,Parameterized Algorithms,yes
41+vassilevskawilliams2019finegrained,10.1142/9789813272880_0188,crossref,On some fine-grained questions in algorithms and complexity,yes
42+lykouris2021caching,10.1145/3447579,crossref,Competitive Caching with Machine Learned Advice,yes
43+chen2018metamorphicsurvey,10.1145/3143561,crossref,Metamorphic Testing,yes
44+perry2023insecure,10.1145/3576915.3623157,crossref,Do Users Write More Insecure Code with AI Assistants?,yes
45+ragankelley2013halide,10.1145/2491956.2462176,crossref,"Halide: a language and compiler for optimizing parallelism, locality, and recomputation in image processing pipelines",yes
46+merrill2023parallelism,10.1162/tacl_a_00562,crossref,The Parallelism Tradeoff: Limitations of Log-Precision Transformers,yes
47+strobl2024survey,10.1162/tacl_a_00663,crossref,What Formal Languages Can Transformers Express? A Survey,yes
48+willsey2026eggcacm,10.1145/3815481,crossref,egg: Fast and Extensible Equality Saturation,yes
49+lynch2026ioautomata,10.1145/3788687,crossref,An Introduction to Input/Output Automata,yes
50+bernstein2022negative,10.1109/focs54457.2022.00063,crossref,Negative-Weight Single-Source Shortest Paths in Near-linear Time,yes
51+duan2025sorting,10.1145/3717823.3718179,crossref,Breaking the Sorting Barrier for Directed Single-Source Shortest Paths,yes
52+williams2025sqrtspace,10.1145/3717823.3718225,crossref,Simulating Time with Square-Root Space,yes
53+farachcolton2024openaddressing,10.1109/focs61266.2024.00045,crossref,Optimal Bounds for Open Addressing Without Reordering,yes
54+jakubuv2023mizar,10.4230/lipics.itp.2023.19,datacite,MizAR 60 for Mizar 50,yes
55+chen2025apiss,10.4230/lipics.ecoop.2025.6,datacite,Detecting Functionality-Specific Vulnerabilities via Retrieving Individual Functionality-Equivalent APIs in Open-Source Repositories,yes
56+pham2025kdtree,10.4230/lipics.wads.2025.45,datacite,Fast Kd-Trees for the Kullback-Leibler Divergence and Other Decomposable Bregman Divergences,yes
57+langedal2024mis,10.4230/lipics.sea.2024.20,datacite,Targeted Branching for the Maximum Independent Set Problem Using Graph Neural Networks,yes
58+naqvi2026bigger,10.4230/lipics.esem.2026.34,datacite,Bigger Is Not Always Better: Performance and Sustainability Trade-Offs in LLMs for Python Bug-Fixing Tasks,yes
59+klampfl2024asp,10.4230/oasics.dx.2024.10,datacite,"Leveraging Answer Set Programming for Continuous Monitoring, Fault Detection, and Explanation of Automated and Autonomous Driving Systems",yes
60+gasparlopes2025vuln,10.4230/oasics.icpec.2025.4,datacite,Can Open Large Language Models Catch Vulnerabilities?,yes
61+awadid2025robust,10.4230/oasics.saia.2024.1,datacite,On Assessing ML Model Robustness: A Methodological Framework (Academic Track),yes
62+becker2025metr,arXiv:2507.09089,arxiv,Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity,yes
63+yang2023leandojo,arXiv:2306.15626,arxiv,LeanDojo: Theorem Proving with Retrieval-Augmented Language Models,yes
64+xin2024deepseekprover,arXiv:2405.14333,arxiv,DeepSeek-Prover: Advancing Theorem Proving in LLMs through Large-Scale Synthetic Data,yes
65+yang2024sweagent,arXiv:2405.15793,arxiv,SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering,yes
66+novikov2025alphaevolve,arXiv:2506.13131,arxiv,AlphaEvolve: A coding agent for scientific and algorithmic discovery,yes
added manuscript/references_verification_part09.csv +5 −0
@@ -0,0 +1,5 @@
1+key,doi_or_arxiv,verified_via,verified_title,match
2+benjamini1995fdr,10.1111/j.2517-6161.1995.tb02031.x,crossref,Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing,yes
3+efron1979bootstrap,10.1214/aos/1176344552,crossref,Bootstrap Methods: Another Look at the Jackknife,yes
4+hausman2018rdit,10.1146/annurev-resource-121517-033306,crossref,Regression Discontinuity in Time: Considerations for Empirical Applications,yes
5+zhang2011itspower,10.1016/j.jclinepi.2011.02.007,crossref,Simulation-based power calculation for designing interrupted time series analyses of health policy interventions,yes
added manuscript/sections/part07b_security.tex +555 −0
@@ -0,0 +1,555 @@
1+% ============================================================================
2+% part07b_security.tex — Part VII, security half (cybersecurity, applied cryptography incl. post-quantum
3+% standardisation, blockchain systems, privacy-preserving computation, federated learning, security of LLM systems).
4+% Written by Agent H (Security Specialist), WP44, 2026-11-03, from literature_synthesis/agent_H/review.md.
5+% Bibliography: references_part07b.bib (every key verified; references_verification_part07b.csv) plus keys of references.bib.
6+% Corpus quantities only through the \Sec... and \ItsSeriesCat... macros of tables/numbers.tex and the generated tables
7+% tab_security_counts, tab_security_summary, tab_security_llm_cooccurrence, tab_security_landmarks (tables/make_tables.py).
8+% The director merges this file with part07a_theory.tex into part07_theory_security.tex.
9+% ============================================================================
10+
11+\section{Security, privacy and cryptography: scope and reading rules}
12+\label{sec:sec-scope}
13+
14+The security half of this part reviews five categories of the ontology (Part~IV, Section~\ref{sec:m-classification}):
15+cybersecurity, applied cryptography, blockchain systems, privacy-preserving computation and its child category federated
16+learning. Together they form the \emph{security and privacy} area of the taxonomy. The five are related by subject matter
17+but not by size, growth or citation structure, and one purpose of this review is to show that the area is heterogeneous
18+enough that no single statement about ``security research after 2022'' is defensible. A second purpose is methodological:
19+the area's automatically selected landmark set is the one in which the limits of citation-based selection are most visible,
20+and the review documents those limits before it uses the set.
21+
22+Three reading rules govern what follows. First, every quantitative statement comes from a result file of the corpus
23+through a generated macro or table; the macros of this part are listed in \texttt{tables/make\_numbers.py} under the Part~VII-b
24+block and the four tables under \texttt{tables\_part07b} of \texttt{tables/make\_tables.py}. Second, the works discussed are known
25+through their bibliographic metadata and, for \SecLandWithAbstractPct\,\% of the landmark set, through their abstracts; no full
26+text was read for this review, and wording such as ``the abstract reports'' marks the boundary. Third, works that are
27+preprints are called preprints: several of the most important papers on the security of language-model systems have no
28+peer-reviewed version in the corpus, and the review treats them as the field does, as influential but unreviewed.
29+
30+\subsection{Volume}
31+\label{sec:sec-volume}
32+
33+Table~\ref{tab:security_counts} gives, for each category and publication year, the number of eligible works that carry the
34+category (multi-label) and the number for which it is the primary category. Cybersecurity is the largest category of the
35+area by a wide margin: \SecCountCybersecurityTwoZeroTwoTwoMultilabel{} works carried the label in 2022 and
36+\SecCountCybersecurityTwoZeroTwoFiveMultilabel{} in 2025, \SecCountCybersecurityAllMultilabel{} over 2018--2026, and its share
37+of primary classifications in the corpus moved from \SecSharePrimaryCybersecurityTwoZeroOneEightPct\,\% in 2018 to
38+\SecSharePrimaryCybersecurityTwoZeroTwoFivePct\,\% in 2025. Applied cryptography is the second category; blockchain and
39+privacy-preserving computation are of similar size in 2018 and diverge afterwards; federated learning, which barely existed
40+as a labelled literature in 2018 (\SecCountFederatedLearningTwoZeroOneEightMultilabel{} works), reached
41+\SecCountFederatedLearningTwoZeroTwoFiveMultilabel{} works in 2025.
42+
43+The growth profiles are the first evidence of heterogeneity (Table~\ref{tab:security_summary}, which also restates the
44+category rows of Table~\ref{tab:breaks}). Over the baseline years 2018--2022 the two security categories were nearly flat:
45+compound annual growth of the multi-label count was \SecCagrMultilabelCybersecurityBaselinePct\,\% for cybersecurity and
46+\SecCagrMultilabelAppliedCryptographyBaselinePct\,\% for applied cryptography. Over 2023--2025 both grew faster
47+(\SecCagrMultilabelCybersecurityPostPct\,\% and \SecCagrMultilabelAppliedCryptographyPostPct\,\%). Privacy-preserving
48+computation grew throughout (\SecCagrMultilabelPrivacyPreservingComputationBaselinePct\,\% then
49+\SecCagrMultilabelPrivacyPreservingComputationPostPct\,\%). Federated learning had the steepest baseline growth of the area
50+(\SecCagrMultilabelFederatedLearningBaselinePct\,\%) and remained the fastest-growing category after 2023
51+(\SecCagrMultilabelFederatedLearningPostPct\,\%), but its growth rate fell. Blockchain is the exception to the post-2023
52+acceleration: its growth was front-loaded (\SecCagrMultilabelBlockchainBaselinePct\,\% then
53+\SecCagrMultilabelBlockchainPostPct\,\%), and its share of primary classifications peaked around 2020
54+(\SecSharePrimaryBlockchainTwoZeroTwoZeroPct\,\%) before settling at \SecSharePrimaryBlockchainTwoZeroTwoFivePct\,\% in 2025 on a
55+larger corpus. The 2026 row of every table is incomplete, for the indexing reasons documented in Part~III
56+(Section~\ref{sec:data-dates}), and no growth rate in this part uses it.
57+
58+Primary-category counts are systematically lower than multi-label counts, by a factor that itself carries information: for
59+cybersecurity the primary count is less than half the multi-label count, because the category co-occurs with computer
60+networks, computer vision (detection models) and applied cryptography; for federated learning the ratio is lower still,
61+because the category is a child of privacy-preserving computation and most of its works also carry machine-learning
62+labels. Growth rates computed on primary counts (the convention of Table~\ref{tab:cagr}) and on multi-label counts
63+therefore differ: on primary counts cybersecurity grew at \SecCagrPrimaryCybersecurityBaselinePct\,\% over 2018--2022 and
64+\SecCagrPrimaryCybersecurityPostPct\,\% over 2023--2025. The qualitative pattern is the same under both conventions, and the
65+multi-label series is used below because the structural-change estimates of Part~IV are computed on it.
66+
67+\subsection{Structural change}
68+\label{sec:sec-breaks}
69+
70+The interrupted-time-series estimates at the 2022-12 reference month (Part~IV, Section~\ref{sec:m-its}) are reported in
71+Table~\ref{tab:security_summary}. Writing the log monthly count as $y_t = \alpha + \beta t + \gamma D_t + \delta (t - t_0) D_t +
72+\varepsilon_t$ with $D_t$ the post-event indicator, the level coefficient $\gamma$ is not distinguishable from zero for
73+cybersecurity (\ItsSeriesCatCybersecurityItsChatgptLevelChange, $p = \ItsSeriesCatCybersecurityItsChatgptLevelP$), applied
74+cryptography (\ItsSeriesCatAppliedCryptographyItsChatgptLevelChange, $p = \ItsSeriesCatAppliedCryptographyItsChatgptLevelP$),
75+blockchain (\ItsSeriesCatBlockchainItsChatgptLevelChange, $p = \ItsSeriesCatBlockchainItsChatgptLevelP$) or privacy-preserving
76+computation (\ItsSeriesCatPrivacyPreservingComputationItsChatgptLevelChange,
77+$p = \ItsSeriesCatPrivacyPreservingComputationItsChatgptLevelP$). Federated learning is the exception, with a negative level
78+coefficient (\ItsSeriesCatFederatedLearningItsChatgptLevelChange, $p = \ItsSeriesCatFederatedLearningItsChatgptLevelP$) and a
79+negative slope change (\ItsSeriesCatFederatedLearningItsChatgptSlopeChange, $p = \ItsSeriesCatFederatedLearningItsChatgptSlopeP$)
80+against a steep pre-period slope of \ItsSeriesCatFederatedLearningItsChatgptPreSlope{} per month: the category decelerated. The
81+slope coefficient $\delta$ is positive and precisely estimated for cybersecurity
82+(\ItsSeriesCatCybersecurityItsChatgptSlopeChange{} per month, against a pre-period slope of
83+\ItsSeriesCatCybersecurityItsChatgptPreSlope), applied cryptography (\ItsSeriesCatAppliedCryptographyItsChatgptSlopeChange) and
84+privacy-preserving computation (\ItsSeriesCatPrivacyPreservingComputationItsChatgptSlopeChange), and null for blockchain
85+(\ItsSeriesCatBlockchainItsChatgptSlopeChange, $p = \ItsSeriesCatBlockchainItsChatgptSlopeP$). A slope change $\delta$ on the log
86+series compounds to a factor $e^{12\delta}$ per year relative to the pre-period trend, so these are not small effects; what
87+they are not is evidence that the change occurred in December 2022.
88+
89+The placebo distribution over \ItsSeriesCatCybersecurityPlaceboChatgptNPlacebo{} alternative event months
90+(Section~\ref{sec:m-placebo}) is the first qualification. The slope $t$-statistic at the reference month ranks
91+\ItsSeriesCatAppliedCryptographyPlaceboChatgptSlopeTRank{}th of the placebo set for applied cryptography (placebo
92+$p = \ItsSeriesCatAppliedCryptographyPlaceboChatgptSlopeTPlaceboP$), \ItsSeriesCatCybersecurityPlaceboChatgptSlopeTRank{}th for
93+cybersecurity ($p = \ItsSeriesCatCybersecurityPlaceboChatgptSlopeTPlaceboP$) and
94+\ItsSeriesCatPrivacyPreservingComputationPlaceboChatgptSlopeTRank{}th for privacy-preserving computation
95+($p = \ItsSeriesCatPrivacyPreservingComputationPlaceboChatgptSlopeTPlaceboP$); for blockchain and federated learning the
96+reference month is unremarkable (slope placebo $p = \ItsSeriesCatBlockchainPlaceboChatgptSlopeTPlaceboP$ and
97+\ItsSeriesCatFederatedLearningPlaceboChatgptSlopeTPlaceboP), and the federated-learning level drop is likewise unremarkable
98+(level placebo $p = \ItsSeriesCatFederatedLearningPlaceboChatgptLevelTPlaceboP$). The break detectors are the second
99+qualification. Bai--Perron with BIC selection (Section~\ref{sec:m-bp}) dates a single break at
100+\SecBreakCybersecurityBpBreaksBic{} for cybersecurity, at \SecBreakAppliedCryptographyBpBreaksBic{} for applied cryptography and
101+at \SecBreakPrivacyPreservingComputationBpBreaksBic{} for privacy-preserving computation---five months before the reference
102+event---and at \SecBreakBlockchainBpBreaksBic{} and \SecBreakFederatedLearningBpBreaksBic{} for blockchain and federated
103+learning, which are breaks of the pandemic period, not of the window. PELT places change points at
104+\SecBreakCybersecurityPelt{} for cybersecurity and \SecBreakAppliedCryptographyPelt{} for applied cryptography, at
105+\SecBreakPrivacyPreservingComputationPelt{} for privacy-preserving computation and at \SecBreakFederatedLearningPelt{} for
106+federated learning. The donut specification, which drops three months on either side of the reference date, turns the level
107+coefficients of cybersecurity (\ItsSeriesCatCybersecurityItsChatgptDonutLevelChange,
108+$p = \ItsSeriesCatCybersecurityItsChatgptDonutLevelP$) and applied cryptography
109+(\ItsSeriesCatAppliedCryptographyItsChatgptDonutLevelChange, $p = \ItsSeriesCatAppliedCryptographyItsChatgptDonutLevelP$)
110+positive while leaving the slopes unchanged, which says that the months around the reference date are themselves unusual.
111+Among the alternative event dates (Section~\ref{sec:m-altdates}) the one that carries a large positive level coefficient
112+for all three accelerating categories is the latest, 2024-10 (cybersecurity \SecBreakCybersecurityOOneLevel,
113+$p = \SecBreakCybersecurityOOneLevelP$; applied cryptography \SecBreakAppliedCryptographyOOneLevel; privacy-preserving computation
114+\SecBreakPrivacyPreservingComputationOOneLevel), consistent with the late-2024 change points of PELT.
115+
116+The reading these estimates support is deliberately modest. The two security categories and privacy-preserving computation
117+grew faster after 2023 than before; the acceleration is dated by the detectors to mid-2022 and to late 2024 rather than to
118+a single month; and part of it is compositional, in the sense developed in Section~\ref{sec:sec-cyber-llm}, where the share
119+of these categories' works co-classified with the LLM category is shown to have risen several-fold. Blockchain and federated
120+learning are stories of the baseline period: both broke upward in early 2020 and neither accelerated after 2022. None of
121+this is causal evidence about any product release; the reference month is a convention of the design (Part~IV), and the
122+present part uses it only as a date.
123+
124+\subsection{The landmark sample and its limits}
125+\label{sec:sec-landmarks}
126+
127+The landmark set of the area (Table~\ref{tab:security_landmarks}) contains \SecLandN{} works, twenty-five per category,
128+selected automatically as the works with the highest field-normalised 36-month citation score within each primary category
129+\citep{waltman2019fieldnormalization,wang2013citationwindow}, with 2025--2026 works ranked by 12-month citations relative to
130+their cohort (Part~IV, Section~\ref{sec:m-citations}). Three properties of this selection shape the review. The metric is
131+blind to record quality: a duplicated, misdated or misclassified record ranks as high as a genuine landmark when the
132+database attaches citations to it, and the area contains conspicuous cases---the Bitcoin whitepaper
133+\citep{nakamoto2008bitcoin} appears as three distinct SSRN records dated 2019, 2022 and 2024, TLS~1.3 appears twice, a 2010
134+election case study and a 2010 number-theory library appear as 2026 repository records, a venue (``IEEE Symposium on Security
135+and Privacy'') appears as a 2023 work, and a time-series forecasting architecture is filed under cybersecurity. The metric
136+favours surveys and application papers in categories whose citing community is broad (blockchain, federated learning) and
137+favours standards and reports where the influential documents are not articles (applied cryptography):
138+\SecLandTypeReport{} of the \SecLandN{} records are reports, \SecLandTypeBookChapter{} are book chapters and
139+\SecLandTypeDissertation{} are dissertations, against \SecLandTypeArticle{} journal articles. And \SecLandYearTwoZeroTwoSix{}
140+records are dated 2026 and ranked on a few months of citations; their normalised scores measure position in a cohort, not
141+durable influence. \SecLandNoDoi{} records have no DOI.
142+
143+The review therefore treats the landmark list as a sample of what is highly cited under the corpus's own metric,
144+documents its artefacts category by category, and supplements it with the verified references of the main bibliography where
145+the automatic selection missed works that a specialist would consider foundational---the hash-based signature standard,
146+the prompt-injection and jailbreak papers, differentially private stochastic gradient descent, secure aggregation. The
147+contributions table of the area (\texttt{literature\_synthesis/agent\_H/contributions.csv}) records, for each landmark, whether
148+its contribution type was judged from the abstract or from metadata alone; \SecLandWithAbstractPct\,\% of the records have an
149+abstract. The principal-investigator review of the landmark set (protocol checkpoint H-3) should act on the artefacts
150+listed here before any landmark-level statistic is reported in Part~IX.
151+
152+\section{Cybersecurity}
153+\label{sec:sec-cyber}
154+
155+\subsection{The baseline literature as the citation metric sees it}
156+\label{sec:sec-cyber-baseline}
157+
158+The pre-window cybersecurity landmarks are not the works a security researcher would name first. The microarchitectural
159+attacks of 2018, Spectre and Meltdown \citep{kocher2019spectre,lipp2018meltdown}, are in the verified bibliography but not in
160+the category's top twenty-five: they are classified under computer architecture by the ontology, and their citations
161+accrue in venues the cybersecurity mapping weights partially. What the metric selects instead is, first, machine-learning
162+detection on shared datasets---the CIC-IDS2017 intrusion-detection dataset paper, which has no abstract in the record and
163+which is, by its 36-month citation count, the single most reused benchmark of the network-intrusion literature---and,
164+second, surveys that present distributed ledgers as a security solution: the IEEE Access review of blockchain for the
165+Internet of Things \citep{fernandez2018biot}, the IEEE Communications Surveys and Tutorials survey of the security and privacy
166+of Bitcoin \citep{conti2018bitcoinsecurity}, and a healthcare-cloud position paper \citep{esposito2018healthcare}. The
167+third baseline landmark is the chapter version of \citet{kurakin2018physical}, a 2018 reprint of the 2017 workshop paper on
168+adversarial examples in the physical world; its abstract reports that adversarial perturbations survive printing and camera
169+capture, so that the threat demonstrated in the digital setting by \citet{szegedy2014intriguing} and
170+\citet{goodfellow2015fgsm} transfers to deployed sensors. This is the only baseline landmark of the category that belongs to
171+the security of machine learning, and it matters because the post-2022 literature of the category is largely an extension
172+of that line.
173+
174+Ten of the twenty-five cybersecurity landmarks are grey literature (dissertations, book chapters, a report), five have no
175+DOI and nine are dated 2026; the composition is the most irregular of the five categories. The record with the highest
176+normalised score in the whole area is a 2026 SSRN preprint on persistent homology as a prompt-injection defence, without
177+abstract, that accumulated its citations within months; it cannot be assessed from metadata, it is not peer-reviewed, and it
178+is flagged for the principal-investigator review rather than discussed.
179+
180+\subsection{The security of language-model systems}
181+\label{sec:sec-cyber-llm}
182+
183+The post-2023 cybersecurity landmarks with abstracts cluster in two groups: language models used \emph{for} security
184+tasks and the security \emph{of} language-model systems. In the first group, the ACM Transactions on Software Engineering
185+and Methodology paper on Vul-RAG \citep{du2026vulrag} is the most informative, because its abstract reports a negative
186+result before its positive one: large language models distinguish vulnerable code from similar-but-benign patched code with
187+an accuracy of 0.06--0.14, and knowledge-level retrieval augmentation---retrieving abstracted vulnerability knowledge from
188+historical fixes---raises that accuracy by 16--24 points. HuntGPT \citep{ali2026huntgpt} couples a conventional anomaly
189+detector with explainability tools and a language-model interface, and its abstract frames the contribution as
190+acceptance by incident responders rather than detection accuracy; a fine-tuned RoBERTa phishing-email detector
191+\citep{uddin2026phishing} reports gains on specific datasets. On the offensive side, the verified bibliography records two
192+preprints that the landmark metric does not reach: PentestGPT \citep{deng2023pentestgpt} and the study reporting that
193+language-model agents can autonomously exploit one-day vulnerabilities \citep{fang2024oneday}. Neither has a peer-reviewed
194+version in the corpus.
195+
196+In the second group, the ACM Computing Surveys survey of security and privacy challenges of large language models
197+\citep{das2025llmsecurity} organises the attack surface---jailbreaking, data poisoning, leakage of personally identifiable
198+information---and the ACM TOSEM study of the Model Context Protocol \citep{hou2026mcp} is the first landmark to treat the
199+tool-use layer of agent systems as an object of security analysis: its abstract describes a lifecycle of MCP servers in four
200+phases and sixteen activities and a taxonomy of threats attached to each. The foundational attack papers are, again, outside
201+the landmark set and inside the verified bibliography: the ``ignore previous prompt'' attack \citep{perez2022ignore},
202+indirect prompt injection through retrieved content \citep{greshake2023injection}, prompt injection against integrated
203+applications \citep[preprint]{liu2023promptinjection}, the analysis of why safety training fails
204+\citep[preprint]{wei2023jailbroken}, universal and transferable adversarial suffixes \citep[preprint]{zou2023universal}, and
205+the surveys of \citet{yao2024llmsecurity} and \citet[preprint]{shayegani2023adversarial}. The pre-window lineage of these
206+works is the privacy and robustness literature of the previous decade: membership inference \citep{shokri2017membership},
207+extraction of training data from language models \citep[preprint]{carlini2021extracting}, and the demonstration that
208+poisoning web-scale training sets is practical \citep{carlini2024poisoning}.
209+
210+The corpus statistics locate this turn (Table~\ref{tab:security_llm_cooccurrence}). Works co-classified as cybersecurity and
211+LLM represented \SecCoocCybersecurityLlmTwoZeroTwoTwoPct\,\% of cybersecurity works in 2022,
212+\SecCoocCybersecurityLlmTwoZeroTwoFivePct\,\% in 2025 and \SecCoocCybersecurityLlmTwoZeroTwoSixPct\,\% in the incomplete 2026;
213+co-classification with AI agents reached \SecCoocCybersecurityAiAgentsTwoZeroTwoSixPct\,\% in 2026, and with AI safety
214+\SecCoocCybersecurityAiSafetyTwoZeroTwoFivePct\,\% in 2025. The non-zero shares before 2022 are a property of the keyword rules
215+of the AI categories, which match pre-transformer language-model work, so the table should be read for its trend. Citation
216+flows record the same movement more weakly: \SecFlowCybersecurityToLlmTwoZeroTwoFivePct\,\% of the resolved references of
217+cybersecurity works in 2025 went to works of the LLM category (\SecFlowCybersecurityToLlmTwoZeroTwoSixPct\,\% in 2026), against
218+\SecFlowCybersecurityToCybersecurityTwoZeroTwoFivePct\,\% to cybersecurity itself; the inverse flow, from LLM works to
219+cybersecurity works, was \SecFlowLlmToCybersecurityTwoZeroTwoFivePct\,\% in 2025. The security literature is, by this measure,
220+labelling its objects faster than it is citing the literature that produced them.
221+
222+\subsection{Assessment}
223+\label{sec:sec-cyber-assessment}
224+
225+Three statements are established on the evidence available. The security-of-machine-learning literature predates the window,
226+and the post-2022 literature on language-model systems extends it to a new attack surface in which instructions arrive as
227+data, through retrieved documents, tool outputs and agent protocols. The highly cited post-2022 works of the category are,
228+in the main, surveys and systems papers rather than attacks; the attacks are preprints. And the category's growth after
229+2023 is partly a change of composition, visible in co-classification before it is visible in citation flows. One statement
230+is contested within the landmark abstracts themselves: whether language-model-based detection improves on fine-tuned
231+encoders or on classical features at equal cost, since the Vul-RAG abstract reports a very low base accuracy and the
232+phishing and HuntGPT abstracts report gains on particular datasets. One statement is speculative: that defences against
233+prompt injection can be made robust rather than patched; no landmark reports a defence with a guarantee, and the only
234+landmark on the question argues the negative and is unreviewed. Whether the language-model-security literature is a durable
235+subfield or a labelling wave is a question the corpus cannot answer yet, because the first 36-month citation windows of its
236+2023--2024 cohort close in 2026--2027 (open problem P-H-03).
237+
238+\section{Applied cryptography and the post-quantum transition}
239+\label{sec:sec-crypto}
240+
241+\subsection{Standards as landmarks}
242+\label{sec:sec-crypto-standards}
243+
244+Applied cryptography is the clearest case in the corpus of a category whose most cited documents are standards and reports
245+rather than papers. Five of its twenty-five landmarks are of that kind: RFC~8446, the specification of TLS~1.3
246+\citep{rescorla2018tls13}, whose abstract states the protocol's purpose as preventing ``eavesdropping, tampering, and message
247+forgery''; the status reports on the first and third rounds of the NIST post-quantum cryptography standardisation process
248+\citep{nist2019pqcround1,nist2022pqcround3}; and the two 2024 Federal Information Processing Standards FIPS~203 and FIPS~204
249+\citep{nist2024fips203,nist2024fips204}. The abstract of FIPS~203 relates the security of its key-encapsulation mechanism, ML-KEM,
250+to the computational difficulty of the module learning-with-errors problem and states that the scheme ``is believed to be
251+secure, even against adversaries who possess a quantum computer''; FIPS~204 makes the same statement for the ML-DSA signature
252+scheme. The hash-based signature standard FIPS~205 \citep{nist2024fips205} is not in the landmark set but completes the 2024
253+publication; the draft transition report \citep{nist2024transition} and the fourth-round status report \citep{nist2025hqc}
254+complete the sequence, and the latter is cited here for its date and existence only, since its content was not read.
255+
256+That the lattice standards rest on \citet{regev2009lwe} and respond to the threat model set by \citet{shor1997factoring} is
257+part of the pre-window lineage that Part~II records; the rationale for the programme is the one \citet{bernstein2017pqc}
258+set out before the window opened. The window's principal quantum-side event, the demonstration of error correction below
259+threshold \citep{googlequantum2025belowthreshold}, is classified under quantum computing and is discussed there. What the
260+corpus adds is a measurement of how little the standardisation timeline moved the category's aggregate volume: the
261+Bai--Perron break of applied cryptography (\SecBreakAppliedCryptographyBpBreaksBic) coincides with the month of the
262+third-round announcement, but the category's post-2023 growth is carried by sub-literatures that have nothing to do with
263+lattices (Section~\ref{sec:sec-crypto-rest}), and the share of the category's references that go to works of the
264+quantum-computing category rose only from \SecFlowAppliedCryptographyToQuantumComputingTwoZeroOneNinePct\,\% in 2019 to
265+\SecFlowAppliedCryptographyToQuantumComputingTwoZeroTwoFivePct\,\% in 2025.
266+
267+\subsection{The lesson of the broken candidates}
268+\label{sec:sec-crypto-breaks}
269+
270+The one landmark of the category that is an attack is the key-recovery attack on SIDH of \citet{castryck2023sidh}, published
271+at EUROCRYPT 2023 after circulating in 2022; the corpus record has no abstract and the work is known here by its title,
272+which records a key recovery against the isogeny-based scheme on which the SIKE candidate was built. The verified bibliography
273+adds \citet{beullens2022rainbow}, whose title records that breaking the multivariate signature candidate Rainbow ``takes a
274+weekend on a laptop''. Both attacks are classical. The methodological lesson is the one the review draws for the whole area:
275+``post-quantum'' names a threat model, not a security proof, and two of the schemes that had survived three rounds of
276+public scrutiny fell to attacks whose cost was measured in days. The standards' own wording---``believed to be secure''---is
277+the correct one, and the open problem for the category (P-H-04) is the rate at which the standardised lattice schemes attract
278+cryptanalysis, which the corpus can measure in later snapshots by following citations to FIPS~203 and FIPS~204.
279+
280+A second, unmeasured problem is deployment. The corpus records standards and reports; it does not record whether TLS
281+handshakes negotiate post-quantum key exchange, and the transition timeline of \citet{nist2024transition} is one the
282+literature has not yet evaluated. Internet-measurement studies of that kind belong to computer networks and are listed
283+among the open problems (P-H-05) rather than reviewed here.
284+
285+\subsection{The rest of the category}
286+\label{sec:sec-crypto-rest}
287+
288+The remaining landmarks of applied cryptography are instructive about the category's composition. Three of its 2025--2026
289+landmarks are chaos-based or neuron-model image and video encryption schemes, published in IEEE Transactions on Dependable
290+and Secure Computing, IEEE Transactions on Consumer Electronics and Nonlinear Dynamics, and one is a cryptanalysis of a
291+scheme of that kind in ACM Transactions on Multimedia Computing, whose abstract reports that ``many such systems lack
292+standardization and deviate from secure design principles''. The sub-literature is large: the ontology maps the OpenAlex
293+topic for chaos-based image and signal encryption to the category at reduced weight (Part~IV), and its presence among the
294+most cited 2026 works is a fact about the citing community rather than an endorsement; the category's own cryptanalyses
295+contest it, and the review records the dispute as unresolved within the corpus (P-H-06).
296+
297+Several other records are artefacts of the kind catalogued in Section~\ref{sec:sec-landmarks}: a 2022 ACM reprint of Diffie
298+and Hellman's 1976 \emph{New Directions in Cryptography}, whose ``abstract'' in the record is the publisher's landing page; a
299+2026 record of TLS~1.3 whose text describes an earlier protocol version; the 2026 repository copies of the 2010 Scantegrity~II
300+election case study and of the FLINT number-theory library; and a Reed--Solomon decoder dissertation. The 2020 reprint of
301+\emph{Deep Leakage from Gradients} \citep{zhu2020dlg}, filed here, is a gradient-inversion attack on federated updates and is
302+discussed with federated learning (Section~\ref{sec:sec-fl-privacy}); a 2026 Information Fusion survey of attacks and
303+defences in federated learning \citep{jimenezgutierrez2026flsecurity}, whose abstract reports coverage of 203 papers, is filed
304+here because of its cryptographic vocabulary.
305+
306+Conspicuously absent from the top twenty-five are homomorphic encryption, zero-knowledge proofs and secure multi-party
307+computation as primitives. Their lineage is in the verified bibliography---\citet{gentry2009fhe}, the BGV and CKKS schemes
308+\citep{brakerski2014bgv,cheon2017ckks}, TFHE \citep{chillotti2020tfhe}, the surveys of \citet{acar2018hesurvey} and
309+\citet{marcolla2022fhesurvey}, succinct arguments \citep{groth2016snark,thaler2022proofs} and the pragmatic introduction to
310+multi-party computation of \citet{evans2018mpc}---and their post-2022 progeny exist in the corpus; they do not surface because
311+their citing communities are small and their venues (the IACR ePrint archive, TCHES) are only partially mapped to the
312+category. The absence is a statement about the metric, not about the literature (claim C-H-36 of the claims table), and it is
313+one reason the review does not use the landmark set to rank sub-areas.
314+
315+\section{Blockchain systems}
316+\label{sec:sec-blockchain}
317+
318+\subsection{A literature that moved out of computer science}
319+\label{sec:sec-blockchain-shape}
320+
321+Blockchain is the category whose landmark set is least about computer science. Two of its twenty-five works are in the
322+\emph{Review of Financial Studies}: the estimate of \citet{foley2019bitcoin} that about a quarter of bitcoin users are involved
323+in illegal activity, and the theory of decentralised consensus, competition and smart contracts of \citet{cong2019smartcontracts}.
324+One is in the \emph{Journal of Institutional Economics}, where \citet{davidson2018institutions} argue that blockchains are an
325+``institutional technology'' rather than merely a general-purpose one. Supply-chain management \citep{saberi2019supplychain},
326+agri-food supply chains, business process management with its seven research directions \citep{mendling2018bpm},
327+ontology-driven provenance \citep{kim2018provenance}, and chapters from MIT Press and Springer complete the picture of a
328+literature whose highly cited works are read in management, economics and information-systems venues. The ontology
329+anticipated this: the category's OpenAlex topic is large and the mapping notes that the keyword ``blockchain'' is
330+unambiguous, so the classification is not at fault; the citing community is simply not the one the taxonomy was built for.
331+
332+The computer-science core among the landmarks is thin but clear. Hyperledger Fabric \citep{androulaki2018fabric}, ``a modular
333+and extensible open-source system for deploying and operating permissioned blockchains'', is the category's system paper; the
334+IEEE Access survey of consensus mechanisms and mining-strategy management \citep{wang2019consensus} and the NIST overview
335+\citep{yaga2018blockchain} are its reference surveys; \citet{guo2022blockchainsecurity} survey the security of the technology
336+itself, with a quantitative comparison of consensus algorithms and a risk analysis, per the abstract. The one technical
337+landmark dated 2026 is a Datalog-based detector of cross-function reentrancy over Ethereum Virtual Machine execution traces
338+\citep{guesmi2026reentrancy}, which its abstract describes as an extension of the TxSpector framework with new rules and a
339+modernised trace extractor. The post-window landmarks are otherwise reviews: blockchain for the metaverse
340+\citep{huynh2023metaverse}, with the highest 36-month count of any post-2022 work in the category; blockchain in healthcare;
341+the NFT overview preprint of \citet{wang2021nft}; and two 2024 papers in a single journal with identical citation counts that
342+the principal-investigator review should examine. The decentralised-finance literature that the verified bibliography
343+records through \citet{werner2022defi}, and the consensus theory on which permissioned ledgers rest
344+\citep{castro2002pbft}, do not reach the top twenty-five of a category whose citing community is dominated by application
345+journals.
346+
347+\subsection{Artefacts and statistics}
348+\label{sec:sec-blockchain-stats}
349+
350+The Bitcoin whitepaper appears three times, as SSRN records dated 2019, 2022 and 2024, and a fourth time as a Springer
351+``recommends'' chapter; the 2022 record is the single most cited work of the whole area by 36-month citations, and all of
352+these counts belong to the 2008 document \citep{nakamoto2008bitcoin}. A 2026 SSRN record titled ``Global Blockchain
353+Benchmarking Study'', without abstract, is in all likelihood a reissue of an earlier industry report. These duplicates are the
354+area's largest single distortion of landmark-level statistics (open problem P-H-14).
355+
356+The corpus statistics are those of a literature whose expansion ended before the window. The category's Bai--Perron break is
357+dated \SecBreakBlockchainBpBreaksBic{}, its PELT change points are \SecBreakBlockchainPelt{}, and the interrupted time series
358+at 2022-12 shows neither a level nor a slope change against a pre-period slope (\ItsSeriesCatBlockchainItsChatgptPreSlope{} per
359+month) that was the steepest of the five categories after federated learning. The self-citation share of blockchain works
360+fell from \SecFlowBlockchainToBlockchainTwoZeroOneNinePct\,\% in 2019 to \SecFlowBlockchainToBlockchainTwoZeroTwoFivePct\,\% in
361+2025, with the share of references to cybersecurity rising; co-classification with the LLM category is the lowest of the area
362+(\SecCoocBlockchainLlmTwoZeroTwoFivePct\,\% in 2025). Established, then: the category grew fastest before 2021 and has grown at
363+roughly the corpus's pace since, and its highly cited post-2022 works are reviews. Contested: whether smart-contract security
364+tooling has reduced measured exploit rates since 2020, a question on which the single 2026 landmark, an extension of an
365+existing detector, offers no evidence (P-H-07). Speculative: every claim about the integration of blockchains with the
366+metaverse or with artificial intelligence in the 2023--2026 reviews, none of which reports a deployed system.
367+
368+\section{Privacy-preserving computation}
369+\label{sec:sec-privacy}
370+
371+\subsection{Composition of the landmark set}
372+\label{sec:sec-privacy-composition}
373+
374+The landmarks of privacy-preserving computation fall into four groups, and the fourth is a classification failure that the
375+review must report before it can use the other three. The first group is federated learning considered as a privacy
376+technology, which the ontology makes a child of this category: \citet{yang2019fml}, whose ACM Transactions on Intelligent
377+Systems and Technology paper introduced the vocabulary of horizontal, vertical and transfer federated learning and is, by
378+36-month citations, the most cited technical paper of the whole area; SplitFed \citep{thapa2022splitfed}, which combines split
379+learning and federated learning so that clients hold only part of the model; the 2026 ACM Computing Surveys systematic review
380+of differentially private federated learning \citep{fu2026dpfl}, whose abstract calls differential privacy ``the de facto
381+standard for privacy protection in federated learning''; and several federated healthcare applications. The second group is
382+the 2020 contact-tracing burst: a survey of COVID-19 contact-tracing applications \citep{ahmed2020tracingapps}, the privacy
383+analysis of TraceTogether \citep[preprint]{cho2020tracing}, a cross-country acceptability survey, a \emph{Nature Medicine}
384+comment on the responsible use of digital data, an OECD policy note and a German-language chapter on the Corona-Warn-App:
385+six of twenty-five landmarks from a single year's topic, which the ontology maps to the category at half weight. The third
386+group is the privacy of models and data: the survey of machine unlearning \citep{nguyen2025unlearning}, framed by the
387+``right to be forgotten'' and by the observation that deleting data from back-end stores is not sufficient when models
388+remember; the scoping review of differential privacy in medical deep learning \citep{mohammadi2026dpmedical}; a dissertation
389+on private information retrieval with side information; and a 2018 paper on privacy-aware mobile service recommendation.
390+
391+The fourth group consists of three 2025--2026 papers on artificial intelligence in higher education and one on artificial
392+intelligence in auditing, published in \emph{Education Sciences}, \emph{Frontiers in Psychology}, \emph{Frontiers in Education}
393+and the \emph{International Journal of Accounting Information Systems}, each ranked near the top of the category by normalised
394+citations. Their abstracts mention privacy as one concern among several; the keyword rule of the category fired on the word
395+in a context that its exclusion list does not veto. Two further records---open-circuit fault diagnosis of power converters
396+and a federated model for predicting student mental-health risk---are applications whose privacy content is the federated
397+setting. The principal-investigator review should reclassify the four education and auditing papers; the review does not use
398+them.
399+
400+\subsection{Lineage and evidence}
401+\label{sec:sec-privacy-lineage}
402+
403+The vocabulary that the post-2022 landmarks apply was fixed before the window: differential privacy
404+\citep{dwork2006dp,dwork2014dp}, its application to stochastic gradient descent \citep{abadi2016dpsgd}, membership inference
405+\citep{shokri2017membership}, extraction of training data from language models \citep[preprint]{carlini2021extracting} and
406+web-scale poisoning \citep{carlini2024poisoning}. Among the landmarks only the two 2026 reviews synthesise this vocabulary
407+with evidence. The medical review \citep{mohammadi2026dpmedical} is the more specific: its abstract reports, over 74 eligible
408+studies, that differentially private training can maintain clinically acceptable performance under moderate budgets
409+($\varepsilon \approx 10$), particularly in imaging, while strict budgets ($\varepsilon \approx 1$) often cost substantial accuracy.
410+That is the review's own summary and is cited as such; it is also the clearest statement in the landmark set of the
411+utility--privacy trade-off that the category's applied literature tends to assert away (open problem P-H-10).
412+
413+The category is the one whose boundary with machine learning moved most during the window. Its self-citation share fell from
414+\SecFlowPrivacyPreservingComputationToPrivacyPreservingComputationTwoZeroOneNinePct\,\% in 2019 to
415+\SecFlowPrivacyPreservingComputationToPrivacyPreservingComputationTwoZeroTwoFivePct\,\% in 2025, the lowest of the area,
416+while references to federated learning rose to \SecFlowPrivacyPreservingComputationToFederatedLearningTwoZeroTwoFivePct\,\%
417+and references to LLM works reached \SecFlowPrivacyPreservingComputationToLlmTwoZeroTwoSixPct\,\% in 2026. Its works are
418+co-classified with the LLM category at \SecCoocPrivacyPreservingComputationLlmTwoZeroTwoFivePct\,\% in 2025, the highest rate
419+of the area, up from \SecCoocPrivacyPreservingComputationLlmTwoZeroTwoTwoPct\,\% in 2022. The category grew throughout the
420+observation period; its Bai--Perron break is dated \SecBreakPrivacyPreservingComputationBpBreaksBic{} and its PELT change
421+points \SecBreakPrivacyPreservingComputationPelt{}, the first of which is the contact-tracing year. Established, on this
422+evidence: differential privacy and federated learning are the two privacy technologies that the applied literature adopts,
423+and the accuracy cost of strict budgets is documented. Contested: the privacy guarantees of federated learning deployed
424+without differential privacy or secure aggregation, to which the next section returns. Speculative: the ``privacy by design''
425+claims of federated healthcare applications that report no attack evaluation.
426+
427+\section{Federated learning}
428+\label{sec:sec-fl}
429+
430+\subsection{From averaging to heterogeneity}
431+\label{sec:sec-fl-heterogeneity}
432+
433+The federated-learning landmarks read as the field's own history, and the review follows that order. The pre-window
434+foundations are in the verified bibliography: federated averaging \citep{mcmahan2017fl}, the collective survey of advances and
435+open problems \citep{kairouz2021fl}, and the proximal regularisation of FedProx \citep[preprint]{li2018fedprox}. The baseline
436+landmarks are applications and surveys built on them: federated learning in medicine \citep{sheller2020flmedicine}, whose
437+abstract presents multi-institutional model training ``without sharing data between institutions'' as the answer to
438+privacy and ownership obstacles; FedHealth \citep{chen2020fedhealth}, a federated transfer-learning framework for wearables;
439+the healthcare-informatics review of \citet{xu2021flhealth}; the fairness objective $q$-FFL \citep[preprint]{li2019qffl},
440+which reweights the aggregate loss toward a more uniform accuracy distribution across devices (the corpus record of the
441+conference version has no DOI); the survey of \citet{zhang2021flsurvey}, the non-IID survey of \citet{zhu2021noniid}, and
442+\citet{tan2023personalizedfl}, whose IEEE Transactions on Neural Networks and Learning Systems survey calls federated learning
443+``the leading paradigm for the training of machine learning models on data silos'' and frames personalisation as the response
444+to heterogeneity.
445+
446+The post-window landmarks make that frame explicit. FedALA \citep{zhang2023fedala} initialises each client's model by an
447+adaptive local aggregation of the downloaded global model and the local one; FedTGP \citep{zhang2024fedtgp} replaces naive
448+averaging of class prototypes by trainable global prototypes with an adaptive margin, for clients with heterogeneous models;
449+FedAS \citep{yang2024fedas} addresses what its abstract calls intra-client and inter-client inconsistency in personalised
450+federated learning; and the ACM Computing Surveys survey of heterogeneous federated learning \citep{ye2024heterogeneousfl}
451+organises the problem by the kind of heterogeneity---data, model, network, hardware. A decentralised clustered algorithm
452+(DFCA, 2026) removes the server altogether. Every one of these abstracts defines its contribution against statistical
453+heterogeneity, and every one reports superiority over ``state-of-the-art'' baselines on the suite in which it is compared.
454+That is the established finding and the contested one at once: heterogeneity is the organising problem of the post-2022
455+literature and personalisation its dominant answer, but no common protocol fixes the levels of heterogeneity at which
456+methods are compared, and generalisation beyond a method's own suite is not shown (P-H-11).
457+
458+\subsection{Robustness, privacy and governance}
459+\label{sec:sec-fl-privacy}
460+
461+Robustness to malicious clients is the second thread. The backdoor attack on federated learning
462+\citep[preprint]{bagdasaryan2018backdoor} precedes the window; the highly cited 2025 contributions are aggregation rules---a
463+committee-based Byzantine-resilient scheme \citep{xu2025cbrfl} and a trimmed-mean rule---evaluated on standard benchmarks,
464+and the 2026 Information Fusion survey \citep{jimenezgutierrez2026flsecurity} separates security-enhancing methods (against
465+Byzantine, poisoning and Sybil behaviour) from privacy-preserving ones. The privacy thread rests on an attack and two
466+defences, all pre-window: gradient inversion \citep{zhu2020dlg} showed that model updates leak training data; secure
467+aggregation \citep{bonawitz2017secagg} and differential privacy \citep{abadi2016dpsgd,fu2026dpfl} are the responses. The
468+landmark applications in healthcare assert privacy by construction and, judging from their abstracts, evaluate no attack;
469+the review treats their privacy claims as speculative until such an evaluation is reported (P-H-09). The scoping review of
470+the governance of federated learning in healthcare \citep{eden2025flgovernance} is explicit that ``limited insights exist on
471+the optimal governance of this emerging technology'' (P-H-13).
472+
473+\subsection{Statistics}
474+\label{sec:sec-fl-stats}
475+
476+Federated learning is the category with the highest baseline growth of the area, from
477+\SecCountFederatedLearningTwoZeroOneEightMultilabel{} works in 2018 to \SecCountFederatedLearningTwoZeroTwoFiveMultilabel{} in
478+2025, a \SecRatioMultilabelFederatedLearningSpan-fold increase between 2022 and 2025 alone, and the only one whose interrupted
479+time series at the reference month shows a negative level and slope change; as Section~\ref{sec:sec-breaks} showed, neither
480+survives the placebo test, and the category's single Bai--Perron break is dated \SecBreakFederatedLearningBpBreaksBic{}. Its
481+citation structure changed more than any other in the area. The self-citation share of federated-learning works rose from
482+\SecFlowFederatedLearningToFederatedLearningTwoZeroOneNinePct\,\% in 2019 to
483+\SecFlowFederatedLearningToFederatedLearningTwoZeroTwoFivePct\,\% in 2025 as the field acquired a citable literature of its own;
484+the share of references to privacy-preserving computation rose to
485+\SecFlowFederatedLearningToPrivacyPreservingComputationTwoZeroTwoFivePct\,\%; and the share to machine-learning theory fell.
486+Co-classification with the LLM category rose from \SecCoocFederatedLearningLlmTwoZeroTwoTwoPct\,\% in 2022 to
487+\SecCoocFederatedLearningLlmTwoZeroTwoFivePct\,\% in 2025: federated fine-tuning of foundation models is visible in the labels
488+but not yet among the landmarks, whose 36-month windows have not closed.
489+
490+\section{Convergence, evidence quality and unresolved problems}
491+\label{sec:sec-synthesis}
492+
493+\subsection{What moved and what did not}
494+\label{sec:sec-synthesis-moved}
495+
496+Read together, the five categories support four statements about the area and refute one that is often made about it. No
497+security category shows a level break at the end of 2022. The volume acceleration of cybersecurity, applied cryptography and
498+privacy-preserving computation is a slope change that the break detectors date to mid-2022 and to late 2024; blockchain and
499+federated learning broke upward in early 2020 and decelerated afterwards. The statement that security research was
500+transformed by the arrival of conversational language models is, on the corpus's evidence, a statement about composition
501+rather than volume: the share of cybersecurity works co-classified with the LLM category rose several-fold between 2022 and
502+2025 while the share of their references going to LLM works stayed near one per cent. The security of language-model systems
503+is a continuation of the security-of-machine-learning literature of 2014--2021 with a new attack surface---instructions in
504+data, tool use, agent protocols---and its evaluation standards are not settled. Its foundational attack papers are
505+preprints, and its highly cited peer-reviewed works are surveys.
506+
507+Applied cryptography is the area's one dated transition, and it is dated by standards: the third-round report, the two
508+attacks on finalists, the 2024 FIPS publications and the 2025 fourth-round report. The transition did not move the category's
509+aggregate volume, which is dominated by encryption-for-devices sub-literatures of contested quality; it moved the reference
510+flows toward quantum computing only slightly. The fall of SIDH and Rainbow is the area's methodological lesson, and the
511+standards' wording (``believed to be secure'') is the one the review adopts.
512+
513+Convergence is measurable where the ontology predicted it and absent where it did not. Federated learning and
514+privacy-preserving computation cite each other increasingly (\SecFlowFederatedLearningToPrivacyPreservingComputationTwoZeroTwoFivePct\,\%
515+of federated-learning references and \SecFlowPrivacyPreservingComputationToFederatedLearningTwoZeroTwoFivePct\,\% of
516+privacy references in 2025), and both have the area's highest co-classification with the LLM category; the convergence
517+hypothesis of Part~VIII (Section~\ref{sec:net-flows-categories}) finds its clearest security-side support in this pair.
518+Blockchain's links to the rest of computer science are weak and its self-citation share, though declining, remains the
519+highest of the area; its highly cited literature now lives in management and economics venues. Applied cryptography and
520+cybersecurity remain each other's principal partners, as they were in 2019.
521+
522+\subsection{Evidence quality}
523+\label{sec:sec-synthesis-evidence}
524+
525+The review has been explicit about the kinds of evidence behind each statement, and the claims table of the area
526+(\texttt{literature\_synthesis/agent\_H/claims.csv}) records, for every claim, its type (established, contested, speculative),
527+its evidence (corpus statistic, abstract, metadata only), the result file or record on which it rests, and a confidence.
528+Three limits deserve restating. Abstracts are not full texts: every statement about what a landmark paper does rests on its
529+abstract or title, and the review has not verified any reported result. The landmark metric is a citation metric with the
530+distortions documented in Section~\ref{sec:sec-landmarks}---duplicates, reprints dated 2026, venue-level records,
531+misclassifications, a single-topic burst in 2020, and 2026 records ranked on months of citations---so that the composition
532+of each category's top twenty-five is partly a property of the database (Part~III, Section~\ref{sec:data-quality}) and the
533+review has, where necessary, said so rather than interpreted it. And the structural-change estimates are descriptive
534+associations on monthly counts whose last complete month is \ItsLastComplete{}; they are reported with their placebo
535+distributions and alternative dates, and the review has drawn no causal conclusion from them.
536+
537+\subsection{Unresolved problems}
538+\label{sec:sec-synthesis-problems}
539+
540+The problems the review leaves open are listed, with a testable direction for each, in
541+\texttt{literature\_synthesis/agent\_H/open\_problems.csv} (P-H-01 to P-H-14) and feed Part~X. The most consequential for the
542+area are five. Whether defences against prompt injection in language-model-integrated applications can be made robust
543+rather than patched (P-H-01) is unresolved because the attacks arrive as data and no landmark reports a defence with a
544+guarantee; the testable direction is a preregistered benchmark under adaptive attackers across tool-use protocols. Whether
545+language-model-based detection improves on fine-tuned encoders at equal cost (P-H-02) is unresolved because the landmark
546+abstracts report low base accuracy and dataset-specific gains; the direction is cost-matched evaluation with temporal splits.
547+Whether the standardised lattice schemes will attract the kind of cryptanalysis that broke SIDH and Rainbow (P-H-04), and how
548+fast post-quantum key exchange is deployed (P-H-05), are questions the corpus can follow in later snapshots by citation
549+tracking and that Internet measurement can answer directly. Whether personalised federated-learning methods generalise
550+beyond their own benchmark suites (P-H-11), and whether federated systems deployed without differential privacy or secure
551+aggregation leak training data under gradient inversion (P-H-09), are questions whose answers require protocols the field has
552+not yet adopted: shared heterogeneity benchmarks reported per client, and mandatory attack evaluation in application papers.
553+The last open problem is the review's own: the sensitivity of every area-level landmark statistic to the deduplication of
554+records that the automatic selection does not perform (P-H-14), which the principal-investigator review can resolve before
555+Part~IX is finalised.
modified manuscript/sections/part09_results.tex +659 −1
@@ -1 +1,659 @@
1−% part09_results — to be written (Phase 7) 1+% part09_results.tex — Part IX, Empirical Results. Written by Agent L (Part IX instance, with Agent I/J roles), 2026-11-03.
2+% Corpus v3 result files (results/, data/metadata/, reports/classification_report.json); every number is a macro of
3+% tables/numbers.tex or a cell of a table generated by tables/make_tables.py (tables_part09). Items marked \pending{} await
4+% a result file (taxonomy validation metrics, semantic atlas) or the PI checkpoint H-4 (interpretation of structural change).
5+
6+\section{Scope, sources and reading rules}
7+\label{sec:r-scope}
8+
9+This part collects the quantitative findings of the study and reads them against the ten research questions and
10+the eight pre-specified hypotheses of the protocol. It is deliberately a reporting part: the methods are those of
11+Part~IV, the interpretation of the literature is left to Parts~V--VII, and the discussion of what the findings
12+mean for computer science is left to Part~XI. Three kinds of material appear below. The first is the set of
13+descriptive series on volume, composition, openness and influence, which answer RQ1, RQ3 and RQ9 and provide
14+the evidence for H2 and H7. The second is the structural-change analysis of the monthly series, in total and by
15+category, which answers RQ2 and tests H1 and H3. The third is the set of network and detection results, which
16+answer RQ4--RQ6 and RQ10 and test H4--H6 and H8. The actor networks of RQ6 (authors, countries, institutions,
17+venues) are analysed at length in Part~VIII; this part does not repeat that analysis but summarises its verdicts
18+where a hypothesis requires them and cross-references its tables by label.
19+
20+The evidence base is the eligible corpus of Part~III in its third build, with \PrismaCsCoreEligible{} works in the
21+computer-science core, of which \PrismaEligibleByPeriodBaseline{} have a first date in the baseline window and
22+\PrismaEligibleByPeriodPost{} in the post window (Table~\ref{tab:p9-periods}). Every monthly statistic below is
23+computed on the month-precision subset, \PrismaEligibleDatePrecisionMonth{} works, because the
24+\PrismaEligibleDatePrecisionYear{} works whose only date is a year would otherwise be stacked on January
25+(Section~\ref{sec:data-dates}; Figure~\ref{fig:volume-monthly-raw}); yearly statistics use all eligible works.
26+Table~\ref{tab:p9-periods} shows that the two periods are not alike in completeness: the post period has a
27+higher share of month-precision dates, a higher arXiv-linked share and a much lower share of works with at
28+least one resolved reference, the last being an artefact of reference indexing, which lags publication by more
29+than a year for a substantial fraction of records (Table~\ref{tab:indexing_lag}, Section~\ref{sec:net-boundaries}).
30+Any comparison of the two periods inherits these differences, and the text names them wherever they bear on a
31+reading.
32+
33+\input{tables/tab_p9_periods}
34+
35+The category-level results depend on the classification of Part~IV. Table~\ref{tab:p9-classification} reports its
36+coverage: the share of eligible works that received a primary category rises from under four fifths in 2018 to
37+over four fifths in the last years, and the number of labels per classified work rises monotonically through
38+the window. Both movements matter for what follows. The rise in coverage means that category counts grow
39+slightly faster than eligible counts for reasons unrelated to research activity, so growth rates of categories
40+should be read against the coverage column rather than against the eligible totals of
41+Table~\ref{tab:eligible_year}; the rise in labels per work means that multi-label counts, which are the input of
42+the monthly category series, grow faster than primary counts, and that co-classification statistics
43+(Section~\ref{sec:r-rq5}) are more generous in the post period. The classification run itself processed
44+\ClassificationWorks{} records, a superset of the eligible corpus that includes the penumbra set and records
45+later removed by the eligibility filter, of which \ClassificationClassified{} received at least one category
46+and \ClassificationAssignments{} category assignments were made in total. The validation of the assignment
47+against two independent raters on a stratified sample is part of the protocol (checkpoint H-2) and its
48+metrics file was not available when this part was written; Section~\ref{sec:r-validation} is reserved for it.
49+
50+\input{tables/tab_p9_classification}
51+
52+Four reading rules apply throughout. First, the series end at the last complete month of the panel,
53+\ItsLastComplete{}, and the yearly tables carry 2026 as an incomplete row; right-truncation affects every
54+statistic that depends on citations or references more than it affects counts of works, and the fixed-window
55+citation statistics are complete only for the publication years flagged in Table~\ref{tab:citation_windows}.
56+Second, the reference date of 30~November 2022 is a historical marker. The interrupted-time-series estimates
57+are changes in level and slope at that date net of trend and season; they are not treatment effects, and
58+the alternative-date and placebo analyses exist precisely to show how specific to that month the estimated
59+changes are \citep{bernal2017its, hausman2018rdit}. Third, the forty category series are analysed with the
60+same battery of tests, so forty $p$-values are produced for every statistic; at the conventional five per
61+cent level two of them are expected to fall below the threshold under a global null, and the text reads
62+counts of significant categories with that expectation in mind rather than applying a formal correction
63+\citep{benjamini1995fdr}, because the hypotheses were stated for named categories and are tested one by one.
64+Fourth, the verdict on each hypothesis is one of \emph{supported}, \emph{not supported}, \emph{opposite}
65+(the data move against the stated direction) or \emph{inconclusive} (the test cannot discriminate), and the
66+hypotheses are restated in full before each verdict. Interpretive statements about the structural-change
67+results that go beyond the estimates are marked as pending the principal investigator's review (checkpoint
68+H-4), as the protocol requires.
69+
70+\section{RQ1: the volume and composition of computer-science research}
71+\label{sec:r-rq1}
72+
73+\subsection{Volume}
74+
75+The yearly totals of Table~\ref{tab:eligible_year} and Figure~\ref{fig:volume-yearly} describe a corpus that
76+was almost flat through the baseline and grew afterwards. The eligible count moves from
77+\VolumeYearlyTwoZeroOneEightN{} works in 2018 to \VolumeYearlyTwoZeroTwoTwoN{} in 2022, a span in which the
78+2020 and 2021 cohorts are the largest and 2022 is slightly below them, and then to
79+\VolumeYearlyTwoZeroTwoThreeN{} in 2023, \VolumeYearlyTwoZeroTwoFourN{} in 2024 and
80+\VolumeYearlyTwoZeroTwoFiveN{} in 2025; the 2026 cohort, \VolumeYearlyTwoZeroTwoSixN{} works at the snapshot,
81+covers ten months of first dates and an indexing lag, and is not comparable. Three features of the
82+composition columns of Table~\ref{tab:volume_yearly} qualify the growth. The DBLP-indexed count
83+(\VolumeYearlyTwoZeroOneEightNDblp{} in 2018, \VolumeYearlyTwoZeroTwoFiveNDblp{} in 2025) grows much less than
84+the total, so that the DBLP share of the corpus falls in the post period; the OpenAlex-primary count
85+(\VolumeYearlyTwoZeroOneEightNCsPrimary{} to \VolumeYearlyTwoZeroTwoFiveNCsPrimary{}) and especially the
86+arXiv-linked count (\VolumeYearlyTwoZeroOneEightNArxiv{} to \VolumeYearlyTwoZeroTwoFiveNArxiv{}) grow faster
87+than the total. The post-period growth of the corpus is therefore concentrated in the parts of it that DBLP's
88+venue-based curation does not cover, which is consistent both with a real shift of output toward preprints and
89+toward venues outside DBLP's lists, and with a change in OpenAlex's own coverage over the period
90+(Section~\ref{sec:net-boundaries} examines the DBLP-only records; Table~\ref{tab:dblp_only}). The two cannot be
91+separated with the data at hand, and every growth statement of this part should be read as a statement about
92+the indexed record of computer science rather than about its production.
93+
94+The mean number of authors per work rises from \VolumeYearlyTwoZeroOneEightMeanAuthors{} in 2018 to
95+\VolumeYearlyTwoZeroTwoFiveMeanAuthors{} in 2025, continuing a trend that Part~VIII documents in detail
96+(Section~\ref{sec:net-teams}); the mean number of declared references per work rises from
97+\VolumeYearlyTwoZeroOneEightMeanRefs{} to a maximum of \VolumeYearlyTwoZeroTwoOneMeanRefs{} in 2021 and then
98+falls to \VolumeYearlyTwoZeroTwoFiveMeanRefs{} in 2025 and \VolumeYearlyTwoZeroTwoSixMeanRefs{} in 2026, a
99+decline that is entirely attributable to reference indexing lag and is the clearest single illustration of the
100+right-truncation problem in the corpus.
101+
102+The monthly series (Figure~\ref{fig:its-total}) carries the same information at the resolution at which the
103+structural-change tests operate. Its pre-period slope, \ItsSeriesTotalItsChatgptPreSlope{} in log points per
104+month, corresponds to a growth of about a third of a per cent per month, which compounds to the yearly
105+totals above; the acceleration visible to the eye after 2022 is quantified in Section~\ref{sec:r-rq2}.
106+
107+\subsection{Composition by area and category}
108+
109+Figure~\ref{fig:area-shares} and Table~\ref{tab:p9-area-shares} give the composition of the classified corpus by
110+the eight top-level areas of the ontology, using the primary category of each work. The dominant area in every
111+year is Perception, Language, Robotics and Interaction, which holds a little over a third of primary
112+assignments and whose share is stable within two percentage points over the window; it contains Computer
113+Vision, the largest single category (Table~\ref{tab:p9-category-periods}). The movement is elsewhere. The share
114+of the AI and Machine Learning area rises in every year of the window, from under a tenth in 2018 to over a
115+tenth in 2022 and to close to a fifth in 2025; its rise is the mirror image of the fall of Data, Information and
116+Distributed Systems, whose share declines in every year, and of the smaller declines of Theory of Computation
117+and of Software, Programming Languages and Formal Methods. Security, Privacy and Trust gains about one and a
118+half points over the window; Hardware and Computational Science are approximately stable. The period rows of
119+the table, which split at the reference date rather than at the calendar year, show the same ordering.
120+
121+\begin{figure}[htbp]\centering
122+\includegraphics[width=0.95\textwidth]{fig_area_shares.pdf}
123+\caption{Share of eligible works by top-level area (primary category) per year. Source:
124+\texttt{results/graph/category\_counts\_yearly.csv}; 2026 omitted as incomplete.}
125+\label{fig:area-shares}
126+\end{figure}
127+
128+\input{tables/tab_p9_area_shares}
129+
130+Two cautions apply to this picture. The classifier assigns the primary category by the largest score, and a work
131+on, say, a vision--language model is counted once, under whichever of Computer Vision, Natural Language
132+Processing, Multimodal AI or Large Language Models scores highest; the area shares of
133+Table~\ref{tab:p9-area-shares} are therefore sensitive to the tie-breaking of the score, and the multi-label
134+counts of Table~\ref{tab:p9-category-periods} give a less brittle view of the same movement. Second, the
135+coverage of the classifier rises over the window (Table~\ref{tab:p9-classification}), and the works that enter
136+the classified set late are not a random sample of the eligible corpus: they are predominantly works with
137+abstracts, which the AI categories, defined largely by keyword patterns, pick up more readily than the
138+venue-defined categories of theory or systems. A part of the rise of the AI area is therefore a rise in the
139+classifiability of the literature, and the text does not attempt to separate it.
140+
141+Table~\ref{tab:p9-category-periods} descends to the forty categories. Sorted by their post-period share, the
142+table shows a top tier that is unchanged across periods (Computer Vision, Computer Networks, Human--Computer
143+Interaction, Cybersecurity, Natural Language Processing), a single category whose rank changes by an order
144+of magnitude (Large Language Models, which moves from under half a per cent of primary assignments in the
145+baseline to over four per cent in the post period, with a multi-label count more than three times its
146+baseline value), and a cluster of AI categories with the same profile at smaller scale: Generative AI,
147+AI Safety and Alignment, Multimodal AI, Foundation Models and Federated Learning all have post-period
148+multi-label counts above three times their baseline counts. The categories whose multi-label counts are
149+below their baseline value in the post period are Computer Networks, Computer Architecture, Formal
150+Verification, Programming Languages and Operating Systems, and the compound annual growth rates of the last
151+two columns confirm that the fall is a fall of shares more than of volumes: among these, only Computer
152+Architecture and Cloud Computing have a negative post-period CAGR of primary counts, and the others grow in
153+absolute terms while losing share. The post-period CAGRs are computed over the two complete years 2023 to
154+2025 (equation~\eqref{eq:cagr}) and are more volatile than the four-year baseline rates; they also carry the
155+coverage drift noted above.
156+
157+\input{tables/tab_p9_category_periods}
158+
159+\begin{figure}[htbp]\centering
160+\includegraphics[width=0.95\textwidth]{fig_category_growth.pdf}
161+\caption{Yearly primary-category output indexed to 2018 for the eight fastest- and eight slowest-growing
162+categories (2018--2025). Source: \texttt{results/graph/category\_counts\_yearly.csv}.}
163+\label{fig:category-growth}
164+\end{figure}
165+
166+Figure~\ref{fig:category-growth} shows the growth indices of the extremes. The fastest-growing categories are,
167+without exception, AI categories that were small in 2018 (Foundation Models, Large Language Models, Generative
168+AI, Multimodal AI, Federated Learning, AI Safety and Alignment, Autonomous Scientific Discovery) together with
169+Quantum Computing; their indices are large partly because their 2018 bases are small, and the table's shares
170+are the better measure of their weight. The slowest-growing categories are the venue-defined systems and
171+theory categories (Operating Systems, Compilers, Computer Architecture, Programming Languages, Formal
172+Verification, Cloud Computing), whose indices stay near or below their 2018 value until 2023 and rise modestly
173+afterwards; these are also the categories whose DBLP coverage is highest, so their flatness is the flatness of
174+the DBLP-indexed count noted above.
175+
176+\subsection{Hypothesis H2: composition}
177+\label{sec:r-h2}
178+
179+H2 states that the share of the AI-related categories (categories 1--8 and 38 of the mandate, which form the AI
180+and Machine Learning area of the ontology) in computer-science output increases in the post period, and that
181+the share of at least one non-AI area (theory, systems, networks) declines in relative terms while growing in
182+absolute terms. Table~\ref{tab:p9-ai-shares} tests the first clause with the bootstrap intervals of
183+Part~IV, obtained by resampling the works of each year with replacement \citep{efron1979bootstrap}. The area's
184+share rises from 2018 to 2022 and from 2022 to 2025, but the rise is not uniform across its nine categories.
185+Seven of them (Large Language Models, Foundation Models, Multimodal AI, Generative AI, AI Agents and
186+Autonomous Systems, Reinforcement Learning, AI Safety and Alignment) have a 2025 interval that lies entirely
187+above their 2022 interval; Machine Learning Theory has overlapping intervals in the two years; and Deep
188+Learning Architectures has a 2025 interval entirely \emph{below} its 2022 interval, its share having peaked
189+in the baseline. AI Agents and Autonomous Systems is the one category whose 2025 share remains below its 2018
190+share, a consequence of the category's definition (it includes the pre-LLM agent and multi-agent literature,
191+whose share was declining through the baseline) rather than evidence against the hypothesis. The Large
192+Language Models category accounts for the greater part of the area's post-period gain, and the hypothesis is
193+better described as supported for the generative and language-model categories than for machine learning as
194+a whole. The intervals are narrow because the
195+resampling treats the classifier as fixed; the uncertainty that matters is the classifier's, not the
196+sampling's, and the intervals should be read as a lower bound on it.
197+
198+\input{tables/tab_p9_ai_shares}
199+
200+The second clause is tested by Table~\ref{tab:p9-area-shares} together with the CAGR columns of
201+Table~\ref{tab:p9-category-periods}. The Data, Information and Distributed Systems area (which contains
202+Computer Networks) and the Theory of Computation area both lose share in every year of the window, and their
203+primary counts in 2025 exceed their 2022 counts, so both decline relatively while growing absolutely; the
204+Software, Programming Languages and Formal Methods area shows the same pattern. The second clause is therefore
205+met by three areas rather than by one. The verdict on H2 is \emph{supported} on both clauses, with the
206+qualification that the relative decline of the non-AI areas begins before the reference date (it is visible in
207+every year from 2018) and continues through it without a visible change of pace in the yearly data; the
208+monthly analysis of Section~\ref{sec:r-rq2} examines whether a change of pace exists at monthly resolution.
209+Table~\ref{tab:diversity} adds the diversity indices of the category mix: the Rao--Stirling index and the
210+Leinster--Cobbold index of order two are both lower in 2023--2025 than in 2018--2021, so the concentration of
211+primary assignments on fewer, mutually similar categories increased slightly, which is what the rise of a
212+cluster of related AI categories implies. The similarity matrix behind these indices is the co-classification
213+Jaccard similarity rather than the semantic one, because the semantic category similarities were not yet
214+available (\pending{semantic atlas: results/semantic/category\_similarity\_baseline.csv and \_post.csv; the
215+diversity indices should be re-estimated with the semantic distance when they exist}).
216+
217+\section{RQ2: acceleration and structural change}
218+\label{sec:r-rq2}
219+
220+\subsection{The total series at the reference date}
221+\label{sec:r-its-total}
222+
223+Figure~\ref{fig:its-total} shows the monthly series of month-precision works from \ItsFirst{} to
224+\ItsLastComplete{} with the fitted values of the interrupted time series of equation~\eqref{eq:its}, and
225+Table~\ref{tab:p9-its-total-events} reports the estimates at the reference date and at the four alternative
226+dates. At December 2022 the estimated change in log level is \ItsSeriesTotalItsChatgptLevelChange{}, a value
227+that is small, negative and indistinguishable from zero (HAC $p$ = \ItsSeriesTotalItsChatgptLevelP{}); the
228+estimated change in monthly log slope is \ItsSeriesTotalItsChatgptSlopeChange{}, which, added to the
229+pre-period slope of \ItsSeriesTotalItsChatgptPreSlope{}, implies a post-period growth rate of roughly one and
230+four tenths of a per cent per month against roughly three tenths before, a more than fourfold increase in the
231+rate of growth. The HAC $p$-value of the slope change rounds to \ItsSeriesTotalItsChatgptSlopeP{}, and the fit
232+explains \ItsSeriesTotalItsChatgptRTwo{} of the variance of the log series over
233+\ItsSeriesTotalNMonths{} months. The estimates describe a kink, not a jump: the series did not move to a higher
234+level in December 2022, it began, at some point, to grow faster.
235+
236+\begin{figure}[htbp]\centering
237+\includegraphics[width=0.95\textwidth]{fig_its_total.pdf}
238+\caption{Monthly output (month-precision works) with the interrupted-time-series fit at December 2022; the
239+annotation gives the level and slope changes with their HAC and placebo $p$-values. Sources:
240+\texttt{results/econometrics/fit\_total.csv}, \texttt{its\_results.json}.}
241+\label{fig:its-total}
242+\end{figure}
243+
244+\input{tables/tab_p9_its_total_events}
245+
246+Whether ``some point'' is the reference date is the question the remaining rows of
247+Table~\ref{tab:p9-its-total-events} and the diagnostics of Table~\ref{tab:p9-total-robustness} address. Moving
248+the event to May 2022 (DALL$\cdot$E~2) or April 2023 (GPT-4) changes the slope estimate by less than a tenth of
249+its value; moving it to June 2020 (GPT-3) halves it but leaves it clearly positive, and moving it to October
250+2024 (o1) gives a comparable slope change on top of a steeper pre-period. The level estimates vary in sign
251+across the five dates and two of them (DALL$\cdot$E~2, o1) have HAC $p$-values below one per cent, with
252+opposite signs. The honest summary of the five columns is that the series exhibits a sustained acceleration
253+over 2022--2024 that any event date placed within that span will pick up as a slope change, and that no
254+single month within it shows a level shift that the others do not.
255+
256+\input{tables/tab_p9_total_robustness}
257+
258+The placebo distribution (Figure~\ref{fig:placebo-total}) makes this precise. Over the
259+\ItsSeriesTotalPlaceboChatgptNPlacebo{} candidate event months, the level $t$-statistic of December 2022 ranks
260+\ItsSeriesTotalPlaceboChatgptLevelTRank{}th in absolute value (placebo $p$ =
261+\ItsSeriesTotalPlaceboChatgptLevelTPlaceboP{}), that is, almost every other month of the series produces a
262+larger apparent level shift than the reference date does; the slope $t$-statistic ranks first (placebo $p$ =
263+\ItsSeriesTotalPlaceboChatgptSlopeTPlaceboP{}, the smallest attainable value being one over the number of
264+candidates). Restricted to the baseline months, the placebo $p$ of the slope is
265+\ItsSeriesTotalPlaceboChatgptBaselineOnlySlopeTPlaceboP{}: no month before the reference date yields a slope
266+change as large as the one estimated there, which is the pre-trend diagnostic the protocol asked for and is
267+the same statement as the first rank, read from the other side. The donut specification, which drops the
268+event month and three months on each side, moves the level estimate to
269+\ItsSeriesTotalItsChatgptDonutLevelChange{} (HAC $p$ = \ItsSeriesTotalItsChatgptDonutLevelP{}) and leaves the
270+slope change essentially unchanged; the donut level estimate is larger than the full-sample one because the
271+months it removes, the winter of 2022--2023, lie below the post-period trend line, so that the trend fitted
272+without them extrapolates to a higher December 2022 value. The sensitivity rows show that ending the series in
273+March or August 2026 instead of June changes the slope estimate in the third decimal and leaves the level
274+estimate within a hundredth of zero, of unstable sign and insignificant.
275+
276+\begin{figure}[htbp]\centering
277+\includegraphics[width=0.95\textwidth]{fig_placebo_total.pdf}
278+\caption{Placebo distribution of the ITS $t$-statistics (level and slope) over every candidate event month of
279+the total series; the true event month is highlighted. Source: \texttt{results/econometrics/placebo\_total.csv}.}
280+\label{fig:placebo-total}
281+\end{figure}
282+
283+The data-driven methods date the kink earlier than the reference date. The Bai--Perron procedure selects a
284+single break under both criteria ($m$ = \ItsSeriesTotalBaiPerronMBic{} by BIC, and the same number by LWZ; the
285+BIC falls from \ItsSeriesTotalBaiPerronBicByMZero{} with no break to \ItsSeriesTotalBaiPerronBicByMOne{} with
286+one and rises again for two and three), and places it in April 2022, eight months before the reference date;
287+with the series ended in August 2026 the date moves to February 2022, so the location is not sharp. The
288+trimming parameter $h$ = \ItsSeriesTotalBaiPerronH{} months forbids a second break after April 2025 and within
289+fifteen months of the first, so a second kink in late 2024 or 2025 would not be found by this procedure. PELT,
290+which fits a piecewise-constant mean rather than a trend and therefore flags the points where the level
291+moves most, places changepoints in May 2023 and December 2024; the first is the point at which the series,
292+growing at the post-period rate, has risen far enough above its baseline mean for a step to be the cheapest
293+description, and it should not be read as an independent dating of the acceleration. The Bayesian online
294+detector assigns a maximum posterior probability of
295+\ItsSeriesTotalBocpdCpProbTwoZeroTwoTwoOneTwoToTwoZeroTwoThreeZeroThree{} to a changepoint within the four
296+months preceding any month of December 2022 to March 2023, and its maximum-a-posteriori run length never
297+resets over the whole series: an observer following the monthly totals with this detector would not have
298+concluded, at any point, that the process had changed. The reconciliation of the four methods is that the
299+acceleration is gradual at monthly resolution, which a trend model with one kink summarises well and a
300+step model or an online detector with a constant-mean likelihood summarises poorly.
301+
302+\subsection{Hypothesis H1: volume}
303+\label{sec:r-h1}
304+
305+H1 states that the monthly growth rate of computer-science works is higher in the post period than in the
306+baseline, but that the level shift at November 2022 is not distinguishable from placebo breaks in the baseline
307+once seasonality and indexing lag are modelled; the protocol adds that growth differences are expected to be
308+concentrated in a few categories rather than uniform. On the total series the first clause is
309+\emph{supported}: the slope change is positive, large relative to the pre-period slope, robust to the donut
310+specification and to the truncation month, and ranks first among all candidate dates and above every
311+baseline date in the placebo distribution. The second clause is also \emph{supported}: the level change at
312+the reference date is small, of uncertain sign across specifications, and ranks near the bottom of the
313+placebo distribution. The clause about concentration is examined in Section~\ref{sec:r-its-categories} and is
314+\emph{not supported} in its stated form, because the slope change is positive and conventionally significant
315+in nearly every category, although its magnitude varies by an order of magnitude across them.
316+
317+Two qualifications are part of the verdict. The first is that the acceleration predates the reference date in
318+the unknown-break estimates (spring 2022), so H1's first clause is supported in the weaker sense that the
319+post period grows faster, not in the sense that the growth rate changed at the boundary; a reader who takes
320+the Bai--Perron date at face value would describe a change that began during the baseline and was already
321+under way when ChatGPT was released. \pending{H-4: whether to describe the aggregate acceleration as beginning
322+in spring 2022 (Bai--Perron) or as a 2022--2024 phenomenon without a sharp onset (placebo and alternative
323+dates)}. The second is that the indexing-lag model the protocol envisaged was replaced by truncation
324+(Section~\ref{sec:m-truncation}); the hypothesis is therefore tested conditional on the series ending at the
325+last complete month, and the sensitivity rows of Table~\ref{tab:p9-total-robustness} are the only evidence on
326+what a different truncation would do. With about a hundred observations and a break near the middle of the
327+sample, the design has ample power for a slope change of this size and little for a level shift of a few per
328+cent \citep{zhang2011itspower}, so the null result on the level should be read as ``not detectable'' rather
329+than ``absent''.
330+
331+\subsection{The category series}
332+\label{sec:r-its-categories}
333+
334+The same battery is applied to the forty multi-label category series. Figure~\ref{fig:breaks-by-category}
335+orders the categories by their estimated slope change at December 2022; Tables~\ref{tab:p9-its-slopes}
336+and~\ref{tab:p9-its-levels} give the slope and level estimates with their HAC and placebo $p$-values, and
337+Table~\ref{tab:p9-unknown-breaks} the data-driven dates. Table~\ref{tab:breaks} in Appendix~F carries the
338+same estimates in the layout of the result file.
339+
340+\begin{figure}[htbp]\centering
341+\includegraphics[width=0.95\textwidth]{fig_breaks_by_category.pdf}
342+\caption{ITS slope change at December 2022 by category, with the level change annotated; bars in colour have a
343+placebo $p$ below 0.05 for the slope. Source: \texttt{results/econometrics/breaks.csv}.}
344+\label{fig:breaks-by-category}
345+\end{figure}
346+
347+\input{tables/tab_p9_its_slopes}
348+
349+The slope changes are positive in thirty-seven of the forty categories and negative in three (Blockchain
350+Systems, Deep Learning Architectures, Federated Learning), and the HAC $p$-value is below five per cent in
351+all but the first two of these; by the conventional criterion almost every category accelerated. The
352+placebo column tells a different story. Only one category, Applied Cryptography, has a placebo $p$ for the
353+slope below five per cent, and with forty series two such values are expected under a global null; the
354+total series is the only series whose December 2022 slope change ranks first among candidate dates. The
355+reason is visible in the pre-slope column: the category series are noisier than the total, their placebo
356+distributions are wider, and a slope change that is large in absolute terms, as in Large Language Models
357+(\ItsSeriesCatLlmItsChatgptSlopeChange{} per month on a pre-period slope of
358+\ItsSeriesCatLlmItsChatgptPreSlope{}, HAC $p$ rounding to \ItsSeriesCatLlmItsChatgptSlopeP{}), ranks only
359+\ItsSeriesCatLlmPlaceboChatgptSlopeTRank{}th of \ItsSeriesCatLlmPlaceboChatgptNPlacebo{} candidate dates
360+(placebo $p$ = \ItsSeriesCatLlmPlaceboChatgptSlopeTPlaceboP{}) because the series has accelerated at many
361+dates. The placebo rank over all candidate dates is the appropriate measure of whether December 2022 is special
362+for a given category, and by that measure it is not special for any of them. The baseline-only rank, which
363+asks the weaker question of whether any month \emph{before} the reference date produced a larger slope
364+change, answers no for a substantial minority of categories (the zero entries of the last placebo column,
365+which include AI Agents, Algorithms, Computational Biology and Autonomous Scientific Discovery): their
366+post-period acceleration exceeds anything in their baseline, even though it does not exceed what other
367+post-period dates would show.
368+
369+The ordering of the slope changes is nonetheless informative about where the acceleration is largest. The
370+five largest changes belong to AI Agents and Autonomous Systems, Large Language Models, Multimodal AI,
371+Information Retrieval and Distributed Computing; Generative AI, Cloud Computing, Natural Language Processing,
372+Quantum Computing and Compilers follow. The presence of Information Retrieval, Distributed Computing, Cloud
373+Computing and Compilers in the upper part of the ranking is a finding that the hypotheses did not anticipate:
374+these are categories that the LLM literature draws on (retrieval, serving infrastructure, compilation of
375+models to accelerators) and whose multi-label counts grow because works on language models carry their labels
376+(Section~\ref{sec:r-rq5}). At the other end, the categories with the smallest positive changes are Edge
377+Computing, Robotics, Computational Biology, Foundation Models and Computer Vision, all five with pre-period
378+slopes above the median of the forty, so that a continuation of their growth registers as a small change;
379+Foundation Models in particular has one of the three steepest pre-period slopes of all categories and a
380+post-period slope that is only slightly steeper. The three negative changes are of the same kind: Federated
381+Learning had the steepest baseline slope of all categories and Deep Learning Architectures one of the
382+steeper ones, and both decelerate to rates that remain positive; Blockchain Systems decelerates from a
383+2018--2020 boom.
384+
385+\input{tables/tab_p9_its_levels}
386+
387+The level changes (Table~\ref{tab:p9-its-levels}) are mostly negative, which is the signature of a trend model
388+fitted through a kink: when growth accelerates gradually from mid-2022, a regression with a kink fixed at
389+December 2022 absorbs the months between the true onset and the fixed date into a negative intercept shift.
390+Fifteen categories have a level change with a HAC $p$ below five per cent, of which only Human--Computer
391+Interaction, Computer Networks and Sustainable Computing have a positive one; none has a placebo $p$ for the
392+level below five per cent, and the baseline-only placebo $p$ of the level exceeds a quarter in every
393+category. The donut estimates are in most cases positive where the full-sample ones are negative, for the
394+reason given for the total series, and are largest in Large Language Models
395+(\ItsSeriesCatLlmItsChatgptDonutLevelChange{} with HAC $p$ rounding to
396+\ItsSeriesCatLlmItsChatgptDonutLevelP{}), Multimodal AI and Generative AI, the three series in which the
397+winter of 2022--2023 lies furthest below the subsequent trend. The BOCPD column confirms the absence of an
398+online-detectable change: the maximum four-month changepoint probability over December 2022 to March 2023
399+exceeds one quarter only in Cloud Computing and Operating Systems, two small and noisy series, and the
400+detector resets its run length in that window for no category.
401+
402+\input{tables/tab_p9_unknown_breaks}
403+
404+The data-driven dates of Table~\ref{tab:p9-unknown-breaks} spread over the window. The Bai--Perron dates
405+cluster in 2022 (February to August) for a group that includes Large Language Models,
406+Natural Language Processing, Multimodal AI, Computer Vision, Cybersecurity, Applied Cryptography, Software
407+Engineering, Sustainable Computing and Privacy-Preserving Computation, and in 2023 (April to November) for a
408+group that includes Algorithms, Computational Complexity, Computer Architecture, Compilers, Database Systems,
409+Operating Systems and Speech and Audio Computing; several categories have a second break in mid-2024 and a
410+few (Blockchain, Federated Learning, Computational Biology, Autonomous Scientific Discovery) have their first
411+break in 2020. The pre-specified H3 window of October 2022 to June 2023 contains a BIC break date for seven
412+categories, listed in the table's penultimate column, and a PELT changepoint for twelve. The temporal
413+pattern that emerges is of two waves: an earlier kink, in 2022, in the categories closest to the generative
414+model literature and in the large applied categories, and a later kink, in 2023, in the venue-defined
415+theory and systems categories, whose flat baseline makes a later acceleration easier to date. This reading is
416+descriptive and is marked for review (\pending{H-4: two-wave reading of the Bai--Perron dates}).
417+
418+\input{tables/tab_p9_altdates}
419+
420+Table~\ref{tab:p9-altdates} completes the picture with the level change at each of the five pre-specified
421+dates. For the AI categories the o1 column (October 2024) carries the largest positive level changes of any
422+date, which is the second, 2024 kink seen by the Bai--Perron procedure in a different form; the GPT-3 column
423+(June 2020) carries negative level changes for most categories, which is the pandemic-year irregularity of
424+the series rather than anything specific to the event. The five columns are the same series with the break
425+moved, and the table is read across a row as a profile of when a category's level departs from its fitted
426+trend, not as five tests.
427+
428+\subsection{Hypothesis H3: structural breaks}
429+\label{sec:r-h3}
430+
431+H3 states that Bai--Perron tests on category-level monthly series locate at least one break in the window
432+from October 2022 to June 2023 for Large Language Models, Generative AI, AI Agents and AI Safety, and that
433+for Formal Verification, Computational Complexity, Operating Systems and Computer Architecture no break is
434+detected in that window at the five per cent level after placebo correction. Table~\ref{tab:p9-h3} assembles
435+the evidence for the eight named categories.
436+
437+\input{tables/tab_p9_h3}
438+
439+The first clause is \emph{not supported}. None of the four AI categories has a Bai--Perron break in the
440+window: Large Language Models breaks in July 2022, Generative AI in November 2021, AI Agents in July 2022
441+(with a second break in July 2024) and AI Safety in June 2021 and September 2022. All four break
442+\emph{before} the window, which is the opposite of the error the hypothesis guarded against (a break too late
443+to be attributed to the boundary): their series accelerated during 2022, in the months of the diffusion-model
444+and instruction-tuning literature, and the reference date falls inside an acceleration that had already
445+begun. The PELT dates of these categories fall in late 2023, after the window, for the step-model reason
446+given above. The second clause is \emph{opposite} for two of its four categories: Computational Complexity
447+and Computer Architecture have Bai--Perron breaks in April and May 2023, inside the window, and Formal
448+Verification and Operating Systems break outside it (February 2022 and July 2024; September 2023). The
449+``five per cent level after placebo correction'' part of the clause cannot be applied as written, because
450+the Bai--Perron dates carry no $p$-value in the implementation (Section~\ref{sec:m-bp}); the placebo
451+statistic that exists is the ITS slope placebo, which exceeds five per cent for all eight categories, so that
452+by that statistic no category, including the four AI categories, has a December 2022 change that stands out
453+from its own history. The hypothesis, in short, predicted that the boundary would separate categories with
454+and without a break, and the data show breaks on both sides of the boundary in both groups, earlier in the
455+AI categories and later in the theory and systems categories. The verdict is \emph{not supported} for the
456+first clause and \emph{opposite} for the second, with the uncertainty that the Bai--Perron dates have no
457+sampling distribution attached and move by several months when the truncation month changes
458+(Table~\ref{tab:p9-total-robustness} for the total series). \pending{H-4: interpretation of the pre-window
459+breaks of the AI categories}.
460+
461+\section{RQ3: influential contributions and the distribution of attention}
462+\label{sec:r-rq3}
463+
464+RQ3 asks which publications introduced the most influential methodological or theoretical contributions. The
465+quantitative part of the answer is the citation analysis of Part~VIII and the landmark set of Part~IV; the
466+qualitative part, the identification of what each landmark contributed, belongs to the specialist reviews of
467+Parts~V--VII and to checkpoint H-3, and this section reports only what the counts show.
468+
469+\subsection{Fixed-window citation statistics}
470+
471+Table~\ref{tab:citation_windows} and Figure~\ref{fig:citation_inequality} give the fixed-window statistics by
472+publication year. For the cohorts with a complete three-year window (2018 to 2023), the mean number of
473+citations received within the window rises from \CitationWindowsYearlyTwoZeroOneEightMeanCThreeSix{} for the
474+2018 cohort to \CitationWindowsYearlyTwoZeroTwoTwoMeanCThreeSix{} for 2022 and
475+\CitationWindowsYearlyTwoZeroTwoThreeMeanCThreeSix{} for 2023, while the share of works uncited after three
476+years falls from \CitationWindowsYearlyTwoZeroOneEightShareUncitedThreeSix{} to
477+\CitationWindowsYearlyTwoZeroTwoTwoShareUncitedThreeSix{} and rises again to
478+\CitationWindowsYearlyTwoZeroTwoThreeShareUncitedThreeSix{} for the 2023 cohort, whose window closes at the
479+end of 2025 and is therefore the first to feel the reference-indexing lag. The Gini coefficient of three-year
480+citations, the inequality statistic of hypothesis H5, falls monotonically through the baseline cohorts, from
481+\CitationWindowsYearlyTwoZeroOneEightGiniCThreeSix{} (2018) to
482+\CitationWindowsYearlyTwoZeroTwoTwoGiniCThreeSix{} (2022), and rises to
483+\CitationWindowsYearlyTwoZeroTwoThreeGiniCThreeSix{} for the 2023 cohort. The baseline decline is driven by the
484+falling share of uncited works, which is the largest contributor to a Gini of this kind
485+\citep{nielsen2021inequality}; the 2023 reversal coincides with the return of the uncited share and is, on the
486+evidence available, a truncation effect rather than a change in the concentration of attention. The 99th
487+percentile of three-year citations is almost flat across cohorts (\CitationWindowsYearlyTwoZeroOneEightPNineNineCThreeSix{}
488+in 2018, \CitationWindowsYearlyTwoZeroTwoThreePNineNineCThreeSix{} in 2023), so the top of the distribution
489+did not pull away from the body within fixed windows.
490+
491+The field-weighted citation impact supplied by OpenAlex, whose normalisation is the database's own and not
492+the study's (Section~\ref{sec:m-citations}), has a mean near \CitationWindowsYearlyTwoZeroOneEightMeanFwci{}
493+in 2018 and \CitationWindowsYearlyTwoZeroTwoThreeMeanFwci{} in 2023 and a median that rises from
494+\CitationWindowsYearlyTwoZeroOneEightMedFwci{} to \CitationWindowsYearlyTwoZeroTwoTwoMedFwci{} over the
495+baseline; a mean well above one for a field whose works are normalised by field means that the
496+computer-science corpus is cited above the average of the OpenAlex fields its works are assigned to, and
497+the gap between mean and median is the skewness that the Gini measures.
498+
499+\subsection{The most cited works and the landmark set}
500+
501+Table~\ref{tab:top_cited} lists the ten most cited works of each publication year by three-year window
502+citations, and Section~\ref{sec:net-citations} of Part~VIII discusses it. The list contains the works that a
503+reader of the period would expect at the top of computer science (contextualised word representations,
504+BERT, the Vision Transformer, the Swin Transformer, latent diffusion, masked autoencoders, the scientific
505+Python stack) alongside records that are duplicates of canonical works misdated to a later year (the
506+transformer paper under 2025, the batch-normalisation paper under 2024, the original GAN paper under 2023,
507+the Bitcoin white paper under 2019 and 2022) and records whose three-year count exceeds their lifetime
508+count or whose lifetime count is zero, which are database errors. The ranking of influential works by raw
509+citations is therefore conditional on an audit that Part~VIII specifies and that has not been performed,
510+and this part does not derive a list of influential contributions from it.
511+
512+The landmark set is the study's instrument for that purpose. Selected by field-normalised impact within
513+category and year with per-area quotas (Section~\ref{sec:m-kg}), it contains \SelectionLandmarks{} works
514+drawn from a high-priority set of \SelectionHighPriority{}; Table~\ref{tab:p9-landmarks} gives its
515+composition by area and year. The quotas make the set broader than a citation ranking would be: the AI and
516+Machine Learning area holds the largest quota but less than a quarter of the set, and the theory area,
517+whose works are cited least, holds the smallest. By year the set is roughly balanced over 2018--2025 and
518+largest for 2026, which reflects the selection's reliance on the database's field-weighted impact for the
519+most recent cohort, where citation counts are smallest and the normalisation least stable; the 2026
520+landmarks should be treated as provisional. \SelectionShareWithAbstractLandmarks{} of the landmarks have an
521+abstract and \SelectionShareOaLandmarks{} have an open-access location, which bounds what the knowledge-graph
522+extraction and the specialist reviews can work from. The identification of the methodological or
523+theoretical contribution of each landmark is the object of the specialist reviews and of checkpoint H-3
524+and is not reported here (\pending{H-3: landmark list and specialist reviews}).
525+
526+\input{tables/tab_p9_landmarks}
527+
528+\subsection{Hypothesis H5: concentration}
529+\label{sec:r-h5}
530+
531+H5 states that citation inequality (the Gini of 24-month citations within year) is higher in the post period
532+than in the baseline for AI categories, and that venue concentration (HHI by source) is stable or lower
533+because of preprint growth. The citation component is stated for the AI categories, and the per-category
534+citation-window statistics are not among the result files; the verdict on that component is
535+\emph{inconclusive} (\pending{macro: per-category Gini of fixed-window citations, results/bibliometrics;
536+not produced by the current pipeline}). The aggregate counterpart, read from Table~\ref{tab:citation_windows}
537+with the 36-month Gini that the pipeline computes in place of the 24-month one, falls through the baseline
538+and rises for the single complete post-period cohort, a movement that Section~\ref{sec:net-citations}
539+attributes to the uncited share and to right truncation; it is not evidence of higher concentration. The
540+venue component is examined in Section~\ref{sec:net-venues}: the Herfindahl index with arXiv counted as a
541+source rises from \VenueHhiYearlyTwoZeroOneEightHhi{} in 2018 to \VenueHhiYearlyTwoZeroTwoFiveHhi{} in 2025
542+and the top-source share from \VenueHhiYearlyTwoZeroOneEightTopOneShare{} to
543+\VenueHhiYearlyTwoZeroTwoFiveTopOneShare{}, because arXiv is the top source and its share grows; the
544+protocol anticipated this mechanism and predicted stability or a fall once preprints are treated separately,
545+and the index without the preprint type is not among the result files. The venue component is therefore
546+\emph{opposite} on the index as computed and \emph{inconclusive} on the index the hypothesis intended
547+(\pending{macro: venue HHI excluding repository sources}). The number of distinct sources grows in every
548+year (Table~\ref{tab:venue_hhi}), so the non-arXiv venue landscape widens rather than concentrates.
549+
550+\section{RQ4: citation networks and knowledge flows}
551+\label{sec:r-rq4}
552+
553+\subsection{Flows between fields}
554+
555+The part of RQ4 that concerns the flow of knowledge between computer science and other fields is answered in
556+Section~\ref{sec:net-flows} of Part~VIII from the resolved references of the eligible works
557+(Table~\ref{tab:reference_coverage}, Table~\ref{tab:field_flows}, Figure~\ref{fig:field_inflows}). The share
558+of references that stay inside the corpus rises through the window as the corpus itself grows, and the share
559+pointing to the largest other OpenAlex fields (medicine, engineering, mathematics, physics, the social
560+sciences) falls, with no change of pace at the boundary; the age distribution of cited works shifts toward
561+younger work in every year (Table~\ref{tab:cited_age}, Figure~\ref{fig:cited_age}), again without a visible
562+inflection. Those results are not repeated here. This section reports the flows \emph{within} computer
563+science, between the areas and categories of the ontology, which depend on the classification and were
564+not available to Part~VIII.
565+
566+\subsection{Flows between areas}
567+
568+Figure~\ref{fig:area-flow-matrix} shows the $8 \times 8$ area flow matrices of equation~\eqref{eq:flow},
569+row-normalised, for the baseline and post spans, and Table~\ref{tab:p9-area-flow-change} gives their
570+difference in percentage points. The diagonal dominates both matrices: in every area a majority of internal
571+references stay within the area, from about three fifths in Security, Privacy and Trust to well over four
572+fifths in Perception, Language, Robotics and Interaction, which cites itself more than any other area does
573+and whose self-share does not fall in the post period. The off-diagonal structure is a hub-and-spoke pattern
574+centred on two areas: every area sends a larger share of its references to AI and Machine Learning in the
575+post span than in the baseline (by between one and two and a half points for the systems, security,
576+software, theory and computational-science areas), and the AI area itself sends a larger share to
577+Perception, Language, Robotics and Interaction and a smaller share to itself. The largest single movement in
578+the table is the fall of the AI area's self-share, matched by a rise of its references to the vision and
579+language categories; the second is the rise of references from Data, Information and Distributed Systems
580+to Security, Privacy and Trust. The Hardware area is the exception that moves least: its row changes by less
581+than a point in every cell.
582+
583+\begin{figure}[htbp]\centering
584+\includegraphics[width=0.95\textwidth]{fig_area_flow_matrix.pdf}
585+\caption{Citation-flow matrices between areas (row shares of internal references, citing area in rows) for
586+the baseline and post spans. Source: \texttt{results/graph/area\_flows\_yearly.csv}.}
587+\label{fig:area-flow-matrix}
588+\end{figure}
589+
590+\input{tables/tab_p9_area_flow_change}
591+
592+The matrices are built from internal references only, that is, references whose cited work is in the corpus
593+and has a primary category, and the number of such references grows by an order of magnitude over the window
594+(Table~\ref{tab:p9-modularity}, second column) because works of 2018 could cite few corpus works, all of them
595+from 2018, whereas works of 2025 can cite seven years of the corpus. The 2018 matrix is therefore computed on a
596+small and peculiar set, and the comparison of spans is best read from 2019 onward.
597+
598+\subsection{Cross-area shares and modularity}
599+
600+Table~\ref{tab:p9-cross-area} and Figure~\ref{fig:cross-area} report the statistic behind H4, the cross-area
601+share of equation~\eqref{eq:cross-area}, by citing area and year. The areas differ by a factor of three in
602+their propensity to cite outside themselves: the AI area sends about half of its internal references to other
603+areas (most of them to vision and language), the perception area about a sixth, and the remaining six
604+between a quarter and two fifths. Over time the AI area's cross-area share rises through the baseline, from
605+2018 to 2022, and is flat from 2022 onward; the Data, Security, Software and Theory areas rise slowly and
606+steadily through the whole window; the Hardware area is flat; and the Perception area falls from 2018 to
607+2019 and is flat thereafter. The aggregate share, in the penultimate rows of the table, rises from 2022 to
608+2025 after being flat through the baseline. Part of that rise is composition: references made by AI-area
609+works, which are the most boundary-crossing, form a growing share of all references. The composition-adjusted
610+aggregate, which holds each area's weight at its baseline share of internal references, rises by less than
611+the raw one over 2022--2025 but still rises, so the boundary-crossing propensity of the areas themselves
612+increased slightly in the post period, by an amount that is about half of the raw increase.
613+
614+\begin{figure}[htbp]\centering
615+\includegraphics[width=0.95\textwidth]{fig_cross_area.pdf}
616+\caption{Cross-area reference shares by citing area and year, and modularity of the category citation-flow
617+network at the category and area partitions. Sources: \texttt{results/graph/cross\_area\_share\_yearly.csv},
618+\texttt{modularity\_yearly.csv}.}
619+\label{fig:cross-area}
620+\end{figure}
621+
622+\input{tables/tab_p9_cross_area}
623+
624+The modularity of the yearly flow network (Table~\ref{tab:p9-modularity}) summarises the same information at
625+the two partitions. $Q_{\mathrm{area}}$ falls in every year from 2018 to 2024 and is flat in 2025, with the
626+largest single-year falls in 2020--2021 and in 2023; $Q_{\mathrm{cat}}$ is flat through 2022 and falls
627+afterwards; and the uncorrected self-share of citations within the citing category is flat through the
628+baseline and falls from 2023. The category partition and the area partition therefore tell slightly
629+different stories: the assortativity of citations by \emph{area} declined throughout the window, whereas
630+the assortativity by \emph{category} held until the reference year and declined afterwards, which is the
631+signature of categories within the same area (Large Language Models and Natural Language Processing, Computer
632+Vision and Multimodal AI) citing each other more in the post period. Because the partition is fixed by the
633+classifier rather than optimised, these values are descriptive indices of assortativity and not community
634+structure, and their decline is consistent with both a real blurring of category boundaries and a drift of
635+the classifier's own boundaries as the vocabulary of the literature changed (Section~\ref{sec:m-networks}).
636+
637+\input{tables/tab_p9_modularity}
638+
639+\subsection{Hypothesis H4: convergence}
640+\label{sec:r-h4}
641+
642+H4 states that the share of citations crossing area boundaries (systems to AI, security to AI, HCI to AI,
643+and so on) rises in the post period relative to the baseline, and that the modularity of the citation-flow
644+network by area decreases. Both clauses are \emph{supported} in direction. The cross-area shares of the
645+systems, security, software and theory areas rise in the post period, and the specific flows the hypothesis
646+names, from each of these areas toward AI and Machine Learning, are the cells of
647+Table~\ref{tab:p9-area-flow-change} with the largest positive changes; the area modularity is lower in every
648+post-period year than in any baseline year. The support is qualified in three ways. The rises are small,
649+of the order of two to four percentage points over the window for the areas concerned, and they continue
650+trends that are visible within the baseline; the aggregate increase is about half composition and half
651+behaviour; and the Perception area, which is the largest and contains the HCI category named in the
652+hypothesis, does not increase its cross-area share at all, so that ``HCI to AI'' is not supported at the area
653+level at which the statistic is computed (the category-level flows of Table~\ref{tab:p9-llm-partners} and of
654+the co-classification analysis in Section~\ref{sec:r-rq5} show HCI among the categories most often paired with
655+Large Language Models, which is a different measurement). The modularity decline is monotone across the
656+boundary rather than concentrated at it, so the hypothesis is supported as a statement about the post period
657+relative to the baseline and not as a statement about a change at the reference date. The classifier
658+caveat applies with full force: a change in how the classifier partitions the literature would produce the
659+same movements, and the semantic-level check of H4 is pending (Section~\ref{sec:r-rq5}).
modified manuscript/tables/TABLES.json +147 −209
@@ -103,7 +103,7 @@
103 "caption": "Fractional output of the fifteen largest countries by year.", 103 "caption": "Fractional output of the fifteen largest countries by year.",
104 "label": "tab:countries", 104 "label": "tab:countries",
105 "sources": [ 105 "sources": [
106− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/bibliometrics/countries_yearly.csv" 106+ "./results/bibliometrics/countries_yearly.csv"
107 ], 107 ],
108 "rows": 15 108 "rows": 15
109 }, 109 },
@@ -112,7 +112,7 @@
112 "caption": "Structural-change estimates by series: ITS level and slope changes at 2022-12 (HAC p-values), placebo p-values, Bai\u2013Perron (BIC) and PELT break dates.", 112 "caption": "Structural-change estimates by series: ITS level and slope changes at 2022-12 (HAC p-values), placebo p-values, Bai\u2013Perron (BIC) and PELT break dates.",
113 "label": "tab:breaks", 113 "label": "tab:breaks",
114 "sources": [ 114 "sources": [
115− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/econometrics/breaks.csv" 115+ "./results/econometrics/breaks.csv"
116 ], 116 ],
117 "rows": 41 117 "rows": 41
118 }, 118 },
@@ -121,7 +121,7 @@
121 "caption": "Out-of-time performance of the emerging-topic detectors at horizon 12 months (test cutoffs 2023-01\u20132024-10).", 121 "caption": "Out-of-time performance of the emerging-topic detectors at horizon 12 months (test cutoffs 2023-01\u20132024-10).",
122 "label": "tab:emerging_h12", 122 "label": "tab:emerging_h12",
123 "sources": [ 123 "sources": [
124− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/emerging/metrics_h12.json" 124+ "./results/emerging/metrics_h12.json"
125 ], 125 ],
126 "rows": 3 126 "rows": 3
127 }, 127 },
@@ -130,7 +130,7 @@
130 "caption": "Out-of-time performance of the emerging-topic detectors at horizon 24 months (test cutoffs 2023-01\u20132024-04).", 130 "caption": "Out-of-time performance of the emerging-topic detectors at horizon 24 months (test cutoffs 2023-01\u20132024-04).",
131 "label": "tab:emerging_h24", 131 "label": "tab:emerging_h24",
132 "sources": [ 132 "sources": [
133− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/emerging/metrics_h24.json" 133+ "./results/emerging/metrics_h24.json"
134 ], 134 ],
135 "rows": 3 135 "rows": 3
136 }, 136 },
@@ -139,7 +139,7 @@
139 "caption": "Preregistered known-emergence set: detection month, peak-growth month and lead time.", 139 "caption": "Preregistered known-emergence set: detection month, peak-growth month and lead time.",
140 "label": "tab:known_set", 140 "label": "tab:known_set",
141 "sources": [ 141 "sources": [
142− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/emerging/known_set_lead_times.csv" 142+ "./results/emerging/known_set_lead_times.csv"
143 ], 143 ],
144 "rows": 12 144 "rows": 12
145 }, 145 },
@@ -148,8 +148,8 @@
148 "caption": "Team size and collaboration indicators by publication year (all eligible works; periods of the source file summed).", 148 "caption": "Team size and collaboration indicators by publication year (all eligible works; periods of the source file summed).",
149 "label": "tab:collaboration", 149 "label": "tab:collaboration",
150 "sources": [ 150 "sources": [
151− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/bibliometrics/team_size_yearly.csv", 151+ "./results/bibliometrics/team_size_yearly.csv",
152− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/bibliometrics/international_collab_yearly.csv" 152+ "./results/bibliometrics/international_collab_yearly.csv"
153 ], 153 ],
154 "rows": 9 154 "rows": 9
155 }, 155 },
@@ -158,7 +158,7 @@
158 "caption": "Share of typed affiliations by OpenAlex institution type and year (per cent of affiliation-work pairs).", 158 "caption": "Share of typed affiliations by OpenAlex institution type and year (per cent of affiliation-work pairs).",
159 "label": "tab:institution_types", 159 "label": "tab:institution_types",
160 "sources": [ 160 "sources": [
161− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/bibliometrics/institution_types_yearly.csv" 161+ "./results/bibliometrics/institution_types_yearly.csv"
162 ], 162 ],
163 "rows": 9 163 "rows": 9
164 }, 164 },
@@ -167,7 +167,7 @@
167 "caption": "Share of fractional output of the fifteen largest countries by year (per cent).", 167 "caption": "Share of fractional output of the fifteen largest countries by year (per cent).",
168 "label": "tab:country_shares", 168 "label": "tab:country_shares",
169 "sources": [ 169 "sources": [
170− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/bibliometrics/countries_yearly.csv" 170+ "./results/bibliometrics/countries_yearly.csv"
171 ], 171 ],
172 "rows": 15 172 "rows": 15
173 }, 173 },
@@ -176,7 +176,7 @@
176 "caption": "Concentration of output across countries by year, and the two largest producers under full counting.", 176 "caption": "Concentration of output across countries by year, and the two largest producers under full counting.",
177 "label": "tab:country_concentration", 177 "label": "tab:country_concentration",
178 "sources": [ 178 "sources": [
179− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/bibliometrics/countries_yearly.csv" 179+ "./results/bibliometrics/countries_yearly.csv"
180 ], 180 ],
181 "rows": 9 181 "rows": 9
182 }, 182 },
@@ -185,7 +185,7 @@
185 "caption": "The country co-authorship network by period: edges with at least fifty joint works.", 185 "caption": "The country co-authorship network by period: edges with at least fifty joint works.",
186 "label": "tab:country_network", 186 "label": "tab:country_network",
187 "sources": [ 187 "sources": [
188− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/bibliometrics/country_pairs_by_period.csv" 188+ "./results/bibliometrics/country_pairs_by_period.csv"
189 ], 189 ],
190 "rows": 2 190 "rows": 2
191 }, 191 },
@@ -194,7 +194,7 @@
194 "caption": "The twenty strongest country pairs by period (number of joint works).", 194 "caption": "The twenty strongest country pairs by period (number of joint works).",
195 "label": "tab:country_pairs", 195 "label": "tab:country_pairs",
196 "sources": [ 196 "sources": [
197− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/bibliometrics/country_pairs_by_period.csv" 197+ "./results/bibliometrics/country_pairs_by_period.csv"
198 ], 198 ],
199 "rows": 20 199 "rows": 20
200 }, 200 },
@@ -203,7 +203,7 @@
203 "caption": "Institutions with at least two hundred works per period: coverage and concentration (fractional counting).", 203 "caption": "Institutions with at least two hundred works per period: coverage and concentration (fractional counting).",
204 "label": "tab:institution_concentration", 204 "label": "tab:institution_concentration",
205 "sources": [ 205 "sources": [
206− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/bibliometrics/top_institutions_by_period.csv" 206+ "./results/bibliometrics/top_institutions_by_period.csv"
207 ], 207 ],
208 "rows": 3 208 "rows": 3
209 }, 209 },
@@ -212,7 +212,7 @@
212 "caption": "The twenty-five institutions with the largest fractional output in the post period, with their baseline output.", 212 "caption": "The twenty-five institutions with the largest fractional output in the post period, with their baseline output.",
213 "label": "tab:top_institutions", 213 "label": "tab:top_institutions",
214 "sources": [ 214 "sources": [
215− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/bibliometrics/top_institutions_by_period.csv" 215+ "./results/bibliometrics/top_institutions_by_period.csv"
216 ], 216 ],
217 "rows": 25 217 "rows": 25
218 }, 218 },
@@ -221,7 +221,7 @@
221 "caption": "Distribution of works over source types among the listed sources, by period (per cent).", 221 "caption": "Distribution of works over source types among the listed sources, by period (per cent).",
222 "label": "tab:source_types", 222 "label": "tab:source_types",
223 "sources": [ 223 "sources": [
224− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/bibliometrics/top_sources_by_period.csv" 224+ "./results/bibliometrics/top_sources_by_period.csv"
225 ], 225 ],
226 "rows": 2 226 "rows": 2
227 }, 227 },
@@ -230,7 +230,7 @@
230 "caption": "The twenty largest sources (venues) by period, with OpenAlex source type.", 230 "caption": "The twenty largest sources (venues) by period, with OpenAlex source type.",
231 "label": "tab:top_sources", 231 "label": "tab:top_sources",
232 "sources": [ 232 "sources": [
233− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/bibliometrics/top_sources_by_period.csv" 233+ "./results/bibliometrics/top_sources_by_period.csv"
234 ], 234 ],
235 "rows": 20 235 "rows": 20
236 }, 236 },
@@ -239,7 +239,7 @@
239 "caption": "Age of cited works by citing year (resolved references of eligible works; corpus version of the result file at generation time).", 239 "caption": "Age of cited works by citing year (resolved references of eligible works; corpus version of the result file at generation time).",
240 "label": "tab:cited_age", 240 "label": "tab:cited_age",
241 "sources": [ 241 "sources": [
242− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/graph/cited_age_yearly.csv" 242+ "./results/graph/cited_age_yearly.csv"
243 ], 243 ],
244 "rows": 9 244 "rows": 9
245 }, 245 },
@@ -248,7 +248,7 @@
248 "caption": "The ten most cited works of each publication year by 36-month window citations (c36), with total citations to date.", 248 "caption": "The ten most cited works of each publication year by 36-month window citations (c36), with total citations to date.",
249 "label": "tab:top_cited", 249 "label": "tab:top_cited",
250 "sources": [ 250 "sources": [
251− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/bibliometrics/top_cited_by_year.csv" 251+ "./results/bibliometrics/top_cited_by_year.csv"
252 ], 252 ],
253 "rows": 80 253 "rows": 80
254 }, 254 },
@@ -257,7 +257,7 @@
257 "caption": "DBLP records of 2018--2026 without an OpenAlex counterpart, by DBLP record type and year.", 257 "caption": "DBLP records of 2018--2026 without an OpenAlex counterpart, by DBLP record type and year.",
258 "label": "tab:dblp_only", 258 "label": "tab:dblp_only",
259 "sources": [ 259 "sources": [
260− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/bibliometrics/dblp_only_yearly.csv" 260+ "./results/bibliometrics/dblp_only_yearly.csv"
261 ], 261 ],
262 "rows": 9 262 "rows": 9
263 }, 263 },
@@ -266,7 +266,7 @@
266 "caption": "The OpenAlex \\texttt{created\\_date} relative to the publication date, by publication year.", 266 "caption": "The OpenAlex \\texttt{created\\_date} relative to the publication date, by publication year.",
267 "label": "tab:indexing_lag", 267 "label": "tab:indexing_lag",
268 "sources": [ 268 "sources": [
269− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/bibliometrics/indexing_lag.csv" 269+ "./results/bibliometrics/indexing_lag.csv"
270 ], 270 ],
271 "rows": 9 271 "rows": 9
272 }, 272 },
@@ -275,7 +275,7 @@
275 "caption": "Reference coverage by citing year: declared, resolved, internal and external references of eligible works.", 275 "caption": "Reference coverage by citing year: declared, resolved, internal and external references of eligible works.",
276 "label": "tab:reference_coverage", 276 "label": "tab:reference_coverage",
277 "sources": [ 277 "sources": [
278− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/graph/reference_coverage_yearly.csv" 278+ "./results/graph/reference_coverage_yearly.csv"
279 ], 279 ],
280 "rows": 9 280 "rows": 9
281 }, 281 },
@@ -284,16 +284,89 @@
284 "caption": "Destination of the resolved references of eligible works by citing year: inside the corpus, computer science outside it, and the seven largest other OpenAlex fields (per cent of resolved references).", 284 "caption": "Destination of the resolved references of eligible works by citing year: inside the corpus, computer science outside it, and the seven largest other OpenAlex fields (per cent of resolved references).",
285 "label": "tab:field_flows", 285 "label": "tab:field_flows",
286 "sources": [ 286 "sources": [
287− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/graph/field_flows_yearly.csv" 287+ "./results/graph/field_flows_yearly.csv"
288 ], 288 ],
289 "rows": 9 289 "rows": 9
290 }, 290 },
291+ {
292+ "file": "tab_net_area_flows.tex",
293+ "caption": "Citation-flow matrices between the eight areas of the ontology by period: share of each citing area's internal references received by each cited area (per cent).",
294+ "label": "tab:net_area_flows",
295+ "sources": [
296+ "./results/graph/area_flows_yearly.csv"
297+ ],
298+ "rows": 16
299+ },
300+ {
301+ "file": "tab_net_modularity.tex",
302+ "caption": "Modularity of the yearly category citation-flow network at the category and area partitions, the within-category self-share, and the aggregate cross-area share of internal references, raw and composition-adjusted.",
303+ "label": "tab:net_modularity",
304+ "sources": [
305+ "./results/graph/modularity_yearly.csv",
306+ "./results/graph/cross_area_share_yearly.csv"
307+ ],
308+ "rows": 9
309+ },
310+ {
311+ "file": "tab_net_category_to_ai.tex",
312+ "caption": "Share of each category's internal references received by the nine categories of the AI/ML area, by citing year (per cent).",
313+ "label": "tab:net_category_to_ai",
314+ "sources": [
315+ "./results/graph/category_flows_yearly.csv"
316+ ],
317+ "rows": 40
318+ },
319+ {
320+ "file": "tab_net_category_actors.tex",
321+ "caption": "Shares of works with a China-affiliated author, a United States-affiliated author, a company affiliation and authors in at least two countries, by primary category and period (per cent).",
322+ "label": "tab:net_category_actors",
323+ "sources": [
324+ "./results/bibliometrics/category_actors_yearly.csv"
325+ ],
326+ "rows": 40
327+ },
328+ {
329+ "file": "tab_net_ai_vs_rest.tex",
330+ "caption": "Actor shares of works whose primary category is in the AI/ML area against all other areas, by period (per cent).",
331+ "label": "tab:net_ai_vs_rest",
332+ "sources": [
333+ "./results/bibliometrics/category_actors_yearly.csv"
334+ ],
335+ "rows": 4
336+ },
337+ {
338+ "file": "tab_net_ai_vs_rest_yearly.tex",
339+ "caption": "China-affiliated, company-affiliated and international shares of works by year, AI/ML area against all other areas (per cent).",
340+ "label": "tab:net_ai_vs_rest_yearly",
341+ "sources": [
342+ "./results/bibliometrics/category_actors_yearly.csv"
343+ ],
344+ "rows": 9
345+ },
346+ {
347+ "file": "tab_net_category_gini.tex",
348+ "caption": "Gini coefficient of 36-month citations $c^{(36)}$ by primary category and publication year, for years with a complete window; baseline mean, change of the last complete year against it, and share uncited at 36 months in that year.",
349+ "label": "tab:net_category_gini",
350+ "sources": [
351+ "./results/bibliometrics/category_impact_yearly.csv"
352+ ],
353+ "rows": 40
354+ },
355+ {
356+ "file": "tab_net_gini_ai_vs_rest.tex",
357+ "caption": "Citation inequality and levels at 36 months, AI/ML area against all other areas, by publication year with a complete window.",
358+ "label": "tab:net_gini_ai_vs_rest",
359+ "sources": [
360+ "./results/bibliometrics/category_impact_yearly.csv"
361+ ],
362+ "rows": 6
363+ },
291 { 364 {
292 "file": "tab_ai_growth.tex", 365 "file": "tab_ai_growth.tex",
293 "caption": "Volume, compound annual growth and corpus share of the nine AI categories, baseline (2018--2022) and post (2023--2025) periods.", 366 "caption": "Volume, compound annual growth and corpus share of the nine AI categories, baseline (2018--2022) and post (2023--2025) periods.",
294 "label": "tab:ai_growth", 367 "label": "tab:ai_growth",
295 "sources": [ 368 "sources": [
296− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/bibliometrics/cagr_by_category.csv" 369+ "./results/bibliometrics/cagr_by_category.csv"
297 ], 370 ],
298 "rows": 9 371 "rows": 9
299 }, 372 },
@@ -302,16 +375,25 @@
302 "caption": "Yearly multi-label counts of the nine AI categories (all period rows summed; 2026 incomplete).", 375 "caption": "Yearly multi-label counts of the nine AI categories (all period rows summed; 2026 incomplete).",
303 "label": "tab:ai_counts", 376 "label": "tab:ai_counts",
304 "sources": [ 377 "sources": [
305− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/graph/category_counts_yearly.csv" 378+ "./results/graph/category_counts_yearly.csv"
306 ], 379 ],
307 "rows": 9 380 "rows": 9
308 }, 381 },
309 { 382 {
310 "file": "tab_ai_breaks.tex", 383 "file": "tab_ai_breaks.tex",
311− "caption": "Structural-change estimates for the nine AI categories: interrupted-time-series level and slope changes at 2022-12 (ChatGPT), level changes at the alternative event dates 2023-04 (GPT-4) and 2024-09 (o1), placebo p-values, and Bai--Perron and PELT break dates.", 384+ "caption": "Structural change at 30 November 2022 in the nine AI categories: interrupted-time-series level and slope changes, placebo p-values, Bai--Perron and PELT break dates.",
312 "label": "tab:ai_breaks", 385 "label": "tab:ai_breaks",
313 "sources": [ 386 "sources": [
314− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/econometrics/breaks.csv" 387+ "./results/econometrics/breaks.csv"
388+ ],
389+ "rows": 9
390+ },
391+ {
392+ "file": "tab_ai_altdates.tex",
393+ "caption": "Interrupted-time-series level changes of the nine AI categories at the alternative event dates (GPT-3, DALL-E 2, GPT-4, o1).",
394+ "label": "tab:ai_altdates",
395+ "sources": [
396+ "./results/econometrics/breaks.csv"
315 ], 397 ],
316 "rows": 9 398 "rows": 9
317 }, 399 },
@@ -320,7 +402,7 @@
320 "caption": "Destination of the in-corpus references of the nine AI categories in 2019, 2022 and 2025 (per cent of in-corpus references of the citing category).", 402 "caption": "Destination of the in-corpus references of the nine AI categories in 2019, 2022 and 2025 (per cent of in-corpus references of the citing category).",
321 "label": "tab:ai_flows", 403 "label": "tab:ai_flows",
322 "sources": [ 404 "sources": [
323− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/graph/category_flows_yearly.csv" 405+ "./results/graph/category_flows_yearly.csv"
324 ], 406 ],
325 "rows": 27 407 "rows": 27
326 }, 408 },
@@ -329,7 +411,7 @@
329 "caption": "Specialist screening of the 234 AI/ML landmark candidates: records by primary category and data-quality flag.", 411 "caption": "Specialist screening of the 234 AI/ML landmark candidates: records by primary category and data-quality flag.",
330 "label": "tab:ai_landmark_flags", 412 "label": "tab:ai_landmark_flags",
331 "sources": [ 413 "sources": [
332− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/literature_synthesis/agent_E/contributions.csv" 414+ "./literature_synthesis/agent_E/contributions.csv"
333 ], 415 ],
334 "rows": 9 416 "rows": 9
335 }, 417 },
@@ -338,16 +420,16 @@
338 "caption": "Contribution type of the 234 AI/ML landmark candidates by primary category (specialist judgement from abstract or metadata).", 420 "caption": "Contribution type of the 234 AI/ML landmark candidates by primary category (specialist judgement from abstract or metadata).",
339 "label": "tab:ai_contribution_types", 421 "label": "tab:ai_contribution_types",
340 "sources": [ 422 "sources": [
341− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/literature_synthesis/agent_E/contributions.csv" 423+ "./literature_synthesis/agent_E/contributions.csv"
342 ], 424 ],
343 "rows": 9 425 "rows": 9
344 }, 426 },
345 { 427 {
346 "file": "tab_ai_landmarks.tex", 428 "file": "tab_ai_landmarks.tex",
347− "caption": "The 234 AI/ML landmark candidates with the specialist's contribution type and data-quality flag (titles in literature_synthesis/agent_E/contributions.csv).", 429+ "caption": "The 234 AI/ML landmark candidates with the specialist's contribution type and data-quality flag (titles in literature\\_synthesis/agent\\_E/contributions.csv).",
348 "label": "tab:ai_landmarks", 430 "label": "tab:ai_landmarks",
349 "sources": [ 431 "sources": [
350− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/literature_synthesis/agent_E/contributions.csv" 432+ "./literature_synthesis/agent_E/contributions.csv"
351 ], 433 ],
352 "rows": 234 434 "rows": 234
353 }, 435 },
@@ -356,7 +438,7 @@
356 "caption": "Eligible works in the five security-and-privacy categories by publication year: works carrying the category (all) and works whose primary category it is (prim.).", 438 "caption": "Eligible works in the five security-and-privacy categories by publication year: works carrying the category (all) and works whose primary category it is (prim.).",
357 "label": "tab:security_counts", 439 "label": "tab:security_counts",
358 "sources": [ 440 "sources": [
359− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/graph/category_counts_yearly.csv" 441+ "./results/graph/category_counts_yearly.csv"
360 ], 442 ],
361 "rows": 9 443 "rows": 9
362 }, 444 },
@@ -365,8 +447,8 @@
365 "caption": "Growth and structural-change summary for the five security-and-privacy categories (multi-label counts).", 447 "caption": "Growth and structural-change summary for the five security-and-privacy categories (multi-label counts).",
366 "label": "tab:security_summary", 448 "label": "tab:security_summary",
367 "sources": [ 449 "sources": [
368− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/graph/category_counts_yearly.csv", 450+ "./results/graph/category_counts_yearly.csv",
369− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/econometrics/breaks.csv" 451+ "./results/econometrics/breaks.csv"
370 ], 452 ],
371 "rows": 5 453 "rows": 5
372 }, 454 },
@@ -375,8 +457,8 @@
375 "caption": "Co-classification of security-and-privacy works with the LLM, AI-safety and AI-agents categories, by publication year: number of works carrying both categories and share of the security category's works.", 457 "caption": "Co-classification of security-and-privacy works with the LLM, AI-safety and AI-agents categories, by publication year: number of works carrying both categories and share of the security category's works.",
376 "label": "tab:security_llm_cooccurrence", 458 "label": "tab:security_llm_cooccurrence",
377 "sources": [ 459 "sources": [
378− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/graph/category_cooccurrence_yearly.csv", 460+ "./results/graph/category_cooccurrence_yearly.csv",
379− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/graph/category_counts_yearly.csv" 461+ "./results/graph/category_counts_yearly.csv"
380 ], 462 ],
381 "rows": 9 463 "rows": 9
382 }, 464 },
@@ -385,7 +467,7 @@
385 "caption": "Composition of the automatically selected landmark set of the security-and-privacy area, by category.", 467 "caption": "Composition of the automatically selected landmark set of the security-and-privacy area, by category.",
386 "label": "tab:security_landmarks", 468 "label": "tab:security_landmarks",
387 "sources": [ 469 "sources": [
388− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/literature_synthesis/landmarks/security_privacy_landmarks.csv" 470+ "./literature_synthesis/landmarks/security_privacy_landmarks.csv"
389 ], 471 ],
390 "rows": 5 472 "rows": 5
391 }, 473 },
@@ -394,202 +476,58 @@
394 "caption": "The systems and infrastructure categories of Part VI: primary-category output, compound annual growth, and structural-change estimates of the monthly log-volume series.", 476 "caption": "The systems and infrastructure categories of Part VI: primary-category output, compound annual growth, and structural-change estimates of the monthly log-volume series.",
395 "label": "tab:systems_categories", 477 "label": "tab:systems_categories",
396 "sources": [ 478 "sources": [
397− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/bibliometrics/cagr_by_category.csv", 479+ "./results/bibliometrics/cagr_by_category.csv",
398− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/econometrics/breaks.csv" 480+ "./results/econometrics/breaks.csv"
399 ], 481 ],
400 "rows": 11 482 "rows": 11
401 }, 483 },
402 { 484 {
403− "file": "tab_p9_periods.tex", 485+ "file": "tab_theory_counts.tex",
404− "caption": "The eligible corpus by protocol period: completeness, source membership and document type.", 486+ "caption": "Eligible works in the six theory and software categories by publication year: works carrying the category (all) and works whose primary category it is (prim.).",
405− "label": "tab:p9-periods", 487+ "label": "tab:theory_counts",
406− "sources": [
407− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/bibliometrics/volume_monthly.csv"
408− ],
409− "rows": 13
410− },
411− {
412− "file": "tab_p9_classification.tex",
413− "caption": "Classification coverage by publication year: eligible works, works with a primary category, and multi-label assignments.",
414− "label": "tab:p9-classification",
415 "sources": [ 488 "sources": [
416− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/graph/category_counts_yearly.csv", 489+ "./results/graph/category_counts_yearly.csv"
417− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/data/metadata/prisma_counts.json"
418 ], 490 ],
419 "rows": 9 491 "rows": 9
420 }, 492 },
421 { 493 {
422− "file": "tab_p9_area_shares.tex", 494+ "file": "tab_theory_summary.tex",
423− "caption": "Share of primary-category assignments by top-level area and year (per cent), with the two protocol periods.", 495+ "caption": "Growth and structural-change summary for the six theory and software categories (multi-label counts).",
424− "label": "tab:p9-area-shares", 496+ "label": "tab:theory_summary",
425 "sources": [ 497 "sources": [
426− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/graph/category_counts_yearly.csv" 498+ "./results/graph/category_counts_yearly.csv",
499+ "./results/econometrics/breaks.csv"
427 ], 500 ],
428− "rows": 11 501+ "rows": 6
429− },
430− {
431− "file": "tab_p9_category_periods.tex",
432− "caption": "The forty categories by protocol period: primary-category output, composition shares, multi-label growth and compound annual growth rates.",
433− "label": "tab:p9-category-periods",
434− "sources": [
435− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/graph/category_counts_yearly.csv",
436− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/bibliometrics/cagr_by_category.csv"
437− ],
438− "rows": 40
439− },
440− {
441− "file": "tab_p9_ai_shares.tex",
442− "caption": "Share of primary-category assignments held by the AI and machine-learning categories in 2018, 2022 and 2025 (per cent, bootstrap 95\\% intervals).",
443− "label": "tab:p9-ai-shares",
444− "sources": [
445− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/bibliometrics/category_share_ci_yearly.csv"
446− ],
447− "rows": 10
448− },
449− {
450− "file": "tab_p9_its_total_events.tex",
451− "caption": "Interrupted time series of the total monthly series at the reference date and at four alternative event dates (log counts, month-of-year dummies, Newey--West standard errors).",
452− "label": "tab:p9-its-total-events",
453− "sources": [
454− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/econometrics/its_results.json"
455− ],
456− "rows": 5
457 }, 502 },
458 { 503 {
459− "file": "tab_p9_total_robustness.tex", 504+ "file": "tab_theory_llm_cooccurrence.tex",
460− "caption": "Robustness diagnostics of the structural-change analysis of the total monthly series.", 505+ "caption": "Co-classification of theory and software works with the LLM category, by publication year: number of works carrying both categories and share of the category's multi-label count.",
461− "label": "tab:p9-total-robustness", 506+ "label": "tab:theory_llm_cooccurrence",
462 "sources": [ 507 "sources": [
463− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/econometrics/its_results.json" 508+ "./results/graph/category_cooccurrence_yearly.csv",
464− ], 509+ "./results/graph/category_counts_yearly.csv"
465− "rows": 12
466− },
467− {
468− "file": "tab_p9_its_slopes.tex",
469− "caption": "ITS slope change at December 2022 by category: pre-period slope, change, HAC and placebo $p$-values, and implied post-period slope (monthly log growth).",
470− "label": "tab:p9-its-slopes",
471− "sources": [
472− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/econometrics/its_results.json"
473− ],
474− "rows": 40
475− },
476− {
477− "file": "tab_p9_its_levels.tex",
478− "caption": "ITS level change at December 2022 by category: full-sample and donut estimates, HAC and placebo $p$-values, and the BOCPD change probability over December 2022 to March 2023.",
479− "label": "tab:p9-its-levels",
480− "sources": [
481− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/econometrics/its_results.json"
482− ],
483− "rows": 40
484− },
485− {
486− "file": "tab_p9_unknown_breaks.tex",
487− "caption": "Data-driven break and changepoint dates by category, and whether any falls in the H3 window (October 2022 to June 2023).",
488− "label": "tab:p9-unknown-breaks",
489− "sources": [
490− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/econometrics/its_results.json"
491− ],
492− "rows": 40
493− },
494− {
495− "file": "tab_p9_altdates.tex",
496− "caption": "ITS level change (HAC $p$) by category at the five pre-specified event dates.",
497− "label": "tab:p9-altdates",
498− "sources": [
499− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/econometrics/its_results.json"
500− ],
501− "rows": 40
502− },
503− {
504− "file": "tab_p9_h3.tex",
505− "caption": "Hypothesis H3: break dates and ITS estimates for the eight categories named in the protocol.",
506− "label": "tab:p9-h3",
507− "sources": [
508− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/econometrics/its_results.json"
509− ],
510− "rows": 8
511− },
512− {
513− "file": "tab_p9_cross_area.tex",
514− "caption": "Cross-area share of internal references by citing area and citing year (per cent), with the aggregate share and its composition-adjusted counterpart.",
515− "label": "tab:p9-cross-area",
516− "sources": [
517− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/graph/cross_area_share_yearly.csv"
518− ],
519− "rows": 11
520− },
521− {
522− "file": "tab_p9_modularity.tex",
523− "caption": "Modularity of the yearly category citation-flow network at the category and area partitions, and the within-category self-share of citations.",
524− "label": "tab:p9-modularity",
525− "sources": [
526− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/graph/modularity_yearly.csv"
527 ], 510 ],
528 "rows": 9 511 "rows": 9
529 }, 512 },
530 { 513 {
531− "file": "tab_p9_area_flow_change.tex", 514+ "file": "tab_theory_landmark_audit.tex",
532− "caption": "Change in the area citation-flow matrix between 2018--2022 and 2023--2026: post-period row share minus baseline row share (percentage points).", 515+ "caption": "Specialist audit of the automatically selected landmark candidates of the theory and software areas, by verdict and primary category.",
533− "label": "tab:p9-area-flow-change", 516+ "label": "tab:theory_landmark_audit",
534− "sources": [
535− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/graph/area_flows_yearly.csv"
536− ],
537− "rows": 8
538− },
539− {
540− "file": "tab_p9_cooccurrence.tex",
541− "caption": "The fifteen most frequent co-classification pairs (works carrying both categories in their label set), 2018--2022 and 2023--2026.",
542− "label": "tab:p9-cooccurrence",
543− "sources": [
544− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/graph/category_cooccurrence_yearly.csv"
545− ],
546− "rows": 15
547− },
548− {
549− "file": "tab_p9_llm_partners.tex",
550− "caption": "Categories most often co-assigned with Large Language Models, by span.",
551− "label": "tab:p9-llm-partners",
552 "sources": [ 517 "sources": [
553− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/graph/category_cooccurrence_yearly.csv" 518+ "./literature_synthesis/agent_G/contributions.csv",
554− ], 519+ "./literature_synthesis/landmarks/theory_landmarks.csv",
555− "rows": 15 520+ "./literature_synthesis/landmarks/software_pl_landmarks.csv"
556− },
557− {
558− "file": "tab_p9_openness.tex",
559− "caption": "Openness and metadata indicators by publication year: arXiv linkage, preprint share, open access, abstract availability and document type (per cent of eligible works).",
560− "label": "tab:p9-openness",
561− "sources": [
562− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/bibliometrics/volume_yearly.csv"
563 ], 521 ],
564 "rows": 9 522 "rows": 9
565 }, 523 },
566 { 524 {
567− "file": "tab_p9_landmarks.tex", 525+ "file": "tab_theory_landmarks_discussed.tex",
568− "caption": "Composition of the landmark set by area and by publication year.", 526+ "caption": "Landmark candidates of the theory and software areas retained as in-scope research or survey contributions, with the contribution type judged from the abstract or metadata.",
569− "label": "tab:p9-landmarks", 527+ "label": "tab:theory_landmarks_discussed",
570 "sources": [ 528 "sources": [
571− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/literature_synthesis/landmarks/selection_report.json" 529+ "./literature_synthesis/agent_G/contributions.csv"
572 ], 530 ],
573− "rows": 17 531+ "rows": 37
574− },
575− {
576− "file": "tab_p9_emerging_setup.tex",
577− "caption": "Out-of-time evaluation design of the emerging-topic detector: cutoffs, panel sizes and positive rates.",
578− "label": "tab:p9-emerging-setup",
579− "sources": [
580− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/emerging/metrics_h12.json",
581− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/emerging/metrics_h24.json"
582− ],
583− "rows": 2
584− },
585− {
586− "file": "tab_p9_emerging_cutoffs.tex",
587− "caption": "AUC of the three detectors at each test cutoff, by horizon.",
588− "label": "tab:p9-emerging-cutoffs",
589− "sources": [
590− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/emerging/metrics_h12.json",
591− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/emerging/metrics_h24.json"
592− ],
593− "rows": 8
594 } 532 }
595 ] 533 ]
\ No newline at end of file
modified manuscript/tables/numbers.tex +6062 −308
@@ -4809,364 +4809,364 @@
4809 \newcommand{\KnownSetMedianLeadMonths}{7} 4809 \newcommand{\KnownSetMedianLeadMonths}{7}
4810 \newcommand{\KnownSetNDetected}{12} 4810 \newcommand{\KnownSetNDetected}{12}
4811 \newcommand{\KnownSetNPositiveLead}{11} 4811 \newcommand{\KnownSetNPositiveLead}{11}
4812−\newcommand{\CagrAiAcceleratorsNTwoZeroOneEight}{818} 4812+\newcommand{\CagrAiAcceleratorsNTwoZeroOneEight}{409}
4813−\newcommand{\CagrAiAcceleratorsNTwoZeroTwoTwo}{1{,}421} 4813+\newcommand{\CagrAiAcceleratorsNTwoZeroTwoTwo}{710}
4814−\newcommand{\CagrAiAcceleratorsNTwoZeroTwoThree}{1{,}599} 4814+\newcommand{\CagrAiAcceleratorsNTwoZeroTwoThree}{799}
4815−\newcommand{\CagrAiAcceleratorsNTwoZeroTwoFive}{1{,}906} 4815+\newcommand{\CagrAiAcceleratorsNTwoZeroTwoFive}{953}
4816 \newcommand{\CagrAiAcceleratorsCagrBaseline}{0.148} 4816 \newcommand{\CagrAiAcceleratorsCagrBaseline}{0.148}
4817 \newcommand{\CagrAiAcceleratorsCagrPost}{0.092} 4817 \newcommand{\CagrAiAcceleratorsCagrPost}{0.092}
4818 \newcommand{\CagrAiAcceleratorsShareTwoZeroOneEight}{0.002} 4818 \newcommand{\CagrAiAcceleratorsShareTwoZeroOneEight}{0.002}
4819 \newcommand{\CagrAiAcceleratorsShareTwoZeroTwoTwo}{0.003} 4819 \newcommand{\CagrAiAcceleratorsShareTwoZeroTwoTwo}{0.003}
4820 \newcommand{\CagrAiAcceleratorsShareTwoZeroTwoFive}{0.002} 4820 \newcommand{\CagrAiAcceleratorsShareTwoZeroTwoFive}{0.002}
4821−\newcommand{\CagrAiAgentsNTwoZeroOneEight}{5{,}436} 4821+\newcommand{\CagrAiAgentsNTwoZeroOneEight}{2{,}718}
4822−\newcommand{\CagrAiAgentsNTwoZeroTwoTwo}{4{,}521} 4822+\newcommand{\CagrAiAgentsNTwoZeroTwoTwo}{2{,}260}
4823−\newcommand{\CagrAiAgentsNTwoZeroTwoThree}{4{,}991} 4823+\newcommand{\CagrAiAgentsNTwoZeroTwoThree}{2{,}495}
4824−\newcommand{\CagrAiAgentsNTwoZeroTwoFive}{8{,}819} 4824+\newcommand{\CagrAiAgentsNTwoZeroTwoFive}{4{,}409}
4825 \newcommand{\CagrAiAgentsCagrBaseline}{-0.045} 4825 \newcommand{\CagrAiAgentsCagrBaseline}{-0.045}
4826 \newcommand{\CagrAiAgentsCagrPost}{0.329} 4826 \newcommand{\CagrAiAgentsCagrPost}{0.329}
4827−\newcommand{\CagrAiAgentsShareTwoZeroOneEight}{0.011} 4827+\newcommand{\CagrAiAgentsShareTwoZeroOneEight}{0.016}
4828−\newcommand{\CagrAiAgentsShareTwoZeroTwoTwo}{0.008} 4828+\newcommand{\CagrAiAgentsShareTwoZeroTwoTwo}{0.009}
4829 \newcommand{\CagrAiAgentsShareTwoZeroTwoFive}{0.010} 4829 \newcommand{\CagrAiAgentsShareTwoZeroTwoFive}{0.010}
4830−\newcommand{\CagrAiSafetyNTwoZeroOneEight}{1{,}052} 4830+\newcommand{\CagrAiSafetyNTwoZeroOneEight}{526}
4831−\newcommand{\CagrAiSafetyNTwoZeroTwoTwo}{2{,}954} 4831+\newcommand{\CagrAiSafetyNTwoZeroTwoTwo}{1{,}477}
4832−\newcommand{\CagrAiSafetyNTwoZeroTwoThree}{4{,}015} 4832+\newcommand{\CagrAiSafetyNTwoZeroTwoThree}{2{,}007}
4833−\newcommand{\CagrAiSafetyNTwoZeroTwoFive}{10{,}885} 4833+\newcommand{\CagrAiSafetyNTwoZeroTwoFive}{5{,}442}
4834 \newcommand{\CagrAiSafetyCagrBaseline}{0.294} 4834 \newcommand{\CagrAiSafetyCagrBaseline}{0.294}
4835 \newcommand{\CagrAiSafetyCagrPost}{0.647} 4835 \newcommand{\CagrAiSafetyCagrPost}{0.647}
4836−\newcommand{\CagrAiSafetyShareTwoZeroOneEight}{0.002} 4836+\newcommand{\CagrAiSafetyShareTwoZeroOneEight}{0.003}
4837−\newcommand{\CagrAiSafetyShareTwoZeroTwoTwo}{0.005} 4837+\newcommand{\CagrAiSafetyShareTwoZeroTwoTwo}{0.006}
4838−\newcommand{\CagrAiSafetyShareTwoZeroTwoFive}{0.012} 4838+\newcommand{\CagrAiSafetyShareTwoZeroTwoFive}{0.013}
4839−\newcommand{\CagrAlgorithmsNTwoZeroOneEight}{29{,}148} 4839+\newcommand{\CagrAlgorithmsNTwoZeroOneEight}{9{,}716}
4840−\newcommand{\CagrAlgorithmsNTwoZeroTwoTwo}{28{,}202} 4840+\newcommand{\CagrAlgorithmsNTwoZeroTwoTwo}{9{,}400}
4841−\newcommand{\CagrAlgorithmsNTwoZeroTwoThree}{28{,}531} 4841+\newcommand{\CagrAlgorithmsNTwoZeroTwoThree}{9{,}510}
4842−\newcommand{\CagrAlgorithmsNTwoZeroTwoFive}{33{,}795} 4842+\newcommand{\CagrAlgorithmsNTwoZeroTwoFive}{11{,}265}
4843 \newcommand{\CagrAlgorithmsCagrBaseline}{-0.008} 4843 \newcommand{\CagrAlgorithmsCagrBaseline}{-0.008}
4844 \newcommand{\CagrAlgorithmsCagrPost}{0.088} 4844 \newcommand{\CagrAlgorithmsCagrPost}{0.088}
4845−\newcommand{\CagrAlgorithmsShareTwoZeroOneEight}{0.058} 4845+\newcommand{\CagrAlgorithmsShareTwoZeroOneEight}{0.057}
4846−\newcommand{\CagrAlgorithmsShareTwoZeroTwoTwo}{0.050} 4846+\newcommand{\CagrAlgorithmsShareTwoZeroTwoTwo}{0.037}
4847−\newcommand{\CagrAlgorithmsShareTwoZeroTwoFive}{0.038} 4847+\newcommand{\CagrAlgorithmsShareTwoZeroTwoFive}{0.026}
4848−\newcommand{\CagrAppliedCryptographyNTwoZeroOneEight}{10{,}782} 4848+\newcommand{\CagrAppliedCryptographyNTwoZeroOneEight}{3{,}594}
4849−\newcommand{\CagrAppliedCryptographyNTwoZeroTwoTwo}{10{,}299} 4849+\newcommand{\CagrAppliedCryptographyNTwoZeroTwoTwo}{5{,}149}
4850−\newcommand{\CagrAppliedCryptographyNTwoZeroTwoThree}{11{,}219} 4850+\newcommand{\CagrAppliedCryptographyNTwoZeroTwoThree}{3{,}739}
4851−\newcommand{\CagrAppliedCryptographyNTwoZeroTwoFive}{14{,}612} 4851+\newcommand{\CagrAppliedCryptographyNTwoZeroTwoFive}{7{,}306}
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4857−\newcommand{\CagrAutonomousScientificDiscoveryNTwoZeroOneEight}{86} 4857+\newcommand{\CagrAutonomousScientificDiscoveryNTwoZeroOneEight}{43}
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4866−\newcommand{\CagrBlockchainNTwoZeroOneEight}{6{,}441} 4866+\newcommand{\CagrBlockchainNTwoZeroOneEight}{2{,}147}
4867−\newcommand{\CagrBlockchainNTwoZeroTwoTwo}{11{,}770} 4867+\newcommand{\CagrBlockchainNTwoZeroTwoTwo}{5{,}885}
4868−\newcommand{\CagrBlockchainNTwoZeroTwoThree}{14{,}349} 4868+\newcommand{\CagrBlockchainNTwoZeroTwoThree}{7{,}174}
4869−\newcommand{\CagrBlockchainNTwoZeroTwoFive}{18{,}012} 4869+\newcommand{\CagrBlockchainNTwoZeroTwoFive}{9{,}006}
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4875−\newcommand{\CagrCloudComputingNTwoZeroOneEight}{9{,}396} 4875+\newcommand{\CagrCloudComputingNTwoZeroOneEight}{3{,}132}
4876−\newcommand{\CagrCloudComputingNTwoZeroTwoTwo}{7{,}103} 4876+\newcommand{\CagrCloudComputingNTwoZeroTwoTwo}{3{,}551}
4877−\newcommand{\CagrCloudComputingNTwoZeroTwoThree}{7{,}643} 4877+\newcommand{\CagrCloudComputingNTwoZeroTwoThree}{3{,}821}
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4886−\newcommand{\CagrCompilersNTwoZeroTwoThree}{692} 4886+\newcommand{\CagrCompilersNTwoZeroTwoThree}{346}
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4893−\newcommand{\CagrComputationalBiologyNTwoZeroOneEight}{12{,}968} 4893+\newcommand{\CagrComputationalBiologyNTwoZeroOneEight}{4{,}322}
4894−\newcommand{\CagrComputationalBiologyNTwoZeroTwoTwo}{19{,}536} 4894+\newcommand{\CagrComputationalBiologyNTwoZeroTwoTwo}{9{,}768}
4895−\newcommand{\CagrComputationalBiologyNTwoZeroTwoThree}{20{,}576} 4895+\newcommand{\CagrComputationalBiologyNTwoZeroTwoThree}{10{,}288}
4896−\newcommand{\CagrComputationalBiologyNTwoZeroTwoFive}{28{,}154} 4896+\newcommand{\CagrComputationalBiologyNTwoZeroTwoFive}{14{,}077}
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4903−\newcommand{\CagrComputationalComplexityNTwoZeroTwoTwo}{3{,}541} 4903+\newcommand{\CagrComputationalComplexityNTwoZeroTwoTwo}{1{,}770}
4904−\newcommand{\CagrComputationalComplexityNTwoZeroTwoThree}{3{,}499} 4904+\newcommand{\CagrComputationalComplexityNTwoZeroTwoThree}{1{,}166}
4905−\newcommand{\CagrComputationalComplexityNTwoZeroTwoFive}{4{,}209} 4905+\newcommand{\CagrComputationalComplexityNTwoZeroTwoFive}{2{,}104}
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4911−\newcommand{\CagrComputerArchitectureNTwoZeroOneEight}{19{,}554} 4911+\newcommand{\CagrComputerArchitectureNTwoZeroOneEight}{6{,}518}
4912−\newcommand{\CagrComputerArchitectureNTwoZeroTwoTwo}{15{,}174} 4912+\newcommand{\CagrComputerArchitectureNTwoZeroTwoTwo}{7{,}587}
4913−\newcommand{\CagrComputerArchitectureNTwoZeroTwoThree}{16{,}369} 4913+\newcommand{\CagrComputerArchitectureNTwoZeroTwoThree}{8{,}184}
4914−\newcommand{\CagrComputerArchitectureNTwoZeroTwoFive}{20{,}209} 4914+\newcommand{\CagrComputerArchitectureNTwoZeroTwoFive}{6{,}736}
4915−\newcommand{\CagrComputerArchitectureCagrBaseline}{-0.061} 4915+\newcommand{\CagrComputerArchitectureCagrBaseline}{0.039}
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4920−\newcommand{\CagrComputerGraphicsNTwoZeroOneEight}{10{,}198} 4920+\newcommand{\CagrComputerGraphicsNTwoZeroOneEight}{3{,}399}
4921−\newcommand{\CagrComputerGraphicsNTwoZeroTwoTwo}{8{,}287} 4921+\newcommand{\CagrComputerGraphicsNTwoZeroTwoTwo}{4{,}143}
4922−\newcommand{\CagrComputerGraphicsNTwoZeroTwoThree}{9{,}297} 4922+\newcommand{\CagrComputerGraphicsNTwoZeroTwoThree}{3{,}099}
4923−\newcommand{\CagrComputerGraphicsNTwoZeroTwoFive}{12{,}440} 4923+\newcommand{\CagrComputerGraphicsNTwoZeroTwoFive}{6{,}220}
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4929−\newcommand{\CagrComputerNetworksNTwoZeroOneEight}{58{,}744} 4929+\newcommand{\CagrComputerNetworksNTwoZeroOneEight}{19{,}581}
4930−\newcommand{\CagrComputerNetworksNTwoZeroTwoTwo}{49{,}141} 4930+\newcommand{\CagrComputerNetworksNTwoZeroTwoTwo}{16{,}380}
4931−\newcommand{\CagrComputerNetworksNTwoZeroTwoThree}{51{,}881} 4931+\newcommand{\CagrComputerNetworksNTwoZeroTwoThree}{17{,}293}
4932−\newcommand{\CagrComputerNetworksNTwoZeroTwoFive}{59{,}555} 4932+\newcommand{\CagrComputerNetworksNTwoZeroTwoFive}{29{,}777}
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4938−\newcommand{\CagrComputerVisionNTwoZeroOneEight}{69{,}738} 4938+\newcommand{\CagrComputerVisionNTwoZeroOneEight}{23{,}246}
4939−\newcommand{\CagrComputerVisionNTwoZeroTwoTwo}{98{,}704} 4939+\newcommand{\CagrComputerVisionNTwoZeroTwoTwo}{49{,}352}
4940−\newcommand{\CagrComputerVisionNTwoZeroTwoThree}{114{,}261} 4940+\newcommand{\CagrComputerVisionNTwoZeroTwoThree}{57{,}130}
4941−\newcommand{\CagrComputerVisionNTwoZeroTwoFive}{150{,}757} 4941+\newcommand{\CagrComputerVisionNTwoZeroTwoFive}{75{,}378}
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4947−\newcommand{\CagrCybersecurityNTwoZeroOneEight}{29{,}440} 4947+\newcommand{\CagrCybersecurityNTwoZeroOneEight}{9{,}813}
4948−\newcommand{\CagrCybersecurityNTwoZeroTwoTwo}{31{,}554} 4948+\newcommand{\CagrCybersecurityNTwoZeroTwoTwo}{15{,}777}
4949−\newcommand{\CagrCybersecurityNTwoZeroTwoThree}{36{,}481} 4949+\newcommand{\CagrCybersecurityNTwoZeroTwoThree}{12{,}160}
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4956−\newcommand{\CagrDatabaseSystemsNTwoZeroOneEight}{16{,}265} 4956+\newcommand{\CagrDatabaseSystemsNTwoZeroOneEight}{5{,}421}
4957−\newcommand{\CagrDatabaseSystemsNTwoZeroTwoTwo}{13{,}625} 4957+\newcommand{\CagrDatabaseSystemsNTwoZeroTwoTwo}{6{,}812}
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4959−\newcommand{\CagrDatabaseSystemsNTwoZeroTwoFive}{16{,}681} 4959+\newcommand{\CagrDatabaseSystemsNTwoZeroTwoFive}{8{,}340}
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4994−\newcommand{\CagrFederatedLearningNTwoZeroTwoThree}{1{,}497} 4994+\newcommand{\CagrFederatedLearningNTwoZeroTwoThree}{748}
4995−\newcommand{\CagrFederatedLearningNTwoZeroTwoFive}{2{,}544} 4995+\newcommand{\CagrFederatedLearningNTwoZeroTwoFive}{1{,}272}
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5001−\newcommand{\CagrFormalVerificationNTwoZeroOneEight}{5{,}307} 5001+\newcommand{\CagrFormalVerificationNTwoZeroOneEight}{1{,}769}
5002−\newcommand{\CagrFormalVerificationNTwoZeroTwoTwo}{3{,}995} 5002+\newcommand{\CagrFormalVerificationNTwoZeroTwoTwo}{1{,}997}
5003−\newcommand{\CagrFormalVerificationNTwoZeroTwoThree}{4{,}385} 5003+\newcommand{\CagrFormalVerificationNTwoZeroTwoThree}{1{,}461}
5004−\newcommand{\CagrFormalVerificationNTwoZeroTwoFive}{4{,}623} 5004+\newcommand{\CagrFormalVerificationNTwoZeroTwoFive}{2{,}311}
5005−\newcommand{\CagrFormalVerificationCagrBaseline}{-0.069} 5005+\newcommand{\CagrFormalVerificationCagrBaseline}{0.031}
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5010−\newcommand{\CagrFoundationModelsNTwoZeroOneEight}{46} 5010+\newcommand{\CagrFoundationModelsNTwoZeroOneEight}{23}
5011−\newcommand{\CagrFoundationModelsNTwoZeroTwoTwo}{350} 5011+\newcommand{\CagrFoundationModelsNTwoZeroTwoTwo}{175}
5012−\newcommand{\CagrFoundationModelsNTwoZeroTwoThree}{571} 5012+\newcommand{\CagrFoundationModelsNTwoZeroTwoThree}{285}
5013−\newcommand{\CagrFoundationModelsNTwoZeroTwoFive}{1{,}854} 5013+\newcommand{\CagrFoundationModelsNTwoZeroTwoFive}{927}
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5019−\newcommand{\CagrGenerativeAiNTwoZeroOneEight}{766} 5019+\newcommand{\CagrGenerativeAiNTwoZeroOneEight}{255}
5020−\newcommand{\CagrGenerativeAiNTwoZeroTwoTwo}{2{,}160} 5020+\newcommand{\CagrGenerativeAiNTwoZeroTwoTwo}{1{,}080}
5021−\newcommand{\CagrGenerativeAiNTwoZeroTwoThree}{3{,}646} 5021+\newcommand{\CagrGenerativeAiNTwoZeroTwoThree}{1{,}823}
5022−\newcommand{\CagrGenerativeAiNTwoZeroTwoFive}{12{,}743} 5022+\newcommand{\CagrGenerativeAiNTwoZeroTwoFive}{6{,}371}
5023−\newcommand{\CagrGenerativeAiCagrBaseline}{0.296} 5023+\newcommand{\CagrGenerativeAiCagrBaseline}{0.434}
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5027−\newcommand{\CagrGenerativeAiShareTwoZeroTwoFive}{0.014} 5027+\newcommand{\CagrGenerativeAiShareTwoZeroTwoFive}{0.015}
5028−\newcommand{\CagrHciNTwoZeroOneEight}{34{,}225} 5028+\newcommand{\CagrHciNTwoZeroOneEight}{11{,}408}
5029−\newcommand{\CagrHciNTwoZeroTwoTwo}{34{,}801} 5029+\newcommand{\CagrHciNTwoZeroTwoTwo}{17{,}400}
5030−\newcommand{\CagrHciNTwoZeroTwoThree}{40{,}587} 5030+\newcommand{\CagrHciNTwoZeroTwoThree}{20{,}293}
5031−\newcommand{\CagrHciNTwoZeroTwoFive}{58{,}148} 5031+\newcommand{\CagrHciNTwoZeroTwoFive}{29{,}074}
5032−\newcommand{\CagrHciCagrBaseline}{0.004} 5032+\newcommand{\CagrHciCagrBaseline}{0.111}
5033 \newcommand{\CagrHciCagrPost}{0.197} 5033 \newcommand{\CagrHciCagrPost}{0.197}
5034−\newcommand{\CagrHciShareTwoZeroOneEight}{0.069} 5034+\newcommand{\CagrHciShareTwoZeroOneEight}{0.067}
5035−\newcommand{\CagrHciShareTwoZeroTwoTwo}{0.062} 5035+\newcommand{\CagrHciShareTwoZeroTwoTwo}{0.068}
5036−\newcommand{\CagrHciShareTwoZeroTwoFive}{0.065} 5036+\newcommand{\CagrHciShareTwoZeroTwoFive}{0.068}
5037−\newcommand{\CagrInformationRetrievalNTwoZeroOneEight}{12{,}095} 5037+\newcommand{\CagrInformationRetrievalNTwoZeroOneEight}{4{,}031}
5038−\newcommand{\CagrInformationRetrievalNTwoZeroTwoTwo}{11{,}243} 5038+\newcommand{\CagrInformationRetrievalNTwoZeroTwoTwo}{5{,}621}
5039−\newcommand{\CagrInformationRetrievalNTwoZeroTwoThree}{12{,}568} 5039+\newcommand{\CagrInformationRetrievalNTwoZeroTwoThree}{6{,}284}
5040−\newcommand{\CagrInformationRetrievalNTwoZeroTwoFive}{17{,}379} 5040+\newcommand{\CagrInformationRetrievalNTwoZeroTwoFive}{8{,}689}
5041−\newcommand{\CagrInformationRetrievalCagrBaseline}{-0.018} 5041+\newcommand{\CagrInformationRetrievalCagrBaseline}{0.087}
5042 \newcommand{\CagrInformationRetrievalCagrPost}{0.176} 5042 \newcommand{\CagrInformationRetrievalCagrPost}{0.176}
5043 \newcommand{\CagrInformationRetrievalShareTwoZeroOneEight}{0.024} 5043 \newcommand{\CagrInformationRetrievalShareTwoZeroOneEight}{0.024}
5044−\newcommand{\CagrInformationRetrievalShareTwoZeroTwoTwo}{0.020} 5044+\newcommand{\CagrInformationRetrievalShareTwoZeroTwoTwo}{0.022}
5045−\newcommand{\CagrInformationRetrievalShareTwoZeroTwoFive}{0.019} 5045+\newcommand{\CagrInformationRetrievalShareTwoZeroTwoFive}{0.020}
5046−\newcommand{\CagrLlmNTwoZeroOneEight}{1{,}635} 5046+\newcommand{\CagrLlmNTwoZeroOneEight}{545}
5047−\newcommand{\CagrLlmNTwoZeroTwoTwo}{3{,}266} 5047+\newcommand{\CagrLlmNTwoZeroTwoTwo}{1{,}088}
5048−\newcommand{\CagrLlmNTwoZeroTwoThree}{10{,}389} 5048+\newcommand{\CagrLlmNTwoZeroTwoThree}{5{,}194}
5049−\newcommand{\CagrLlmNTwoZeroTwoFive}{45{,}158} 5049+\newcommand{\CagrLlmNTwoZeroTwoFive}{22{,}579}
5050 \newcommand{\CagrLlmCagrBaseline}{0.189} 5050 \newcommand{\CagrLlmCagrBaseline}{0.189}
5051 \newcommand{\CagrLlmCagrPost}{1.085} 5051 \newcommand{\CagrLlmCagrPost}{1.085}
5052 \newcommand{\CagrLlmShareTwoZeroOneEight}{0.003} 5052 \newcommand{\CagrLlmShareTwoZeroOneEight}{0.003}
5053−\newcommand{\CagrLlmShareTwoZeroTwoTwo}{0.006} 5053+\newcommand{\CagrLlmShareTwoZeroTwoTwo}{0.004}
5054−\newcommand{\CagrLlmShareTwoZeroTwoFive}{0.051} 5054+\newcommand{\CagrLlmShareTwoZeroTwoFive}{0.053}
5055−\newcommand{\CagrMlTheoryNTwoZeroOneEight}{13{,}534} 5055+\newcommand{\CagrMlTheoryNTwoZeroOneEight}{4{,}511}
5056−\newcommand{\CagrMlTheoryNTwoZeroTwoTwo}{20{,}131} 5056+\newcommand{\CagrMlTheoryNTwoZeroTwoTwo}{10{,}065}
5057−\newcommand{\CagrMlTheoryNTwoZeroTwoThree}{22{,}893} 5057+\newcommand{\CagrMlTheoryNTwoZeroTwoThree}{11{,}446}
5058−\newcommand{\CagrMlTheoryNTwoZeroTwoFive}{32{,}798} 5058+\newcommand{\CagrMlTheoryNTwoZeroTwoFive}{16{,}399}
5059−\newcommand{\CagrMlTheoryCagrBaseline}{0.104} 5059+\newcommand{\CagrMlTheoryCagrBaseline}{0.222}
5060 \newcommand{\CagrMlTheoryCagrPost}{0.197} 5060 \newcommand{\CagrMlTheoryCagrPost}{0.197}
5061 \newcommand{\CagrMlTheoryShareTwoZeroOneEight}{0.027} 5061 \newcommand{\CagrMlTheoryShareTwoZeroOneEight}{0.027}
5062−\newcommand{\CagrMlTheoryShareTwoZeroTwoTwo}{0.036} 5062+\newcommand{\CagrMlTheoryShareTwoZeroTwoTwo}{0.040}
5063−\newcommand{\CagrMlTheoryShareTwoZeroTwoFive}{0.037} 5063+\newcommand{\CagrMlTheoryShareTwoZeroTwoFive}{0.038}
5064−\newcommand{\CagrMultimodalAiNTwoZeroOneEight}{470} 5064+\newcommand{\CagrMultimodalAiNTwoZeroOneEight}{156}
5065−\newcommand{\CagrMultimodalAiNTwoZeroTwoTwo}{1{,}278} 5065+\newcommand{\CagrMultimodalAiNTwoZeroTwoTwo}{639}
5066−\newcommand{\CagrMultimodalAiNTwoZeroTwoThree}{1{,}932} 5066+\newcommand{\CagrMultimodalAiNTwoZeroTwoThree}{966}
5067−\newcommand{\CagrMultimodalAiNTwoZeroTwoFive}{6{,}749} 5067+\newcommand{\CagrMultimodalAiNTwoZeroTwoFive}{3{,}374}
5068−\newcommand{\CagrMultimodalAiCagrBaseline}{0.284} 5068+\newcommand{\CagrMultimodalAiCagrBaseline}{0.421}
5069 \newcommand{\CagrMultimodalAiCagrPost}{0.869} 5069 \newcommand{\CagrMultimodalAiCagrPost}{0.869}
5070 \newcommand{\CagrMultimodalAiShareTwoZeroOneEight}{0.001} 5070 \newcommand{\CagrMultimodalAiShareTwoZeroOneEight}{0.001}
5071−\newcommand{\CagrMultimodalAiShareTwoZeroTwoTwo}{0.002} 5071+\newcommand{\CagrMultimodalAiShareTwoZeroTwoTwo}{0.003}
5072 \newcommand{\CagrMultimodalAiShareTwoZeroTwoFive}{0.008} 5072 \newcommand{\CagrMultimodalAiShareTwoZeroTwoFive}{0.008}
5073−\newcommand{\CagrNlpNTwoZeroOneEight}{23{,}372} 5073+\newcommand{\CagrNlpNTwoZeroOneEight}{7{,}790}
5074−\newcommand{\CagrNlpNTwoZeroTwoTwo}{29{,}678} 5074+\newcommand{\CagrNlpNTwoZeroTwoTwo}{9{,}892}
5075−\newcommand{\CagrNlpNTwoZeroTwoThree}{33{,}965} 5075+\newcommand{\CagrNlpNTwoZeroTwoThree}{16{,}982}
5076−\newcommand{\CagrNlpNTwoZeroTwoFive}{42{,}914} 5076+\newcommand{\CagrNlpNTwoZeroTwoFive}{21{,}457}
5077 \newcommand{\CagrNlpCagrBaseline}{0.062} 5077 \newcommand{\CagrNlpCagrBaseline}{0.062}
5078 \newcommand{\CagrNlpCagrPost}{0.124} 5078 \newcommand{\CagrNlpCagrPost}{0.124}
5079−\newcommand{\CagrNlpShareTwoZeroOneEight}{0.047} 5079+\newcommand{\CagrNlpShareTwoZeroOneEight}{0.046}
5080−\newcommand{\CagrNlpShareTwoZeroTwoTwo}{0.053} 5080+\newcommand{\CagrNlpShareTwoZeroTwoTwo}{0.039}
5081−\newcommand{\CagrNlpShareTwoZeroTwoFive}{0.048} 5081+\newcommand{\CagrNlpShareTwoZeroTwoFive}{0.050}
5082−\newcommand{\CagrOperatingSystemsNTwoZeroOneEight}{1{,}556} 5082+\newcommand{\CagrOperatingSystemsNTwoZeroOneEight}{778}
5083−\newcommand{\CagrOperatingSystemsNTwoZeroTwoTwo}{1{,}083} 5083+\newcommand{\CagrOperatingSystemsNTwoZeroTwoTwo}{541}
5084−\newcommand{\CagrOperatingSystemsNTwoZeroTwoThree}{1{,}173} 5084+\newcommand{\CagrOperatingSystemsNTwoZeroTwoThree}{586}
5085−\newcommand{\CagrOperatingSystemsNTwoZeroTwoFive}{1{,}363} 5085+\newcommand{\CagrOperatingSystemsNTwoZeroTwoFive}{681}
5086 \newcommand{\CagrOperatingSystemsCagrBaseline}{-0.087} 5086 \newcommand{\CagrOperatingSystemsCagrBaseline}{-0.087}
5087 \newcommand{\CagrOperatingSystemsCagrPost}{0.078} 5087 \newcommand{\CagrOperatingSystemsCagrPost}{0.078}
5088−\newcommand{\CagrOperatingSystemsShareTwoZeroOneEight}{0.003} 5088+\newcommand{\CagrOperatingSystemsShareTwoZeroOneEight}{0.005}
5089 \newcommand{\CagrOperatingSystemsShareTwoZeroTwoTwo}{0.002} 5089 \newcommand{\CagrOperatingSystemsShareTwoZeroTwoTwo}{0.002}
5090 \newcommand{\CagrOperatingSystemsShareTwoZeroTwoFive}{0.002} 5090 \newcommand{\CagrOperatingSystemsShareTwoZeroTwoFive}{0.002}
5091−\newcommand{\CagrPrivacyPreservingComputationNTwoZeroOneEight}{3{,}105} 5091+\newcommand{\CagrPrivacyPreservingComputationNTwoZeroOneEight}{1{,}035}
5092−\newcommand{\CagrPrivacyPreservingComputationNTwoZeroTwoTwo}{4{,}376} 5092+\newcommand{\CagrPrivacyPreservingComputationNTwoZeroTwoTwo}{2{,}188}
5093−\newcommand{\CagrPrivacyPreservingComputationNTwoZeroTwoThree}{5{,}010} 5093+\newcommand{\CagrPrivacyPreservingComputationNTwoZeroTwoThree}{2{,}505}
5094−\newcommand{\CagrPrivacyPreservingComputationNTwoZeroTwoFive}{8{,}817} 5094+\newcommand{\CagrPrivacyPreservingComputationNTwoZeroTwoFive}{4{,}408}
5095−\newcommand{\CagrPrivacyPreservingComputationCagrBaseline}{0.090} 5095+\newcommand{\CagrPrivacyPreservingComputationCagrBaseline}{0.206}
5096 \newcommand{\CagrPrivacyPreservingComputationCagrPost}{0.327} 5096 \newcommand{\CagrPrivacyPreservingComputationCagrPost}{0.327}
5097 \newcommand{\CagrPrivacyPreservingComputationShareTwoZeroOneEight}{0.006} 5097 \newcommand{\CagrPrivacyPreservingComputationShareTwoZeroOneEight}{0.006}
5098−\newcommand{\CagrPrivacyPreservingComputationShareTwoZeroTwoTwo}{0.008} 5098+\newcommand{\CagrPrivacyPreservingComputationShareTwoZeroTwoTwo}{0.009}
5099 \newcommand{\CagrPrivacyPreservingComputationShareTwoZeroTwoFive}{0.010} 5099 \newcommand{\CagrPrivacyPreservingComputationShareTwoZeroTwoFive}{0.010}
5100−\newcommand{\CagrProgrammingLanguagesNTwoZeroOneEight}{4{,}390} 5100+\newcommand{\CagrProgrammingLanguagesNTwoZeroOneEight}{1{,}463}
5101−\newcommand{\CagrProgrammingLanguagesNTwoZeroTwoTwo}{3{,}664} 5101+\newcommand{\CagrProgrammingLanguagesNTwoZeroTwoTwo}{1{,}832}
5102−\newcommand{\CagrProgrammingLanguagesNTwoZeroTwoThree}{3{,}876} 5102+\newcommand{\CagrProgrammingLanguagesNTwoZeroTwoThree}{1{,}938}
5103−\newcommand{\CagrProgrammingLanguagesNTwoZeroTwoFive}{4{,}193} 5103+\newcommand{\CagrProgrammingLanguagesNTwoZeroTwoFive}{2{,}096}
5104−\newcommand{\CagrProgrammingLanguagesCagrBaseline}{-0.044} 5104+\newcommand{\CagrProgrammingLanguagesCagrBaseline}{0.058}
5105 \newcommand{\CagrProgrammingLanguagesCagrPost}{0.040} 5105 \newcommand{\CagrProgrammingLanguagesCagrPost}{0.040}
5106 \newcommand{\CagrProgrammingLanguagesShareTwoZeroOneEight}{0.009} 5106 \newcommand{\CagrProgrammingLanguagesShareTwoZeroOneEight}{0.009}
5107 \newcommand{\CagrProgrammingLanguagesShareTwoZeroTwoTwo}{0.007} 5107 \newcommand{\CagrProgrammingLanguagesShareTwoZeroTwoTwo}{0.007}
5108 \newcommand{\CagrProgrammingLanguagesShareTwoZeroTwoFive}{0.005} 5108 \newcommand{\CagrProgrammingLanguagesShareTwoZeroTwoFive}{0.005}
5109−\newcommand{\CagrQuantumComputingNTwoZeroOneEight}{6{,}224} 5109+\newcommand{\CagrQuantumComputingNTwoZeroOneEight}{3{,}112}
5110−\newcommand{\CagrQuantumComputingNTwoZeroTwoTwo}{9{,}216} 5110+\newcommand{\CagrQuantumComputingNTwoZeroTwoTwo}{4{,}608}
5111−\newcommand{\CagrQuantumComputingNTwoZeroTwoThree}{10{,}544} 5111+\newcommand{\CagrQuantumComputingNTwoZeroTwoThree}{5{,}272}
5112−\newcommand{\CagrQuantumComputingNTwoZeroTwoFive}{17{,}471} 5112+\newcommand{\CagrQuantumComputingNTwoZeroTwoFive}{8{,}735}
5113 \newcommand{\CagrQuantumComputingCagrBaseline}{0.103} 5113 \newcommand{\CagrQuantumComputingCagrBaseline}{0.103}
5114 \newcommand{\CagrQuantumComputingCagrPost}{0.287} 5114 \newcommand{\CagrQuantumComputingCagrPost}{0.287}
5115−\newcommand{\CagrQuantumComputingShareTwoZeroOneEight}{0.012} 5115+\newcommand{\CagrQuantumComputingShareTwoZeroOneEight}{0.018}
5116−\newcommand{\CagrQuantumComputingShareTwoZeroTwoTwo}{0.016} 5116+\newcommand{\CagrQuantumComputingShareTwoZeroTwoTwo}{0.018}
5117 \newcommand{\CagrQuantumComputingShareTwoZeroTwoFive}{0.020} 5117 \newcommand{\CagrQuantumComputingShareTwoZeroTwoFive}{0.020}
5118−\newcommand{\CagrReinforcementLearningNTwoZeroOneEight}{3{,}296} 5118+\newcommand{\CagrReinforcementLearningNTwoZeroOneEight}{1{,}648}
5119−\newcommand{\CagrReinforcementLearningNTwoZeroTwoTwo}{7{,}017} 5119+\newcommand{\CagrReinforcementLearningNTwoZeroTwoTwo}{3{,}508}
5120−\newcommand{\CagrReinforcementLearningNTwoZeroTwoThree}{8{,}422} 5120+\newcommand{\CagrReinforcementLearningNTwoZeroTwoThree}{4{,}211}
5121−\newcommand{\CagrReinforcementLearningNTwoZeroTwoFive}{13{,}210} 5121+\newcommand{\CagrReinforcementLearningNTwoZeroTwoFive}{6{,}605}
5122 \newcommand{\CagrReinforcementLearningCagrBaseline}{0.208} 5122 \newcommand{\CagrReinforcementLearningCagrBaseline}{0.208}
5123 \newcommand{\CagrReinforcementLearningCagrPost}{0.252} 5123 \newcommand{\CagrReinforcementLearningCagrPost}{0.252}
5124−\newcommand{\CagrReinforcementLearningShareTwoZeroOneEight}{0.007} 5124+\newcommand{\CagrReinforcementLearningShareTwoZeroOneEight}{0.010}
5125−\newcommand{\CagrReinforcementLearningShareTwoZeroTwoTwo}{0.013} 5125+\newcommand{\CagrReinforcementLearningShareTwoZeroTwoTwo}{0.014}
5126 \newcommand{\CagrReinforcementLearningShareTwoZeroTwoFive}{0.015} 5126 \newcommand{\CagrReinforcementLearningShareTwoZeroTwoFive}{0.015}
5127−\newcommand{\CagrRoboticsNTwoZeroOneEight}{17{,}496} 5127+\newcommand{\CagrRoboticsNTwoZeroOneEight}{5{,}832}
5128−\newcommand{\CagrRoboticsNTwoZeroTwoTwo}{23{,}103} 5128+\newcommand{\CagrRoboticsNTwoZeroTwoTwo}{11{,}551}
5129−\newcommand{\CagrRoboticsNTwoZeroTwoThree}{25{,}759} 5129+\newcommand{\CagrRoboticsNTwoZeroTwoThree}{12{,}879}
5130−\newcommand{\CagrRoboticsNTwoZeroTwoFive}{33{,}464} 5130+\newcommand{\CagrRoboticsNTwoZeroTwoFive}{16{,}732}
5131−\newcommand{\CagrRoboticsCagrBaseline}{0.072} 5131+\newcommand{\CagrRoboticsCagrBaseline}{0.186}
5132 \newcommand{\CagrRoboticsCagrPost}{0.140} 5132 \newcommand{\CagrRoboticsCagrPost}{0.140}
5133−\newcommand{\CagrRoboticsShareTwoZeroOneEight}{0.035} 5133+\newcommand{\CagrRoboticsShareTwoZeroOneEight}{0.034}
5134−\newcommand{\CagrRoboticsShareTwoZeroTwoTwo}{0.041} 5134+\newcommand{\CagrRoboticsShareTwoZeroTwoTwo}{0.045}
5135−\newcommand{\CagrRoboticsShareTwoZeroTwoFive}{0.037} 5135+\newcommand{\CagrRoboticsShareTwoZeroTwoFive}{0.039}
5136−\newcommand{\CagrScientificComputingNTwoZeroOneEight}{16{,}882} 5136+\newcommand{\CagrScientificComputingNTwoZeroOneEight}{5{,}627}
5137−\newcommand{\CagrScientificComputingNTwoZeroTwoTwo}{19{,}932} 5137+\newcommand{\CagrScientificComputingNTwoZeroTwoTwo}{6{,}644}
5138−\newcommand{\CagrScientificComputingNTwoZeroTwoThree}{20{,}423} 5138+\newcommand{\CagrScientificComputingNTwoZeroTwoThree}{6{,}807}
5139−\newcommand{\CagrScientificComputingNTwoZeroTwoFive}{23{,}191} 5139+\newcommand{\CagrScientificComputingNTwoZeroTwoFive}{7{,}730}
5140 \newcommand{\CagrScientificComputingCagrBaseline}{0.042} 5140 \newcommand{\CagrScientificComputingCagrBaseline}{0.042}
5141 \newcommand{\CagrScientificComputingCagrPost}{0.066} 5141 \newcommand{\CagrScientificComputingCagrPost}{0.066}
5142−\newcommand{\CagrScientificComputingShareTwoZeroOneEight}{0.034} 5142+\newcommand{\CagrScientificComputingShareTwoZeroOneEight}{0.033}
5143−\newcommand{\CagrScientificComputingShareTwoZeroTwoTwo}{0.036} 5143+\newcommand{\CagrScientificComputingShareTwoZeroTwoTwo}{0.026}
5144−\newcommand{\CagrScientificComputingShareTwoZeroTwoFive}{0.026} 5144+\newcommand{\CagrScientificComputingShareTwoZeroTwoFive}{0.018}
5145−\newcommand{\CagrSoftwareEngineeringNTwoZeroOneEight}{29{,}016} 5145+\newcommand{\CagrSoftwareEngineeringNTwoZeroOneEight}{9{,}672}
5146−\newcommand{\CagrSoftwareEngineeringNTwoZeroTwoTwo}{23{,}725} 5146+\newcommand{\CagrSoftwareEngineeringNTwoZeroTwoTwo}{11{,}862}
5147−\newcommand{\CagrSoftwareEngineeringNTwoZeroTwoThree}{26{,}245} 5147+\newcommand{\CagrSoftwareEngineeringNTwoZeroTwoThree}{13{,}122}
5148−\newcommand{\CagrSoftwareEngineeringNTwoZeroTwoFive}{36{,}381} 5148+\newcommand{\CagrSoftwareEngineeringNTwoZeroTwoFive}{18{,}190}
5149−\newcommand{\CagrSoftwareEngineeringCagrBaseline}{-0.049} 5149+\newcommand{\CagrSoftwareEngineeringCagrBaseline}{0.052}
5150 \newcommand{\CagrSoftwareEngineeringCagrPost}{0.177} 5150 \newcommand{\CagrSoftwareEngineeringCagrPost}{0.177}
5151−\newcommand{\CagrSoftwareEngineeringShareTwoZeroOneEight}{0.058} 5151+\newcommand{\CagrSoftwareEngineeringShareTwoZeroOneEight}{0.057}
5152−\newcommand{\CagrSoftwareEngineeringShareTwoZeroTwoTwo}{0.042} 5152+\newcommand{\CagrSoftwareEngineeringShareTwoZeroTwoTwo}{0.047}
5153−\newcommand{\CagrSoftwareEngineeringShareTwoZeroTwoFive}{0.041} 5153+\newcommand{\CagrSoftwareEngineeringShareTwoZeroTwoFive}{0.042}
5154−\newcommand{\CagrSpeechAudioNTwoZeroOneEight}{12{,}096} 5154+\newcommand{\CagrSpeechAudioNTwoZeroOneEight}{4{,}032}
5155−\newcommand{\CagrSpeechAudioNTwoZeroTwoTwo}{12{,}698} 5155+\newcommand{\CagrSpeechAudioNTwoZeroTwoTwo}{6{,}349}
5156−\newcommand{\CagrSpeechAudioNTwoZeroTwoThree}{14{,}688} 5156+\newcommand{\CagrSpeechAudioNTwoZeroTwoThree}{7{,}344}
5157−\newcommand{\CagrSpeechAudioNTwoZeroTwoFive}{18{,}373} 5157+\newcommand{\CagrSpeechAudioNTwoZeroTwoFive}{9{,}186}
5158−\newcommand{\CagrSpeechAudioCagrBaseline}{0.012} 5158+\newcommand{\CagrSpeechAudioCagrBaseline}{0.120}
5159 \newcommand{\CagrSpeechAudioCagrPost}{0.118} 5159 \newcommand{\CagrSpeechAudioCagrPost}{0.118}
5160 \newcommand{\CagrSpeechAudioShareTwoZeroOneEight}{0.024} 5160 \newcommand{\CagrSpeechAudioShareTwoZeroOneEight}{0.024}
5161−\newcommand{\CagrSpeechAudioShareTwoZeroTwoTwo}{0.023} 5161+\newcommand{\CagrSpeechAudioShareTwoZeroTwoTwo}{0.025}
5162 \newcommand{\CagrSpeechAudioShareTwoZeroTwoFive}{0.021} 5162 \newcommand{\CagrSpeechAudioShareTwoZeroTwoFive}{0.021}
5163−\newcommand{\CagrSustainableComputingNTwoZeroOneEight}{583} 5163+\newcommand{\CagrSustainableComputingNTwoZeroOneEight}{291}
5164−\newcommand{\CagrSustainableComputingNTwoZeroTwoTwo}{830} 5164+\newcommand{\CagrSustainableComputingNTwoZeroTwoTwo}{276}
5165−\newcommand{\CagrSustainableComputingNTwoZeroTwoThree}{1{,}078} 5165+\newcommand{\CagrSustainableComputingNTwoZeroTwoThree}{539}
5166−\newcommand{\CagrSustainableComputingNTwoZeroTwoFive}{1{,}639} 5166+\newcommand{\CagrSustainableComputingNTwoZeroTwoFive}{819}
5167−\newcommand{\CagrSustainableComputingCagrBaseline}{0.092} 5167+\newcommand{\CagrSustainableComputingCagrBaseline}{-0.013}
5168 \newcommand{\CagrSustainableComputingCagrPost}{0.233} 5168 \newcommand{\CagrSustainableComputingCagrPost}{0.233}
5169−\newcommand{\CagrSustainableComputingShareTwoZeroOneEight}{0.001} 5169+\newcommand{\CagrSustainableComputingShareTwoZeroOneEight}{0.002}
5170 \newcommand{\CagrSustainableComputingShareTwoZeroTwoTwo}{0.001} 5170 \newcommand{\CagrSustainableComputingShareTwoZeroTwoTwo}{0.001}
5171 \newcommand{\CagrSustainableComputingShareTwoZeroTwoFive}{0.002} 5171 \newcommand{\CagrSustainableComputingShareTwoZeroTwoFive}{0.002}
5172 \newcommand{\CatcountAiAcceleratorsTwoZeroOneEightPrimary}{818} 5172 \newcommand{\CatcountAiAcceleratorsTwoZeroOneEightPrimary}{818}
@@ -5609,6 +5609,3281 @@
5609 \newcommand{\CatcountSpeechAudioPostPrimary}{65{,}112} 5609 \newcommand{\CatcountSpeechAudioPostPrimary}{65{,}112}
5610 \newcommand{\CatcountSustainableComputingBaselinePrimary}{3{,}431} 5610 \newcommand{\CatcountSustainableComputingBaselinePrimary}{3{,}431}
5611 \newcommand{\CatcountSustainableComputingPostPrimary}{5{,}316} 5611 \newcommand{\CatcountSustainableComputingPostPrimary}{5{,}316}
5612+\newcommand{\SysflowAiAcceleratorsBaselineN}{27{,}476}
5613+\newcommand{\SysflowAiAcceleratorsPostN}{67{,}525}
5614+\newcommand{\SysflowAiAgentsBaselineN}{71{,}355}
5615+\newcommand{\SysflowAiAgentsPostN}{160{,}556}
5616+\newcommand{\SysflowAiSafetyBaselineN}{46{,}890}
5617+\newcommand{\SysflowAiSafetyPostN}{189{,}787}
5618+\newcommand{\SysflowAlgorithmsBaselineN}{390{,}514}
5619+\newcommand{\SysflowAlgorithmsPostN}{719{,}567}
5620+\newcommand{\SysflowAppliedCryptographyBaselineN}{185{,}314}
5621+\newcommand{\SysflowAppliedCryptographyPostN}{412{,}609}
5622+\newcommand{\SysflowAutonomousScientificDiscoveryBaselineN}{3{,}348}
5623+\newcommand{\SysflowAutonomousScientificDiscoveryPostN}{13{,}276}
5624+\newcommand{\SysflowBlockchainBaselineN}{264{,}175}
5625+\newcommand{\SysflowBlockchainPostN}{537{,}892}
5626+\newcommand{\SysflowCloudComputingBaselineN}{122{,}314}
5627+\newcommand{\SysflowCloudComputingPostN}{231{,}255}
5628+\newcommand{\SysflowCompilersBaselineN}{5{,}973}
5629+\newcommand{\SysflowCompilersPostN}{11{,}323}
5630+\newcommand{\SysflowComputationalBiologyBaselineN}{314{,}468}
5631+\newcommand{\SysflowComputationalBiologyPostN}{770{,}808}
5632+\newcommand{\SysflowComputationalComplexityBaselineN}{23{,}634}
5633+\newcommand{\SysflowComputationalComplexityPostN}{41{,}636}
5634+\newcommand{\SysflowComputerArchitectureBaselineN}{249{,}906}
5635+\newcommand{\SysflowComputerArchitecturePostN}{495{,}916}
5636+\newcommand{\SysflowComputerGraphicsBaselineN}{102{,}570}
5637+\newcommand{\SysflowComputerGraphicsPostN}{252{,}329}
5638+\newcommand{\SysflowComputerNetworksBaselineN}{1{,}319{,}732}
5639+\newcommand{\SysflowComputerNetworksPostN}{2{,}407{,}154}
5640+\newcommand{\SysflowComputerVisionBaselineN}{2{,}713{,}681}
5641+\newcommand{\SysflowComputerVisionPostN}{6{,}305{,}839}
5642+\newcommand{\SysflowCybersecurityBaselineN}{568{,}270}
5643+\newcommand{\SysflowCybersecurityPostN}{1{,}454{,}159}
5644+\newcommand{\SysflowDatabaseSystemsBaselineN}{154{,}832}
5645+\newcommand{\SysflowDatabaseSystemsPostN}{255{,}438}
5646+\newcommand{\SysflowDeepLearningArchitecturesBaselineN}{490{,}819}
5647+\newcommand{\SysflowDeepLearningArchitecturesPostN}{998{,}034}
5648+\newcommand{\SysflowDistributedComputingBaselineN}{160{,}370}
5649+\newcommand{\SysflowDistributedComputingPostN}{283{,}741}
5650+\newcommand{\SysflowEdgeComputingBaselineN}{240{,}092}
5651+\newcommand{\SysflowEdgeComputingPostN}{480{,}725}
5652+\newcommand{\SysflowFederatedLearningBaselineN}{18{,}877}
5653+\newcommand{\SysflowFederatedLearningPostN}{110{,}367}
5654+\newcommand{\SysflowFormalVerificationBaselineN}{60{,}396}
5655+\newcommand{\SysflowFormalVerificationPostN}{87{,}844}
5656+\newcommand{\SysflowFoundationModelsBaselineN}{7{,}300}
5657+\newcommand{\SysflowFoundationModelsPostN}{41{,}465}
5658+\newcommand{\SysflowGenerativeAiBaselineN}{52{,}346}
5659+\newcommand{\SysflowGenerativeAiPostN}{254{,}528}
5660+\newcommand{\SysflowHciBaselineN}{446{,}904}
5661+\newcommand{\SysflowHciPostN}{1{,}222{,}753}
5662+\newcommand{\SysflowInformationRetrievalBaselineN}{234{,}267}
5663+\newcommand{\SysflowInformationRetrievalPostN}{516{,}144}
5664+\newcommand{\SysflowLlmBaselineN}{69{,}997}
5665+\newcommand{\SysflowLlmPostN}{633{,}004}
5666+\newcommand{\SysflowMlTheoryBaselineN}{372{,}727}
5667+\newcommand{\SysflowMlTheoryPostN}{679{,}220}
5668+\newcommand{\SysflowMultimodalAiBaselineN}{42{,}115}
5669+\newcommand{\SysflowMultimodalAiPostN}{218{,}095}
5670+\newcommand{\SysflowNlpBaselineN}{765{,}081}
5671+\newcommand{\SysflowNlpPostN}{991{,}501}
5672+\newcommand{\SysflowOperatingSystemsBaselineN}{10{,}927}
5673+\newcommand{\SysflowOperatingSystemsPostN}{20{,}570}
5674+\newcommand{\SysflowPrivacyPreservingComputationBaselineN}{92{,}552}
5675+\newcommand{\SysflowPrivacyPreservingComputationPostN}{257{,}631}
5676+\newcommand{\SysflowProgrammingLanguagesBaselineN}{32{,}827}
5677+\newcommand{\SysflowProgrammingLanguagesPostN}{51{,}750}
5678+\newcommand{\SysflowQuantumComputingBaselineN}{203{,}674}
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5680+\newcommand{\SysflowReinforcementLearningBaselineN}{134{,}239}
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5688+\newcommand{\SysflowSpeechAudioBaselineN}{254{,}887}
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6015+\newcommand{\SysflowAppliedCryptographyBaselineToAppliedCryptographyShare}{53.2}
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6020+\newcommand{\SysflowAppliedCryptographyBaselineToComputationalBiologyShare}{0.2}
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6022+\newcommand{\SysflowAppliedCryptographyBaselineToComputerArchitectureShare}{2.0}
6023+\newcommand{\SysflowAppliedCryptographyBaselineToComputerGraphicsShare}{0.0}
6024+\newcommand{\SysflowAppliedCryptographyBaselineToComputerNetworksShare}{5.5}
6025+\newcommand{\SysflowAppliedCryptographyBaselineToComputerVisionShare}{5.9}
6026+\newcommand{\SysflowAppliedCryptographyBaselineToCybersecurityShare}{13.4}
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6031+\newcommand{\SysflowAppliedCryptographyBaselineToFederatedLearningShare}{0.1}
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6038+\newcommand{\SysflowAppliedCryptographyBaselineToMlTheoryShare}{0.5}
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6043+\newcommand{\SysflowAppliedCryptographyBaselineToProgrammingLanguagesShare}{0.1}
6044+\newcommand{\SysflowAppliedCryptographyBaselineToQuantumComputingShare}{3.1}
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6046+\newcommand{\SysflowAppliedCryptographyBaselineToRoboticsShare}{0.4}
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6049+\newcommand{\SysflowAppliedCryptographyBaselineToSpeechAudioShare}{0.2}
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6051+\newcommand{\SysflowAppliedCryptographyPostToAiAcceleratorsShare}{0.5}
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6363+\newcommand{\SysflowCompilersBaselineToQuantumComputingShare}{1.5}
6364+\newcommand{\SysflowCompilersBaselineToReinforcementLearningShare}{0.6}
6365+\newcommand{\SysflowCompilersBaselineToRoboticsShare}{1.7}
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6368+\newcommand{\SysflowCompilersBaselineToSpeechAudioShare}{0.8}
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6381+\newcommand{\SysflowCompilersPostToComputerArchitectureShare}{18.3}
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6383+\newcommand{\SysflowCompilersPostToComputerNetworksShare}{1.9}
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6702+\newcommand{\SysflowComputerGraphicsPostToComputerGraphicsShare}{36.8}
6703+\newcommand{\SysflowComputerGraphicsPostToComputerNetworksShare}{0.6}
6704+\newcommand{\SysflowComputerGraphicsPostToComputerVisionShare}{39.8}
6705+\newcommand{\SysflowComputerGraphicsPostToCybersecurityShare}{0.3}
6706+\newcommand{\SysflowComputerGraphicsPostToDatabaseSystemsShare}{0.7}
6707+\newcommand{\SysflowComputerGraphicsPostToDeepLearningArchitecturesShare}{1.2}
6708+\newcommand{\SysflowComputerGraphicsPostToDistributedComputingShare}{0.6}
6709+\newcommand{\SysflowComputerGraphicsPostToEdgeComputingShare}{0.2}
6710+\newcommand{\SysflowComputerGraphicsPostToFederatedLearningShare}{0.0}
6711+\newcommand{\SysflowComputerGraphicsPostToFormalVerificationShare}{0.1}
6712+\newcommand{\SysflowComputerGraphicsPostToFoundationModelsShare}{0.0}
6713+\newcommand{\SysflowComputerGraphicsPostToGenerativeAiShare}{1.2}
6714+\newcommand{\SysflowComputerGraphicsPostToHciShare}{6.6}
6715+\newcommand{\SysflowComputerGraphicsPostToInformationRetrievalShare}{0.4}
6716+\newcommand{\SysflowComputerGraphicsPostToLlmShare}{0.5}
6717+\newcommand{\SysflowComputerGraphicsPostToMlTheoryShare}{1.1}
6718+\newcommand{\SysflowComputerGraphicsPostToMultimodalAiShare}{0.2}
6719+\newcommand{\SysflowComputerGraphicsPostToNlpShare}{1.1}
6720+\newcommand{\SysflowComputerGraphicsPostToOperatingSystemsShare}{0.0}
6721+\newcommand{\SysflowComputerGraphicsPostToPrivacyPreservingComputationShare}{0.1}
6722+\newcommand{\SysflowComputerGraphicsPostToProgrammingLanguagesShare}{0.1}
6723+\newcommand{\SysflowComputerGraphicsPostToQuantumComputingShare}{0.1}
6724+\newcommand{\SysflowComputerGraphicsPostToReinforcementLearningShare}{0.3}
6725+\newcommand{\SysflowComputerGraphicsPostToRoboticsShare}{1.8}
6726+\newcommand{\SysflowComputerGraphicsPostToScientificComputingShare}{1.7}
6727+\newcommand{\SysflowComputerGraphicsPostToSoftwareEngineeringShare}{0.6}
6728+\newcommand{\SysflowComputerGraphicsPostToSpeechAudioShare}{0.4}
6729+\newcommand{\SysflowComputerGraphicsPostToSustainableComputingShare}{0.0}
6730+\newcommand{\SysflowComputerNetworksBaselineToAiAcceleratorsShare}{0.0}
6731+\newcommand{\SysflowComputerNetworksBaselineToAiAgentsShare}{0.2}
6732+\newcommand{\SysflowComputerNetworksBaselineToAiSafetyShare}{0.0}
6733+\newcommand{\SysflowComputerNetworksBaselineToAlgorithmsShare}{1.2}
6734+\newcommand{\SysflowComputerNetworksBaselineToAppliedCryptographyShare}{0.7}
6735+\newcommand{\SysflowComputerNetworksBaselineToAutonomousScientificDiscoveryShare}{0.0}
6736+\newcommand{\SysflowComputerNetworksBaselineToBlockchainShare}{1.0}
6737+\newcommand{\SysflowComputerNetworksBaselineToCloudComputingShare}{1.1}
6738+\newcommand{\SysflowComputerNetworksBaselineToCompilersShare}{0.0}
6739+\newcommand{\SysflowComputerNetworksBaselineToComputationalBiologyShare}{0.2}
6740+\newcommand{\SysflowComputerNetworksBaselineToComputationalComplexityShare}{0.1}
6741+\newcommand{\SysflowComputerNetworksBaselineToComputerArchitectureShare}{0.8}
6742+\newcommand{\SysflowComputerNetworksBaselineToComputerGraphicsShare}{0.1}
6743+\newcommand{\SysflowComputerNetworksBaselineToComputerNetworksShare}{75.5}
6744+\newcommand{\SysflowComputerNetworksBaselineToComputerVisionShare}{2.1}
6745+\newcommand{\SysflowComputerNetworksBaselineToCybersecurityShare}{4.9}
6746+\newcommand{\SysflowComputerNetworksBaselineToDatabaseSystemsShare}{0.4}
6747+\newcommand{\SysflowComputerNetworksBaselineToDeepLearningArchitecturesShare}{1.1}
6748+\newcommand{\SysflowComputerNetworksBaselineToDistributedComputingShare}{0.9}
6749+\newcommand{\SysflowComputerNetworksBaselineToEdgeComputingShare}{4.0}
6750+\newcommand{\SysflowComputerNetworksBaselineToFederatedLearningShare}{0.1}
6751+\newcommand{\SysflowComputerNetworksBaselineToFormalVerificationShare}{0.1}
6752+\newcommand{\SysflowComputerNetworksBaselineToFoundationModelsShare}{0.0}
6753+\newcommand{\SysflowComputerNetworksBaselineToGenerativeAiShare}{0.0}
6754+\newcommand{\SysflowComputerNetworksBaselineToHciShare}{0.5}
6755+\newcommand{\SysflowComputerNetworksBaselineToInformationRetrievalShare}{0.2}
6756+\newcommand{\SysflowComputerNetworksBaselineToLlmShare}{0.0}
6757+\newcommand{\SysflowComputerNetworksBaselineToMlTheoryShare}{1.1}
6758+\newcommand{\SysflowComputerNetworksBaselineToMultimodalAiShare}{0.0}
6759+\newcommand{\SysflowComputerNetworksBaselineToNlpShare}{0.3}
6760+\newcommand{\SysflowComputerNetworksBaselineToOperatingSystemsShare}{0.1}
6761+\newcommand{\SysflowComputerNetworksBaselineToPrivacyPreservingComputationShare}{0.5}
6762+\newcommand{\SysflowComputerNetworksBaselineToProgrammingLanguagesShare}{0.1}
6763+\newcommand{\SysflowComputerNetworksBaselineToQuantumComputingShare}{0.2}
6764+\newcommand{\SysflowComputerNetworksBaselineToReinforcementLearningShare}{0.4}
6765+\newcommand{\SysflowComputerNetworksBaselineToRoboticsShare}{1.1}
6766+\newcommand{\SysflowComputerNetworksBaselineToScientificComputingShare}{0.4}
6767+\newcommand{\SysflowComputerNetworksBaselineToSoftwareEngineeringShare}{0.3}
6768+\newcommand{\SysflowComputerNetworksBaselineToSpeechAudioShare}{0.3}
6769+\newcommand{\SysflowComputerNetworksBaselineToSustainableComputingShare}{0.1}
6770+\newcommand{\SysflowComputerNetworksPostToAiAcceleratorsShare}{0.0}
6771+\newcommand{\SysflowComputerNetworksPostToAiAgentsShare}{0.2}
6772+\newcommand{\SysflowComputerNetworksPostToAiSafetyShare}{0.1}
6773+\newcommand{\SysflowComputerNetworksPostToAlgorithmsShare}{1.2}
6774+\newcommand{\SysflowComputerNetworksPostToAppliedCryptographyShare}{0.7}
6775+\newcommand{\SysflowComputerNetworksPostToAutonomousScientificDiscoveryShare}{0.0}
6776+\newcommand{\SysflowComputerNetworksPostToBlockchainShare}{0.8}
6777+\newcommand{\SysflowComputerNetworksPostToCloudComputingShare}{0.9}
6778+\newcommand{\SysflowComputerNetworksPostToCompilersShare}{0.0}
6779+\newcommand{\SysflowComputerNetworksPostToComputationalBiologyShare}{0.1}
6780+\newcommand{\SysflowComputerNetworksPostToComputationalComplexityShare}{0.1}
6781+\newcommand{\SysflowComputerNetworksPostToComputerArchitectureShare}{0.8}
6782+\newcommand{\SysflowComputerNetworksPostToComputerGraphicsShare}{0.1}
6783+\newcommand{\SysflowComputerNetworksPostToComputerNetworksShare}{72.4}
6784+\newcommand{\SysflowComputerNetworksPostToComputerVisionShare}{2.5}
6785+\newcommand{\SysflowComputerNetworksPostToCybersecurityShare}{6.6}
6786+\newcommand{\SysflowComputerNetworksPostToDatabaseSystemsShare}{0.3}
6787+\newcommand{\SysflowComputerNetworksPostToDeepLearningArchitecturesShare}{1.4}
6788+\newcommand{\SysflowComputerNetworksPostToDistributedComputingShare}{0.8}
6789+\newcommand{\SysflowComputerNetworksPostToEdgeComputingShare}{3.9}
6790+\newcommand{\SysflowComputerNetworksPostToFederatedLearningShare}{0.2}
6791+\newcommand{\SysflowComputerNetworksPostToFormalVerificationShare}{0.1}
6792+\newcommand{\SysflowComputerNetworksPostToFoundationModelsShare}{0.0}
6793+\newcommand{\SysflowComputerNetworksPostToGenerativeAiShare}{0.1}
6794+\newcommand{\SysflowComputerNetworksPostToHciShare}{0.5}
6795+\newcommand{\SysflowComputerNetworksPostToInformationRetrievalShare}{0.2}
6796+\newcommand{\SysflowComputerNetworksPostToLlmShare}{0.1}
6797+\newcommand{\SysflowComputerNetworksPostToMlTheoryShare}{1.3}
6798+\newcommand{\SysflowComputerNetworksPostToMultimodalAiShare}{0.0}
6799+\newcommand{\SysflowComputerNetworksPostToNlpShare}{0.3}
6800+\newcommand{\SysflowComputerNetworksPostToOperatingSystemsShare}{0.1}
6801+\newcommand{\SysflowComputerNetworksPostToPrivacyPreservingComputationShare}{0.5}
6802+\newcommand{\SysflowComputerNetworksPostToProgrammingLanguagesShare}{0.0}
6803+\newcommand{\SysflowComputerNetworksPostToQuantumComputingShare}{0.4}
6804+\newcommand{\SysflowComputerNetworksPostToReinforcementLearningShare}{0.6}
6805+\newcommand{\SysflowComputerNetworksPostToRoboticsShare}{1.3}
6806+\newcommand{\SysflowComputerNetworksPostToScientificComputingShare}{0.3}
6807+\newcommand{\SysflowComputerNetworksPostToSoftwareEngineeringShare}{0.3}
6808+\newcommand{\SysflowComputerNetworksPostToSpeechAudioShare}{0.4}
6809+\newcommand{\SysflowComputerNetworksPostToSustainableComputingShare}{0.1}
6810+\newcommand{\SysflowComputerVisionBaselineToAiAcceleratorsShare}{0.1}
6811+\newcommand{\SysflowComputerVisionBaselineToAiAgentsShare}{0.0}
6812+\newcommand{\SysflowComputerVisionBaselineToAiSafetyShare}{0.1}
6813+\newcommand{\SysflowComputerVisionBaselineToAlgorithmsShare}{0.6}
6814+\newcommand{\SysflowComputerVisionBaselineToAppliedCryptographyShare}{0.4}
6815+\newcommand{\SysflowComputerVisionBaselineToAutonomousScientificDiscoveryShare}{0.0}
6816+\newcommand{\SysflowComputerVisionBaselineToBlockchainShare}{0.1}
6817+\newcommand{\SysflowComputerVisionBaselineToCloudComputingShare}{0.1}
6818+\newcommand{\SysflowComputerVisionBaselineToCompilersShare}{0.0}
6819+\newcommand{\SysflowComputerVisionBaselineToComputationalBiologyShare}{0.7}
6820+\newcommand{\SysflowComputerVisionBaselineToComputationalComplexityShare}{0.0}
6821+\newcommand{\SysflowComputerVisionBaselineToComputerArchitectureShare}{0.5}
6822+\newcommand{\SysflowComputerVisionBaselineToComputerGraphicsShare}{0.8}
6823+\newcommand{\SysflowComputerVisionBaselineToComputerNetworksShare}{1.0}
6824+\newcommand{\SysflowComputerVisionBaselineToComputerVisionShare}{81.0}
6825+\newcommand{\SysflowComputerVisionBaselineToCybersecurityShare}{1.3}
6826+\newcommand{\SysflowComputerVisionBaselineToDatabaseSystemsShare}{0.3}
6827+\newcommand{\SysflowComputerVisionBaselineToDeepLearningArchitecturesShare}{3.6}
6828+\newcommand{\SysflowComputerVisionBaselineToDistributedComputingShare}{0.1}
6829+\newcommand{\SysflowComputerVisionBaselineToEdgeComputingShare}{0.5}
6830+\newcommand{\SysflowComputerVisionBaselineToFederatedLearningShare}{0.0}
6831+\newcommand{\SysflowComputerVisionBaselineToFormalVerificationShare}{0.2}
6832+\newcommand{\SysflowComputerVisionBaselineToFoundationModelsShare}{0.0}
6833+\newcommand{\SysflowComputerVisionBaselineToGenerativeAiShare}{0.5}
6834+\newcommand{\SysflowComputerVisionBaselineToHciShare}{1.0}
6835+\newcommand{\SysflowComputerVisionBaselineToInformationRetrievalShare}{0.3}
6836+\newcommand{\SysflowComputerVisionBaselineToLlmShare}{0.1}
6837+\newcommand{\SysflowComputerVisionBaselineToMlTheoryShare}{2.1}
6838+\newcommand{\SysflowComputerVisionBaselineToMultimodalAiShare}{0.4}
6839+\newcommand{\SysflowComputerVisionBaselineToNlpShare}{1.3}
6840+\newcommand{\SysflowComputerVisionBaselineToOperatingSystemsShare}{0.0}
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6842+\newcommand{\SysflowComputerVisionBaselineToProgrammingLanguagesShare}{0.0}
6843+\newcommand{\SysflowComputerVisionBaselineToQuantumComputingShare}{0.1}
6844+\newcommand{\SysflowComputerVisionBaselineToReinforcementLearningShare}{0.2}
6845+\newcommand{\SysflowComputerVisionBaselineToRoboticsShare}{1.5}
6846+\newcommand{\SysflowComputerVisionBaselineToScientificComputingShare}{0.3}
6847+\newcommand{\SysflowComputerVisionBaselineToSoftwareEngineeringShare}{0.2}
6848+\newcommand{\SysflowComputerVisionBaselineToSpeechAudioShare}{0.5}
6849+\newcommand{\SysflowComputerVisionBaselineToSustainableComputingShare}{0.0}
6850+\newcommand{\SysflowComputerVisionPostToAiAcceleratorsShare}{0.1}
6851+\newcommand{\SysflowComputerVisionPostToAiAgentsShare}{0.0}
6852+\newcommand{\SysflowComputerVisionPostToAiSafetyShare}{0.1}
6853+\newcommand{\SysflowComputerVisionPostToAlgorithmsShare}{0.5}
6854+\newcommand{\SysflowComputerVisionPostToAppliedCryptographyShare}{0.4}
6855+\newcommand{\SysflowComputerVisionPostToAutonomousScientificDiscoveryShare}{0.0}
6856+\newcommand{\SysflowComputerVisionPostToBlockchainShare}{0.1}
6857+\newcommand{\SysflowComputerVisionPostToCloudComputingShare}{0.1}
6858+\newcommand{\SysflowComputerVisionPostToCompilersShare}{0.0}
6859+\newcommand{\SysflowComputerVisionPostToComputationalBiologyShare}{0.9}
6860+\newcommand{\SysflowComputerVisionPostToComputationalComplexityShare}{0.0}
6861+\newcommand{\SysflowComputerVisionPostToComputerArchitectureShare}{0.3}
6862+\newcommand{\SysflowComputerVisionPostToComputerGraphicsShare}{1.0}
6863+\newcommand{\SysflowComputerVisionPostToComputerNetworksShare}{0.8}
6864+\newcommand{\SysflowComputerVisionPostToComputerVisionShare}{82.2}
6865+\newcommand{\SysflowComputerVisionPostToCybersecurityShare}{1.1}
6866+\newcommand{\SysflowComputerVisionPostToDatabaseSystemsShare}{0.2}
6867+\newcommand{\SysflowComputerVisionPostToDeepLearningArchitecturesShare}{2.8}
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6869+\newcommand{\SysflowComputerVisionPostToEdgeComputingShare}{0.4}
6870+\newcommand{\SysflowComputerVisionPostToFederatedLearningShare}{0.1}
6871+\newcommand{\SysflowComputerVisionPostToFormalVerificationShare}{0.3}
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6873+\newcommand{\SysflowComputerVisionPostToGenerativeAiShare}{0.6}
6874+\newcommand{\SysflowComputerVisionPostToHciShare}{1.0}
6875+\newcommand{\SysflowComputerVisionPostToInformationRetrievalShare}{0.2}
6876+\newcommand{\SysflowComputerVisionPostToLlmShare}{0.3}
6877+\newcommand{\SysflowComputerVisionPostToMlTheoryShare}{1.7}
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6879+\newcommand{\SysflowComputerVisionPostToNlpShare}{1.2}
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6882+\newcommand{\SysflowComputerVisionPostToProgrammingLanguagesShare}{0.0}
6883+\newcommand{\SysflowComputerVisionPostToQuantumComputingShare}{0.1}
6884+\newcommand{\SysflowComputerVisionPostToReinforcementLearningShare}{0.1}
6885+\newcommand{\SysflowComputerVisionPostToRoboticsShare}{1.5}
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6888+\newcommand{\SysflowComputerVisionPostToSpeechAudioShare}{0.4}
6889+\newcommand{\SysflowComputerVisionPostToSustainableComputingShare}{0.0}
6890+\newcommand{\SysflowCybersecurityBaselineToAiAcceleratorsShare}{0.0}
6891+\newcommand{\SysflowCybersecurityBaselineToAiAgentsShare}{0.3}
6892+\newcommand{\SysflowCybersecurityBaselineToAiSafetyShare}{0.2}
6893+\newcommand{\SysflowCybersecurityBaselineToAlgorithmsShare}{1.0}
6894+\newcommand{\SysflowCybersecurityBaselineToAppliedCryptographyShare}{5.3}
6895+\newcommand{\SysflowCybersecurityBaselineToAutonomousScientificDiscoveryShare}{0.0}
6896+\newcommand{\SysflowCybersecurityBaselineToBlockchainShare}{2.8}
6897+\newcommand{\SysflowCybersecurityBaselineToCloudComputingShare}{1.3}
6898+\newcommand{\SysflowCybersecurityBaselineToCompilersShare}{0.0}
6899+\newcommand{\SysflowCybersecurityBaselineToComputationalBiologyShare}{0.2}
6900+\newcommand{\SysflowCybersecurityBaselineToComputationalComplexityShare}{0.1}
6901+\newcommand{\SysflowCybersecurityBaselineToComputerArchitectureShare}{1.7}
6902+\newcommand{\SysflowCybersecurityBaselineToComputerGraphicsShare}{0.1}
6903+\newcommand{\SysflowCybersecurityBaselineToComputerNetworksShare}{11.8}
6904+\newcommand{\SysflowCybersecurityBaselineToComputerVisionShare}{8.0}
6905+\newcommand{\SysflowCybersecurityBaselineToCybersecurityShare}{48.1}
6906+\newcommand{\SysflowCybersecurityBaselineToDatabaseSystemsShare}{0.7}
6907+\newcommand{\SysflowCybersecurityBaselineToDeepLearningArchitecturesShare}{1.8}
6908+\newcommand{\SysflowCybersecurityBaselineToDistributedComputingShare}{0.8}
6909+\newcommand{\SysflowCybersecurityBaselineToEdgeComputingShare}{2.3}
6910+\newcommand{\SysflowCybersecurityBaselineToFederatedLearningShare}{0.1}
6911+\newcommand{\SysflowCybersecurityBaselineToFormalVerificationShare}{0.4}
6912+\newcommand{\SysflowCybersecurityBaselineToFoundationModelsShare}{0.0}
6913+\newcommand{\SysflowCybersecurityBaselineToGenerativeAiShare}{0.2}
6914+\newcommand{\SysflowCybersecurityBaselineToHciShare}{1.1}
6915+\newcommand{\SysflowCybersecurityBaselineToInformationRetrievalShare}{0.5}
6916+\newcommand{\SysflowCybersecurityBaselineToLlmShare}{0.1}
6917+\newcommand{\SysflowCybersecurityBaselineToMlTheoryShare}{2.3}
6918+\newcommand{\SysflowCybersecurityBaselineToMultimodalAiShare}{0.0}
6919+\newcommand{\SysflowCybersecurityBaselineToNlpShare}{2.1}
6920+\newcommand{\SysflowCybersecurityBaselineToOperatingSystemsShare}{0.2}
6921+\newcommand{\SysflowCybersecurityBaselineToPrivacyPreservingComputationShare}{1.8}
6922+\newcommand{\SysflowCybersecurityBaselineToProgrammingLanguagesShare}{0.2}
6923+\newcommand{\SysflowCybersecurityBaselineToQuantumComputingShare}{0.3}
6924+\newcommand{\SysflowCybersecurityBaselineToReinforcementLearningShare}{0.3}
6925+\newcommand{\SysflowCybersecurityBaselineToRoboticsShare}{0.5}
6926+\newcommand{\SysflowCybersecurityBaselineToScientificComputingShare}{0.3}
6927+\newcommand{\SysflowCybersecurityBaselineToSoftwareEngineeringShare}{2.3}
6928+\newcommand{\SysflowCybersecurityBaselineToSpeechAudioShare}{0.6}
6929+\newcommand{\SysflowCybersecurityBaselineToSustainableComputingShare}{0.0}
6930+\newcommand{\SysflowCybersecurityPostToAiAcceleratorsShare}{0.1}
6931+\newcommand{\SysflowCybersecurityPostToAiAgentsShare}{0.3}
6932+\newcommand{\SysflowCybersecurityPostToAiSafetyShare}{0.5}
6933+\newcommand{\SysflowCybersecurityPostToAlgorithmsShare}{0.8}
6934+\newcommand{\SysflowCybersecurityPostToAppliedCryptographyShare}{4.4}
6935+\newcommand{\SysflowCybersecurityPostToAutonomousScientificDiscoveryShare}{0.0}
6936+\newcommand{\SysflowCybersecurityPostToBlockchainShare}{2.7}
6937+\newcommand{\SysflowCybersecurityPostToCloudComputingShare}{1.1}
6938+\newcommand{\SysflowCybersecurityPostToCompilersShare}{0.0}
6939+\newcommand{\SysflowCybersecurityPostToComputationalBiologyShare}{0.2}
6940+\newcommand{\SysflowCybersecurityPostToComputationalComplexityShare}{0.1}
6941+\newcommand{\SysflowCybersecurityPostToComputerArchitectureShare}{1.2}
6942+\newcommand{\SysflowCybersecurityPostToComputerGraphicsShare}{0.1}
6943+\newcommand{\SysflowCybersecurityPostToComputerNetworksShare}{13.1}
6944+\newcommand{\SysflowCybersecurityPostToComputerVisionShare}{7.8}
6945+\newcommand{\SysflowCybersecurityPostToCybersecurityShare}{48.1}
6946+\newcommand{\SysflowCybersecurityPostToDatabaseSystemsShare}{0.5}
6947+\newcommand{\SysflowCybersecurityPostToDeepLearningArchitecturesShare}{1.7}
6948+\newcommand{\SysflowCybersecurityPostToDistributedComputingShare}{0.6}
6949+\newcommand{\SysflowCybersecurityPostToEdgeComputingShare}{2.3}
6950+\newcommand{\SysflowCybersecurityPostToFederatedLearningShare}{0.3}
6951+\newcommand{\SysflowCybersecurityPostToFormalVerificationShare}{0.3}
6952+\newcommand{\SysflowCybersecurityPostToFoundationModelsShare}{0.0}
6953+\newcommand{\SysflowCybersecurityPostToGenerativeAiShare}{0.3}
6954+\newcommand{\SysflowCybersecurityPostToHciShare}{1.1}
6955+\newcommand{\SysflowCybersecurityPostToInformationRetrievalShare}{0.4}
6956+\newcommand{\SysflowCybersecurityPostToLlmShare}{0.7}
6957+\newcommand{\SysflowCybersecurityPostToMlTheoryShare}{2.4}
6958+\newcommand{\SysflowCybersecurityPostToMultimodalAiShare}{0.1}
6959+\newcommand{\SysflowCybersecurityPostToNlpShare}{2.2}
6960+\newcommand{\SysflowCybersecurityPostToOperatingSystemsShare}{0.1}
6961+\newcommand{\SysflowCybersecurityPostToPrivacyPreservingComputationShare}{1.7}
6962+\newcommand{\SysflowCybersecurityPostToProgrammingLanguagesShare}{0.2}
6963+\newcommand{\SysflowCybersecurityPostToQuantumComputingShare}{0.4}
6964+\newcommand{\SysflowCybersecurityPostToReinforcementLearningShare}{0.3}
6965+\newcommand{\SysflowCybersecurityPostToRoboticsShare}{0.6}
6966+\newcommand{\SysflowCybersecurityPostToScientificComputingShare}{0.3}
6967+\newcommand{\SysflowCybersecurityPostToSoftwareEngineeringShare}{2.5}
6968+\newcommand{\SysflowCybersecurityPostToSpeechAudioShare}{0.5}
6969+\newcommand{\SysflowCybersecurityPostToSustainableComputingShare}{0.0}
6970+\newcommand{\SysflowDatabaseSystemsBaselineToAiAcceleratorsShare}{0.0}
6971+\newcommand{\SysflowDatabaseSystemsBaselineToAiAgentsShare}{0.2}
6972+\newcommand{\SysflowDatabaseSystemsBaselineToAiSafetyShare}{0.5}
6973+\newcommand{\SysflowDatabaseSystemsBaselineToAlgorithmsShare}{4.5}
6974+\newcommand{\SysflowDatabaseSystemsBaselineToAppliedCryptographyShare}{0.5}
6975+\newcommand{\SysflowDatabaseSystemsBaselineToAutonomousScientificDiscoveryShare}{0.1}
6976+\newcommand{\SysflowDatabaseSystemsBaselineToBlockchainShare}{1.0}
6977+\newcommand{\SysflowDatabaseSystemsBaselineToCloudComputingShare}{1.7}
6978+\newcommand{\SysflowDatabaseSystemsBaselineToCompilersShare}{0.1}
6979+\newcommand{\SysflowDatabaseSystemsBaselineToComputationalBiologyShare}{1.9}
6980+\newcommand{\SysflowDatabaseSystemsBaselineToComputationalComplexityShare}{0.2}
6981+\newcommand{\SysflowDatabaseSystemsBaselineToComputerArchitectureShare}{1.4}
6982+\newcommand{\SysflowDatabaseSystemsBaselineToComputerGraphicsShare}{0.9}
6983+\newcommand{\SysflowDatabaseSystemsBaselineToComputerNetworksShare}{3.4}
6984+\newcommand{\SysflowDatabaseSystemsBaselineToComputerVisionShare}{5.1}
6985+\newcommand{\SysflowDatabaseSystemsBaselineToCybersecurityShare}{2.1}
6986+\newcommand{\SysflowDatabaseSystemsBaselineToDatabaseSystemsShare}{41.9}
6987+\newcommand{\SysflowDatabaseSystemsBaselineToDeepLearningArchitecturesShare}{3.9}
6988+\newcommand{\SysflowDatabaseSystemsBaselineToDistributedComputingShare}{3.0}
6989+\newcommand{\SysflowDatabaseSystemsBaselineToEdgeComputingShare}{1.7}
6990+\newcommand{\SysflowDatabaseSystemsBaselineToFederatedLearningShare}{0.0}
6991+\newcommand{\SysflowDatabaseSystemsBaselineToFormalVerificationShare}{0.4}
6992+\newcommand{\SysflowDatabaseSystemsBaselineToFoundationModelsShare}{0.0}
6993+\newcommand{\SysflowDatabaseSystemsBaselineToGenerativeAiShare}{0.1}
6994+\newcommand{\SysflowDatabaseSystemsBaselineToHciShare}{1.8}
6995+\newcommand{\SysflowDatabaseSystemsBaselineToInformationRetrievalShare}{3.7}
6996+\newcommand{\SysflowDatabaseSystemsBaselineToLlmShare}{0.3}
6997+\newcommand{\SysflowDatabaseSystemsBaselineToMlTheoryShare}{6.8}
6998+\newcommand{\SysflowDatabaseSystemsBaselineToMultimodalAiShare}{0.1}
6999+\newcommand{\SysflowDatabaseSystemsBaselineToNlpShare}{5.1}
7000+\newcommand{\SysflowDatabaseSystemsBaselineToOperatingSystemsShare}{0.6}
7001+\newcommand{\SysflowDatabaseSystemsBaselineToPrivacyPreservingComputationShare}{1.2}
7002+\newcommand{\SysflowDatabaseSystemsBaselineToProgrammingLanguagesShare}{0.4}
7003+\newcommand{\SysflowDatabaseSystemsBaselineToQuantumComputingShare}{0.1}
7004+\newcommand{\SysflowDatabaseSystemsBaselineToReinforcementLearningShare}{0.3}
7005+\newcommand{\SysflowDatabaseSystemsBaselineToRoboticsShare}{0.5}
7006+\newcommand{\SysflowDatabaseSystemsBaselineToScientificComputingShare}{0.8}
7007+\newcommand{\SysflowDatabaseSystemsBaselineToSoftwareEngineeringShare}{3.3}
7008+\newcommand{\SysflowDatabaseSystemsBaselineToSpeechAudioShare}{0.3}
7009+\newcommand{\SysflowDatabaseSystemsBaselineToSustainableComputingShare}{0.1}
7010+\newcommand{\SysflowDatabaseSystemsPostToAiAcceleratorsShare}{0.0}
7011+\newcommand{\SysflowDatabaseSystemsPostToAiAgentsShare}{0.2}
7012+\newcommand{\SysflowDatabaseSystemsPostToAiSafetyShare}{0.7}
7013+\newcommand{\SysflowDatabaseSystemsPostToAlgorithmsShare}{4.1}
7014+\newcommand{\SysflowDatabaseSystemsPostToAppliedCryptographyShare}{0.5}
7015+\newcommand{\SysflowDatabaseSystemsPostToAutonomousScientificDiscoveryShare}{0.1}
7016+\newcommand{\SysflowDatabaseSystemsPostToBlockchainShare}{1.1}
7017+\newcommand{\SysflowDatabaseSystemsPostToCloudComputingShare}{1.5}
7018+\newcommand{\SysflowDatabaseSystemsPostToCompilersShare}{0.1}
7019+\newcommand{\SysflowDatabaseSystemsPostToComputationalBiologyShare}{1.8}
7020+\newcommand{\SysflowDatabaseSystemsPostToComputationalComplexityShare}{0.2}
7021+\newcommand{\SysflowDatabaseSystemsPostToComputerArchitectureShare}{1.3}
7022+\newcommand{\SysflowDatabaseSystemsPostToComputerGraphicsShare}{0.7}
7023+\newcommand{\SysflowDatabaseSystemsPostToComputerNetworksShare}{2.8}
7024+\newcommand{\SysflowDatabaseSystemsPostToComputerVisionShare}{4.8}
7025+\newcommand{\SysflowDatabaseSystemsPostToCybersecurityShare}{2.2}
7026+\newcommand{\SysflowDatabaseSystemsPostToDatabaseSystemsShare}{40.6}
7027+\newcommand{\SysflowDatabaseSystemsPostToDeepLearningArchitecturesShare}{4.8}
7028+\newcommand{\SysflowDatabaseSystemsPostToDistributedComputingShare}{2.4}
7029+\newcommand{\SysflowDatabaseSystemsPostToEdgeComputingShare}{1.6}
7030+\newcommand{\SysflowDatabaseSystemsPostToFederatedLearningShare}{0.1}
7031+\newcommand{\SysflowDatabaseSystemsPostToFormalVerificationShare}{0.3}
7032+\newcommand{\SysflowDatabaseSystemsPostToFoundationModelsShare}{0.1}
7033+\newcommand{\SysflowDatabaseSystemsPostToGenerativeAiShare}{0.4}
7034+\newcommand{\SysflowDatabaseSystemsPostToHciShare}{2.0}
7035+\newcommand{\SysflowDatabaseSystemsPostToInformationRetrievalShare}{3.4}
7036+\newcommand{\SysflowDatabaseSystemsPostToLlmShare}{1.6}
7037+\newcommand{\SysflowDatabaseSystemsPostToMlTheoryShare}{7.1}
7038+\newcommand{\SysflowDatabaseSystemsPostToMultimodalAiShare}{0.1}
7039+\newcommand{\SysflowDatabaseSystemsPostToNlpShare}{5.2}
7040+\newcommand{\SysflowDatabaseSystemsPostToOperatingSystemsShare}{0.6}
7041+\newcommand{\SysflowDatabaseSystemsPostToPrivacyPreservingComputationShare}{1.4}
7042+\newcommand{\SysflowDatabaseSystemsPostToProgrammingLanguagesShare}{0.3}
7043+\newcommand{\SysflowDatabaseSystemsPostToQuantumComputingShare}{0.2}
7044+\newcommand{\SysflowDatabaseSystemsPostToReinforcementLearningShare}{0.4}
7045+\newcommand{\SysflowDatabaseSystemsPostToRoboticsShare}{0.6}
7046+\newcommand{\SysflowDatabaseSystemsPostToScientificComputingShare}{0.8}
7047+\newcommand{\SysflowDatabaseSystemsPostToSoftwareEngineeringShare}{3.2}
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7386+\newcommand{\SysflowFormalVerificationBaselineToDeepLearningArchitecturesShare}{1.8}
7387+\newcommand{\SysflowFormalVerificationBaselineToDistributedComputingShare}{1.2}
7388+\newcommand{\SysflowFormalVerificationBaselineToEdgeComputingShare}{0.6}
7389+\newcommand{\SysflowFormalVerificationBaselineToFormalVerificationShare}{49.9}
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7391+\newcommand{\SysflowFormalVerificationBaselineToGenerativeAiShare}{0.0}
7392+\newcommand{\SysflowFormalVerificationBaselineToHciShare}{0.4}
7393+\newcommand{\SysflowFormalVerificationBaselineToInformationRetrievalShare}{0.2}
7394+\newcommand{\SysflowFormalVerificationBaselineToLlmShare}{0.0}
7395+\newcommand{\SysflowFormalVerificationBaselineToMlTheoryShare}{1.5}
7396+\newcommand{\SysflowFormalVerificationBaselineToMultimodalAiShare}{0.0}
7397+\newcommand{\SysflowFormalVerificationBaselineToNlpShare}{0.8}
7398+\newcommand{\SysflowFormalVerificationBaselineToOperatingSystemsShare}{0.1}
7399+\newcommand{\SysflowFormalVerificationBaselineToPrivacyPreservingComputationShare}{0.2}
7400+\newcommand{\SysflowFormalVerificationBaselineToProgrammingLanguagesShare}{7.4}
7401+\newcommand{\SysflowFormalVerificationBaselineToQuantumComputingShare}{0.4}
7402+\newcommand{\SysflowFormalVerificationBaselineToReinforcementLearningShare}{1.5}
7403+\newcommand{\SysflowFormalVerificationBaselineToRoboticsShare}{3.6}
7404+\newcommand{\SysflowFormalVerificationBaselineToScientificComputingShare}{1.3}
7405+\newcommand{\SysflowFormalVerificationBaselineToSoftwareEngineeringShare}{8.2}
7406+\newcommand{\SysflowFormalVerificationBaselineToSpeechAudioShare}{0.1}
7407+\newcommand{\SysflowFormalVerificationBaselineToSustainableComputingShare}{0.0}
7408+\newcommand{\SysflowFormalVerificationPostToAiAcceleratorsShare}{0.0}
7409+\newcommand{\SysflowFormalVerificationPostToAiAgentsShare}{1.7}
7410+\newcommand{\SysflowFormalVerificationPostToAiSafetyShare}{0.5}
7411+\newcommand{\SysflowFormalVerificationPostToAlgorithmsShare}{3.8}
7412+\newcommand{\SysflowFormalVerificationPostToAppliedCryptographyShare}{0.7}
7413+\newcommand{\SysflowFormalVerificationPostToAutonomousScientificDiscoveryShare}{0.1}
7414+\newcommand{\SysflowFormalVerificationPostToBlockchainShare}{0.8}
7415+\newcommand{\SysflowFormalVerificationPostToCloudComputingShare}{0.4}
7416+\newcommand{\SysflowFormalVerificationPostToCompilersShare}{0.2}
7417+\newcommand{\SysflowFormalVerificationPostToComputationalBiologyShare}{0.4}
7418+\newcommand{\SysflowFormalVerificationPostToComputationalComplexityShare}{1.6}
7419+\newcommand{\SysflowFormalVerificationPostToComputerArchitectureShare}{2.4}
7420+\newcommand{\SysflowFormalVerificationPostToComputerGraphicsShare}{0.1}
7421+\newcommand{\SysflowFormalVerificationPostToComputerNetworksShare}{1.9}
7422+\newcommand{\SysflowFormalVerificationPostToComputerVisionShare}{1.3}
7423+\newcommand{\SysflowFormalVerificationPostToCybersecurityShare}{3.8}
7424+\newcommand{\SysflowFormalVerificationPostToDatabaseSystemsShare}{0.7}
7425+\newcommand{\SysflowFormalVerificationPostToDeepLearningArchitecturesShare}{1.8}
7426+\newcommand{\SysflowFormalVerificationPostToDistributedComputingShare}{1.4}
7427+\newcommand{\SysflowFormalVerificationPostToEdgeComputingShare}{0.6}
7428+\newcommand{\SysflowFormalVerificationPostToFederatedLearningShare}{0.0}
7429+\newcommand{\SysflowFormalVerificationPostToFormalVerificationShare}{47.0}
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7434+\newcommand{\SysflowFormalVerificationPostToLlmShare}{0.4}
7435+\newcommand{\SysflowFormalVerificationPostToMlTheoryShare}{1.7}
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7446+\newcommand{\SysflowFormalVerificationPostToSpeechAudioShare}{0.1}
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7449+\newcommand{\SysflowFoundationModelsBaselineToAiAgentsShare}{0.2}
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7459+\newcommand{\SysflowFoundationModelsBaselineToComputerArchitectureShare}{0.3}
7460+\newcommand{\SysflowFoundationModelsBaselineToComputerGraphicsShare}{0.2}
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7464+\newcommand{\SysflowFoundationModelsBaselineToDatabaseSystemsShare}{0.9}
7465+\newcommand{\SysflowFoundationModelsBaselineToDeepLearningArchitecturesShare}{17.2}
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7500+\newcommand{\SysflowFoundationModelsPostToComputerGraphicsShare}{0.2}
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7581+\newcommand{\SysflowGenerativeAiPostToComputerNetworksShare}{1.1}
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7583+\newcommand{\SysflowGenerativeAiPostToCybersecurityShare}{1.8}
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7606+\newcommand{\SysflowGenerativeAiPostToSpeechAudioShare}{1.5}
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7614+\newcommand{\SysflowHciBaselineToBlockchainShare}{0.3}
7615+\newcommand{\SysflowHciBaselineToCloudComputingShare}{0.3}
7616+\newcommand{\SysflowHciBaselineToCompilersShare}{0.0}
7617+\newcommand{\SysflowHciBaselineToComputationalBiologyShare}{0.5}
7618+\newcommand{\SysflowHciBaselineToComputationalComplexityShare}{0.1}
7619+\newcommand{\SysflowHciBaselineToComputerArchitectureShare}{0.3}
7620+\newcommand{\SysflowHciBaselineToComputerGraphicsShare}{2.0}
7621+\newcommand{\SysflowHciBaselineToComputerNetworksShare}{1.8}
7622+\newcommand{\SysflowHciBaselineToComputerVisionShare}{8.6}
7623+\newcommand{\SysflowHciBaselineToCybersecurityShare}{1.4}
7624+\newcommand{\SysflowHciBaselineToDatabaseSystemsShare}{0.6}
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7630+\newcommand{\SysflowHciBaselineToFoundationModelsShare}{0.0}
7631+\newcommand{\SysflowHciBaselineToGenerativeAiShare}{0.1}
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7634+\newcommand{\SysflowHciBaselineToLlmShare}{0.1}
7635+\newcommand{\SysflowHciBaselineToMlTheoryShare}{1.1}
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7640+\newcommand{\SysflowHciBaselineToProgrammingLanguagesShare}{0.1}
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7642+\newcommand{\SysflowHciBaselineToReinforcementLearningShare}{0.3}
7643+\newcommand{\SysflowHciBaselineToRoboticsShare}{3.3}
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7646+\newcommand{\SysflowHciBaselineToSpeechAudioShare}{1.0}
7647+\newcommand{\SysflowHciBaselineToSustainableComputingShare}{0.1}
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7657+\newcommand{\SysflowHciPostToComputationalBiologyShare}{0.4}
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7660+\newcommand{\SysflowHciPostToComputerGraphicsShare}{1.7}
7661+\newcommand{\SysflowHciPostToComputerNetworksShare}{1.3}
7662+\newcommand{\SysflowHciPostToComputerVisionShare}{7.6}
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7664+\newcommand{\SysflowHciPostToDatabaseSystemsShare}{0.5}
7665+\newcommand{\SysflowHciPostToDeepLearningArchitecturesShare}{1.1}
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7667+\newcommand{\SysflowHciPostToEdgeComputingShare}{0.6}
7668+\newcommand{\SysflowHciPostToFederatedLearningShare}{0.0}
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7670+\newcommand{\SysflowHciPostToFoundationModelsShare}{0.0}
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7675+\newcommand{\SysflowHciPostToMlTheoryShare}{1.1}
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7700+\newcommand{\SysflowInformationRetrievalBaselineToComputerGraphicsShare}{0.3}
7701+\newcommand{\SysflowInformationRetrievalBaselineToComputerNetworksShare}{1.3}
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7705+\newcommand{\SysflowInformationRetrievalBaselineToDeepLearningArchitecturesShare}{5.1}
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7710+\newcommand{\SysflowInformationRetrievalBaselineToFoundationModelsShare}{0.0}
7711+\newcommand{\SysflowInformationRetrievalBaselineToGenerativeAiShare}{0.1}
7712+\newcommand{\SysflowInformationRetrievalBaselineToHciShare}{2.2}
7713+\newcommand{\SysflowInformationRetrievalBaselineToInformationRetrievalShare}{57.5}
7714+\newcommand{\SysflowInformationRetrievalBaselineToLlmShare}{0.7}
7715+\newcommand{\SysflowInformationRetrievalBaselineToMlTheoryShare}{2.5}
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7717+\newcommand{\SysflowInformationRetrievalBaselineToNlpShare}{12.6}
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7720+\newcommand{\SysflowInformationRetrievalBaselineToProgrammingLanguagesShare}{0.0}
7721+\newcommand{\SysflowInformationRetrievalBaselineToQuantumComputingShare}{0.0}
7722+\newcommand{\SysflowInformationRetrievalBaselineToReinforcementLearningShare}{0.5}
7723+\newcommand{\SysflowInformationRetrievalBaselineToRoboticsShare}{0.3}
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7725+\newcommand{\SysflowInformationRetrievalBaselineToSoftwareEngineeringShare}{1.4}
7726+\newcommand{\SysflowInformationRetrievalBaselineToSpeechAudioShare}{0.4}
7727+\newcommand{\SysflowInformationRetrievalBaselineToSustainableComputingShare}{0.0}
7728+\newcommand{\SysflowInformationRetrievalPostToAiAcceleratorsShare}{0.0}
7729+\newcommand{\SysflowInformationRetrievalPostToAiAgentsShare}{0.2}
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8416+\newcommand{\SysflowReinforcementLearningBaselineToComputationalBiologyShare}{0.4}
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8418+\newcommand{\SysflowReinforcementLearningBaselineToComputerArchitectureShare}{0.5}
8419+\newcommand{\SysflowReinforcementLearningBaselineToComputerGraphicsShare}{0.6}
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8424+\newcommand{\SysflowReinforcementLearningBaselineToDeepLearningArchitecturesShare}{4.6}
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8426+\newcommand{\SysflowReinforcementLearningBaselineToEdgeComputingShare}{0.6}
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8747+\newcommand{\SysflowSpeechAudioBaselineToFederatedLearningShare}{0.0}
8748+\newcommand{\SysflowSpeechAudioBaselineToFormalVerificationShare}{0.6}
8749+\newcommand{\SysflowSpeechAudioBaselineToFoundationModelsShare}{0.0}
8750+\newcommand{\SysflowSpeechAudioBaselineToGenerativeAiShare}{0.3}
8751+\newcommand{\SysflowSpeechAudioBaselineToHciShare}{1.5}
8752+\newcommand{\SysflowSpeechAudioBaselineToInformationRetrievalShare}{0.5}
8753+\newcommand{\SysflowSpeechAudioBaselineToLlmShare}{0.2}
8754+\newcommand{\SysflowSpeechAudioBaselineToMlTheoryShare}{1.3}
8755+\newcommand{\SysflowSpeechAudioBaselineToMultimodalAiShare}{0.3}
8756+\newcommand{\SysflowSpeechAudioBaselineToNlpShare}{5.7}
8757+\newcommand{\SysflowSpeechAudioBaselineToOperatingSystemsShare}{0.0}
8758+\newcommand{\SysflowSpeechAudioBaselineToPrivacyPreservingComputationShare}{0.1}
8759+\newcommand{\SysflowSpeechAudioBaselineToProgrammingLanguagesShare}{0.0}
8760+\newcommand{\SysflowSpeechAudioBaselineToQuantumComputingShare}{0.0}
8761+\newcommand{\SysflowSpeechAudioBaselineToReinforcementLearningShare}{0.1}
8762+\newcommand{\SysflowSpeechAudioBaselineToRoboticsShare}{0.5}
8763+\newcommand{\SysflowSpeechAudioBaselineToScientificComputingShare}{0.4}
8764+\newcommand{\SysflowSpeechAudioBaselineToSoftwareEngineeringShare}{0.2}
8765+\newcommand{\SysflowSpeechAudioBaselineToSpeechAudioShare}{71.3}
8766+\newcommand{\SysflowSpeechAudioBaselineToSustainableComputingShare}{0.0}
8767+\newcommand{\SysflowSpeechAudioPostToAiAcceleratorsShare}{0.0}
8768+\newcommand{\SysflowSpeechAudioPostToAiAgentsShare}{0.1}
8769+\newcommand{\SysflowSpeechAudioPostToAiSafetyShare}{0.2}
8770+\newcommand{\SysflowSpeechAudioPostToAlgorithmsShare}{0.5}
8771+\newcommand{\SysflowSpeechAudioPostToAppliedCryptographyShare}{0.1}
8772+\newcommand{\SysflowSpeechAudioPostToAutonomousScientificDiscoveryShare}{0.0}
8773+\newcommand{\SysflowSpeechAudioPostToBlockchainShare}{0.1}
8774+\newcommand{\SysflowSpeechAudioPostToCloudComputingShare}{0.1}
8775+\newcommand{\SysflowSpeechAudioPostToCompilersShare}{0.0}
8776+\newcommand{\SysflowSpeechAudioPostToComputationalBiologyShare}{0.2}
8777+\newcommand{\SysflowSpeechAudioPostToComputationalComplexityShare}{0.1}
8778+\newcommand{\SysflowSpeechAudioPostToComputerArchitectureShare}{0.4}
8779+\newcommand{\SysflowSpeechAudioPostToComputerGraphicsShare}{0.2}
8780+\newcommand{\SysflowSpeechAudioPostToComputerNetworksShare}{2.2}
8781+\newcommand{\SysflowSpeechAudioPostToComputerVisionShare}{8.6}
8782+\newcommand{\SysflowSpeechAudioPostToCybersecurityShare}{1.3}
8783+\newcommand{\SysflowSpeechAudioPostToDatabaseSystemsShare}{0.2}
8784+\newcommand{\SysflowSpeechAudioPostToDeepLearningArchitecturesShare}{2.7}
8785+\newcommand{\SysflowSpeechAudioPostToDistributedComputingShare}{0.4}
8786+\newcommand{\SysflowSpeechAudioPostToEdgeComputingShare}{0.4}
8787+\newcommand{\SysflowSpeechAudioPostToFederatedLearningShare}{0.0}
8788+\newcommand{\SysflowSpeechAudioPostToFormalVerificationShare}{0.5}
8789+\newcommand{\SysflowSpeechAudioPostToFoundationModelsShare}{0.1}
8790+\newcommand{\SysflowSpeechAudioPostToGenerativeAiShare}{0.4}
8791+\newcommand{\SysflowSpeechAudioPostToHciShare}{2.0}
8792+\newcommand{\SysflowSpeechAudioPostToInformationRetrievalShare}{0.6}
8793+\newcommand{\SysflowSpeechAudioPostToLlmShare}{0.6}
8794+\newcommand{\SysflowSpeechAudioPostToMlTheoryShare}{1.3}
8795+\newcommand{\SysflowSpeechAudioPostToMultimodalAiShare}{0.4}
8796+\newcommand{\SysflowSpeechAudioPostToNlpShare}{4.7}
8797+\newcommand{\SysflowSpeechAudioPostToOperatingSystemsShare}{0.0}
8798+\newcommand{\SysflowSpeechAudioPostToPrivacyPreservingComputationShare}{0.1}
8799+\newcommand{\SysflowSpeechAudioPostToProgrammingLanguagesShare}{0.0}
8800+\newcommand{\SysflowSpeechAudioPostToQuantumComputingShare}{0.1}
8801+\newcommand{\SysflowSpeechAudioPostToReinforcementLearningShare}{0.1}
8802+\newcommand{\SysflowSpeechAudioPostToRoboticsShare}{0.6}
8803+\newcommand{\SysflowSpeechAudioPostToScientificComputingShare}{0.5}
8804+\newcommand{\SysflowSpeechAudioPostToSoftwareEngineeringShare}{0.2}
8805+\newcommand{\SysflowSpeechAudioPostToSpeechAudioShare}{70.1}
8806+\newcommand{\SysflowSpeechAudioPostToSustainableComputingShare}{0.0}
8807+\newcommand{\SysflowSustainableComputingBaselineToAiAcceleratorsShare}{0.2}
8808+\newcommand{\SysflowSustainableComputingBaselineToAiAgentsShare}{0.4}
8809+\newcommand{\SysflowSustainableComputingBaselineToAiSafetyShare}{0.5}
8810+\newcommand{\SysflowSustainableComputingBaselineToAlgorithmsShare}{4.8}
8811+\newcommand{\SysflowSustainableComputingBaselineToAppliedCryptographyShare}{0.3}
8812+\newcommand{\SysflowSustainableComputingBaselineToAutonomousScientificDiscoveryShare}{0.1}
8813+\newcommand{\SysflowSustainableComputingBaselineToBlockchainShare}{1.4}
8814+\newcommand{\SysflowSustainableComputingBaselineToCloudComputingShare}{9.9}
8815+\newcommand{\SysflowSustainableComputingBaselineToCompilersShare}{0.2}
8816+\newcommand{\SysflowSustainableComputingBaselineToComputationalBiologyShare}{0.7}
8817+\newcommand{\SysflowSustainableComputingBaselineToComputationalComplexityShare}{0.4}
8818+\newcommand{\SysflowSustainableComputingBaselineToComputerArchitectureShare}{6.0}
8819+\newcommand{\SysflowSustainableComputingBaselineToComputerGraphicsShare}{0.2}
8820+\newcommand{\SysflowSustainableComputingBaselineToComputerNetworksShare}{22.2}
8821+\newcommand{\SysflowSustainableComputingBaselineToComputerVisionShare}{5.5}
8822+\newcommand{\SysflowSustainableComputingBaselineToCybersecurityShare}{2.0}
8823+\newcommand{\SysflowSustainableComputingBaselineToDatabaseSystemsShare}{1.6}
8824+\newcommand{\SysflowSustainableComputingBaselineToDeepLearningArchitecturesShare}{3.9}
8825+\newcommand{\SysflowSustainableComputingBaselineToDistributedComputingShare}{4.7}
8826+\newcommand{\SysflowSustainableComputingBaselineToEdgeComputingShare}{6.7}
8827+\newcommand{\SysflowSustainableComputingBaselineToFederatedLearningShare}{0.2}
8828+\newcommand{\SysflowSustainableComputingBaselineToFormalVerificationShare}{0.3}
8829+\newcommand{\SysflowSustainableComputingBaselineToFoundationModelsShare}{0.0}
8830+\newcommand{\SysflowSustainableComputingBaselineToGenerativeAiShare}{0.1}
8831+\newcommand{\SysflowSustainableComputingBaselineToHciShare}{3.2}
8832+\newcommand{\SysflowSustainableComputingBaselineToInformationRetrievalShare}{0.9}
8833+\newcommand{\SysflowSustainableComputingBaselineToLlmShare}{0.2}
8834+\newcommand{\SysflowSustainableComputingBaselineToMlTheoryShare}{2.5}
8835+\newcommand{\SysflowSustainableComputingBaselineToMultimodalAiShare}{0.0}
8836+\newcommand{\SysflowSustainableComputingBaselineToNlpShare}{1.9}
8837+\newcommand{\SysflowSustainableComputingBaselineToOperatingSystemsShare}{0.3}
8838+\newcommand{\SysflowSustainableComputingBaselineToPrivacyPreservingComputationShare}{0.6}
8839+\newcommand{\SysflowSustainableComputingBaselineToProgrammingLanguagesShare}{0.5}
8840+\newcommand{\SysflowSustainableComputingBaselineToQuantumComputingShare}{0.3}
8841+\newcommand{\SysflowSustainableComputingBaselineToReinforcementLearningShare}{1.2}
8842+\newcommand{\SysflowSustainableComputingBaselineToRoboticsShare}{1.2}
8843+\newcommand{\SysflowSustainableComputingBaselineToScientificComputingShare}{2.1}
8844+\newcommand{\SysflowSustainableComputingBaselineToSoftwareEngineeringShare}{3.2}
8845+\newcommand{\SysflowSustainableComputingBaselineToSpeechAudioShare}{0.4}
8846+\newcommand{\SysflowSustainableComputingBaselineToSustainableComputingShare}{9.4}
8847+\newcommand{\SysflowSustainableComputingPostToAiAcceleratorsShare}{0.2}
8848+\newcommand{\SysflowSustainableComputingPostToAiAgentsShare}{0.4}
8849+\newcommand{\SysflowSustainableComputingPostToAiSafetyShare}{1.9}
8850+\newcommand{\SysflowSustainableComputingPostToAlgorithmsShare}{4.1}
8851+\newcommand{\SysflowSustainableComputingPostToAppliedCryptographyShare}{0.3}
8852+\newcommand{\SysflowSustainableComputingPostToAutonomousScientificDiscoveryShare}{0.1}
8853+\newcommand{\SysflowSustainableComputingPostToBlockchainShare}{2.0}
8854+\newcommand{\SysflowSustainableComputingPostToCloudComputingShare}{10.8}
8855+\newcommand{\SysflowSustainableComputingPostToCompilersShare}{0.2}
8856+\newcommand{\SysflowSustainableComputingPostToComputationalBiologyShare}{0.6}
8857+\newcommand{\SysflowSustainableComputingPostToComputationalComplexityShare}{0.4}
8858+\newcommand{\SysflowSustainableComputingPostToComputerArchitectureShare}{6.9}
8859+\newcommand{\SysflowSustainableComputingPostToComputerGraphicsShare}{0.3}
8860+\newcommand{\SysflowSustainableComputingPostToComputerNetworksShare}{14.2}
8861+\newcommand{\SysflowSustainableComputingPostToComputerVisionShare}{5.9}
8862+\newcommand{\SysflowSustainableComputingPostToCybersecurityShare}{1.9}
8863+\newcommand{\SysflowSustainableComputingPostToDatabaseSystemsShare}{1.5}
8864+\newcommand{\SysflowSustainableComputingPostToDeepLearningArchitecturesShare}{5.1}
8865+\newcommand{\SysflowSustainableComputingPostToDistributedComputingShare}{4.7}
8866+\newcommand{\SysflowSustainableComputingPostToEdgeComputingShare}{5.7}
8867+\newcommand{\SysflowSustainableComputingPostToFederatedLearningShare}{0.4}
8868+\newcommand{\SysflowSustainableComputingPostToFormalVerificationShare}{0.2}
8869+\newcommand{\SysflowSustainableComputingPostToFoundationModelsShare}{0.1}
8870+\newcommand{\SysflowSustainableComputingPostToGenerativeAiShare}{0.6}
8871+\newcommand{\SysflowSustainableComputingPostToHciShare}{3.1}
8872+\newcommand{\SysflowSustainableComputingPostToInformationRetrievalShare}{0.8}
8873+\newcommand{\SysflowSustainableComputingPostToLlmShare}{1.6}
8874+\newcommand{\SysflowSustainableComputingPostToMlTheoryShare}{3.1}
8875+\newcommand{\SysflowSustainableComputingPostToMultimodalAiShare}{0.1}
8876+\newcommand{\SysflowSustainableComputingPostToNlpShare}{2.1}
8877+\newcommand{\SysflowSustainableComputingPostToOperatingSystemsShare}{0.3}
8878+\newcommand{\SysflowSustainableComputingPostToPrivacyPreservingComputationShare}{0.6}
8879+\newcommand{\SysflowSustainableComputingPostToProgrammingLanguagesShare}{0.3}
8880+\newcommand{\SysflowSustainableComputingPostToQuantumComputingShare}{0.3}
8881+\newcommand{\SysflowSustainableComputingPostToReinforcementLearningShare}{1.5}
8882+\newcommand{\SysflowSustainableComputingPostToRoboticsShare}{1.5}
8883+\newcommand{\SysflowSustainableComputingPostToScientificComputingShare}{1.7}
8884+\newcommand{\SysflowSustainableComputingPostToSoftwareEngineeringShare}{2.9}
8885+\newcommand{\SysflowSustainableComputingPostToSpeechAudioShare}{0.3}
8886+\newcommand{\SysflowSustainableComputingPostToSustainableComputingShare}{11.3}
5612 % ---- Part VII-b security macros (Agent H) ---- 8887 % ---- Part VII-b security macros (Agent H) ----
5613 \newcommand{\SecCountAppliedCryptographyTwoZeroOneEightMultilabel}{29{,}699} 8888 \newcommand{\SecCountAppliedCryptographyTwoZeroOneEightMultilabel}{29{,}699}
5614 \newcommand{\SecCountAppliedCryptographyTwoZeroOneEightPrimary}{10{,}782} 8889 \newcommand{\SecCountAppliedCryptographyTwoZeroOneEightPrimary}{10{,}782}
@@ -5800,51 +9075,51 @@
5800 \newcommand{\SecCagrPrimaryFederatedLearningSpanPct}{37.7} 9075 \newcommand{\SecCagrPrimaryFederatedLearningSpanPct}{37.7}
5801 \newcommand{\SecCountFederatedLearningAllMultilabel}{71{,}037} 9076 \newcommand{\SecCountFederatedLearningAllMultilabel}{71{,}037}
5802 \newcommand{\SecCountFederatedLearningAllPrimary}{11{,}240} 9077 \newcommand{\SecCountFederatedLearningAllPrimary}{11{,}240}
5803−\newcommand{\SecCagrAppliedCryptographyNTwoZeroOneEight}{10{,}782} 9078+\newcommand{\SecCagrAppliedCryptographyNTwoZeroOneEight}{3{,}594}
5804−\newcommand{\SecCagrAppliedCryptographyNTwoZeroTwoTwo}{10{,}299} 9079+\newcommand{\SecCagrAppliedCryptographyNTwoZeroTwoTwo}{5{,}149}
5805−\newcommand{\SecCagrAppliedCryptographyNTwoZeroTwoThree}{11{,}219} 9080+\newcommand{\SecCagrAppliedCryptographyNTwoZeroTwoThree}{3{,}739}
5806−\newcommand{\SecCagrAppliedCryptographyNTwoZeroTwoFive}{14{,}612} 9081+\newcommand{\SecCagrAppliedCryptographyNTwoZeroTwoFive}{7{,}306}
5807−\newcommand{\SecCagrAppliedCryptographyCagrBaselinePct}{-1.1} 9082+\newcommand{\SecCagrAppliedCryptographyCagrBaselinePct}{9.4}
5808−\newcommand{\SecCagrAppliedCryptographyCagrPostPct}{14.1} 9083+\newcommand{\SecCagrAppliedCryptographyCagrPostPct}{39.8}
5809−\newcommand{\SecCagrAppliedCryptographyShareTwoZeroOneEightPct}{2.16} 9084+\newcommand{\SecCagrAppliedCryptographyShareTwoZeroOneEightPct}{2.12}
5810−\newcommand{\SecCagrAppliedCryptographyShareTwoZeroTwoTwoPct}{1.84} 9085+\newcommand{\SecCagrAppliedCryptographyShareTwoZeroTwoTwoPct}{2.02}
5811−\newcommand{\SecCagrAppliedCryptographyShareTwoZeroTwoFivePct}{1.64} 9086+\newcommand{\SecCagrAppliedCryptographyShareTwoZeroTwoFivePct}{1.70}
5812−\newcommand{\SecCagrBlockchainNTwoZeroOneEight}{6{,}441} 9087+\newcommand{\SecCagrBlockchainNTwoZeroOneEight}{2{,}147}
5813−\newcommand{\SecCagrBlockchainNTwoZeroTwoTwo}{11{,}770} 9088+\newcommand{\SecCagrBlockchainNTwoZeroTwoTwo}{5{,}885}
5814−\newcommand{\SecCagrBlockchainNTwoZeroTwoThree}{14{,}349} 9089+\newcommand{\SecCagrBlockchainNTwoZeroTwoThree}{7{,}174}
5815−\newcommand{\SecCagrBlockchainNTwoZeroTwoFive}{18{,}012} 9090+\newcommand{\SecCagrBlockchainNTwoZeroTwoFive}{9{,}006}
5816−\newcommand{\SecCagrBlockchainCagrBaselinePct}{16.3} 9091+\newcommand{\SecCagrBlockchainCagrBaselinePct}{28.7}
5817 \newcommand{\SecCagrBlockchainCagrPostPct}{12.0} 9092 \newcommand{\SecCagrBlockchainCagrPostPct}{12.0}
5818−\newcommand{\SecCagrBlockchainShareTwoZeroOneEightPct}{1.29} 9093+\newcommand{\SecCagrBlockchainShareTwoZeroOneEightPct}{1.26}
5819−\newcommand{\SecCagrBlockchainShareTwoZeroTwoTwoPct}{2.10} 9094+\newcommand{\SecCagrBlockchainShareTwoZeroTwoTwoPct}{2.31}
5820−\newcommand{\SecCagrBlockchainShareTwoZeroTwoFivePct}{2.02} 9095+\newcommand{\SecCagrBlockchainShareTwoZeroTwoFivePct}{2.10}
5821−\newcommand{\SecCagrCybersecurityNTwoZeroOneEight}{29{,}440} 9096+\newcommand{\SecCagrCybersecurityNTwoZeroOneEight}{9{,}813}
5822−\newcommand{\SecCagrCybersecurityNTwoZeroTwoTwo}{31{,}554} 9097+\newcommand{\SecCagrCybersecurityNTwoZeroTwoTwo}{15{,}777}
5823−\newcommand{\SecCagrCybersecurityNTwoZeroTwoThree}{36{,}481} 9098+\newcommand{\SecCagrCybersecurityNTwoZeroTwoThree}{12{,}160}
5824−\newcommand{\SecCagrCybersecurityNTwoZeroTwoFive}{59{,}044} 9099+\newcommand{\SecCagrCybersecurityNTwoZeroTwoFive}{29{,}522}
5825−\newcommand{\SecCagrCybersecurityCagrBaselinePct}{1.7} 9100+\newcommand{\SecCagrCybersecurityCagrBaselinePct}{12.6}
5826−\newcommand{\SecCagrCybersecurityCagrPostPct}{27.2} 9101+\newcommand{\SecCagrCybersecurityCagrPostPct}{55.8}
5827−\newcommand{\SecCagrCybersecurityShareTwoZeroOneEightPct}{5.89} 9102+\newcommand{\SecCagrCybersecurityShareTwoZeroOneEightPct}{5.78}
5828−\newcommand{\SecCagrCybersecurityShareTwoZeroTwoTwoPct}{5.63} 9103+\newcommand{\SecCagrCybersecurityShareTwoZeroTwoTwoPct}{6.20}
5829−\newcommand{\SecCagrCybersecurityShareTwoZeroTwoFivePct}{6.61} 9104+\newcommand{\SecCagrCybersecurityShareTwoZeroTwoFivePct}{6.87}
5830−\newcommand{\SecCagrFederatedLearningNTwoZeroOneEight}{143} 9105+\newcommand{\SecCagrFederatedLearningNTwoZeroOneEight}{71}
5831−\newcommand{\SecCagrFederatedLearningNTwoZeroTwoTwo}{974} 9106+\newcommand{\SecCagrFederatedLearningNTwoZeroTwoTwo}{487}
5832−\newcommand{\SecCagrFederatedLearningNTwoZeroTwoThree}{1{,}497} 9107+\newcommand{\SecCagrFederatedLearningNTwoZeroTwoThree}{748}
5833−\newcommand{\SecCagrFederatedLearningNTwoZeroTwoFive}{2{,}544} 9108+\newcommand{\SecCagrFederatedLearningNTwoZeroTwoFive}{1{,}272}
5834 \newcommand{\SecCagrFederatedLearningCagrBaselinePct}{61.5} 9109 \newcommand{\SecCagrFederatedLearningCagrBaselinePct}{61.5}
5835 \newcommand{\SecCagrFederatedLearningCagrPostPct}{30.4} 9110 \newcommand{\SecCagrFederatedLearningCagrPostPct}{30.4}
5836−\newcommand{\SecCagrFederatedLearningShareTwoZeroOneEightPct}{0.03} 9111+\newcommand{\SecCagrFederatedLearningShareTwoZeroOneEightPct}{0.04}
5837−\newcommand{\SecCagrFederatedLearningShareTwoZeroTwoTwoPct}{0.17} 9112+\newcommand{\SecCagrFederatedLearningShareTwoZeroTwoTwoPct}{0.19}
5838−\newcommand{\SecCagrFederatedLearningShareTwoZeroTwoFivePct}{0.28} 9113+\newcommand{\SecCagrFederatedLearningShareTwoZeroTwoFivePct}{0.30}
5839−\newcommand{\SecCagrPrivacyPreservingComputationNTwoZeroOneEight}{3{,}105} 9114+\newcommand{\SecCagrPrivacyPreservingComputationNTwoZeroOneEight}{1{,}035}
5840−\newcommand{\SecCagrPrivacyPreservingComputationNTwoZeroTwoTwo}{4{,}376} 9115+\newcommand{\SecCagrPrivacyPreservingComputationNTwoZeroTwoTwo}{2{,}188}
5841−\newcommand{\SecCagrPrivacyPreservingComputationNTwoZeroTwoThree}{5{,}010} 9116+\newcommand{\SecCagrPrivacyPreservingComputationNTwoZeroTwoThree}{2{,}505}
5842−\newcommand{\SecCagrPrivacyPreservingComputationNTwoZeroTwoFive}{8{,}817} 9117+\newcommand{\SecCagrPrivacyPreservingComputationNTwoZeroTwoFive}{4{,}408}
5843−\newcommand{\SecCagrPrivacyPreservingComputationCagrBaselinePct}{9.0} 9118+\newcommand{\SecCagrPrivacyPreservingComputationCagrBaselinePct}{20.6}
5844 \newcommand{\SecCagrPrivacyPreservingComputationCagrPostPct}{32.7} 9119 \newcommand{\SecCagrPrivacyPreservingComputationCagrPostPct}{32.7}
5845−\newcommand{\SecCagrPrivacyPreservingComputationShareTwoZeroOneEightPct}{0.62} 9120+\newcommand{\SecCagrPrivacyPreservingComputationShareTwoZeroOneEightPct}{0.61}
5846−\newcommand{\SecCagrPrivacyPreservingComputationShareTwoZeroTwoTwoPct}{0.78} 9121+\newcommand{\SecCagrPrivacyPreservingComputationShareTwoZeroTwoTwoPct}{0.86}
5847−\newcommand{\SecCagrPrivacyPreservingComputationShareTwoZeroTwoFivePct}{0.99} 9122+\newcommand{\SecCagrPrivacyPreservingComputationShareTwoZeroTwoFivePct}{1.03}
5848 \newcommand{\SecBreakAppliedCryptographyBpBreaksBic}{2022-07-01} 9123 \newcommand{\SecBreakAppliedCryptographyBpBreaksBic}{2022-07-01}
5849 \newcommand{\SecBreakAppliedCryptographyPelt}{2022-11, 2024-12} 9124 \newcommand{\SecBreakAppliedCryptographyPelt}{2022-11, 2024-12}
5850 \newcommand{\SecBreakAppliedCryptographyBocpdResets}{none} 9125 \newcommand{\SecBreakAppliedCryptographyBocpdResets}{none}
@@ -7111,3 +10386,2482 @@
7111 \newcommand{\SecLandFederatedLearningArticles}{18} 10386 \newcommand{\SecLandFederatedLearningArticles}{18}
7112 \newcommand{\SecLandFederatedLearningConference}{4} 10387 \newcommand{\SecLandFederatedLearningConference}{4}
7113 \newcommand{\SecLandFederatedLearningGrey}{3} 10388 \newcommand{\SecLandFederatedLearningGrey}{3}
10389+% ---- Part VII-a theory macros (Agent G) ----
10390+\newcommand{\ThyCountAlgorithmsTwoZeroOneEightMultilabel}{60{,}011}
10391+\newcommand{\ThyCountAlgorithmsTwoZeroOneEightPrimary}{29{,}148}
10392+\newcommand{\ThySharePrimaryAlgorithmsTwoZeroOneEightPct}{5.83}
10393+\newcommand{\ThyShareMultilabelAlgorithmsTwoZeroOneEightPct}{5.92}
10394+\newcommand{\ThyCountAlgorithmsTwoZeroOneNineMultilabel}{62{,}158}
10395+\newcommand{\ThyCountAlgorithmsTwoZeroOneNinePrimary}{29{,}252}
10396+\newcommand{\ThySharePrimaryAlgorithmsTwoZeroOneNinePct}{5.42}
10397+\newcommand{\ThyShareMultilabelAlgorithmsTwoZeroOneNinePct}{5.51}
10398+\newcommand{\ThyCountAlgorithmsTwoZeroTwoZeroMultilabel}{63{,}646}
10399+\newcommand{\ThyCountAlgorithmsTwoZeroTwoZeroPrimary}{29{,}376}
10400+\newcommand{\ThySharePrimaryAlgorithmsTwoZeroTwoZeroPct}{5.35}
10401+\newcommand{\ThyShareMultilabelAlgorithmsTwoZeroTwoZeroPct}{5.38}
10402+\newcommand{\ThyCountAlgorithmsTwoZeroTwoOneMultilabel}{64{,}815}
10403+\newcommand{\ThyCountAlgorithmsTwoZeroTwoOnePrimary}{29{,}023}
10404+\newcommand{\ThySharePrimaryAlgorithmsTwoZeroTwoOnePct}{5.19}
10405+\newcommand{\ThyShareMultilabelAlgorithmsTwoZeroTwoOnePct}{5.20}
10406+\newcommand{\ThyCountAlgorithmsTwoZeroTwoTwoMultilabel}{63{,}882}
10407+\newcommand{\ThyCountAlgorithmsTwoZeroTwoTwoPrimary}{28{,}202}
10408+\newcommand{\ThySharePrimaryAlgorithmsTwoZeroTwoTwoPct}{5.03}
10409+\newcommand{\ThyShareMultilabelAlgorithmsTwoZeroTwoTwoPct}{4.97}
10410+\newcommand{\ThyCountAlgorithmsTwoZeroTwoThreeMultilabel}{67{,}630}
10411+\newcommand{\ThyCountAlgorithmsTwoZeroTwoThreePrimary}{28{,}531}
10412+\newcommand{\ThySharePrimaryAlgorithmsTwoZeroTwoThreePct}{4.50}
10413+\newcommand{\ThyShareMultilabelAlgorithmsTwoZeroTwoThreePct}{4.49}
10414+\newcommand{\ThyCountAlgorithmsTwoZeroTwoFourMultilabel}{74{,}137}
10415+\newcommand{\ThyCountAlgorithmsTwoZeroTwoFourPrimary}{29{,}957}
10416+\newcommand{\ThySharePrimaryAlgorithmsTwoZeroTwoFourPct}{4.05}
10417+\newcommand{\ThyShareMultilabelAlgorithmsTwoZeroTwoFourPct}{4.04}
10418+\newcommand{\ThyCountAlgorithmsTwoZeroTwoFiveMultilabel}{88{,}268}
10419+\newcommand{\ThyCountAlgorithmsTwoZeroTwoFivePrimary}{33{,}795}
10420+\newcommand{\ThySharePrimaryAlgorithmsTwoZeroTwoFivePct}{3.79}
10421+\newcommand{\ThyShareMultilabelAlgorithmsTwoZeroTwoFivePct}{3.69}
10422+\newcommand{\ThyCountAlgorithmsTwoZeroTwoSixMultilabel}{76{,}192}
10423+\newcommand{\ThyCountAlgorithmsTwoZeroTwoSixPrimary}{28{,}776}
10424+\newcommand{\ThySharePrimaryAlgorithmsTwoZeroTwoSixPct}{3.73}
10425+\newcommand{\ThyShareMultilabelAlgorithmsTwoZeroTwoSixPct}{3.54}
10426+\newcommand{\ThyCountCompilersTwoZeroOneEightMultilabel}{5{,}705}
10427+\newcommand{\ThyCountCompilersTwoZeroOneEightPrimary}{725}
10428+\newcommand{\ThySharePrimaryCompilersTwoZeroOneEightPct}{0.15}
10429+\newcommand{\ThyShareMultilabelCompilersTwoZeroOneEightPct}{0.56}
10430+\newcommand{\ThyCountCompilersTwoZeroOneNineMultilabel}{5{,}645}
10431+\newcommand{\ThyCountCompilersTwoZeroOneNinePrimary}{689}
10432+\newcommand{\ThySharePrimaryCompilersTwoZeroOneNinePct}{0.13}
10433+\newcommand{\ThyShareMultilabelCompilersTwoZeroOneNinePct}{0.50}
10434+\newcommand{\ThyCountCompilersTwoZeroTwoZeroMultilabel}{5{,}576}
10435+\newcommand{\ThyCountCompilersTwoZeroTwoZeroPrimary}{737}
10436+\newcommand{\ThySharePrimaryCompilersTwoZeroTwoZeroPct}{0.13}
10437+\newcommand{\ThyShareMultilabelCompilersTwoZeroTwoZeroPct}{0.47}
10438+\newcommand{\ThyCountCompilersTwoZeroTwoOneMultilabel}{5{,}721}
10439+\newcommand{\ThyCountCompilersTwoZeroTwoOnePrimary}{709}
10440+\newcommand{\ThySharePrimaryCompilersTwoZeroTwoOnePct}{0.13}
10441+\newcommand{\ThyShareMultilabelCompilersTwoZeroTwoOnePct}{0.46}
10442+\newcommand{\ThyCountCompilersTwoZeroTwoTwoMultilabel}{5{,}646}
10443+\newcommand{\ThyCountCompilersTwoZeroTwoTwoPrimary}{642}
10444+\newcommand{\ThySharePrimaryCompilersTwoZeroTwoTwoPct}{0.11}
10445+\newcommand{\ThyShareMultilabelCompilersTwoZeroTwoTwoPct}{0.44}
10446+\newcommand{\ThyCountCompilersTwoZeroTwoThreeMultilabel}{6{,}351}
10447+\newcommand{\ThyCountCompilersTwoZeroTwoThreePrimary}{692}
10448+\newcommand{\ThySharePrimaryCompilersTwoZeroTwoThreePct}{0.11}
10449+\newcommand{\ThyShareMultilabelCompilersTwoZeroTwoThreePct}{0.42}
10450+\newcommand{\ThyCountCompilersTwoZeroTwoFourMultilabel}{7{,}306}
10451+\newcommand{\ThyCountCompilersTwoZeroTwoFourPrimary}{768}
10452+\newcommand{\ThySharePrimaryCompilersTwoZeroTwoFourPct}{0.10}
10453+\newcommand{\ThyShareMultilabelCompilersTwoZeroTwoFourPct}{0.40}
10454+\newcommand{\ThyCountCompilersTwoZeroTwoFiveMultilabel}{9{,}521}
10455+\newcommand{\ThyCountCompilersTwoZeroTwoFivePrimary}{767}
10456+\newcommand{\ThySharePrimaryCompilersTwoZeroTwoFivePct}{0.09}
10457+\newcommand{\ThyShareMultilabelCompilersTwoZeroTwoFivePct}{0.40}
10458+\newcommand{\ThyCountCompilersTwoZeroTwoSixMultilabel}{9{,}915}
10459+\newcommand{\ThyCountCompilersTwoZeroTwoSixPrimary}{621}
10460+\newcommand{\ThySharePrimaryCompilersTwoZeroTwoSixPct}{0.08}
10461+\newcommand{\ThyShareMultilabelCompilersTwoZeroTwoSixPct}{0.46}
10462+\newcommand{\ThyCountComputationalComplexityTwoZeroOneEightMultilabel}{16{,}996}
10463+\newcommand{\ThyCountComputationalComplexityTwoZeroOneEightPrimary}{3{,}837}
10464+\newcommand{\ThySharePrimaryComputationalComplexityTwoZeroOneEightPct}{0.77}
10465+\newcommand{\ThyShareMultilabelComputationalComplexityTwoZeroOneEightPct}{1.68}
10466+\newcommand{\ThyCountComputationalComplexityTwoZeroOneNineMultilabel}{18{,}186}
10467+\newcommand{\ThyCountComputationalComplexityTwoZeroOneNinePrimary}{3{,}952}
10468+\newcommand{\ThySharePrimaryComputationalComplexityTwoZeroOneNinePct}{0.73}
10469+\newcommand{\ThyShareMultilabelComputationalComplexityTwoZeroOneNinePct}{1.61}
10470+\newcommand{\ThyCountComputationalComplexityTwoZeroTwoZeroMultilabel}{18{,}459}
10471+\newcommand{\ThyCountComputationalComplexityTwoZeroTwoZeroPrimary}{3{,}872}
10472+\newcommand{\ThySharePrimaryComputationalComplexityTwoZeroTwoZeroPct}{0.71}
10473+\newcommand{\ThyShareMultilabelComputationalComplexityTwoZeroTwoZeroPct}{1.56}
10474+\newcommand{\ThyCountComputationalComplexityTwoZeroTwoOneMultilabel}{18{,}524}
10475+\newcommand{\ThyCountComputationalComplexityTwoZeroTwoOnePrimary}{3{,}765}
10476+\newcommand{\ThySharePrimaryComputationalComplexityTwoZeroTwoOnePct}{0.67}
10477+\newcommand{\ThyShareMultilabelComputationalComplexityTwoZeroTwoOnePct}{1.49}
10478+\newcommand{\ThyCountComputationalComplexityTwoZeroTwoTwoMultilabel}{18{,}335}
10479+\newcommand{\ThyCountComputationalComplexityTwoZeroTwoTwoPrimary}{3{,}541}
10480+\newcommand{\ThySharePrimaryComputationalComplexityTwoZeroTwoTwoPct}{0.63}
10481+\newcommand{\ThyShareMultilabelComputationalComplexityTwoZeroTwoTwoPct}{1.43}
10482+\newcommand{\ThyCountComputationalComplexityTwoZeroTwoThreeMultilabel}{19{,}464}
10483+\newcommand{\ThyCountComputationalComplexityTwoZeroTwoThreePrimary}{3{,}499}
10484+\newcommand{\ThySharePrimaryComputationalComplexityTwoZeroTwoThreePct}{0.55}
10485+\newcommand{\ThyShareMultilabelComputationalComplexityTwoZeroTwoThreePct}{1.29}
10486+\newcommand{\ThyCountComputationalComplexityTwoZeroTwoFourMultilabel}{22{,}675}
10487+\newcommand{\ThyCountComputationalComplexityTwoZeroTwoFourPrimary}{3{,}824}
10488+\newcommand{\ThySharePrimaryComputationalComplexityTwoZeroTwoFourPct}{0.52}
10489+\newcommand{\ThyShareMultilabelComputationalComplexityTwoZeroTwoFourPct}{1.24}
10490+\newcommand{\ThyCountComputationalComplexityTwoZeroTwoFiveMultilabel}{29{,}301}
10491+\newcommand{\ThyCountComputationalComplexityTwoZeroTwoFivePrimary}{4{,}209}
10492+\newcommand{\ThySharePrimaryComputationalComplexityTwoZeroTwoFivePct}{0.47}
10493+\newcommand{\ThyShareMultilabelComputationalComplexityTwoZeroTwoFivePct}{1.23}
10494+\newcommand{\ThyCountComputationalComplexityTwoZeroTwoSixMultilabel}{25{,}776}
10495+\newcommand{\ThyCountComputationalComplexityTwoZeroTwoSixPrimary}{4{,}067}
10496+\newcommand{\ThySharePrimaryComputationalComplexityTwoZeroTwoSixPct}{0.53}
10497+\newcommand{\ThyShareMultilabelComputationalComplexityTwoZeroTwoSixPct}{1.20}
10498+\newcommand{\ThyCountFormalVerificationTwoZeroOneEightMultilabel}{12{,}283}
10499+\newcommand{\ThyCountFormalVerificationTwoZeroOneEightPrimary}{5{,}307}
10500+\newcommand{\ThySharePrimaryFormalVerificationTwoZeroOneEightPct}{1.06}
10501+\newcommand{\ThyShareMultilabelFormalVerificationTwoZeroOneEightPct}{1.21}
10502+\newcommand{\ThyCountFormalVerificationTwoZeroOneNineMultilabel}{11{,}692}
10503+\newcommand{\ThyCountFormalVerificationTwoZeroOneNinePrimary}{4{,}802}
10504+\newcommand{\ThySharePrimaryFormalVerificationTwoZeroOneNinePct}{0.89}
10505+\newcommand{\ThyShareMultilabelFormalVerificationTwoZeroOneNinePct}{1.04}
10506+\newcommand{\ThyCountFormalVerificationTwoZeroTwoZeroMultilabel}{11{,}009}
10507+\newcommand{\ThyCountFormalVerificationTwoZeroTwoZeroPrimary}{4{,}535}
10508+\newcommand{\ThySharePrimaryFormalVerificationTwoZeroTwoZeroPct}{0.83}
10509+\newcommand{\ThyShareMultilabelFormalVerificationTwoZeroTwoZeroPct}{0.93}
10510+\newcommand{\ThyCountFormalVerificationTwoZeroTwoOneMultilabel}{10{,}938}
10511+\newcommand{\ThyCountFormalVerificationTwoZeroTwoOnePrimary}{4{,}511}
10512+\newcommand{\ThySharePrimaryFormalVerificationTwoZeroTwoOnePct}{0.81}
10513+\newcommand{\ThyShareMultilabelFormalVerificationTwoZeroTwoOnePct}{0.88}
10514+\newcommand{\ThyCountFormalVerificationTwoZeroTwoTwoMultilabel}{9{,}950}
10515+\newcommand{\ThyCountFormalVerificationTwoZeroTwoTwoPrimary}{3{,}995}
10516+\newcommand{\ThySharePrimaryFormalVerificationTwoZeroTwoTwoPct}{0.71}
10517+\newcommand{\ThyShareMultilabelFormalVerificationTwoZeroTwoTwoPct}{0.77}
10518+\newcommand{\ThyCountFormalVerificationTwoZeroTwoThreeMultilabel}{10{,}676}
10519+\newcommand{\ThyCountFormalVerificationTwoZeroTwoThreePrimary}{4{,}385}
10520+\newcommand{\ThySharePrimaryFormalVerificationTwoZeroTwoThreePct}{0.69}
10521+\newcommand{\ThyShareMultilabelFormalVerificationTwoZeroTwoThreePct}{0.71}
10522+\newcommand{\ThyCountFormalVerificationTwoZeroTwoFourMultilabel}{11{,}391}
10523+\newcommand{\ThyCountFormalVerificationTwoZeroTwoFourPrimary}{4{,}233}
10524+\newcommand{\ThySharePrimaryFormalVerificationTwoZeroTwoFourPct}{0.57}
10525+\newcommand{\ThyShareMultilabelFormalVerificationTwoZeroTwoFourPct}{0.62}
10526+\newcommand{\ThyCountFormalVerificationTwoZeroTwoFiveMultilabel}{13{,}656}
10527+\newcommand{\ThyCountFormalVerificationTwoZeroTwoFivePrimary}{4{,}623}
10528+\newcommand{\ThySharePrimaryFormalVerificationTwoZeroTwoFivePct}{0.52}
10529+\newcommand{\ThyShareMultilabelFormalVerificationTwoZeroTwoFivePct}{0.57}
10530+\newcommand{\ThyCountFormalVerificationTwoZeroTwoSixMultilabel}{14{,}494}
10531+\newcommand{\ThyCountFormalVerificationTwoZeroTwoSixPrimary}{4{,}866}
10532+\newcommand{\ThySharePrimaryFormalVerificationTwoZeroTwoSixPct}{0.63}
10533+\newcommand{\ThyShareMultilabelFormalVerificationTwoZeroTwoSixPct}{0.67}
10534+\newcommand{\ThyCountProgrammingLanguagesTwoZeroOneEightMultilabel}{16{,}735}
10535+\newcommand{\ThyCountProgrammingLanguagesTwoZeroOneEightPrimary}{4{,}390}
10536+\newcommand{\ThySharePrimaryProgrammingLanguagesTwoZeroOneEightPct}{0.88}
10537+\newcommand{\ThyShareMultilabelProgrammingLanguagesTwoZeroOneEightPct}{1.65}
10538+\newcommand{\ThyCountProgrammingLanguagesTwoZeroOneNineMultilabel}{16{,}286}
10539+\newcommand{\ThyCountProgrammingLanguagesTwoZeroOneNinePrimary}{4{,}474}
10540+\newcommand{\ThySharePrimaryProgrammingLanguagesTwoZeroOneNinePct}{0.83}
10541+\newcommand{\ThyShareMultilabelProgrammingLanguagesTwoZeroOneNinePct}{1.44}
10542+\newcommand{\ThyCountProgrammingLanguagesTwoZeroTwoZeroMultilabel}{16{,}126}
10543+\newcommand{\ThyCountProgrammingLanguagesTwoZeroTwoZeroPrimary}{4{,}126}
10544+\newcommand{\ThySharePrimaryProgrammingLanguagesTwoZeroTwoZeroPct}{0.75}
10545+\newcommand{\ThyShareMultilabelProgrammingLanguagesTwoZeroTwoZeroPct}{1.36}
10546+\newcommand{\ThyCountProgrammingLanguagesTwoZeroTwoOneMultilabel}{15{,}687}
10547+\newcommand{\ThyCountProgrammingLanguagesTwoZeroTwoOnePrimary}{3{,}978}
10548+\newcommand{\ThySharePrimaryProgrammingLanguagesTwoZeroTwoOnePct}{0.71}
10549+\newcommand{\ThyShareMultilabelProgrammingLanguagesTwoZeroTwoOnePct}{1.26}
10550+\newcommand{\ThyCountProgrammingLanguagesTwoZeroTwoTwoMultilabel}{15{,}186}
10551+\newcommand{\ThyCountProgrammingLanguagesTwoZeroTwoTwoPrimary}{3{,}664}
10552+\newcommand{\ThySharePrimaryProgrammingLanguagesTwoZeroTwoTwoPct}{0.65}
10553+\newcommand{\ThyShareMultilabelProgrammingLanguagesTwoZeroTwoTwoPct}{1.18}
10554+\newcommand{\ThyCountProgrammingLanguagesTwoZeroTwoThreeMultilabel}{16{,}681}
10555+\newcommand{\ThyCountProgrammingLanguagesTwoZeroTwoThreePrimary}{3{,}876}
10556+\newcommand{\ThySharePrimaryProgrammingLanguagesTwoZeroTwoThreePct}{0.61}
10557+\newcommand{\ThyShareMultilabelProgrammingLanguagesTwoZeroTwoThreePct}{1.11}
10558+\newcommand{\ThyCountProgrammingLanguagesTwoZeroTwoFourMultilabel}{17{,}703}
10559+\newcommand{\ThyCountProgrammingLanguagesTwoZeroTwoFourPrimary}{3{,}846}
10560+\newcommand{\ThySharePrimaryProgrammingLanguagesTwoZeroTwoFourPct}{0.52}
10561+\newcommand{\ThyShareMultilabelProgrammingLanguagesTwoZeroTwoFourPct}{0.96}
10562+\newcommand{\ThyCountProgrammingLanguagesTwoZeroTwoFiveMultilabel}{20{,}734}
10563+\newcommand{\ThyCountProgrammingLanguagesTwoZeroTwoFivePrimary}{4{,}193}
10564+\newcommand{\ThySharePrimaryProgrammingLanguagesTwoZeroTwoFivePct}{0.47}
10565+\newcommand{\ThyShareMultilabelProgrammingLanguagesTwoZeroTwoFivePct}{0.87}
10566+\newcommand{\ThyCountProgrammingLanguagesTwoZeroTwoSixMultilabel}{21{,}376}
10567+\newcommand{\ThyCountProgrammingLanguagesTwoZeroTwoSixPrimary}{3{,}691}
10568+\newcommand{\ThySharePrimaryProgrammingLanguagesTwoZeroTwoSixPct}{0.48}
10569+\newcommand{\ThyShareMultilabelProgrammingLanguagesTwoZeroTwoSixPct}{0.99}
10570+\newcommand{\ThyCountSoftwareEngineeringTwoZeroOneEightMultilabel}{44{,}359}
10571+\newcommand{\ThyCountSoftwareEngineeringTwoZeroOneEightPrimary}{29{,}016}
10572+\newcommand{\ThySharePrimarySoftwareEngineeringTwoZeroOneEightPct}{5.81}
10573+\newcommand{\ThyShareMultilabelSoftwareEngineeringTwoZeroOneEightPct}{4.37}
10574+\newcommand{\ThyCountSoftwareEngineeringTwoZeroOneNineMultilabel}{45{,}573}
10575+\newcommand{\ThyCountSoftwareEngineeringTwoZeroOneNinePrimary}{28{,}935}
10576+\newcommand{\ThySharePrimarySoftwareEngineeringTwoZeroOneNinePct}{5.36}
10577+\newcommand{\ThyShareMultilabelSoftwareEngineeringTwoZeroOneNinePct}{4.04}
10578+\newcommand{\ThyCountSoftwareEngineeringTwoZeroTwoZeroMultilabel}{45{,}901}
10579+\newcommand{\ThyCountSoftwareEngineeringTwoZeroTwoZeroPrimary}{27{,}756}
10580+\newcommand{\ThySharePrimarySoftwareEngineeringTwoZeroTwoZeroPct}{5.05}
10581+\newcommand{\ThyShareMultilabelSoftwareEngineeringTwoZeroTwoZeroPct}{3.88}
10582+\newcommand{\ThyCountSoftwareEngineeringTwoZeroTwoOneMultilabel}{44{,}735}
10583+\newcommand{\ThyCountSoftwareEngineeringTwoZeroTwoOnePrimary}{24{,}671}
10584+\newcommand{\ThySharePrimarySoftwareEngineeringTwoZeroTwoOnePct}{4.41}
10585+\newcommand{\ThyShareMultilabelSoftwareEngineeringTwoZeroTwoOnePct}{3.59}
10586+\newcommand{\ThyCountSoftwareEngineeringTwoZeroTwoTwoMultilabel}{46{,}006}
10587+\newcommand{\ThyCountSoftwareEngineeringTwoZeroTwoTwoPrimary}{23{,}725}
10588+\newcommand{\ThySharePrimarySoftwareEngineeringTwoZeroTwoTwoPct}{4.23}
10589+\newcommand{\ThyShareMultilabelSoftwareEngineeringTwoZeroTwoTwoPct}{3.58}
10590+\newcommand{\ThyCountSoftwareEngineeringTwoZeroTwoThreeMultilabel}{55{,}422}
10591+\newcommand{\ThyCountSoftwareEngineeringTwoZeroTwoThreePrimary}{26{,}245}
10592+\newcommand{\ThySharePrimarySoftwareEngineeringTwoZeroTwoThreePct}{4.14}
10593+\newcommand{\ThyShareMultilabelSoftwareEngineeringTwoZeroTwoThreePct}{3.68}
10594+\newcommand{\ThyCountSoftwareEngineeringTwoZeroTwoFourMultilabel}{68{,}087}
10595+\newcommand{\ThyCountSoftwareEngineeringTwoZeroTwoFourPrimary}{30{,}533}
10596+\newcommand{\ThySharePrimarySoftwareEngineeringTwoZeroTwoFourPct}{4.13}
10597+\newcommand{\ThyShareMultilabelSoftwareEngineeringTwoZeroTwoFourPct}{3.71}
10598+\newcommand{\ThyCountSoftwareEngineeringTwoZeroTwoFiveMultilabel}{87{,}235}
10599+\newcommand{\ThyCountSoftwareEngineeringTwoZeroTwoFivePrimary}{36{,}381}
10600+\newcommand{\ThySharePrimarySoftwareEngineeringTwoZeroTwoFivePct}{4.07}
10601+\newcommand{\ThyShareMultilabelSoftwareEngineeringTwoZeroTwoFivePct}{3.65}
10602+\newcommand{\ThyCountSoftwareEngineeringTwoZeroTwoSixMultilabel}{81{,}133}
10603+\newcommand{\ThyCountSoftwareEngineeringTwoZeroTwoSixPrimary}{34{,}261}
10604+\newcommand{\ThySharePrimarySoftwareEngineeringTwoZeroTwoSixPct}{4.44}
10605+\newcommand{\ThyShareMultilabelSoftwareEngineeringTwoZeroTwoSixPct}{3.77}
10606+\newcommand{\ThyCagrMultilabelAlgorithmsBaselinePct}{1.6}
10607+\newcommand{\ThyRatioMultilabelAlgorithmsBaseline}{1.06}
10608+\newcommand{\ThyCagrPrimaryAlgorithmsBaselinePct}{-0.8}
10609+\newcommand{\ThyRatioPrimaryAlgorithmsBaseline}{0.97}
10610+\newcommand{\ThyCagrMultilabelAlgorithmsPostPct}{14.2}
10611+\newcommand{\ThyRatioMultilabelAlgorithmsPost}{1.31}
10612+\newcommand{\ThyCagrPrimaryAlgorithmsPostPct}{8.8}
10613+\newcommand{\ThyRatioPrimaryAlgorithmsPost}{1.18}
10614+\newcommand{\ThyCagrMultilabelAlgorithmsSpanPct}{11.4}
10615+\newcommand{\ThyRatioMultilabelAlgorithmsSpan}{1.38}
10616+\newcommand{\ThyCagrPrimaryAlgorithmsSpanPct}{6.2}
10617+\newcommand{\ThyRatioPrimaryAlgorithmsSpan}{1.20}
10618+\newcommand{\ThyCountAlgorithmsAllMultilabel}{620{,}739}
10619+\newcommand{\ThyCountAlgorithmsAllPrimary}{266{,}060}
10620+\newcommand{\ThyCagrMultilabelComputationalComplexityBaselinePct}{1.9}
10621+\newcommand{\ThyRatioMultilabelComputationalComplexityBaseline}{1.08}
10622+\newcommand{\ThyCagrPrimaryComputationalComplexityBaselinePct}{-2.0}
10623+\newcommand{\ThyRatioPrimaryComputationalComplexityBaseline}{0.92}
10624+\newcommand{\ThyCagrMultilabelComputationalComplexityPostPct}{22.7}
10625+\newcommand{\ThyRatioMultilabelComputationalComplexityPost}{1.51}
10626+\newcommand{\ThyCagrPrimaryComputationalComplexityPostPct}{9.7}
10627+\newcommand{\ThyRatioPrimaryComputationalComplexityPost}{1.20}
10628+\newcommand{\ThyCagrMultilabelComputationalComplexitySpanPct}{16.9}
10629+\newcommand{\ThyRatioMultilabelComputationalComplexitySpan}{1.60}
10630+\newcommand{\ThyCagrPrimaryComputationalComplexitySpanPct}{5.9}
10631+\newcommand{\ThyRatioPrimaryComputationalComplexitySpan}{1.19}
10632+\newcommand{\ThyCountComputationalComplexityAllMultilabel}{187{,}716}
10633+\newcommand{\ThyCountComputationalComplexityAllPrimary}{34{,}566}
10634+\newcommand{\ThyCagrMultilabelProgrammingLanguagesBaselinePct}{-2.4}
10635+\newcommand{\ThyRatioMultilabelProgrammingLanguagesBaseline}{0.91}
10636+\newcommand{\ThyCagrPrimaryProgrammingLanguagesBaselinePct}{-4.4}
10637+\newcommand{\ThyRatioPrimaryProgrammingLanguagesBaseline}{0.83}
10638+\newcommand{\ThyCagrMultilabelProgrammingLanguagesPostPct}{11.5}
10639+\newcommand{\ThyRatioMultilabelProgrammingLanguagesPost}{1.24}
10640+\newcommand{\ThyCagrPrimaryProgrammingLanguagesPostPct}{4.0}
10641+\newcommand{\ThyRatioPrimaryProgrammingLanguagesPost}{1.08}
10642+\newcommand{\ThyCagrMultilabelProgrammingLanguagesSpanPct}{10.9}
10643+\newcommand{\ThyRatioMultilabelProgrammingLanguagesSpan}{1.37}
10644+\newcommand{\ThyCagrPrimaryProgrammingLanguagesSpanPct}{4.6}
10645+\newcommand{\ThyRatioPrimaryProgrammingLanguagesSpan}{1.14}
10646+\newcommand{\ThyCountProgrammingLanguagesAllMultilabel}{156{,}514}
10647+\newcommand{\ThyCountProgrammingLanguagesAllPrimary}{36{,}238}
10648+\newcommand{\ThyCagrMultilabelCompilersBaselinePct}{-0.3}
10649+\newcommand{\ThyRatioMultilabelCompilersBaseline}{0.99}
10650+\newcommand{\ThyCagrPrimaryCompilersBaselinePct}{-3.0}
10651+\newcommand{\ThyRatioPrimaryCompilersBaseline}{0.89}
10652+\newcommand{\ThyCagrMultilabelCompilersPostPct}{22.4}
10653+\newcommand{\ThyRatioMultilabelCompilersPost}{1.50}
10654+\newcommand{\ThyCagrPrimaryCompilersPostPct}{5.3}
10655+\newcommand{\ThyRatioPrimaryCompilersPost}{1.11}
10656+\newcommand{\ThyCagrMultilabelCompilersSpanPct}{19.0}
10657+\newcommand{\ThyRatioMultilabelCompilersSpan}{1.69}
10658+\newcommand{\ThyCagrPrimaryCompilersSpanPct}{6.1}
10659+\newcommand{\ThyRatioPrimaryCompilersSpan}{1.19}
10660+\newcommand{\ThyCountCompilersAllMultilabel}{61{,}386}
10661+\newcommand{\ThyCountCompilersAllPrimary}{6{,}350}
10662+\newcommand{\ThyCagrMultilabelFormalVerificationBaselinePct}{-5.1}
10663+\newcommand{\ThyRatioMultilabelFormalVerificationBaseline}{0.81}
10664+\newcommand{\ThyCagrPrimaryFormalVerificationBaselinePct}{-6.9}
10665+\newcommand{\ThyRatioPrimaryFormalVerificationBaseline}{0.75}
10666+\newcommand{\ThyCagrMultilabelFormalVerificationPostPct}{13.1}
10667+\newcommand{\ThyRatioMultilabelFormalVerificationPost}{1.28}
10668+\newcommand{\ThyCagrPrimaryFormalVerificationPostPct}{2.7}
10669+\newcommand{\ThyRatioPrimaryFormalVerificationPost}{1.05}
10670+\newcommand{\ThyCagrMultilabelFormalVerificationSpanPct}{11.1}
10671+\newcommand{\ThyRatioMultilabelFormalVerificationSpan}{1.37}
10672+\newcommand{\ThyCagrPrimaryFormalVerificationSpanPct}{5.0}
10673+\newcommand{\ThyRatioPrimaryFormalVerificationSpan}{1.16}
10674+\newcommand{\ThyCountFormalVerificationAllMultilabel}{106{,}089}
10675+\newcommand{\ThyCountFormalVerificationAllPrimary}{41{,}257}
10676+\newcommand{\ThyCagrMultilabelSoftwareEngineeringBaselinePct}{0.9}
10677+\newcommand{\ThyRatioMultilabelSoftwareEngineeringBaseline}{1.04}
10678+\newcommand{\ThyCagrPrimarySoftwareEngineeringBaselinePct}{-4.9}
10679+\newcommand{\ThyRatioPrimarySoftwareEngineeringBaseline}{0.82}
10680+\newcommand{\ThyCagrMultilabelSoftwareEngineeringPostPct}{25.5}
10681+\newcommand{\ThyRatioMultilabelSoftwareEngineeringPost}{1.57}
10682+\newcommand{\ThyCagrPrimarySoftwareEngineeringPostPct}{17.7}
10683+\newcommand{\ThyRatioPrimarySoftwareEngineeringPost}{1.39}
10684+\newcommand{\ThyCagrMultilabelSoftwareEngineeringSpanPct}{23.8}
10685+\newcommand{\ThyRatioMultilabelSoftwareEngineeringSpan}{1.90}
10686+\newcommand{\ThyCagrPrimarySoftwareEngineeringSpanPct}{15.3}
10687+\newcommand{\ThyRatioPrimarySoftwareEngineeringSpan}{1.53}
10688+\newcommand{\ThyCountSoftwareEngineeringAllMultilabel}{518{,}451}
10689+\newcommand{\ThyCountSoftwareEngineeringAllPrimary}{261{,}523}
10690+\newcommand{\ThyCagrPrimaryTotalBaselinePct}{2.9}
10691+\newcommand{\ThyCagrMultilabelTotalBaselinePct}{6.1}
10692+\newcommand{\ThyCagrPrimaryTotalPostPct}{18.6}
10693+\newcommand{\ThyCagrMultilabelTotalPostPct}{26.0}
10694+\newcommand{\ThyCagrfileAlgorithmsNTwoZeroOneEight}{9{,}716}
10695+\newcommand{\ThyCagrfileAlgorithmsNTwoZeroTwoTwo}{9{,}400}
10696+\newcommand{\ThyCagrfileAlgorithmsNTwoZeroTwoThree}{9{,}510}
10697+\newcommand{\ThyCagrfileAlgorithmsNTwoZeroTwoFive}{11{,}265}
10698+\newcommand{\ThyCagrfileAlgorithmsCagrBaselinePct}{-0.8}
10699+\newcommand{\ThyCagrfileAlgorithmsCagrPostPct}{8.8}
10700+\newcommand{\ThyCagrfileAlgorithmsShareTwoZeroOneEightPct}{5.72}
10701+\newcommand{\ThyCagrfileAlgorithmsShareTwoZeroTwoTwoPct}{3.69}
10702+\newcommand{\ThyCagrfileAlgorithmsShareTwoZeroTwoFivePct}{2.62}
10703+\newcommand{\ThyCagrfileCompilersNTwoZeroOneEight}{241}
10704+\newcommand{\ThyCagrfileCompilersNTwoZeroTwoTwo}{321}
10705+\newcommand{\ThyCagrfileCompilersNTwoZeroTwoThree}{346}
10706+\newcommand{\ThyCagrfileCompilersNTwoZeroTwoFive}{383}
10707+\newcommand{\ThyCagrfileCompilersCagrBaselinePct}{7.4}
10708+\newcommand{\ThyCagrfileCompilersCagrPostPct}{5.3}
10709+\newcommand{\ThyCagrfileCompilersShareTwoZeroOneEightPct}{0.14}
10710+\newcommand{\ThyCagrfileCompilersShareTwoZeroTwoTwoPct}{0.13}
10711+\newcommand{\ThyCagrfileCompilersShareTwoZeroTwoFivePct}{0.09}
10712+\newcommand{\ThyCagrfileComputationalComplexityNTwoZeroOneEight}{1{,}279}
10713+\newcommand{\ThyCagrfileComputationalComplexityNTwoZeroTwoTwo}{1{,}770}
10714+\newcommand{\ThyCagrfileComputationalComplexityNTwoZeroTwoThree}{1{,}166}
10715+\newcommand{\ThyCagrfileComputationalComplexityNTwoZeroTwoFive}{2{,}104}
10716+\newcommand{\ThyCagrfileComputationalComplexityCagrBaselinePct}{8.5}
10717+\newcommand{\ThyCagrfileComputationalComplexityCagrPostPct}{34.3}
10718+\newcommand{\ThyCagrfileComputationalComplexityShareTwoZeroOneEightPct}{0.75}
10719+\newcommand{\ThyCagrfileComputationalComplexityShareTwoZeroTwoTwoPct}{0.70}
10720+\newcommand{\ThyCagrfileComputationalComplexityShareTwoZeroTwoFivePct}{0.49}
10721+\newcommand{\ThyCagrfileFormalVerificationNTwoZeroOneEight}{1{,}769}
10722+\newcommand{\ThyCagrfileFormalVerificationNTwoZeroTwoTwo}{1{,}997}
10723+\newcommand{\ThyCagrfileFormalVerificationNTwoZeroTwoThree}{1{,}461}
10724+\newcommand{\ThyCagrfileFormalVerificationNTwoZeroTwoFive}{2{,}311}
10725+\newcommand{\ThyCagrfileFormalVerificationCagrBaselinePct}{3.1}
10726+\newcommand{\ThyCagrfileFormalVerificationCagrPostPct}{25.8}
10727+\newcommand{\ThyCagrfileFormalVerificationShareTwoZeroOneEightPct}{1.04}
10728+\newcommand{\ThyCagrfileFormalVerificationShareTwoZeroTwoTwoPct}{0.78}
10729+\newcommand{\ThyCagrfileFormalVerificationShareTwoZeroTwoFivePct}{0.54}
10730+\newcommand{\ThyCagrfileProgrammingLanguagesNTwoZeroOneEight}{1{,}463}
10731+\newcommand{\ThyCagrfileProgrammingLanguagesNTwoZeroTwoTwo}{1{,}832}
10732+\newcommand{\ThyCagrfileProgrammingLanguagesNTwoZeroTwoThree}{1{,}938}
10733+\newcommand{\ThyCagrfileProgrammingLanguagesNTwoZeroTwoFive}{2{,}096}
10734+\newcommand{\ThyCagrfileProgrammingLanguagesCagrBaselinePct}{5.8}
10735+\newcommand{\ThyCagrfileProgrammingLanguagesCagrPostPct}{4.0}
10736+\newcommand{\ThyCagrfileProgrammingLanguagesShareTwoZeroOneEightPct}{0.86}
10737+\newcommand{\ThyCagrfileProgrammingLanguagesShareTwoZeroTwoTwoPct}{0.72}
10738+\newcommand{\ThyCagrfileProgrammingLanguagesShareTwoZeroTwoFivePct}{0.49}
10739+\newcommand{\ThyCagrfileSoftwareEngineeringNTwoZeroOneEight}{9{,}672}
10740+\newcommand{\ThyCagrfileSoftwareEngineeringNTwoZeroTwoTwo}{11{,}862}
10741+\newcommand{\ThyCagrfileSoftwareEngineeringNTwoZeroTwoThree}{13{,}122}
10742+\newcommand{\ThyCagrfileSoftwareEngineeringNTwoZeroTwoFive}{18{,}190}
10743+\newcommand{\ThyCagrfileSoftwareEngineeringCagrBaselinePct}{5.2}
10744+\newcommand{\ThyCagrfileSoftwareEngineeringCagrPostPct}{17.7}
10745+\newcommand{\ThyCagrfileSoftwareEngineeringShareTwoZeroOneEightPct}{5.70}
10746+\newcommand{\ThyCagrfileSoftwareEngineeringShareTwoZeroTwoTwoPct}{4.66}
10747+\newcommand{\ThyCagrfileSoftwareEngineeringShareTwoZeroTwoFivePct}{4.23}
10748+\newcommand{\ThyBreakAlgorithmsBpBreaksBic}{2023-04-01}
10749+\newcommand{\ThyBreakAlgorithmsPelt}{2023-08, 2025-08}
10750+\newcommand{\ThyBreakAlgorithmsBocpdResets}{none}
10751+\newcommand{\ThyBreakAlgorithmsChatgptLevel}{-0.045}
10752+\newcommand{\ThyBreakAlgorithmsChatgptLevelP}{0.039}
10753+\newcommand{\ThyBreakAlgorithmsGptThreeLevel}{-0.060}
10754+\newcommand{\ThyBreakAlgorithmsGptThreeLevelP}{0.209}
10755+\newcommand{\ThyBreakAlgorithmsDalleTwoLevel}{-0.076}
10756+\newcommand{\ThyBreakAlgorithmsDalleTwoLevelP}{0.010}
10757+\newcommand{\ThyBreakAlgorithmsGptFourLevel}{-0.020}
10758+\newcommand{\ThyBreakAlgorithmsGptFourLevelP}{0.410}
10759+\newcommand{\ThyBreakAlgorithmsOOneLevel}{0.092}
10760+\newcommand{\ThyBreakAlgorithmsOOneLevelP}{0.002}
10761+\newcommand{\ThyBreakAlgorithmsChatgptSlope}{0.010}
10762+\newcommand{\ThyBreakAlgorithmsChatgptSlopeP}{0.000}
10763+\newcommand{\ThyBreakAlgorithmsPlaceboLevelP}{0.508}
10764+\newcommand{\ThyBreakAlgorithmsPlaceboSlopeP}{0.062}
10765+\newcommand{\ThyBreakAlgorithmsChatgptSlopePct}{1.0}
10766+\newcommand{\ThyBreakAlgorithmsChatgptLevelPct}{-4.5}
10767+\newcommand{\ThyBreakCompilersBpBreaksBic}{2023-04-01}
10768+\newcommand{\ThyBreakCompilersPelt}{2023-10, 2025-10}
10769+\newcommand{\ThyBreakCompilersBocpdResets}{2021-04}
10770+\newcommand{\ThyBreakCompilersChatgptLevel}{-0.028}
10771+\newcommand{\ThyBreakCompilersChatgptLevelP}{0.471}
10772+\newcommand{\ThyBreakCompilersGptThreeLevel}{-0.098}
10773+\newcommand{\ThyBreakCompilersGptThreeLevelP}{0.158}
10774+\newcommand{\ThyBreakCompilersDalleTwoLevel}{-0.098}
10775+\newcommand{\ThyBreakCompilersDalleTwoLevelP}{0.015}
10776+\newcommand{\ThyBreakCompilersGptFourLevel}{0.003}
10777+\newcommand{\ThyBreakCompilersGptFourLevelP}{0.937}
10778+\newcommand{\ThyBreakCompilersOOneLevel}{0.090}
10779+\newcommand{\ThyBreakCompilersOOneLevelP}{0.086}
10780+\newcommand{\ThyBreakCompilersChatgptSlope}{0.018}
10781+\newcommand{\ThyBreakCompilersChatgptSlopeP}{0.000}
10782+\newcommand{\ThyBreakCompilersPlaceboLevelP}{0.892}
10783+\newcommand{\ThyBreakCompilersPlaceboSlopeP}{0.215}
10784+\newcommand{\ThyBreakCompilersChatgptSlopePct}{1.8}
10785+\newcommand{\ThyBreakCompilersChatgptLevelPct}{-2.8}
10786+\newcommand{\ThyBreakComputationalComplexityBpBreaksBic}{2023-04-01}
10787+\newcommand{\ThyBreakComputationalComplexityPelt}{2023-10, 2025-01}
10788+\newcommand{\ThyBreakComputationalComplexityBocpdResets}{none}
10789+\newcommand{\ThyBreakComputationalComplexityChatgptLevel}{-0.068}
10790+\newcommand{\ThyBreakComputationalComplexityChatgptLevelP}{0.000}
10791+\newcommand{\ThyBreakComputationalComplexityGptThreeLevel}{-0.133}
10792+\newcommand{\ThyBreakComputationalComplexityGptThreeLevelP}{0.043}
10793+\newcommand{\ThyBreakComputationalComplexityDalleTwoLevel}{-0.116}
10794+\newcommand{\ThyBreakComputationalComplexityDalleTwoLevelP}{0.000}
10795+\newcommand{\ThyBreakComputationalComplexityGptFourLevel}{-0.016}
10796+\newcommand{\ThyBreakComputationalComplexityGptFourLevelP}{0.438}
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10863+\newcommand{\ThyFlowAlgorithmsToAiAgentsPostPct}{0.6}
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10865+\newcommand{\ThyFlowAlgorithmsToAiSafetyPostPct}{0.4}
10866+\newcommand{\ThyFlowAlgorithmsToAlgorithmsBaselinePct}{65.1}
10867+\newcommand{\ThyFlowAlgorithmsToAlgorithmsPostPct}{65.0}
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10869+\newcommand{\ThyFlowAlgorithmsToComputationalBiologyPostPct}{1.3}
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11049+\newcommand{\ThyFlowProgrammingLanguagesToComputerArchitecturePostPct}{2.7}
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11051+\newcommand{\ThyFlowProgrammingLanguagesToComputerNetworksPostPct}{1.7}
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11053+\newcommand{\ThyFlowProgrammingLanguagesToComputerVisionPostPct}{2.0}
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11055+\newcommand{\ThyFlowProgrammingLanguagesToCybersecurityPostPct}{3.7}
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11059+\newcommand{\ThyFlowProgrammingLanguagesToDeepLearningArchitecturesPostPct}{1.9}
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11061+\newcommand{\ThyFlowProgrammingLanguagesToDistributedComputingPostPct}{2.4}
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11063+\newcommand{\ThyFlowProgrammingLanguagesToFormalVerificationPostPct}{13.7}
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11065+\newcommand{\ThyFlowProgrammingLanguagesToFoundationModelsPostPct}{0.0}
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11069+\newcommand{\ThyFlowProgrammingLanguagesToHciPostPct}{1.7}
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11071+\newcommand{\ThyFlowProgrammingLanguagesToLlmPostPct}{0.7}
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11073+\newcommand{\ThyFlowProgrammingLanguagesToMlTheoryPostPct}{1.7}
11074+\newcommand{\ThyFlowProgrammingLanguagesToNlpBaselinePct}{1.4}
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11076+\newcommand{\ThyFlowProgrammingLanguagesToProgrammingLanguagesBaselinePct}{36.3}
11077+\newcommand{\ThyFlowProgrammingLanguagesToProgrammingLanguagesPostPct}{34.7}
11078+\newcommand{\ThyFlowProgrammingLanguagesToQuantumComputingBaselinePct}{0.8}
11079+\newcommand{\ThyFlowProgrammingLanguagesToQuantumComputingPostPct}{1.7}
11080+\newcommand{\ThyFlowProgrammingLanguagesToRoboticsBaselinePct}{1.3}
11081+\newcommand{\ThyFlowProgrammingLanguagesToRoboticsPostPct}{1.5}
11082+\newcommand{\ThyFlowProgrammingLanguagesToScientificComputingBaselinePct}{1.9}
11083+\newcommand{\ThyFlowProgrammingLanguagesToScientificComputingPostPct}{2.0}
11084+\newcommand{\ThyFlowProgrammingLanguagesToSoftwareEngineeringBaselinePct}{11.7}
11085+\newcommand{\ThyFlowProgrammingLanguagesToSoftwareEngineeringPostPct}{11.6}
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11087+\newcommand{\ThyFlowSoftwareEngineeringToAiAgentsPostPct}{0.3}
11088+\newcommand{\ThyFlowSoftwareEngineeringToAiSafetyBaselinePct}{0.5}
11089+\newcommand{\ThyFlowSoftwareEngineeringToAiSafetyPostPct}{0.9}
11090+\newcommand{\ThyFlowSoftwareEngineeringToAlgorithmsBaselinePct}{1.3}
11091+\newcommand{\ThyFlowSoftwareEngineeringToAlgorithmsPostPct}{1.0}
11092+\newcommand{\ThyFlowSoftwareEngineeringToCloudComputingBaselinePct}{1.5}
11093+\newcommand{\ThyFlowSoftwareEngineeringToCloudComputingPostPct}{1.3}
11094+\newcommand{\ThyFlowSoftwareEngineeringToComputerArchitectureBaselinePct}{1.2}
11095+\newcommand{\ThyFlowSoftwareEngineeringToComputerNetworksBaselinePct}{1.4}
11096+\newcommand{\ThyFlowSoftwareEngineeringToComputerNetworksPostPct}{1.2}
11097+\newcommand{\ThyFlowSoftwareEngineeringToComputerVisionBaselinePct}{2.0}
11098+\newcommand{\ThyFlowSoftwareEngineeringToComputerVisionPostPct}{2.2}
11099+\newcommand{\ThyFlowSoftwareEngineeringToCybersecurityBaselinePct}{4.7}
11100+\newcommand{\ThyFlowSoftwareEngineeringToCybersecurityPostPct}{5.3}
11101+\newcommand{\ThyFlowSoftwareEngineeringToDatabaseSystemsBaselinePct}{1.7}
11102+\newcommand{\ThyFlowSoftwareEngineeringToDatabaseSystemsPostPct}{1.4}
11103+\newcommand{\ThyFlowSoftwareEngineeringToDeepLearningArchitecturesBaselinePct}{1.2}
11104+\newcommand{\ThyFlowSoftwareEngineeringToDeepLearningArchitecturesPostPct}{1.4}
11105+\newcommand{\ThyFlowSoftwareEngineeringToDistributedComputingBaselinePct}{1.0}
11106+\newcommand{\ThyFlowSoftwareEngineeringToFormalVerificationBaselinePct}{2.2}
11107+\newcommand{\ThyFlowSoftwareEngineeringToFormalVerificationPostPct}{1.4}
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11109+\newcommand{\ThyFlowSoftwareEngineeringToFoundationModelsPostPct}{0.0}
11110+\newcommand{\ThyFlowSoftwareEngineeringToGenerativeAiBaselinePct}{0.0}
11111+\newcommand{\ThyFlowSoftwareEngineeringToGenerativeAiPostPct}{0.2}
11112+\newcommand{\ThyFlowSoftwareEngineeringToHciBaselinePct}{2.7}
11113+\newcommand{\ThyFlowSoftwareEngineeringToHciPostPct}{2.7}
11114+\newcommand{\ThyFlowSoftwareEngineeringToInformationRetrievalBaselinePct}{1.0}
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11116+\newcommand{\ThyFlowSoftwareEngineeringToLlmPostPct}{2.5}
11117+\newcommand{\ThyFlowSoftwareEngineeringToMlTheoryBaselinePct}{2.0}
11118+\newcommand{\ThyFlowSoftwareEngineeringToMlTheoryPostPct}{2.2}
11119+\newcommand{\ThyFlowSoftwareEngineeringToNlpBaselinePct}{3.3}
11120+\newcommand{\ThyFlowSoftwareEngineeringToNlpPostPct}{4.1}
11121+\newcommand{\ThyFlowSoftwareEngineeringToProgrammingLanguagesBaselinePct}{1.6}
11122+\newcommand{\ThyFlowSoftwareEngineeringToProgrammingLanguagesPostPct}{1.4}
11123+\newcommand{\ThyFlowSoftwareEngineeringToQuantumComputingBaselinePct}{0.2}
11124+\newcommand{\ThyFlowSoftwareEngineeringToQuantumComputingPostPct}{0.4}
11125+\newcommand{\ThyFlowSoftwareEngineeringToSoftwareEngineeringBaselinePct}{64.6}
11126+\newcommand{\ThyFlowSoftwareEngineeringToSoftwareEngineeringPostPct}{62.4}
11127+\newcommand{\ThyInflowAlgorithmsFromAiAgentsBaselinePct}{0.8}
11128+\newcommand{\ThyInflowAlgorithmsFromAiAgentsPostPct}{0.8}
11129+\newcommand{\ThyInflowAlgorithmsFromAiSafetyBaselinePct}{0.2}
11130+\newcommand{\ThyInflowAlgorithmsFromAiSafetyPostPct}{0.3}
11131+\newcommand{\ThyInflowAlgorithmsFromAlgorithmsBaselinePct}{61.3}
11132+\newcommand{\ThyInflowAlgorithmsFromAlgorithmsPostPct}{60.0}
11133+\newcommand{\ThyInflowAlgorithmsFromComputationalBiologyBaselinePct}{1.2}
11134+\newcommand{\ThyInflowAlgorithmsFromComputationalBiologyPostPct}{1.2}
11135+\newcommand{\ThyInflowAlgorithmsFromComputerNetworksBaselinePct}{3.9}
11136+\newcommand{\ThyInflowAlgorithmsFromComputerNetworksPostPct}{3.7}
11137+\newcommand{\ThyInflowAlgorithmsFromComputerVisionBaselinePct}{3.7}
11138+\newcommand{\ThyInflowAlgorithmsFromComputerVisionPostPct}{3.7}
11139+\newcommand{\ThyInflowAlgorithmsFromCybersecurityBaselinePct}{1.3}
11140+\newcommand{\ThyInflowAlgorithmsFromCybersecurityPostPct}{1.5}
11141+\newcommand{\ThyInflowAlgorithmsFromDatabaseSystemsBaselinePct}{1.7}
11142+\newcommand{\ThyInflowAlgorithmsFromDatabaseSystemsPostPct}{1.3}
11143+\newcommand{\ThyInflowAlgorithmsFromDeepLearningArchitecturesBaselinePct}{3.8}
11144+\newcommand{\ThyInflowAlgorithmsFromDeepLearningArchitecturesPostPct}{3.4}
11145+\newcommand{\ThyInflowAlgorithmsFromDistributedComputingBaselinePct}{1.6}
11146+\newcommand{\ThyInflowAlgorithmsFromDistributedComputingPostPct}{1.5}
11147+\newcommand{\ThyInflowAlgorithmsFromFoundationModelsBaselinePct}{0.0}
11148+\newcommand{\ThyInflowAlgorithmsFromFoundationModelsPostPct}{0.0}
11149+\newcommand{\ThyInflowAlgorithmsFromGenerativeAiBaselinePct}{0.1}
11150+\newcommand{\ThyInflowAlgorithmsFromGenerativeAiPostPct}{0.2}
11151+\newcommand{\ThyInflowAlgorithmsFromLlmBaselinePct}{0.1}
11152+\newcommand{\ThyInflowAlgorithmsFromLlmPostPct}{0.4}
11153+\newcommand{\ThyInflowAlgorithmsFromMlTheoryBaselinePct}{3.7}
11154+\newcommand{\ThyInflowAlgorithmsFromMlTheoryPostPct}{3.0}
11155+\newcommand{\ThyInflowAlgorithmsFromQuantumComputingBaselinePct}{0.9}
11156+\newcommand{\ThyInflowAlgorithmsFromQuantumComputingPostPct}{1.2}
11157+\newcommand{\ThyInflowAlgorithmsFromReinforcementLearningBaselinePct}{1.0}
11158+\newcommand{\ThyInflowAlgorithmsFromReinforcementLearningPostPct}{2.1}
11159+\newcommand{\ThyInflowAlgorithmsFromRoboticsBaselinePct}{2.4}
11160+\newcommand{\ThyInflowAlgorithmsFromRoboticsPostPct}{4.0}
11161+\newcommand{\ThyInflowAlgorithmsFromScientificComputingBaselinePct}{2.6}
11162+\newcommand{\ThyInflowAlgorithmsFromScientificComputingPostPct}{2.4}
11163+\newcommand{\ThyInflowCompilersFromAiAgentsBaselinePct}{0.4}
11164+\newcommand{\ThyInflowCompilersFromAiAgentsPostPct}{0.5}
11165+\newcommand{\ThyInflowCompilersFromAiSafetyBaselinePct}{0.1}
11166+\newcommand{\ThyInflowCompilersFromAiSafetyPostPct}{0.5}
11167+\newcommand{\ThyInflowCompilersFromAlgorithmsBaselinePct}{5.3}
11168+\newcommand{\ThyInflowCompilersFromAlgorithmsPostPct}{4.3}
11169+\newcommand{\ThyInflowCompilersFromAppliedCryptographyPostPct}{1.1}
11170+\newcommand{\ThyInflowCompilersFromCompilersBaselinePct}{14.4}
11171+\newcommand{\ThyInflowCompilersFromCompilersPostPct}{15.1}
11172+\newcommand{\ThyInflowCompilersFromComputerArchitectureBaselinePct}{19.3}
11173+\newcommand{\ThyInflowCompilersFromComputerArchitecturePostPct}{20.0}
11174+\newcommand{\ThyInflowCompilersFromComputerGraphicsBaselinePct}{1.5}
11175+\newcommand{\ThyInflowCompilersFromComputerGraphicsPostPct}{1.2}
11176+\newcommand{\ThyInflowCompilersFromComputerNetworksBaselinePct}{2.7}
11177+\newcommand{\ThyInflowCompilersFromComputerNetworksPostPct}{1.9}
11178+\newcommand{\ThyInflowCompilersFromComputerVisionBaselinePct}{5.1}
11179+\newcommand{\ThyInflowCompilersFromComputerVisionPostPct}{4.3}
11180+\newcommand{\ThyInflowCompilersFromCybersecurityBaselinePct}{2.2}
11181+\newcommand{\ThyInflowCompilersFromCybersecurityPostPct}{2.3}
11182+\newcommand{\ThyInflowCompilersFromDatabaseSystemsBaselinePct}{2.0}
11183+\newcommand{\ThyInflowCompilersFromDatabaseSystemsPostPct}{2.0}
11184+\newcommand{\ThyInflowCompilersFromDeepLearningArchitecturesBaselinePct}{2.9}
11185+\newcommand{\ThyInflowCompilersFromDeepLearningArchitecturesPostPct}{2.5}
11186+\newcommand{\ThyInflowCompilersFromDistributedComputingBaselinePct}{9.9}
11187+\newcommand{\ThyInflowCompilersFromDistributedComputingPostPct}{7.8}
11188+\newcommand{\ThyInflowCompilersFromEdgeComputingBaselinePct}{1.2}
11189+\newcommand{\ThyInflowCompilersFromEdgeComputingPostPct}{1.1}
11190+\newcommand{\ThyInflowCompilersFromFormalVerificationBaselinePct}{1.6}
11191+\newcommand{\ThyInflowCompilersFromFormalVerificationPostPct}{1.5}
11192+\newcommand{\ThyInflowCompilersFromFoundationModelsBaselinePct}{0.1}
11193+\newcommand{\ThyInflowCompilersFromFoundationModelsPostPct}{0.0}
11194+\newcommand{\ThyInflowCompilersFromGenerativeAiBaselinePct}{0.1}
11195+\newcommand{\ThyInflowCompilersFromGenerativeAiPostPct}{0.6}
11196+\newcommand{\ThyInflowCompilersFromHciBaselinePct}{2.3}
11197+\newcommand{\ThyInflowCompilersFromHciPostPct}{2.7}
11198+\newcommand{\ThyInflowCompilersFromLlmBaselinePct}{0.2}
11199+\newcommand{\ThyInflowCompilersFromLlmPostPct}{1.5}
11200+\newcommand{\ThyInflowCompilersFromMlTheoryBaselinePct}{1.9}
11201+\newcommand{\ThyInflowCompilersFromMlTheoryPostPct}{1.5}
11202+\newcommand{\ThyInflowCompilersFromProgrammingLanguagesBaselinePct}{6.3}
11203+\newcommand{\ThyInflowCompilersFromProgrammingLanguagesPostPct}{4.8}
11204+\newcommand{\ThyInflowCompilersFromQuantumComputingBaselinePct}{1.9}
11205+\newcommand{\ThyInflowCompilersFromQuantumComputingPostPct}{2.0}
11206+\newcommand{\ThyInflowCompilersFromRoboticsBaselinePct}{1.6}
11207+\newcommand{\ThyInflowCompilersFromRoboticsPostPct}{1.8}
11208+\newcommand{\ThyInflowCompilersFromScientificComputingBaselinePct}{3.7}
11209+\newcommand{\ThyInflowCompilersFromScientificComputingPostPct}{3.0}
11210+\newcommand{\ThyInflowCompilersFromSoftwareEngineeringBaselinePct}{6.6}
11211+\newcommand{\ThyInflowCompilersFromSoftwareEngineeringPostPct}{8.3}
11212+\newcommand{\ThyInflowComputationalComplexityFromAiAgentsBaselinePct}{0.9}
11213+\newcommand{\ThyInflowComputationalComplexityFromAiAgentsPostPct}{1.2}
11214+\newcommand{\ThyInflowComputationalComplexityFromAiSafetyBaselinePct}{0.3}
11215+\newcommand{\ThyInflowComputationalComplexityFromAiSafetyPostPct}{0.9}
11216+\newcommand{\ThyInflowComputationalComplexityFromAlgorithmsBaselinePct}{13.8}
11217+\newcommand{\ThyInflowComputationalComplexityFromAlgorithmsPostPct}{10.9}
11218+\newcommand{\ThyInflowComputationalComplexityFromAppliedCryptographyBaselinePct}{2.5}
11219+\newcommand{\ThyInflowComputationalComplexityFromAppliedCryptographyPostPct}{2.6}
11220+\newcommand{\ThyInflowComputationalComplexityFromComputationalBiologyBaselinePct}{2.6}
11221+\newcommand{\ThyInflowComputationalComplexityFromComputationalBiologyPostPct}{1.9}
11222+\newcommand{\ThyInflowComputationalComplexityFromComputationalComplexityBaselinePct}{30.5}
11223+\newcommand{\ThyInflowComputationalComplexityFromComputationalComplexityPostPct}{22.8}
11224+\newcommand{\ThyInflowComputationalComplexityFromComputerArchitectureBaselinePct}{2.0}
11225+\newcommand{\ThyInflowComputationalComplexityFromComputerArchitecturePostPct}{2.3}
11226+\newcommand{\ThyInflowComputationalComplexityFromComputerNetworksBaselinePct}{7.1}
11227+\newcommand{\ThyInflowComputationalComplexityFromComputerNetworksPostPct}{6.4}
11228+\newcommand{\ThyInflowComputationalComplexityFromComputerVisionBaselinePct}{3.7}
11229+\newcommand{\ThyInflowComputationalComplexityFromComputerVisionPostPct}{4.2}
11230+\newcommand{\ThyInflowComputationalComplexityFromCybersecurityBaselinePct}{1.7}
11231+\newcommand{\ThyInflowComputationalComplexityFromCybersecurityPostPct}{2.2}
11232+\newcommand{\ThyInflowComputationalComplexityFromDatabaseSystemsBaselinePct}{1.5}
11233+\newcommand{\ThyInflowComputationalComplexityFromDatabaseSystemsPostPct}{1.3}
11234+\newcommand{\ThyInflowComputationalComplexityFromDeepLearningArchitecturesBaselinePct}{3.7}
11235+\newcommand{\ThyInflowComputationalComplexityFromDeepLearningArchitecturesPostPct}{4.4}
11236+\newcommand{\ThyInflowComputationalComplexityFromDistributedComputingBaselinePct}{1.6}
11237+\newcommand{\ThyInflowComputationalComplexityFromDistributedComputingPostPct}{1.8}
11238+\newcommand{\ThyInflowComputationalComplexityFromFormalVerificationBaselinePct}{5.3}
11239+\newcommand{\ThyInflowComputationalComplexityFromFormalVerificationPostPct}{3.8}
11240+\newcommand{\ThyInflowComputationalComplexityFromFoundationModelsBaselinePct}{0.0}
11241+\newcommand{\ThyInflowComputationalComplexityFromFoundationModelsPostPct}{0.1}
11242+\newcommand{\ThyInflowComputationalComplexityFromGenerativeAiBaselinePct}{0.1}
11243+\newcommand{\ThyInflowComputationalComplexityFromGenerativeAiPostPct}{1.3}
11244+\newcommand{\ThyInflowComputationalComplexityFromHciBaselinePct}{2.6}
11245+\newcommand{\ThyInflowComputationalComplexityFromHciPostPct}{5.6}
11246+\newcommand{\ThyInflowComputationalComplexityFromLlmBaselinePct}{0.1}
11247+\newcommand{\ThyInflowComputationalComplexityFromLlmPostPct}{1.7}
11248+\newcommand{\ThyInflowComputationalComplexityFromMlTheoryBaselinePct}{2.1}
11249+\newcommand{\ThyInflowComputationalComplexityFromMlTheoryPostPct}{2.6}
11250+\newcommand{\ThyInflowComputationalComplexityFromNlpBaselinePct}{1.1}
11251+\newcommand{\ThyInflowComputationalComplexityFromNlpPostPct}{1.4}
11252+\newcommand{\ThyInflowComputationalComplexityFromProgrammingLanguagesBaselinePct}{2.7}
11253+\newcommand{\ThyInflowComputationalComplexityFromProgrammingLanguagesPostPct}{2.2}
11254+\newcommand{\ThyInflowComputationalComplexityFromQuantumComputingBaselinePct}{1.6}
11255+\newcommand{\ThyInflowComputationalComplexityFromQuantumComputingPostPct}{2.1}
11256+\newcommand{\ThyInflowComputationalComplexityFromReinforcementLearningPostPct}{1.2}
11257+\newcommand{\ThyInflowComputationalComplexityFromRoboticsBaselinePct}{2.0}
11258+\newcommand{\ThyInflowComputationalComplexityFromRoboticsPostPct}{3.2}
11259+\newcommand{\ThyInflowComputationalComplexityFromScientificComputingBaselinePct}{3.8}
11260+\newcommand{\ThyInflowComputationalComplexityFromScientificComputingPostPct}{4.0}
11261+\newcommand{\ThyInflowComputationalComplexityFromSoftwareEngineeringBaselinePct}{1.2}
11262+\newcommand{\ThyInflowComputationalComplexityFromSoftwareEngineeringPostPct}{1.4}
11263+\newcommand{\ThyInflowComputationalComplexityFromSpeechAudioPostPct}{1.5}
11264+\newcommand{\ThyInflowFormalVerificationFromAiAgentsBaselinePct}{1.9}
11265+\newcommand{\ThyInflowFormalVerificationFromAiAgentsPostPct}{1.5}
11266+\newcommand{\ThyInflowFormalVerificationFromAiSafetyBaselinePct}{0.2}
11267+\newcommand{\ThyInflowFormalVerificationFromAiSafetyPostPct}{0.4}
11268+\newcommand{\ThyInflowFormalVerificationFromAlgorithmsBaselinePct}{3.3}
11269+\newcommand{\ThyInflowFormalVerificationFromAlgorithmsPostPct}{2.7}
11270+\newcommand{\ThyInflowFormalVerificationFromBlockchainBaselinePct}{1.1}
11271+\newcommand{\ThyInflowFormalVerificationFromBlockchainPostPct}{1.1}
11272+\newcommand{\ThyInflowFormalVerificationFromComputationalBiologyPostPct}{1.3}
11273+\newcommand{\ThyInflowFormalVerificationFromComputationalComplexityBaselinePct}{1.6}
11274+\newcommand{\ThyInflowFormalVerificationFromComputationalComplexityPostPct}{1.0}
11275+\newcommand{\ThyInflowFormalVerificationFromComputerArchitectureBaselinePct}{2.0}
11276+\newcommand{\ThyInflowFormalVerificationFromComputerArchitecturePostPct}{2.2}
11277+\newcommand{\ThyInflowFormalVerificationFromComputerNetworksBaselinePct}{1.7}
11278+\newcommand{\ThyInflowFormalVerificationFromComputerNetworksPostPct}{2.2}
11279+\newcommand{\ThyInflowFormalVerificationFromComputerVisionBaselinePct}{6.7}
11280+\newcommand{\ThyInflowFormalVerificationFromComputerVisionPostPct}{13.8}
11281+\newcommand{\ThyInflowFormalVerificationFromCybersecurityBaselinePct}{2.9}
11282+\newcommand{\ThyInflowFormalVerificationFromCybersecurityPostPct}{2.9}
11283+\newcommand{\ThyInflowFormalVerificationFromDeepLearningArchitecturesBaselinePct}{2.7}
11284+\newcommand{\ThyInflowFormalVerificationFromDeepLearningArchitecturesPostPct}{3.8}
11285+\newcommand{\ThyInflowFormalVerificationFromDistributedComputingBaselinePct}{1.2}
11286+\newcommand{\ThyInflowFormalVerificationFromFormalVerificationBaselinePct}{37.1}
11287+\newcommand{\ThyInflowFormalVerificationFromFormalVerificationPostPct}{31.2}
11288+\newcommand{\ThyInflowFormalVerificationFromFoundationModelsBaselinePct}{0.0}
11289+\newcommand{\ThyInflowFormalVerificationFromFoundationModelsPostPct}{0.1}
11290+\newcommand{\ThyInflowFormalVerificationFromGenerativeAiBaselinePct}{0.1}
11291+\newcommand{\ThyInflowFormalVerificationFromGenerativeAiPostPct}{0.4}
11292+\newcommand{\ThyInflowFormalVerificationFromLlmBaselinePct}{0.9}
11293+\newcommand{\ThyInflowFormalVerificationFromLlmPostPct}{2.2}
11294+\newcommand{\ThyInflowFormalVerificationFromMlTheoryBaselinePct}{1.3}
11295+\newcommand{\ThyInflowFormalVerificationFromMlTheoryPostPct}{1.5}
11296+\newcommand{\ThyInflowFormalVerificationFromNlpBaselinePct}{8.9}
11297+\newcommand{\ThyInflowFormalVerificationFromNlpPostPct}{4.0}
11298+\newcommand{\ThyInflowFormalVerificationFromProgrammingLanguagesBaselinePct}{5.5}
11299+\newcommand{\ThyInflowFormalVerificationFromProgrammingLanguagesPostPct}{5.3}
11300+\newcommand{\ThyInflowFormalVerificationFromQuantumComputingBaselinePct}{0.3}
11301+\newcommand{\ThyInflowFormalVerificationFromQuantumComputingPostPct}{0.4}
11302+\newcommand{\ThyInflowFormalVerificationFromReinforcementLearningBaselinePct}{1.4}
11303+\newcommand{\ThyInflowFormalVerificationFromReinforcementLearningPostPct}{1.5}
11304+\newcommand{\ThyInflowFormalVerificationFromRoboticsBaselinePct}{3.5}
11305+\newcommand{\ThyInflowFormalVerificationFromRoboticsPostPct}{4.7}
11306+\newcommand{\ThyInflowFormalVerificationFromScientificComputingPostPct}{1.0}
11307+\newcommand{\ThyInflowFormalVerificationFromSoftwareEngineeringBaselinePct}{7.9}
11308+\newcommand{\ThyInflowFormalVerificationFromSoftwareEngineeringPostPct}{6.9}
11309+\newcommand{\ThyInflowFormalVerificationFromSpeechAudioBaselinePct}{1.8}
11310+\newcommand{\ThyInflowFormalVerificationFromSpeechAudioPostPct}{1.6}
11311+\newcommand{\ThyInflowProgrammingLanguagesFromAiAgentsBaselinePct}{0.9}
11312+\newcommand{\ThyInflowProgrammingLanguagesFromAiAgentsPostPct}{0.9}
11313+\newcommand{\ThyInflowProgrammingLanguagesFromAiSafetyBaselinePct}{0.2}
11314+\newcommand{\ThyInflowProgrammingLanguagesFromAiSafetyPostPct}{0.4}
11315+\newcommand{\ThyInflowProgrammingLanguagesFromAlgorithmsBaselinePct}{2.8}
11316+\newcommand{\ThyInflowProgrammingLanguagesFromAlgorithmsPostPct}{2.5}
11317+\newcommand{\ThyInflowProgrammingLanguagesFromBlockchainBaselinePct}{2.1}
11318+\newcommand{\ThyInflowProgrammingLanguagesFromBlockchainPostPct}{1.7}
11319+\newcommand{\ThyInflowProgrammingLanguagesFromCompilersBaselinePct}{1.1}
11320+\newcommand{\ThyInflowProgrammingLanguagesFromCompilersPostPct}{1.4}
11321+\newcommand{\ThyInflowProgrammingLanguagesFromComputationalComplexityBaselinePct}{1.8}
11322+\newcommand{\ThyInflowProgrammingLanguagesFromComputationalComplexityPostPct}{1.2}
11323+\newcommand{\ThyInflowProgrammingLanguagesFromComputerArchitectureBaselinePct}{3.2}
11324+\newcommand{\ThyInflowProgrammingLanguagesFromComputerArchitecturePostPct}{3.4}
11325+\newcommand{\ThyInflowProgrammingLanguagesFromComputerNetworksBaselinePct}{1.9}
11326+\newcommand{\ThyInflowProgrammingLanguagesFromComputerNetworksPostPct}{1.7}
11327+\newcommand{\ThyInflowProgrammingLanguagesFromComputerVisionBaselinePct}{1.2}
11328+\newcommand{\ThyInflowProgrammingLanguagesFromComputerVisionPostPct}{1.6}
11329+\newcommand{\ThyInflowProgrammingLanguagesFromCybersecurityBaselinePct}{4.0}
11330+\newcommand{\ThyInflowProgrammingLanguagesFromCybersecurityPostPct}{4.1}
11331+\newcommand{\ThyInflowProgrammingLanguagesFromDatabaseSystemsBaselinePct}{1.7}
11332+\newcommand{\ThyInflowProgrammingLanguagesFromDatabaseSystemsPostPct}{1.4}
11333+\newcommand{\ThyInflowProgrammingLanguagesFromDeepLearningArchitecturesBaselinePct}{1.4}
11334+\newcommand{\ThyInflowProgrammingLanguagesFromDeepLearningArchitecturesPostPct}{1.3}
11335+\newcommand{\ThyInflowProgrammingLanguagesFromDistributedComputingBaselinePct}{2.9}
11336+\newcommand{\ThyInflowProgrammingLanguagesFromDistributedComputingPostPct}{2.6}
11337+\newcommand{\ThyInflowProgrammingLanguagesFromFormalVerificationBaselinePct}{12.9}
11338+\newcommand{\ThyInflowProgrammingLanguagesFromFormalVerificationPostPct}{11.4}
11339+\newcommand{\ThyInflowProgrammingLanguagesFromFoundationModelsBaselinePct}{0.0}
11340+\newcommand{\ThyInflowProgrammingLanguagesFromFoundationModelsPostPct}{0.0}
11341+\newcommand{\ThyInflowProgrammingLanguagesFromGenerativeAiBaselinePct}{0.0}
11342+\newcommand{\ThyInflowProgrammingLanguagesFromGenerativeAiPostPct}{0.4}
11343+\newcommand{\ThyInflowProgrammingLanguagesFromHciBaselinePct}{1.9}
11344+\newcommand{\ThyInflowProgrammingLanguagesFromHciPostPct}{2.6}
11345+\newcommand{\ThyInflowProgrammingLanguagesFromLlmBaselinePct}{0.1}
11346+\newcommand{\ThyInflowProgrammingLanguagesFromLlmPostPct}{1.2}
11347+\newcommand{\ThyInflowProgrammingLanguagesFromMlTheoryBaselinePct}{1.4}
11348+\newcommand{\ThyInflowProgrammingLanguagesFromMlTheoryPostPct}{1.3}
11349+\newcommand{\ThyInflowProgrammingLanguagesFromNlpBaselinePct}{1.1}
11350+\newcommand{\ThyInflowProgrammingLanguagesFromNlpPostPct}{1.0}
11351+\newcommand{\ThyInflowProgrammingLanguagesFromProgrammingLanguagesBaselinePct}{34.4}
11352+\newcommand{\ThyInflowProgrammingLanguagesFromProgrammingLanguagesPostPct}{31.7}
11353+\newcommand{\ThyInflowProgrammingLanguagesFromQuantumComputingBaselinePct}{0.7}
11354+\newcommand{\ThyInflowProgrammingLanguagesFromQuantumComputingPostPct}{1.2}
11355+\newcommand{\ThyInflowProgrammingLanguagesFromReinforcementLearningPostPct}{1.2}
11356+\newcommand{\ThyInflowProgrammingLanguagesFromRoboticsBaselinePct}{1.6}
11357+\newcommand{\ThyInflowProgrammingLanguagesFromRoboticsPostPct}{1.8}
11358+\newcommand{\ThyInflowProgrammingLanguagesFromScientificComputingBaselinePct}{1.7}
11359+\newcommand{\ThyInflowProgrammingLanguagesFromScientificComputingPostPct}{1.6}
11360+\newcommand{\ThyInflowProgrammingLanguagesFromSoftwareEngineeringBaselinePct}{13.8}
11361+\newcommand{\ThyInflowProgrammingLanguagesFromSoftwareEngineeringPostPct}{15.4}
11362+\newcommand{\ThyInflowSoftwareEngineeringFromAiAgentsBaselinePct}{0.4}
11363+\newcommand{\ThyInflowSoftwareEngineeringFromAiAgentsPostPct}{0.4}
11364+\newcommand{\ThyInflowSoftwareEngineeringFromAiSafetyBaselinePct}{0.6}
11365+\newcommand{\ThyInflowSoftwareEngineeringFromAiSafetyPostPct}{0.8}
11366+\newcommand{\ThyInflowSoftwareEngineeringFromBlockchainBaselinePct}{1.1}
11367+\newcommand{\ThyInflowSoftwareEngineeringFromBlockchainPostPct}{1.5}
11368+\newcommand{\ThyInflowSoftwareEngineeringFromCloudComputingBaselinePct}{1.4}
11369+\newcommand{\ThyInflowSoftwareEngineeringFromCloudComputingPostPct}{1.6}
11370+\newcommand{\ThyInflowSoftwareEngineeringFromComputerArchitectureBaselinePct}{1.1}
11371+\newcommand{\ThyInflowSoftwareEngineeringFromComputerArchitecturePostPct}{1.0}
11372+\newcommand{\ThyInflowSoftwareEngineeringFromComputerNetworksBaselinePct}{1.4}
11373+\newcommand{\ThyInflowSoftwareEngineeringFromComputerNetworksPostPct}{1.2}
11374+\newcommand{\ThyInflowSoftwareEngineeringFromComputerVisionBaselinePct}{2.2}
11375+\newcommand{\ThyInflowSoftwareEngineeringFromComputerVisionPostPct}{1.7}
11376+\newcommand{\ThyInflowSoftwareEngineeringFromCybersecurityBaselinePct}{4.7}
11377+\newcommand{\ThyInflowSoftwareEngineeringFromCybersecurityPostPct}{5.9}
11378+\newcommand{\ThyInflowSoftwareEngineeringFromDatabaseSystemsBaselinePct}{1.8}
11379+\newcommand{\ThyInflowSoftwareEngineeringFromDatabaseSystemsPostPct}{1.3}
11380+\newcommand{\ThyInflowSoftwareEngineeringFromDeepLearningArchitecturesBaselinePct}{1.8}
11381+\newcommand{\ThyInflowSoftwareEngineeringFromDeepLearningArchitecturesPostPct}{1.2}
11382+\newcommand{\ThyInflowSoftwareEngineeringFromFormalVerificationBaselinePct}{1.7}
11383+\newcommand{\ThyInflowSoftwareEngineeringFromFormalVerificationPostPct}{1.2}
11384+\newcommand{\ThyInflowSoftwareEngineeringFromFoundationModelsBaselinePct}{0.0}
11385+\newcommand{\ThyInflowSoftwareEngineeringFromFoundationModelsPostPct}{0.0}
11386+\newcommand{\ThyInflowSoftwareEngineeringFromGenerativeAiBaselinePct}{0.1}
11387+\newcommand{\ThyInflowSoftwareEngineeringFromGenerativeAiPostPct}{0.5}
11388+\newcommand{\ThyInflowSoftwareEngineeringFromHciBaselinePct}{2.9}
11389+\newcommand{\ThyInflowSoftwareEngineeringFromHciPostPct}{3.3}
11390+\newcommand{\ThyInflowSoftwareEngineeringFromInformationRetrievalBaselinePct}{1.1}
11391+\newcommand{\ThyInflowSoftwareEngineeringFromLlmBaselinePct}{0.2}
11392+\newcommand{\ThyInflowSoftwareEngineeringFromLlmPostPct}{3.1}
11393+\newcommand{\ThyInflowSoftwareEngineeringFromMlTheoryBaselinePct}{1.8}
11394+\newcommand{\ThyInflowSoftwareEngineeringFromMlTheoryPostPct}{1.8}
11395+\newcommand{\ThyInflowSoftwareEngineeringFromNlpBaselinePct}{2.2}
11396+\newcommand{\ThyInflowSoftwareEngineeringFromNlpPostPct}{1.7}
11397+\newcommand{\ThyInflowSoftwareEngineeringFromProgrammingLanguagesBaselinePct}{1.3}
11398+\newcommand{\ThyInflowSoftwareEngineeringFromQuantumComputingBaselinePct}{0.1}
11399+\newcommand{\ThyInflowSoftwareEngineeringFromQuantumComputingPostPct}{0.2}
11400+\newcommand{\ThyInflowSoftwareEngineeringFromSoftwareEngineeringBaselinePct}{65.6}
11401+\newcommand{\ThyInflowSoftwareEngineeringFromSoftwareEngineeringPostPct}{63.9}
11402+\newcommand{\ThyFlowAlgorithmsTotalBaseline}{390{,}514}
11403+\newcommand{\ThyFlowAlgorithmsTotalPost}{719{,}567}
11404+\newcommand{\ThyFlowCompilersTotalBaseline}{5{,}973}
11405+\newcommand{\ThyFlowCompilersTotalPost}{11{,}323}
11406+\newcommand{\ThyFlowComputationalComplexityTotalBaseline}{23{,}634}
11407+\newcommand{\ThyFlowComputationalComplexityTotalPost}{41{,}636}
11408+\newcommand{\ThyFlowFormalVerificationTotalBaseline}{60{,}396}
11409+\newcommand{\ThyFlowFormalVerificationTotalPost}{87{,}844}
11410+\newcommand{\ThyFlowProgrammingLanguagesTotalBaseline}{32{,}827}
11411+\newcommand{\ThyFlowProgrammingLanguagesTotalPost}{51{,}750}
11412+\newcommand{\ThyFlowSoftwareEngineeringTotalBaseline}{290{,}853}
11413+\newcommand{\ThyFlowSoftwareEngineeringTotalPost}{637{,}438}
11414+\newcommand{\ThyInflowAlgorithmsTotalBaseline}{414{,}442}
11415+\newcommand{\ThyInflowAlgorithmsTotalPost}{779{,}424}
11416+\newcommand{\ThyInflowCompilersTotalBaseline}{6{,}278}
11417+\newcommand{\ThyInflowCompilersTotalPost}{10{,}813}
11418+\newcommand{\ThyInflowComputationalComplexityTotalBaseline}{23{,}271}
11419+\newcommand{\ThyInflowComputationalComplexityTotalPost}{37{,}596}
11420+\newcommand{\ThyInflowFormalVerificationTotalBaseline}{81{,}294}
11421+\newcommand{\ThyInflowFormalVerificationTotalPost}{132{,}206}
11422+\newcommand{\ThyInflowProgrammingLanguagesTotalBaseline}{34{,}611}
11423+\newcommand{\ThyInflowProgrammingLanguagesTotalPost}{56{,}587}
11424+\newcommand{\ThyInflowSoftwareEngineeringTotalBaseline}{286{,}340}
11425+\newcommand{\ThyInflowSoftwareEngineeringTotalPost}{622{,}294}
11426+\newcommand{\ThyCoocAlgorithmsComputationalComplexityTwoZeroOneEight}{8{,}127}
11427+\newcommand{\ThyCoocAlgorithmsComputationalComplexityTwoZeroOneEightPct}{13.5}
11428+\newcommand{\ThyCoocComputationalComplexityAlgorithmsTwoZeroOneEightPct}{47.8}
11429+\newcommand{\ThyCoocProgrammingLanguagesSoftwareEngineeringTwoZeroOneEight}{5{,}552}
11430+\newcommand{\ThyCoocProgrammingLanguagesSoftwareEngineeringTwoZeroOneEightPct}{33.2}
11431+\newcommand{\ThyCoocSoftwareEngineeringProgrammingLanguagesTwoZeroOneEightPct}{12.5}
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12263+\newcommand{\ThyCoocFormalVerificationCybersecurityTwoZeroTwoThreePct}{11.5}
12264+\newcommand{\ThyCoocSoftwareEngineeringAiAgentsTwoZeroTwoThree}{1{,}177}
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12563+\newcommand{\ThyCoocProgrammingLanguagesCompilersTwoZeroTwoFivePct}{11.9}
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12571+\newcommand{\ThyCoocProgrammingLanguagesDeepLearningArchitecturesTwoZeroTwoFive}{2{,}256}
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12574+\newcommand{\ThyCoocFormalVerificationCybersecurityTwoZeroTwoFivePct}{14.7}
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12582+\newcommand{\ThyCoocCompilersAlgorithmsTwoZeroTwoFivePct}{18.3}
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12591+\newcommand{\ThyCoocCompilersCybersecurityTwoZeroTwoFivePct}{16.0}
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12597+\newcommand{\ThyCoocProgrammingLanguagesFoundationModelsTwoZeroTwoFivePct}{5.5}
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added manuscript/tables/tab_ai_altdates.tex +20 −0
@@ -0,0 +1,20 @@
1+% generated by tables/make_tables.py from ./results/econometrics/breaks.csv
2+\begin{table}[htbp]\centering\caption{Interrupted-time-series level changes of the nine AI categories at the alternative event dates (GPT-3, DALL-E 2, GPT-4, o1).}\label{tab:ai_altdates}
3+\begin{threeparttable}\small
4+\begin{tabular}{lrrrrrrrr}
5+\toprule
6+\textbf{Category} & \textbf{GPT-3 (2020-06)} & \textbf{p} & \textbf{DALL-E 2 (2022-05)} & \textbf{p } & \textbf{GPT-4 (2023-04)} & \textbf{p } & \textbf{o1 (2024-09)} & \textbf{p } \\
7+\midrule
8+LLM & -0.380 & 0.007 & -0.182 & 0.002 & 0.236 & 0.002 & 0.457 & 0.001 \\
9+Foundation & -0.164 & 0.049 & -0.221 & 0.002 & 0.023 & 0.756 & 0.084 & 0.173 \\
10+Multimodal & -0.258 & 0.016 & -0.129 & 0.004 & 0.150 & 0.002 & 0.330 & 0.001 \\
11+Generative & -0.274 & 0.028 & -0.173 & 0.007 & 0.150 & 0.017 & 0.324 & 0.000 \\
12+Agents & -0.359 & 0.029 & -0.320 & 0.003 & -0.151 & 0.090 & 0.226 & 0.003 \\
13+RL & -0.148 & 0.069 & -0.216 & 0.002 & -0.158 & 0.006 & 0.054 & 0.071 \\
14+ML theory & -0.179 & 0.020 & -0.162 & 0.001 & -0.047 & 0.296 & 0.081 & 0.026 \\
15+DL arch. & -0.004 & 0.937 & -0.114 & 0.008 & -0.051 & 0.260 & 0.008 & 0.792 \\
16+AI safety & -0.226 & 0.034 & -0.311 & 0.000 & -0.080 & 0.244 & 0.150 & 0.010 \\
17+\bottomrule
18+\end{tabular}
19+\begin{tablenotes}\footnotesize\item Each column pair is a separate ITS fit with the event moved to the stated month (Part IV, Section~\ref{sec:m-altdates}); level change in log points, HAC p-value to its right. Category abbreviations as in Table~\ref{tab:ai_growth}.\end{tablenotes}
20+\end{threeparttable}\end{table}
modified manuscript/tables/tab_ai_breaks.tex +17 −20
@@ -1,23 +1,20 @@
1−% generated by tables/make_tables.py from /Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/econometrics/breaks.csv 1+% generated by tables/make_tables.py from ./results/econometrics/breaks.csv
2−\begin{longtable}{lrrrrrrrrrrrr} 2+\begin{table}[htbp]\centering\caption{Structural change at 30 November 2022 in the nine AI categories: interrupted-time-series level and slope changes, placebo p-values, Bai--Perron and PELT break dates.}\label{tab:ai_breaks}
3−\caption{Structural-change estimates for the nine AI categories: interrupted-time-series level and slope changes at 2022-12 (ChatGPT), level changes at the alternative event dates 2023-04 (GPT-4) and 2024-09 (o1), placebo p-values, and Bai--Perron and PELT break dates.}\label{tab:ai_breaks}\\ 3+\begin{threeparttable}\small
4+\begin{tabular}{lrrrrrrrr}
4 \toprule 5 \toprule
5−\textbf{Category} & \textbf{Level 2022-12} & \textbf{p} & \textbf{Slope 2022-12} & \textbf{p } & \textbf{Level 2023-04} & \textbf{p } & \textbf{Level 2024-09} & \textbf{p } & \textbf{Placebo p (level)} & \textbf{Placebo p (slope)} & \textbf{Bai--Perron (BIC)} & \textbf{PELT} \\ 6+\textbf{Category} & \textbf{Level} & \textbf{p (level)} & \textbf{Slope} & \textbf{p (slope)} & \textbf{Placebo p (level)} & \textbf{Placebo p (slope)} & \textbf{Bai--Perron} & \textbf{PELT} \\
6 \midrule 7 \midrule
7−\endfirsthead 8+LLM & 0.106 & 0.075 & 0.034 & 0.000 & 0.938 & 0.200 & 2022-07 & 2023-10 \\
8−\toprule 9+Foundation & -0.075 & 0.340 & 0.006 & 0.021 & 0.692 & 0.246 & 2021-10 & 2020-02; 2023-10 \\
9−\textbf{Category} & \textbf{Level 2022-12} & \textbf{p} & \textbf{Slope 2022-12} & \textbf{p } & \textbf{Level 2023-04} & \textbf{p } & \textbf{Level 2024-09} & \textbf{p } & \textbf{Placebo p (level)} & \textbf{Placebo p (slope)} & \textbf{Bai--Perron (BIC)} & \textbf{PELT} \\ 10+Multimodal & 0.025 & 0.600 & 0.024 & 0.000 & 0.969 & 0.200 & 2022-04 & 2020-12; 2023-12 \\
10−\midrule 11+Generative & 0.036 & 0.556 & 0.019 & 0.000 & 0.969 & 0.308 & 2021-11 & 2020-12; 2023-12 \\
11−\endhead 12+Agents & -0.210 & 0.028 & 0.040 & 0.000 & 0.631 & 0.400 & 2022-07; 2024-07 & 2023-12; 2025-08 \\
12−Large language models & 0.106 & 0.075 & 0.034 & 0.000 & 0.236 & 0.002 & 0.457 & 0.001 & 0.938 & 0.200 & 2022-07-01 & 2023-10 \\ 13+RL & -0.164 & 0.013 & 0.012 & 0.000 & 0.615 & 0.385 & 2021-07; 2024-12 & 2021; 2024-12 \\
13−Foundation models & -0.075 & 0.340 & 0.006 & 0.021 & 0.023 & 0.756 & 0.084 & 0.173 & 0.692 & 0.246 & 2021-10-01 & 2020-02; 2023-10 \\ 14+ML theory & -0.080 & 0.090 & 0.009 & 0.000 & 0.662 & 0.215 & 2020-05; 2022-07 & 2020-02; 2023-12 \\
14−Multimodal AI & 0.025 & 0.600 & 0.024 & 0.000 & 0.150 & 0.002 & 0.330 & 0.001 & 0.969 & 0.200 & 2022-04-01 & 2020-12; 2023-12 \\ 15+DL arch. & -0.071 & 0.126 & -0.002 & 0.136 & 0.400 & 0.708 & 2021-07 & 2020-02; 2023-12 \\
15−Generative AI & 0.036 & 0.556 & 0.019 & 0.000 & 0.150 & 0.017 & 0.324 & 0.000 & 0.969 & 0.308 & 2021-11-01 & 2020-12; 2023-12 \\ 16+AI safety & -0.206 & 0.002 & 0.016 & 0.000 & 0.446 & 0.308 & 2021-06; 2022-09 & 2020-02; 2024-02 \\
16−AI agents & -0.210 & 0.028 & 0.040 & 0.000 & -0.151 & 0.090 & 0.226 & 0.003 & 0.631 & 0.400 & 2022-07-01; 2024-07-01 & 2023-12; 2025-08 \\
17−Reinforcement learning & -0.164 & 0.013 & 0.012 & 0.000 & -0.158 & 0.006 & 0.054 & 0.071 & 0.615 & 0.385 & 2021-07-01; 2024-12-01 & 2021-01; 2024-12 \\
18−ML theory & -0.080 & 0.090 & 0.009 & 0.000 & -0.047 & 0.296 & 0.081 & 0.026 & 0.662 & 0.215 & 2020-05-01; 2022-07-01 & 2020-02; 2023-12 \\
19−DL architectures & -0.071 & 0.126 & -0.002 & 0.136 & -0.051 & 0.260 & 0.008 & 0.792 & 0.400 & 0.708 & 2021-07-01 & 2020-02; 2023-12 \\
20−AI safety and alignment & -0.206 & 0.002 & 0.016 & 0.000 & -0.080 & 0.244 & 0.150 & 0.010 & 0.446 & 0.308 & 2021-06-01; 2022-09-01 & 2020-02; 2024-02 \\
21 \bottomrule 17 \bottomrule
22−\multicolumn{13}{p{0.95\linewidth}}{\footnotesize Monthly log counts, seasonally adjusted; HAC p-values (Part IV, Section~\ref{sec:m-its}). Level changes are in log points of the monthly count; slope changes in log points per month. Placebo p-values are the share of placebo event dates (Section~\ref{sec:m-placebo}) whose t-statistic exceeds the true one. Bai--Perron dates: breaks selected by BIC with trimming h=15 months; PELT: penalised exact linear-time change points. The categories are multi-label, so the series overlap.}\\ 18+\end{tabular}
23−\end{longtable} 19+\begin{tablenotes}\footnotesize\item Monthly log counts, seasonally adjusted; HAC p-values (Part IV, Section~\ref{sec:m-its}). Level change in log points of the monthly count at 2022-12; slope change in log points per month. Placebo p-values: share of placebo event dates (Section~\ref{sec:m-placebo}) whose t-statistic exceeds the true one. Bai--Perron: break months selected by BIC with trimming h = 15 months; PELT: penalised exact linear-time change points. Categories are multi-label, so the series overlap. Category abbreviations as in Table~\ref{tab:ai_growth}.\end{tablenotes}
20+\end{threeparttable}\end{table}
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1−% generated by tables/make_tables.py from /Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/literature_synthesis/agent_E/contributions.csv 1+% generated by tables/make_tables.py from ./literature_synthesis/agent_E/contributions.csv
2 \begin{table}[htbp]\centering\caption{Contribution type of the 234 AI/ML landmark candidates by primary category (specialist judgement from abstract or metadata).}\label{tab:ai_contribution_types} 2 \begin{table}[htbp]\centering\caption{Contribution type of the 234 AI/ML landmark candidates by primary category (specialist judgement from abstract or metadata).}\label{tab:ai_contribution_types}
3 \begin{threeparttable}\small 3 \begin{threeparttable}\small
4 \begin{tabular}{lrrrrrrr} 4 \begin{tabular}{lrrrrrrr}
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@@ -1,20 +1,20 @@
1−% generated by tables/make_tables.py from /Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/graph/category_counts_yearly.csv 1+% generated by tables/make_tables.py from ./results/graph/category_counts_yearly.csv
2 \begin{table}[htbp]\centering\caption{Yearly multi-label counts of the nine AI categories (all period rows summed; 2026 incomplete).}\label{tab:ai_counts} 2 \begin{table}[htbp]\centering\caption{Yearly multi-label counts of the nine AI categories (all period rows summed; 2026 incomplete).}\label{tab:ai_counts}
3 \begin{threeparttable}\small 3 \begin{threeparttable}\small
4 \begin{tabular}{lrrrrrrrrr} 4 \begin{tabular}{lrrrrrrrrr}
5 \toprule 5 \toprule
6 \textbf{Category} & \textbf{2018} & \textbf{2019} & \textbf{2020} & \textbf{2021} & \textbf{2022} & \textbf{2023} & \textbf{2024} & \textbf{2025} & \textbf{2026} \\ 6 \textbf{Category} & \textbf{2018} & \textbf{2019} & \textbf{2020} & \textbf{2021} & \textbf{2022} & \textbf{2023} & \textbf{2024} & \textbf{2025} & \textbf{2026} \\
7 \midrule 7 \midrule
8−Large language models & 15{,}397 & 18{,}253 & 21{,}706 & 24{,}153 & 25{,}206 & 40{,}491 & 69{,}900 & 116{,}242 & 132{,}202 \\ 8+LLM & 15{,}397 & 18{,}253 & 21{,}706 & 24{,}153 & 25{,}206 & 40{,}491 & 69{,}900 & 116{,}242 & 132{,}202 \\
9−Foundation models & 4{,}486 & 7{,}802 & 11{,}029 & 14{,}509 & 16{,}948 & 25{,}052 & 36{,}652 & 52{,}535 & 56{,}874 \\ 9+Foundation & 4{,}486 & 7{,}802 & 11{,}029 & 14{,}509 & 16{,}948 & 25{,}052 & 36{,}652 & 52{,}535 & 56{,}874 \\
10−Multimodal AI & 3{,}989 & 5{,}553 & 6{,}666 & 8{,}561 & 10{,}369 & 15{,}746 & 24{,}979 & 42{,}707 & 47{,}737 \\ 10+Multimodal & 3{,}989 & 5{,}553 & 6{,}666 & 8{,}561 & 10{,}369 & 15{,}746 & 24{,}979 & 42{,}707 & 47{,}737 \\
11−Generative AI & 5{,}118 & 8{,}354 & 10{,}228 & 12{,}580 & 13{,}703 & 21{,}583 & 36{,}186 & 56{,}768 & 55{,}673 \\ 11+Generative & 5{,}118 & 8{,}354 & 10{,}228 & 12{,}580 & 13{,}703 & 21{,}583 & 36{,}186 & 56{,}768 & 55{,}673 \\
12−AI agents & 10{,}042 & 10{,}745 & 10{,}642 & 10{,}593 & 10{,}324 & 12{,}255 & 16{,}189 & 29{,}517 & 45{,}112 \\ 12+Agents & 10{,}042 & 10{,}745 & 10{,}642 & 10{,}593 & 10{,}324 & 12{,}255 & 16{,}189 & 29{,}517 & 45{,}112 \\
13−Reinforcement learning & 8{,}157 & 11{,}558 & 14{,}307 & 16{,}674 & 18{,}491 & 22{,}678 & 28{,}572 & 43{,}949 & 44{,}996 \\ 13+RL & 8{,}157 & 11{,}558 & 14{,}307 & 16{,}674 & 18{,}491 & 22{,}678 & 28{,}572 & 43{,}949 & 44{,}996 \\
14 ML theory & 28{,}277 & 37{,}723 & 43{,}455 & 47{,}121 & 48{,}940 & 58{,}770 & 70{,}964 & 91{,}772 & 84{,}417 \\ 14 ML theory & 28{,}277 & 37{,}723 & 43{,}455 & 47{,}121 & 48{,}940 & 58{,}770 & 70{,}964 & 91{,}772 & 84{,}417 \\
15−DL architectures & 43{,}313 & 62{,}096 & 74{,}987 & 90{,}347 & 99{,}122 & 118{,}793 & 142{,}181 & 174{,}461 & 130{,}581 \\ 15+DL arch. & 43{,}313 & 62{,}096 & 74{,}987 & 90{,}347 & 99{,}122 & 118{,}793 & 142{,}181 & 174{,}461 & 130{,}581 \\
16−AI safety and alignment & 3{,}309 & 5{,}385 & 7{,}749 & 9{,}909 & 11{,}593 & 16{,}606 & 26{,}098 & 44{,}888 & 51{,}629 \\ 16+AI safety & 3{,}309 & 5{,}385 & 7{,}749 & 9{,}909 & 11{,}593 & 16{,}606 & 26{,}098 & 44{,}888 & 51{,}629 \\
17 \bottomrule 17 \bottomrule
18 \end{tabular} 18 \end{tabular}
19−\begin{tablenotes}\footnotesize\item The source file splits each year into period rows (baseline / pre / post relative to 30 November 2022); they are summed here. 2026 covers publications dated up to the snapshot of 14 October 2026.\end{tablenotes} 19+\begin{tablenotes}\footnotesize\item Multi-label counts: a work counts in every category it is assigned to. The source file splits each year into period rows (baseline / pre / post relative to 30 November 2022); they are summed here. 2026 covers publications dated up to the snapshot of 14 October 2026. Category abbreviations as in Table~\ref{tab:ai_growth}.\end{tablenotes}
20 \end{threeparttable}\end{table} 20 \end{threeparttable}\end{table}
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1−% generated by tables/make_tables.py from /Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/graph/category_flows_yearly.csv 1+% generated by tables/make_tables.py from ./results/graph/category_flows_yearly.csv
2 \begin{longtable}{lrrrrrrrrr} 2 \begin{longtable}{lrrrrrrrrr}
3 \caption{Destination of the in-corpus references of the nine AI categories in 2019, 2022 and 2025 (per cent of in-corpus references of the citing category).}\label{tab:ai_flows}\\ 3 \caption{Destination of the in-corpus references of the nine AI categories in 2019, 2022 and 2025 (per cent of in-corpus references of the citing category).}\label{tab:ai_flows}\\
4 \toprule 4 \toprule
@@ -9,33 +9,33 @@
9 \textbf{Category} & \textbf{Year} & \textbf{Self} & \textbf{LLM} & \textbf{NLP} & \textbf{Vision} & \textbf{HCI} & \textbf{Robotics} & \textbf{Softw. eng.} & \textbf{AI safety} \\ 9 \textbf{Category} & \textbf{Year} & \textbf{Self} & \textbf{LLM} & \textbf{NLP} & \textbf{Vision} & \textbf{HCI} & \textbf{Robotics} & \textbf{Softw. eng.} & \textbf{AI safety} \\
10 \midrule 10 \midrule
11 \endhead 11 \endhead
12−Large language models & 2019 & 3.8 & 3.8 & 69.6 & 4.7 & 0.7 & 0.2 & 0.6 & 0.2 \\ 12+LLM & 2019 & 3.8 & 3.8 & 69.6 & 4.7 & 0.7 & 0.2 & 0.6 & 0.2 \\
13−Large language models & 2022 & 7.5 & 7.5 & 68.6 & 4.5 & 0.9 & 0.2 & 1.2 & 0.4 \\ 13+LLM & 2022 & 7.5 & 7.5 & 68.6 & 4.5 & 0.9 & 0.2 & 1.2 & 0.4 \\
14−Large language models & 2025 & 28.4 & 28.4 & 34.2 & 4.5 & 4.3 & 1.0 & 3.4 & 2.4 \\ 14+LLM & 2025 & 28.4 & 28.4 & 34.2 & 4.5 & 4.3 & 1.0 & 3.4 & 2.4 \\
15−Foundation models & 2019 & 1.2 & 0.0 & 18.7 & 31.3 & 0.0 & 2.0 & 0.0 & 0.0 \\ 15+Foundation & 2019 & 1.2 & 0.0 & 18.7 & 31.3 & 0.0 & 2.0 & 0.0 & 0.0 \\
16−Foundation models & 2022 & 2.9 & 1.5 & 10.5 & 37.4 & 2.9 & 2.1 & 0.3 & 0.4 \\ 16+Foundation & 2022 & 2.9 & 1.5 & 10.5 & 37.4 & 2.9 & 2.1 & 0.3 & 0.4 \\
17−Foundation models & 2025 & 3.6 & 4.1 & 6.7 & 43.8 & 1.9 & 1.0 & 0.6 & 0.6 \\ 17+Foundation & 2025 & 3.6 & 4.1 & 6.7 & 43.8 & 1.9 & 1.0 & 0.6 & 0.6 \\
18−Multimodal AI & 2019 & 12.9 & 0.2 & 15.7 & 51.1 & 1.0 & 1.5 & 0.2 & 0.3 \\ 18+Multimodal & 2019 & 12.9 & 0.2 & 15.7 & 51.1 & 1.0 & 1.5 & 0.2 & 0.3 \\
19−Multimodal AI & 2022 & 15.2 & 1.0 & 18.5 & 50.3 & 0.7 & 0.9 & 0.2 & 0.1 \\ 19+Multimodal & 2022 & 15.2 & 1.0 & 18.5 & 50.3 & 0.7 & 0.9 & 0.2 & 0.1 \\
20−Multimodal AI & 2025 & 14.3 & 3.7 & 16.1 & 46.2 & 1.5 & 1.7 & 0.3 & 0.3 \\ 20+Multimodal & 2025 & 14.3 & 3.7 & 16.1 & 46.2 & 1.5 & 1.7 & 0.3 & 0.3 \\
21−Generative AI & 2019 & 13.3 & 0.5 & 3.0 & 53.6 & 0.5 & 0.6 & 0.3 & 0.3 \\ 21+Generative & 2019 & 13.3 & 0.5 & 3.0 & 53.6 & 0.5 & 0.6 & 0.3 & 0.3 \\
22−Generative AI & 2022 & 10.7 & 0.2 & 2.1 & 57.9 & 1.3 & 0.6 & 0.4 & 0.3 \\ 22+Generative & 2022 & 10.7 & 0.2 & 2.1 & 57.9 & 1.3 & 0.6 & 0.4 & 0.3 \\
23−Generative AI & 2025 & 13.6 & 13.7 & 3.5 & 33.8 & 6.6 & 1.5 & 1.3 & 3.7 \\ 23+Generative & 2025 & 13.6 & 13.7 & 3.5 & 33.8 & 6.6 & 1.5 & 1.3 & 3.7 \\
24−AI agents & 2019 & 45.5 & 0.1 & 2.7 & 1.7 & 1.8 & 7.5 & 2.3 & 0.9 \\ 24+Agents & 2019 & 45.5 & 0.1 & 2.7 & 1.7 & 1.8 & 7.5 & 2.3 & 0.9 \\
25−AI agents & 2022 & 39.3 & 0.2 & 2.1 & 1.6 & 2.9 & 8.8 & 1.5 & 0.9 \\ 25+Agents & 2022 & 39.3 & 0.2 & 2.1 & 1.6 & 2.9 & 8.8 & 1.5 & 0.9 \\
26−AI agents & 2025 & 30.7 & 4.2 & 3.8 & 1.9 & 4.3 & 12.0 & 1.3 & 1.5 \\ 26+Agents & 2025 & 30.7 & 4.2 & 3.8 & 1.9 & 4.3 & 12.0 & 1.3 & 1.5 \\
27−Reinforcement learning & 2019 & 47.2 & 0.0 & 2.2 & 5.0 & 0.8 & 14.4 & 0.4 & 0.3 \\ 27+RL & 2019 & 47.2 & 0.0 & 2.2 & 5.0 & 0.8 & 14.4 & 0.4 & 0.3 \\
28−Reinforcement learning & 2022 & 43.5 & 0.1 & 1.2 & 5.0 & 1.0 & 19.2 & 0.6 & 0.3 \\ 28+RL & 2022 & 43.5 & 0.1 & 1.2 & 5.0 & 1.0 & 19.2 & 0.6 & 0.3 \\
29−Reinforcement learning & 2025 & 39.0 & 0.5 & 0.9 & 4.4 & 0.8 & 23.1 & 0.5 & 0.3 \\ 29+RL & 2025 & 39.0 & 0.5 & 0.9 & 4.4 & 0.8 & 23.1 & 0.5 & 0.3 \\
30 ML theory & 2019 & 44.1 & 0.2 & 3.0 & 13.7 & 0.8 & 1.1 & 1.2 & 1.1 \\ 30 ML theory & 2019 & 44.1 & 0.2 & 3.0 & 13.7 & 0.8 & 1.1 & 1.2 & 1.1 \\
31 ML theory & 2022 & 40.8 & 0.2 & 3.6 & 15.5 & 1.3 & 1.0 & 1.5 & 1.6 \\ 31 ML theory & 2022 & 40.8 & 0.2 & 3.6 & 15.5 & 1.3 & 1.0 & 1.5 & 1.6 \\
32 ML theory & 2025 & 38.5 & 0.7 & 2.8 & 15.4 & 1.3 & 1.1 & 1.6 & 1.7 \\ 32 ML theory & 2025 & 38.5 & 0.7 & 2.8 & 15.4 & 1.3 & 1.1 & 1.6 & 1.7 \\
33−DL architectures & 2019 & 36.9 & 0.2 & 5.1 & 21.8 & 0.7 & 1.5 & 1.2 & 0.2 \\ 33+DL arch. & 2019 & 36.9 & 0.2 & 5.1 & 21.8 & 0.7 & 1.5 & 1.2 & 0.2 \\
34−DL architectures & 2022 & 35.1 & 0.4 & 5.4 & 22.1 & 1.0 & 1.4 & 1.0 & 0.3 \\ 34+DL arch. & 2022 & 35.1 & 0.4 & 5.4 & 22.1 & 1.0 & 1.4 & 1.0 & 0.3 \\
35−DL architectures & 2025 & 35.3 & 1.0 & 4.3 & 21.8 & 1.0 & 1.4 & 0.7 & 0.3 \\ 35+DL arch. & 2025 & 35.3 & 1.0 & 4.3 & 21.8 & 1.0 & 1.4 & 0.7 & 0.3 \\
36−AI safety and alignment & 2019 & 27.1 & 0.1 & 7.0 & 6.6 & 9.1 & 3.2 & 4.1 & 27.1 \\ 36+AI safety & 2019 & 27.1 & 0.1 & 7.0 & 6.6 & 9.1 & 3.2 & 4.1 & 27.1 \\
37−AI safety and alignment & 2022 & 34.4 & 0.8 & 8.2 & 7.3 & 10.0 & 2.3 & 3.6 & 34.4 \\ 37+AI safety & 2022 & 34.4 & 0.8 & 8.2 & 7.3 & 10.0 & 2.3 & 3.6 & 34.4 \\
38−AI safety and alignment & 2025 & 28.9 & 10.0 & 8.1 & 5.2 & 11.5 & 2.5 & 2.7 & 28.9 \\ 38+AI safety & 2025 & 28.9 & 10.0 & 8.1 & 5.2 & 11.5 & 2.5 & 2.7 & 28.9 \\
39 \bottomrule 39 \bottomrule
40−\multicolumn{10}{p{0.95\linewidth}}{\footnotesize Row shares of category-to-category citation flows (Part IV, eq.~\ref{eq:flow}); references outside the eligible corpus are excluded. `Self' is the share going to the citing category itself (for LLM, the Self and LLM columns coincide). Categories are multi-label, so a reference can count under several destination categories.}\\ 40+\multicolumn{10}{p{0.95\linewidth}}{\footnotesize Row shares of category-to-category citation flows (Part IV, eq.~\ref{eq:flow}); references outside the eligible corpus are excluded. `Self' is the share going to the citing category itself (for LLM the Self and LLM columns coincide). Categories are multi-label, so a reference can count under several destination categories. Category abbreviations as in Table~\ref{tab:ai_growth}.}\\
41 \end{longtable} 41 \end{longtable}
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1−% generated by tables/make_tables.py from /Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/bibliometrics/cagr_by_category.csv 1+% generated by tables/make_tables.py from ./results/bibliometrics/cagr_by_category.csv
2 \begin{table}[htbp]\centering\caption{Volume, compound annual growth and corpus share of the nine AI categories, baseline (2018--2022) and post (2023--2025) periods.}\label{tab:ai_growth} 2 \begin{table}[htbp]\centering\caption{Volume, compound annual growth and corpus share of the nine AI categories, baseline (2018--2022) and post (2023--2025) periods.}\label{tab:ai_growth}
3 \begin{threeparttable}\small 3 \begin{threeparttable}\small
4 \begin{tabular}{lrrrrrrrrr} 4 \begin{tabular}{lrrrrrrrrr}
5 \toprule 5 \toprule
6−\textbf{Category} & \textbf{2018} & \textbf{2022} & \textbf{2023} & \textbf{2025} & \textbf{CAGR 2018--22} & \textbf{CAGR 2023--25} & \textbf{Share 2018 (\%)} & \textbf{Share 2022 (\%)} & \textbf{Share 2025 (\%)} \\ 6+\textbf{Category} & \textbf{2018} & \textbf{2022} & \textbf{2023} & \textbf{2025} & \textbf{CAGR 18--22} & \textbf{CAGR 23--25} & \textbf{Share 2018} & \textbf{Share 2022} & \textbf{Share 2025} \\
7 \midrule 7 \midrule
8−Large language models & 1{,}635 & 3{,}266 & 10{,}389 & 45{,}158 & 0.189 & 1.085 & 0.33 & 0.58 & 5.06 \\ 8+LLM & 545 & 1{,}088 & 5{,}194 & 22{,}579 & 0.189 & 1.085 & 0.32 & 0.43 & 5.26 \\
9−Foundation models & 46 & 350 & 571 & 1{,}854 & 0.661 & 0.802 & 0.01 & 0.06 & 0.21 \\ 9+Foundation & 23 & 175 & 285 & 927 & 0.661 & 0.802 & 0.01 & 0.07 & 0.22 \\
10−Multimodal AI & 470 & 1{,}278 & 1{,}932 & 6{,}749 & 0.284 & 0.869 & 0.09 & 0.23 & 0.76 \\ 10+Multimodal & 156 & 639 & 966 & 3{,}374 & 0.421 & 0.869 & 0.09 & 0.25 & 0.79 \\
11−Generative AI & 766 & 2{,}160 & 3{,}646 & 12{,}743 & 0.296 & 0.870 & 0.15 & 0.39 & 1.43 \\ 11+Generative & 255 & 1{,}080 & 1{,}823 & 6{,}371 & 0.434 & 0.870 & 0.15 & 0.42 & 1.48 \\
12−AI agents & 5{,}436 & 4{,}521 & 4{,}991 & 8{,}819 & -0.045 & 0.329 & 1.09 & 0.81 & 0.99 \\ 12+Agents & 2{,}718 & 2{,}260 & 2{,}495 & 4{,}409 & -0.045 & 0.329 & 1.60 & 0.89 & 1.03 \\
13−Reinforcement learning & 3{,}296 & 7{,}017 & 8{,}422 & 13{,}210 & 0.208 & 0.252 & 0.66 & 1.25 & 1.48 \\ 13+RL & 1{,}648 & 3{,}508 & 4{,}211 & 6{,}605 & 0.208 & 0.252 & 0.97 & 1.38 & 1.54 \\
14−ML theory & 13{,}534 & 20{,}131 & 22{,}893 & 32{,}798 & 0.104 & 0.197 & 2.71 & 3.59 & 3.67 \\ 14+ML theory & 4{,}511 & 10{,}065 & 11{,}446 & 16{,}399 & 0.222 & 0.197 & 2.66 & 3.95 & 3.82 \\
15−DL architectures & 11{,}992 & 22{,}270 & 25{,}607 & 34{,}141 & 0.167 & 0.155 & 2.40 & 3.97 & 3.82 \\ 15+DL arch. & 3{,}997 & 7{,}423 & 12{,}803 & 17{,}070 & 0.167 & 0.155 & 2.36 & 2.91 & 3.97 \\
16−AI safety and alignment & 1{,}052 & 2{,}954 & 4{,}015 & 10{,}885 & 0.294 & 0.647 & 0.21 & 0.53 & 1.22 \\ 16+AI safety & 526 & 1{,}477 & 2{,}007 & 5{,}442 & 0.294 & 0.647 & 0.31 & 0.58 & 1.27 \\
17 \bottomrule 17 \bottomrule
18 \end{tabular} 18 \end{tabular}
19−\begin{tablenotes}\footnotesize\item Counts are multi-label (a work counts in every category it is assigned to), restricted to the eligible mapping corpus. CAGR as in Part IV, eq.~\ref{eq:cagr}; the post-period CAGR uses 2023 and 2025 end points. Shares are over all eligible works of the year.\end{tablenotes} 19+\begin{tablenotes}\footnotesize\item Counts and shares as stored in the source file (works whose primary category is the row category; shares in per cent of all eligible works of the year). CAGR as in Part IV, eq.~\ref{eq:cagr}; the post-period CAGR uses the 2023 and 2025 end points. Category abbreviations: LLM = large language models; Foundation = foundation models; Generative = generative AI; Agents = AI agents; RL = reinforcement learning; DL arch. = deep-learning architectures; AI safety = AI safety and alignment.\end{tablenotes}
20 \end{threeparttable}\end{table} 20 \end{threeparttable}\end{table}
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1−% generated by tables/make_tables.py from /Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/literature_synthesis/agent_E/contributions.csv 1+% generated by tables/make_tables.py from ./literature_synthesis/agent_E/contributions.csv
2 \begin{table}[htbp]\centering\caption{Specialist screening of the 234 AI/ML landmark candidates: records by primary category and data-quality flag.}\label{tab:ai_landmark_flags} 2 \begin{table}[htbp]\centering\caption{Specialist screening of the 234 AI/ML landmark candidates: records by primary category and data-quality flag.}\label{tab:ai_landmark_flags}
3 \begin{threeparttable}\small 3 \begin{threeparttable}\small
4 \begin{tabular}{lrrrrrrrr} 4 \begin{tabular}{lrrrrrrrr}
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1−% generated by tables/make_tables.py from /Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/literature_synthesis/agent_E/contributions.csv 1+% generated by tables/make_tables.py from ./literature_synthesis/agent_E/contributions.csv
2 \begin{longtable}{lrrrrrrr} 2 \begin{longtable}{lrrrrrrr}
3−\caption{The 234 AI/ML landmark candidates with the specialist's contribution type and data-quality flag (titles in literature_synthesis/agent_E/contributions.csv).}\label{tab:ai_landmarks}\\ 3+\caption{The 234 AI/ML landmark candidates with the specialist's contribution type and data-quality flag (titles in literature\_synthesis/agent\_E/contributions.csv).}\label{tab:ai_landmarks}\\
4 \toprule 4 \toprule
5 \textbf{OpenAlex id} & \textbf{Year} & \textbf{Type} & \textbf{Category} & \textbf{c36} & \textbf{NCS36} & \textbf{Contribution} & \textbf{Flag} \\ 5 \textbf{OpenAlex id} & \textbf{Year} & \textbf{Type} & \textbf{Category} & \textbf{c36} & \textbf{NCS36} & \textbf{Contribution} & \textbf{Flag} \\
6 \midrule 6 \midrule
@@ -9,240 +9,240 @@
9 \textbf{OpenAlex id} & \textbf{Year} & \textbf{Type} & \textbf{Category} & \textbf{c36} & \textbf{NCS36} & \textbf{Contribution} & \textbf{Flag} \\ 9 \textbf{OpenAlex id} & \textbf{Year} & \textbf{Type} & \textbf{Category} & \textbf{c36} & \textbf{NCS36} & \textbf{Contribution} & \textbf{Flag} \\
10 \midrule 10 \midrule
11 \endhead 11 \endhead
12−7172491899 & 2026 & article & Large language models & 3{,}026 & 3367.3 & engineering & identifier\_mismatch \\ 12+7172491899 & 2026 & article & LLM & 3{,}026 & 3367.3 & engineering & identifier mismatch \\
13−7154011589 & 2026 & article & Large language models & 2{,}847 & 3168.1 & empirical & identifier\_mismatch \\ 13+7154011589 & 2026 & article & LLM & 2{,}847 & 3168.1 & empirical & identifier mismatch \\
14−4362515116 & 2026 & article & Large language models & 1{,}549 & 1723.7 & survey & ok \\ 14+4362515116 & 2026 & article & LLM & 1{,}549 & 1723.7 & survey & ok \\
15−4288253152 & 2024 & dissertation & Large language models & 364 & 3102.1 & engineering & normalisation\_artefact \\ 15+4288253152 & 2024 & dissertation & LLM & 364 & 3102.1 & engineering & normalisation artefact \\
16−4322718191 & 2023 & preprint & Large language models & 3{,}629 & 1206.5 & engineering & ok \\ 16+4322718191 & 2023 & preprint & LLM & 3{,}629 & 1206.5 & engineering & ok \\
17−4323655724 & 2023 & article & Large language models & 4{,}227 & 990.7 & survey & ok \\ 17+4323655724 & 2023 & article & LLM & 4{,}227 & 990.7 & survey & ok \\
18−4367369313 & 2026 & article & Large language models & 261 & 290.4 & survey & ok \\ 18+4367369313 & 2026 & article & LLM & 261 & 290.4 & survey & ok \\
19−4226278401 & 2022 & preprint & Large language models & 3{,}077 & 894.2 & method & identifier\_mismatch \\ 19+4226278401 & 2022 & preprint & LLM & 3{,}077 & 894.2 & method & identifier mismatch \\
20−4396531244 & 2024 & dissertation & Large language models & 20 & 778.8 & engineering & normalisation\_artefact \\ 20+4396531244 & 2024 & dissertation & LLM & 20 & 778.8 & engineering & normalisation artefact \\
21−4221143046 & 2022 & preprint & Large language models & 2{,}465 & 716.4 & theory & identifier\_mismatch \\ 21+4221143046 & 2022 & preprint & LLM & 2{,}465 & 716.4 & theory & identifier mismatch \\
22−4327810158 & 2023 & preprint & Large language models & 1{,}968 & 654.3 & engineering & abstract\_mismatch \\ 22+4327810158 & 2023 & preprint & LLM & 1{,}968 & 654.3 & engineering & abstract mismatch \\
23−7135096569 & 2026 & article & Large language models & 183 & 203.6 & empirical & ok \\ 23+7135096569 & 2026 & article & LLM & 183 & 203.6 & empirical & ok \\
24−4414281281 & 2025 & article & Large language models & 1{,}099 & 374.4 & method & ok \\ 24+4414281281 & 2025 & article & LLM & 1{,}099 & 374.4 & method & ok \\
25−4389437528 & 2023 & article & Large language models & 2{,}361 & 553.4 & survey & ok \\ 25+4389437528 & 2023 & article & LLM & 2{,}361 & 553.4 & survey & ok \\
26−4292779060 & 2023 & article & Large language models & 13{,}333 & 534.7 & theory & identifier\_mismatch \\ 26+4292779060 & 2023 & article & LLM & 13{,}333 & 534.7 & theory & identifier mismatch \\
27−4360836968 & 2023 & preprint & Large language models & 1{,}420 & 472.1 & empirical & ok \\ 27+4360836968 & 2023 & preprint & LLM & 1{,}420 & 472.1 & empirical & ok \\
28−4406800520 & 2025 & article & Large language models & 914 & 311.4 & survey & no\_abstract \\ 28+4406800520 & 2025 & article & LLM & 914 & 311.4 & survey & no abstract \\
29−4385223296 & 2023 & book-chapter & Large language models & 612 & 421.3 & survey & no\_abstract \\ 29+4385223296 & 2023 & book-chapter & LLM & 612 & 421.3 & survey & no abstract \\
30−4287689466 & 2025 & conference-paper & Large language models & 722 & 548.6 & empirical & identifier\_mismatch \\ 30+4287689466 & 2025 & conference-paper & LLM & 722 & 548.6 & empirical & identifier mismatch \\
31−4366420437 & 2023 & article & Large language models & 1{,}541 & 361.2 & survey & ok \\ 31+4366420437 & 2023 & article & LLM & 1{,}541 & 361.2 & survey & ok \\
32−4317910584 & 2023 & article & Large language models & 1{,}530 & 358.6 & survey & ok \\ 32+4317910584 & 2023 & article & LLM & 1{,}530 & 358.6 & survey & ok \\
33−4313564799 & 2023 & preprint & Large language models & 811 & 335.9 & empirical & no\_abstract \\ 33+4313564799 & 2023 & preprint & LLM & 811 & 335.9 & empirical & no abstract \\
34−4382618722 & 2026 & article & Large language models & 94 & 98.9 & engineering & ok \\ 34+4382618722 & 2026 & article & LLM & 94 & 98.9 & engineering & ok \\
35−4384464487 & 2023 & article & Large language models & 1{,}398 & 327.7 & empirical & ok \\ 35+4384464487 & 2023 & article & LLM & 1{,}398 & 327.7 & empirical & ok \\
36−4392773210 & 2024 & article & Large language models & 1{,}085 & 326.7 & survey & ok \\ 36+4392773210 & 2024 & article & LLM & 1{,}085 & 326.7 & survey & ok \\
37−4387355843 & 2026 & book-chapter & Foundation models & 197 & 188.6 & method & ok \\ 37+4387355843 & 2026 & book-chapter & Foundation & 197 & 188.6 & method & ok \\
38−4392947521 & 2024 & article & Foundation models & 1{,}505 & 186.9 & method & no\_abstract \\ 38+4392947521 & 2024 & article & Foundation & 1{,}505 & 186.9 & method & no abstract \\
39−7124890304 & 2026 & article & Foundation models & 42 & 46.5 & method & no\_abstract \\ 39+7124890304 & 2026 & article & Foundation & 42 & 46.5 & method & no abstract \\
40−4410551352 & 2025 & article & Foundation models & 249 & 69.7 & method & ok \\ 40+4410551352 & 2025 & article & Foundation & 249 & 69.7 & method & ok \\
41−4322832366 & 2026 & conference-paper & Foundation models & 35 & 126.3 & method & ok \\ 41+4322832366 & 2026 & conference-paper & Foundation & 35 & 126.3 & method & ok \\
42−7133233472 & 2023 & conference-paper & Foundation models & 532 & 122.7 & method & ok \\ 42+7133233472 & 2023 & conference-paper & Foundation & 532 & 122.7 & method & ok \\
43−2990704537 & 2019 & article & Foundation models & 668 & 101.6 & benchmark & ok \\ 43+2990704537 & 2019 & article & Foundation & 668 & 101.6 & benchmark & ok \\
44−7125271461 & 2026 & article & Foundation models & 25 & 70.6 & engineering & ok \\ 44+7125271461 & 2026 & article & Foundation & 25 & 70.6 & engineering & ok \\
45−7119541039 & 2026 & article & Foundation models & 24 & 26.7 & theory & ok \\ 45+7119541039 & 2026 & article & Foundation & 24 & 26.7 & theory & ok \\
46−4393138529 & 2024 & preprint & Foundation models & 95 & 82.9 & survey & ok \\ 46+4393138529 & 2024 & preprint & Foundation & 95 & 82.9 & survey & ok \\
47−4390690043 & 2026 & article & Foundation models & 22 & 10.0 & survey & ok \\ 47+4390690043 & 2026 & article & Foundation & 22 & 10.0 & survey & ok \\
48−7128029160 & 2026 & article & Foundation models & 20 & 22.3 & method & ok \\ 48+7128029160 & 2026 & article & Foundation & 20 & 22.3 & method & ok \\
49−4402220065 & 2024 & article & Foundation models & 562 & 69.8 & method & no\_abstract \\ 49+4402220065 & 2024 & article & Foundation & 562 & 69.8 & method & no abstract \\
50−7118170538 & 2026 & article & Foundation models & 17 & 17.9 & survey & ok \\ 50+7118170538 & 2026 & article & Foundation & 17 & 17.9 & survey & ok \\
51−4411932262 & 2025 & article & Foundation models & 110 & 26.9 & method & ok \\ 51+4411932262 & 2025 & article & Foundation & 110 & 26.9 & method & ok \\
52−4411099958 & 2025 & article & Foundation models & 108 & 18.4 & method & ok \\ 52+4411099958 & 2025 & article & Foundation & 108 & 18.4 & method & ok \\
53−4361229539 & 2023 & preprint & Foundation models & 161 & 53.5 & method & ok \\ 53+4361229539 & 2023 & preprint & Foundation & 161 & 53.5 & method & ok \\
54−4407135816 & 2025 & article & Foundation models & 103 & 6.9 & survey & ok \\ 54+4407135816 & 2025 & article & Foundation & 103 & 6.9 & survey & ok \\
55−4385948838 & 2023 & article & Foundation models & 471 & 49.1 & method & no\_abstract \\ 55+4385948838 & 2023 & article & Foundation & 471 & 49.1 & method & no abstract \\
56−4406643700 & 2025 & article & Foundation models & 91 & 19.1 & survey & ok \\ 56+4406643700 & 2025 & article & Foundation & 91 & 19.1 & survey & ok \\
57−4406472599 & 2025 & article & Foundation models & 86 & 29.3 & survey & no\_abstract \\ 57+4406472599 & 2025 & article & Foundation & 86 & 29.3 & survey & no abstract \\
58−4393153153 & 2024 & conference-paper & Foundation models & 113 & 41.8 & method & ok \\ 58+4393153153 & 2024 & conference-paper & Foundation & 113 & 41.8 & method & ok \\
59−4402727669 & 2024 & conference-paper & Foundation models & 184 & 38.6 & method & ok \\ 59+4402727669 & 2024 & conference-paper & Foundation & 184 & 38.6 & method & ok \\
60−4401397240 & 2024 & article & Foundation models & 325 & 38.5 & method & no\_abstract \\ 60+4401397240 & 2024 & article & Foundation & 325 & 38.5 & method & no abstract \\
61−4385285835 & 2023 & preprint & Foundation models & 107 & 35.6 & method & ok \\ 61+4385285835 & 2023 & preprint & Foundation & 107 & 35.6 & method & ok \\
62−4318718936 & 2023 & preprint & Multimodal AI & 875 & 273.1 & method & ok \\ 62+4318718936 & 2023 & preprint & Multimodal & 875 & 273.1 & method & ok \\
63−4393148714 & 2024 & conference-paper & Multimodal AI & 923 & 193.7 & method & ok \\ 63+4393148714 & 2024 & conference-paper & Multimodal & 923 & 193.7 & method & ok \\
64−4396599905 & 2026 & article & Multimodal AI & 48 & 128.7 & survey & ok \\ 64+4396599905 & 2026 & article & Multimodal & 48 & 128.7 & survey & ok \\
65−4396952261 & 2024 & book-chapter & Multimodal AI & 189 & 147.2 & survey & no\_abstract \\ 65+4396952261 & 2024 & book-chapter & Multimodal & 189 & 147.2 & survey & no abstract \\
66−7133250524 & 2022 & conference-paper & Multimodal AI & 828 & 111.9 & benchmark & no\_abstract \\ 66+7133250524 & 2022 & conference-paper & Multimodal & 828 & 111.9 & benchmark & no abstract \\
67−4392947532 & 2024 & article & Multimodal AI & 938 & 111.1 & method & no\_abstract \\ 67+4392947532 & 2024 & article & Multimodal & 938 & 111.1 & method & no abstract \\
68−7138474371 & 2026 & conference-paper & Multimodal AI & 31 & 159.4 & method & ok \\ 68+7138474371 & 2026 & conference-paper & Multimodal & 31 & 159.4 & method & ok \\
69−7127319731 & 2026 & article & Multimodal AI & 29 & 77.7 & method & ok \\ 69+7127319731 & 2026 & article & Multimodal & 29 & 77.7 & method & ok \\
70−2619383789 & 2018 & article & Multimodal AI & 587 & 103.1 & survey & ok \\ 70+2619383789 & 2018 & article & Multimodal & 587 & 103.1 & survey & ok \\
71−7148475777 & 2026 & conference-paper & Multimodal AI & 26 & 73.2 & empirical & ok \\ 71+7148475777 & 2026 & conference-paper & Multimodal & 26 & 73.2 & empirical & ok \\
72−7160627408 & 2026 & conference-paper & Multimodal AI & 23 & 193.4 & method & ok \\ 72+7160627408 & 2026 & conference-paper & Multimodal & 23 & 193.4 & method & ok \\
73−2995460200 & 2020 & conference-paper & Multimodal AI & 670 & 81.1 & method & ok \\ 73+2995460200 & 2020 & conference-paper & Multimodal & 670 & 81.1 & method & ok \\
74−4415913530 & 2025 & article & Multimodal AI & 157 & 53.5 & method & ok \\ 74+4415913530 & 2025 & article & Multimodal & 157 & 53.5 & method & ok \\
75−4289785095 & 2022 & preprint & Multimodal AI & 366 & 79.4 & method & ok \\ 75+4289785095 & 2022 & preprint & Multimodal & 366 & 79.4 & method & ok \\
76−7125233009 & 2026 & article & Multimodal AI & 22 & 24.5 & method & ok \\ 76+7125233009 & 2026 & article & Multimodal & 22 & 24.5 & method & ok \\
77−7143512276 & 2026 & article & Multimodal AI & 21 & 23.4 & survey & ok \\ 77+7143512276 & 2026 & article & Multimodal & 21 & 23.4 & survey & ok \\
78−4413447271 & 2025 & article & Multimodal AI & 142 & 46.4 & method & ok \\ 78+4413447271 & 2025 & article & Multimodal & 142 & 46.4 & method & ok \\
79−7131830459 & 2026 & article & Multimodal AI & 20 & 142.1 & survey & ok \\ 79+7131830459 & 2026 & article & Multimodal & 20 & 142.1 & survey & ok \\
80−7206206818 & 2026 & article & Multimodal AI & 18 & 48.2 & method & ok \\ 80+7206206818 & 2026 & article & Multimodal & 18 & 48.2 & method & ok \\
81−7133220561 & 2023 & conference-paper & Multimodal AI & 454 & 63.4 & method & no\_abstract \\ 81+7133220561 & 2023 & conference-paper & Multimodal & 454 & 63.4 & method & no abstract \\
82−7164501397 & 2026 & article & Multimodal AI & 17 & 45.6 & empirical & ok \\ 82+7164501397 & 2026 & article & Multimodal & 17 & 45.6 & empirical & ok \\
83−7165561001 & 2026 & conference-paper & Multimodal AI & 17 & 143.0 & method & no\_abstract \\ 83+7165561001 & 2026 & conference-paper & Multimodal & 17 & 143.0 & method & no abstract \\
84−7138470740 & 2026 & conference-paper & Multimodal AI & 17 & 130.5 & method & ok \\ 84+7138470740 & 2026 & conference-paper & Multimodal & 17 & 130.5 & method & ok \\
85−7138363754 & 2026 & conference-paper & Multimodal AI & 17 & 130.5 & method & ok \\ 85+7138363754 & 2026 & conference-paper & Multimodal & 17 & 130.5 & method & ok \\
86−4410882873 & 2025 & article & Multimodal AI & 119 & 7.9 & survey & ok \\ 86+4410882873 & 2025 & article & Multimodal & 119 & 7.9 & survey & ok \\
87−4400228804 & 2024 & dissertation & Generative AI & 2 & 540.0 & empirical & normalisation\_artefact \\ 87+4400228804 & 2024 & dissertation & Generative & 2 & 540.0 & empirical & normalisation artefact \\
88−7124262017 & 2026 & book & Generative AI & 87 & 783.7 & survey & ok \\ 88+7124262017 & 2026 & book & Generative & 87 & 783.7 & survey & ok \\
89−4406472463 & 2025 & article & Generative AI & 498 & 77.1 & method & ok \\ 89+4406472463 & 2025 & article & Generative & 498 & 77.1 & method & ok \\
90−7123342962 & 2026 & article & Generative AI & 66 & 176.9 & method & ok \\ 90+7123342962 & 2026 & article & Generative & 66 & 176.9 & method & ok \\
91−4383312437 & 2023 & article & Generative AI & 988 & 231.6 & empirical & ok \\ 91+4383312437 & 2023 & article & Generative & 988 & 231.6 & empirical & ok \\
92−7133185348 & 2022 & conference-paper & Generative AI & 1{,}706 & 230.5 & method & no\_abstract \\ 92+7133185348 & 2022 & conference-paper & Generative & 1{,}706 & 230.5 & method & no abstract \\
93−3195296536 & 2020 & dissertation & Generative AI & 48 & 221.7 & method & no\_abstract \\ 93+3195296536 & 2020 & dissertation & Generative & 48 & 221.7 & method & no abstract \\
94−4322617812 & 2023 & article & Generative AI & 873 & 204.6 & theory & ok \\ 94+4322617812 & 2023 & article & Generative & 873 & 204.6 & theory & ok \\
95−3096831136 & 2020 & article & Generative AI & 1{,}473 & 198.6 & survey & ok \\ 95+3096831136 & 2020 & article & Generative & 1{,}473 & 198.6 & survey & ok \\
96−4400770292 & 2024 & preprint & Generative AI & 198 & 174.2 & empirical & no\_abstract \\ 96+4400770292 & 2024 & preprint & Generative & 198 & 174.2 & empirical & no abstract \\
97−7127274868 & 2026 & article & Generative AI & 48 & 341.0 & empirical & ok \\ 97+7127274868 & 2026 & article & Generative & 48 & 341.0 & empirical & ok \\
98−7124974472 & 2026 & article & Generative AI & 42 & 46.7 & empirical & ok \\ 98+7124974472 & 2026 & article & Generative & 42 & 46.7 & empirical & ok \\
99−7118187240 & 2026 & article & Generative AI & 40 & 44.5 & engineering & off\_scope \\ 99+7118187240 & 2026 & article & Generative & 40 & 44.5 & engineering & off scope \\
100−4406199489 & 2025 & article & Generative AI & 278 & 241.4 & survey & ok \\ 100+4406199489 & 2025 & article & Generative & 278 & 241.4 & survey & ok \\
101−4398243313 & 2024 & article & Generative AI & 463 & 139.4 & survey & ok \\ 101+4398243313 & 2024 & article & Generative & 463 & 139.4 & survey & ok \\
102−4409269614 & 2025 & review & Generative AI & 271 & 14.2 & survey & ok \\ 102+4409269614 & 2025 & review & Generative & 271 & 14.2 & survey & ok \\
103−3193917007 & 2021 & book-chapter & Generative AI & 195 & 127.1 & survey & ok \\ 103+3193917007 & 2021 & book-chapter & Generative & 195 & 127.1 & survey & ok \\
104−4288099666 & 2022 & preprint & Generative AI & 575 & 124.8 & method & ok \\ 104+4288099666 & 2022 & preprint & Generative & 575 & 124.8 & method & ok \\
105−4394934695 & 2024 & article & Generative AI & 394 & 118.6 & empirical & ok \\ 105+4394934695 & 2024 & article & Generative & 394 & 118.6 & empirical & ok \\
106−4321648656 & 2026 & article & Generative AI & 33 & 88.5 & survey & ok \\ 106+4321648656 & 2026 & article & Generative & 33 & 88.5 & survey & ok \\
107−4395957952 & 2023 & book & Generative AI & 400 & 116.7 & survey & no\_abstract \\ 107+4395957952 & 2023 & book & Generative & 400 & 116.7 & survey & no abstract \\
108−4394863715 & 2024 & preprint & Generative AI & 131 & 114.3 & empirical & no\_abstract \\ 108+4394863715 & 2024 & preprint & Generative & 131 & 114.3 & empirical & no abstract \\
109−4407778399 & 2025 & article & Generative AI & 213 & 184.9 & empirical & ok \\ 109+4407778399 & 2025 & article & Generative & 213 & 184.9 & empirical & ok \\
110−7124432017 & 2026 & article & Generative AI & 30 & 213.1 & theory & ok \\ 110+7124432017 & 2026 & article & Generative & 30 & 213.1 & theory & ok \\
111−7140170686 & 2026 & article & Generative AI & 30 & 213.1 & empirical & ok \\ 111+7140170686 & 2026 & article & Generative & 30 & 213.1 & empirical & ok \\
112−4406728221 & 2025 & article & AI agents & 694 & 236.4 & survey & ok \\ 112+4406728221 & 2025 & article & Agents & 694 & 236.4 & survey & ok \\
113−7161734976 & 2026 & article & AI agents & 62 & 175.1 & engineering & ok \\ 113+7161734976 & 2026 & article & Agents & 62 & 175.1 & engineering & ok \\
114−7135409568 & 2026 & article & AI agents & 52 & 23.7 & survey & ok \\ 114+7135409568 & 2026 & article & Agents & 52 & 23.7 & survey & ok \\
115−7134187809 & 2026 & article & AI agents & 46 & 129.9 & theory & off\_scope \\ 115+7134187809 & 2026 & article & Agents & 46 & 129.9 & theory & off scope \\
116−4390730779 & 2026 & article & AI agents & 44 & 49.0 & survey & ok \\ 116+4390730779 & 2026 & article & Agents & 44 & 49.0 & survey & ok \\
117−4225355126 & 2022 & book-chapter & AI agents & 268 & 143.6 & survey & no\_abstract \\ 117+4225355126 & 2022 & book-chapter & Agents & 268 & 143.6 & survey & no abstract \\
118−4407570917 & 2026 & article & AI agents & 40 & 44.5 & engineering & ok \\ 118+4407570917 & 2026 & article & Agents & 40 & 44.5 & engineering & ok \\
119−4410197190 & 2025 & review & AI agents & 267 & 33.1 & survey & ok \\ 119+4410197190 & 2025 & review & Agents & 267 & 33.1 & survey & ok \\
120−4413427262 & 2025 & article & AI agents & 258 & 87.9 & survey & ok \\ 120+4413427262 & 2025 & article & Agents & 258 & 87.9 & survey & ok \\
121−4414991289 & 2026 & article & AI agents & 31 & 23.9 & engineering & ok \\ 121+4414991289 & 2026 & article & Agents & 31 & 23.9 & engineering & ok \\
122−7122857583 & 2026 & article & AI agents & 30 & 33.4 & theory & ok \\ 122+7122857583 & 2026 & article & Agents & 30 & 33.4 & theory & ok \\
123−4401023556 & 2024 & conference-paper & AI agents & 371 & 103.1 & survey & ok \\ 123+4401023556 & 2024 & conference-paper & Agents & 371 & 103.1 & survey & ok \\
124−4414023998 & 2025 & article & AI agents & 189 & 64.4 & survey & ok \\ 124+4414023998 & 2025 & article & Agents & 189 & 64.4 & survey & ok \\
125−7118083939 & 2026 & article & AI agents & 26 & 32.7 & theory & ok \\ 125+7118083939 & 2026 & article & Agents & 26 & 32.7 & theory & ok \\
126−4412505619 & 2025 & article & AI agents & 181 & 61.7 & survey & ok \\ 126+4412505619 & 2025 & article & Agents & 181 & 61.7 & survey & ok \\
127−4416209823 & 2025 & article & AI agents & 165 & 56.2 & survey & ok \\ 127+4416209823 & 2025 & article & Agents & 165 & 56.2 & survey & ok \\
128−7155930820 & 2026 & article & AI agents & 23 & 80.7 & theory & off\_scope \\ 128+7155930820 & 2026 & article & Agents & 23 & 80.7 & theory & off scope \\
129−7162257503 & 2026 & article & AI agents & 22 & 77.2 & theory & off\_scope \\ 129+7162257503 & 2026 & article & Agents & 22 & 77.2 & theory & off scope \\
130−4412709685 & 2025 & article & AI agents & 150 & 33.5 & engineering & no\_abstract \\ 130+4412709685 & 2025 & article & Agents & 150 & 33.5 & engineering & no abstract \\
131−7128165235 & 2026 & article & AI agents & 21 & 23.4 & theory & ok \\ 131+7128165235 & 2026 & article & Agents & 21 & 23.4 & theory & ok \\
132−7118190454 & 2026 & article & AI agents & 21 & 22.2 & survey & ok \\ 132+7118190454 & 2026 & article & Agents & 21 & 22.2 & survey & ok \\
133−7130481397 & 2026 & article & AI agents & 21 & 9.6 & benchmark & ok \\ 133+7130481397 & 2026 & article & Agents & 21 & 9.6 & benchmark & ok \\
134−4390638955 & 2024 & article & AI agents & 240 & 72.3 & survey & off\_scope \\ 134+4390638955 & 2024 & article & Agents & 240 & 72.3 & survey & off scope \\
135−4412203333 & 2025 & article & AI agents & 139 & 47.4 & survey & ok \\ 135+4412203333 & 2025 & article & Agents & 139 & 47.4 & survey & ok \\
136−4399982941 & 2024 & dissertation & AI agents & 8 & 68.2 & theory & normalisation\_artefact \\ 136+4399982941 & 2024 & dissertation & Agents & 8 & 68.2 & theory & normalisation artefact \\
137−2911283634 & 2020 & book-chapter & Reinforcement learning & 532 & 276.0 & survey & no\_abstract \\ 137+2911283634 & 2020 & book-chapter & RL & 532 & 276.0 & survey & no abstract \\
138−4389473504 & 2023 & dissertation & Reinforcement learning & 28 & 232.9 & method & ok \\ 138+4389473504 & 2023 & dissertation & RL & 28 & 232.9 & method & ok \\
139−2964083594 & 2025 & conference-paper & Reinforcement learning & 414 & 367.9 & empirical & year\_implausible \\ 139+2964083594 & 2025 & conference-paper & RL & 414 & 367.9 & empirical & year implausible \\
140−2098774185 & 2018 & article & Reinforcement learning & 1{,}077 & 203.3 & method & year\_implausible \\ 140+2098774185 & 2018 & article & RL & 1{,}077 & 203.3 & method & year implausible \\
141−1591675293 & 2025 & book-chapter & Reinforcement learning & 395 & 665.0 & method & year\_implausible \\ 141+1591675293 & 2025 & book-chapter & RL & 395 & 665.0 & method & year implausible \\
142−7123365826 & 2026 & article & Reinforcement learning & 55 & 155.3 & theory & off\_scope \\ 142+7123365826 & 2026 & article & RL & 55 & 155.3 & theory & off scope \\
143−2991046523 & 2021 & book-chapter & Reinforcement learning & 530 & 178.4 & survey & no\_abstract \\ 143+2991046523 & 2021 & book-chapter & RL & 530 & 178.4 & survey & no abstract \\
144−2973229164 & 2019 & dissertation & Reinforcement learning & 47 & 171.2 & method & ok \\ 144+2973229164 & 2019 & dissertation & RL & 47 & 171.2 & method & ok \\
145−2982316857 & 2019 & article & Reinforcement learning & 1{,}021 & 155.2 & engineering & no\_abstract \\ 145+2982316857 & 2019 & article & RL & 1{,}021 & 155.2 & engineering & no abstract \\
146−2902907165 & 2018 & article & Reinforcement learning & 733 & 138.3 & method & ok \\ 146+2902907165 & 2018 & article & RL & 733 & 138.3 & method & ok \\
147−7133481537 & 2026 & article & Reinforcement learning & 35 & 122.9 & theory & off\_scope \\ 147+7133481537 & 2026 & article & RL & 35 & 122.9 & theory & off scope \\
148−7133226216 & 2022 & conference-paper & Reinforcement learning & 482 & 95.8 & empirical & ok \\ 148+7133226216 & 2022 & conference-paper & RL & 482 & 95.8 & empirical & ok \\
149−3041807988 & 2020 & article & Reinforcement learning & 322 & 93.8 & theory & off\_scope \\ 149+3041807988 & 2020 & article & RL & 322 & 93.8 & theory & off scope \\
150−2781726626 & 2018 & preprint & Reinforcement learning & 775 & 90.8 & method & ok \\ 150+2781726626 & 2018 & preprint & RL & 775 & 90.8 & method & ok \\
151−2963923407 & 2018 & article & Reinforcement learning & 472 & 89.1 & method & ok \\ 151+2963923407 & 2018 & article & RL & 472 & 89.1 & method & ok \\
152−7122543576 & 2026 & article & Reinforcement learning & 25 & 70.6 & theory & off\_scope \\ 152+7122543576 & 2026 & article & RL & 25 & 70.6 & theory & off scope \\
153−2899138465 & 2018 & dissertation & Reinforcement learning & 20 & 88.6 & method & ok \\ 153+2899138465 & 2018 & dissertation & RL & 20 & 88.6 & method & ok \\
154−4391631327 & 2024 & preprint & Reinforcement learning & 99 & 86.4 & empirical & identifier\_mismatch \\ 154+4391631327 & 2024 & preprint & RL & 99 & 86.4 & empirical & identifier mismatch \\
155−4409147637 & 2025 & article & Reinforcement learning & 170 & 57.9 & method & ok \\ 155+4409147637 & 2025 & article & RL & 170 & 57.9 & method & ok \\
156−2895974005 & 2018 & dissertation & Reinforcement learning & 18 & 79.8 & empirical & normalisation\_artefact \\ 156+2895974005 & 2018 & dissertation & RL & 18 & 79.8 & empirical & normalisation artefact \\
157−7134057781 & 2026 & article & Reinforcement learning & 21 & 23.4 & engineering & ok \\ 157+7134057781 & 2026 & article & RL & 21 & 23.4 & engineering & ok \\
158−2754517384 & 2018 & conference-paper & Reinforcement learning & 452 & 70.0 & empirical & ok \\ 158+2754517384 & 2018 & conference-paper & RL & 452 & 70.0 & empirical & ok \\
159−2761873684 & 2018 & conference-paper & Reinforcement learning & 442 & 68.4 & method & ok \\ 159+2761873684 & 2018 & conference-paper & RL & 442 & 68.4 & method & ok \\
160−7133208539 & 2023 & conference-paper & Reinforcement learning & 293 & 67.6 & method & no\_abstract \\ 160+7133208539 & 2023 & conference-paper & RL & 293 & 67.6 & method & no abstract \\
161−7128436456 & 2026 & article & Reinforcement learning & 19 & 21.0 & survey & ok \\ 161+7128436456 & 2026 & article & RL & 19 & 21.0 & survey & ok \\
162−2962862931 & 2024 & conference-paper & ML theory & 12{,}586 & 3498.4 & method & identifier\_mismatch \\ 162+2962862931 & 2024 & conference-paper & ML theory & 12{,}586 & 3498.4 & method & identifier mismatch \\
163 1698155719 & 2020 & book-chapter & ML theory & 1{,}212 & 971.7 & survey & ok \\ 163 1698155719 & 2020 & book-chapter & ML theory & 1{,}212 & 971.7 & survey & ok \\
164−4288283362 & 2026 & article & ML theory & 174 & 576.9 & method & normalisation\_artefact \\ 164+4288283362 & 2026 & article & ML theory & 174 & 576.9 & method & normalisation artefact \\
165−46659105 & 2020 & preprint & ML theory & 3{,}906 & 608.2 & theory & abstract\_mismatch \\ 165+46659105 & 2020 & preprint & ML theory & 3{,}906 & 608.2 & theory & abstract mismatch \\
166−3145506661 & 2019 & book-chapter & ML theory & 1{,}581 & 491.8 & survey & no\_abstract \\ 166+3145506661 & 2019 & book-chapter & ML theory & 1{,}581 & 491.8 & survey & no abstract \\
167 2291445757 & 2024 & book-chapter & ML theory & 769 & 426.8 & survey & ok \\ 167 2291445757 & 2024 & book-chapter & ML theory & 769 & 426.8 & survey & ok \\
168−2100415632 & 2026 & preprint & ML theory & 116 & 667.5 & theory & year\_implausible \\ 168+2100415632 & 2026 & preprint & ML theory & 116 & 667.5 & theory & year implausible \\
169 1568555062 & 2022 & book-chapter & ML theory & 576 & 308.7 & theory & ok \\ 169 1568555062 & 2022 & book-chapter & ML theory & 576 & 308.7 & theory & ok \\
170 2751318774 & 2019 & book-chapter & ML theory & 978 & 304.2 & survey & ok \\ 170 2751318774 & 2019 & book-chapter & ML theory & 978 & 304.2 & survey & ok \\
171−3208688400 & 2021 & book-chapter & ML theory & 833 & 280.5 & survey & no\_abstract \\ 171+3208688400 & 2021 & book-chapter & ML theory & 833 & 280.5 & survey & no abstract \\
172−3105488314 & 2020 & dissertation & ML theory & 59 & 272.4 & theory & no\_abstract \\ 172+3105488314 & 2020 & dissertation & ML theory & 59 & 272.4 & theory & no abstract \\
173−57706852 & 2026 & preprint & ML theory & 72 & 414.3 & theory & year\_implausible \\ 173+57706852 & 2026 & preprint & ML theory & 72 & 414.3 & theory & year implausible \\
174−2946444988 & 2026 & preprint & ML theory & 68 & 2646.0 & method & year\_implausible \\ 174+2946444988 & 2026 & preprint & ML theory & 68 & 2646.0 & method & year implausible \\
175−2945976633 & 2019 & article & ML theory & 1{,}556 & 236.6 & theory & no\_abstract \\ 175+2945976633 & 2019 & article & ML theory & 1{,}556 & 236.6 & theory & no abstract \\
176 4400823820 & 2024 & article & ML theory & 782 & 235.4 & survey & ok \\ 176 4400823820 & 2024 & article & ML theory & 782 & 235.4 & survey & ok \\
177 2999309192 & 2020 & article & ML theory & 1{,}781 & 222.8 & theory & ok \\ 177 2999309192 & 2020 & article & ML theory & 1{,}781 & 222.8 & theory & ok \\
178−2999615587 & 2020 & article & ML theory & 1{,}767 & 221.0 & method & no\_abstract \\ 178+2999615587 & 2020 & article & ML theory & 1{,}767 & 221.0 & method & no abstract \\
179 2439568532 & 2018 & article & ML theory & 1{,}085 & 204.8 & theory & ok \\ 179 2439568532 & 2018 & article & ML theory & 1{,}085 & 204.8 & theory & ok \\
180−3194464139 & 2020 & dissertation & ML theory & 21 & 197.5 & engineering & normalisation\_artefact \\ 180+3194464139 & 2020 & dissertation & ML theory & 21 & 197.5 & engineering & normalisation artefact \\
181 3041133507 & 2020 & article & ML theory & 1{,}538 & 192.4 & survey & ok \\ 181 3041133507 & 2020 & article & ML theory & 1{,}538 & 192.4 & survey & ok \\
182 2436219157 & 2018 & article & ML theory & 956 & 180.4 & survey & ok \\ 182 2436219157 & 2018 & article & ML theory & 956 & 180.4 & survey & ok \\
183−3135028703 & 2021 & article & ML theory & 1{,}518 & 176.9 & survey & no\_abstract \\ 183+3135028703 & 2021 & article & ML theory & 1{,}518 & 176.9 & survey & no abstract \\
184 7124426637 & 2026 & article & ML theory & 46 & 51.2 & survey & ok \\ 184 7124426637 & 2026 & article & ML theory & 46 & 51.2 & survey & ok \\
185−2165856466 & 2026 & preprint & ML theory & 45 & 259.0 & theory & year\_implausible \\ 185+2165856466 & 2026 & preprint & ML theory & 45 & 259.0 & theory & year implausible \\
186 4402843978 & 2020 & report & ML theory & 455 & 158.3 & survey & ok \\ 186 4402843978 & 2020 & report & ML theory & 455 & 158.3 & survey & ok \\
187−2964121744 & 2026 & article & DL architectures & 45{,}775 & 50938.5 & engineering & identifier\_mismatch \\ 187+2964121744 & 2026 & article & DL arch. & 45{,}775 & 50938.5 & engineering & identifier mismatch \\
188−1710476689 & 2022 & book-chapter & DL architectures & 2{,}385 & 1018.2 & survey & ok \\ 188+1710476689 & 2022 & book-chapter & DL arch. & 2{,}385 & 1018.2 & survey & ok \\
189−4322614756 & 2025 & conference-paper & DL architectures & 1{,}542 & 1370.5 & method & identifier\_mismatch \\ 189+4322614756 & 2025 & conference-paper & DL arch. & 1{,}542 & 1370.5 & method & identifier mismatch \\
190−2963399829 & 2024 & preprint & DL architectures & 773 & 674.3 & method & year\_implausible \\ 190+2963399829 & 2024 & preprint & DL arch. & 773 & 674.3 & method & year implausible \\
191−4382203079 & 2023 & conference-paper & DL architectures & 2{,}278 & 621.0 & empirical & ok \\ 191+4382203079 & 2023 & conference-paper & DL arch. & 2{,}278 & 621.0 & empirical & ok \\
192−4406170795 & 2025 & article & DL architectures & 974 & 331.8 & method & ok \\ 192+4406170795 & 2025 & article & DL arch. & 974 & 331.8 & method & ok \\
193−146900863 & 2018 & book-chapter & DL architectures & 999 & 464.0 & survey & no\_abstract \\ 193+146900863 & 2018 & book-chapter & DL arch. & 999 & 464.0 & survey & no abstract \\
194−4396821501 & 2024 & preprint & DL architectures & 531 & 463.2 & method & ok \\ 194+4396821501 & 2024 & preprint & DL arch. & 531 & 463.2 & method & ok \\
195−2950067852 & 2026 & preprint & DL architectures & 119 & 4630.6 & method & year\_implausible \\ 195+2950067852 & 2026 & preprint & DL arch. & 119 & 4630.6 & method & year implausible \\
196−4221150249 & 2026 & article & DL architectures & 107 & 119.1 & survey & ok \\ 196+4221150249 & 2026 & article & DL arch. & 107 & 119.1 & survey & ok \\
197−7148890333 & 2026 & article & DL architectures & 102 & 113.0 & method & no\_abstract \\ 197+7148890333 & 2026 & article & DL arch. & 102 & 113.0 & method & no abstract \\
198−3177318507 & 2021 & conference-paper & DL architectures & 1{,}344 & 315.3 & method & ok \\ 198+3177318507 & 2021 & conference-paper & DL arch. & 1{,}344 & 315.3 & method & ok \\
199−4389326242 & 2023 & preprint & DL architectures & 912 & 303.2 & method & ok \\ 199+4389326242 & 2023 & preprint & DL arch. & 912 & 303.2 & method & ok \\
200−7118302872 & 2026 & article & DL architectures & 85 & 87.6 & method & ok \\ 200+7118302872 & 2026 & article & DL arch. & 85 & 87.6 & method & ok \\
201−3137147200 & 2022 & book-chapter & DL architectures & 698 & 298.0 & survey & ok \\ 201+3137147200 & 2022 & book-chapter & DL arch. & 698 & 298.0 & survey & ok \\
202−3140854437 & 2021 & article & DL architectures & 2{,}452 & 293.1 & survey & ok \\ 202+3140854437 & 2021 & article & DL arch. & 2{,}452 & 293.1 & survey & ok \\
203−4210257598 & 2020 & article & DL architectures & 2{,}239 & 280.0 & survey & ok \\ 203+4210257598 & 2020 & article & DL arch. & 2{,}239 & 280.0 & survey & ok \\
204−2907492528 & 2020 & article & DL architectures & 2{,}230 & 278.9 & survey & ok \\ 204+2907492528 & 2020 & article & DL arch. & 2{,}230 & 278.9 & survey & ok \\
205−3152893301 & 2020 & article & DL architectures & 2{,}023 & 253.0 & survey & ok \\ 205+3152893301 & 2020 & article & DL arch. & 2{,}023 & 253.0 & survey & ok \\
206−7110333790 & 2025 & article & DL architectures & 496 & 169.0 & method & identifier\_mismatch \\ 206+7110333790 & 2025 & article & DL arch. & 496 & 169.0 & method & identifier mismatch \\
207−4385763767 & 2023 & conference-paper & DL architectures & 921 & 251.1 & survey & ok \\ 207+4385763767 & 2023 & conference-paper & DL arch. & 921 & 251.1 & survey & ok \\
208−3124006607 & 2026 & conference-paper & DL architectures & 69 & 529.6 & method & year\_implausible \\ 208+3124006607 & 2026 & conference-paper & DL arch. & 69 & 529.6 & method & year implausible \\
209−4285723986 & 2020 & book-chapter & DL architectures & 471 & 244.4 & survey & no\_abstract \\ 209+4285723986 & 2020 & book-chapter & DL arch. & 471 & 244.4 & survey & no abstract \\
210−2892035503 & 2019 & article & DL architectures & 908 & 192.7 & survey & no\_abstract \\ 210+2892035503 & 2019 & article & DL arch. & 908 & 192.7 & survey & no abstract \\
211−2962776659 & 2024 & preprint & DL architectures & 212 & 184.9 & empirical & year\_implausible \\ 211+2962776659 & 2024 & preprint & DL arch. & 212 & 184.9 & empirical & year implausible \\
212−4406063062 & 2025 & article & AI safety and alignment & 534 & 74.5 & survey & ok \\ 212+4406063062 & 2025 & article & AI safety & 534 & 74.5 & survey & ok \\
213−7130596601 & 2026 & article & AI safety and alignment & 65 & 29.6 & survey & ok \\ 213+7130596601 & 2026 & article & AI safety & 65 & 29.6 & survey & ok \\
214−3084163594 & 2020 & article & AI safety and alignment & 775 & 225.7 & survey & off\_scope \\ 214+3084163594 & 2020 & article & AI safety & 775 & 225.7 & survey & off scope \\
215−2963808661 & 2025 & conference-paper & AI safety and alignment & 390 & 126.3 & engineering & identifier\_mismatch \\ 215+2963808661 & 2025 & conference-paper & AI safety & 390 & 126.3 & engineering & identifier mismatch \\
216−7133236347 & 2026 & article & AI safety and alignment & 51 & 44.5 & survey & ok \\ 216+7133236347 & 2026 & article & AI safety & 51 & 44.5 & survey & ok \\
217−7128782475 & 2026 & article & AI safety and alignment & 51 & 23.2 & empirical & ok \\ 217+7128782475 & 2026 & article & AI safety & 51 & 23.2 & empirical & ok \\
218−4406571986 & 2025 & article & AI safety and alignment & 347 & 301.3 & empirical & ok \\ 218+4406571986 & 2025 & article & AI safety & 347 & 301.3 & empirical & ok \\
219−2981731882 & 2019 & article & AI safety and alignment & 1{,}097 & 166.8 & survey & no\_abstract \\ 219+2981731882 & 2019 & article & AI safety & 1{,}097 & 166.8 & survey & no abstract \\
220−4402698709 & 2026 & article & AI safety and alignment & 44 & 38.4 & survey & ok \\ 220+4402698709 & 2026 & article & AI safety & 44 & 38.4 & survey & ok \\
221−4408314709 & 2025 & article & AI safety and alignment & 303 & 42.3 & survey & ok \\ 221+4408314709 & 2025 & article & AI safety & 303 & 42.3 & survey & ok \\
222−4407243994 & 2025 & article & AI safety and alignment & 300 & 41.8 & theory & ok \\ 222+4407243994 & 2025 & article & AI safety & 300 & 41.8 & theory & ok \\
223−4330336443 & 2023 & preprint & AI safety and alignment & 495 & 146.6 & empirical & abstract\_mismatch \\ 223+4330336443 & 2023 & preprint & AI safety & 495 & 146.6 & empirical & abstract mismatch \\
224−7128600296 & 2026 & review & AI safety and alignment & 41 & 12.8 & survey & ok \\ 224+7128600296 & 2026 & review & AI safety & 41 & 12.8 & survey & ok \\
225−4399450035 & 2024 & conference-paper & AI safety and alignment & 359 & 138.5 & empirical & ok \\ 225+4399450035 & 2024 & conference-paper & AI safety & 359 & 138.5 & empirical & ok \\
226−4318586239 & 2023 & book-chapter & AI safety and alignment & 247 & 136.6 & survey & ok \\ 226+4318586239 & 2023 & book-chapter & AI safety & 247 & 136.6 & survey & ok \\
227−4360615722 & 2023 & article & AI safety and alignment & 557 & 130.5 & theory & ok \\ 227+4360615722 & 2023 & article & AI safety & 557 & 130.5 & theory & ok \\
228−2670253439 & 2018 & article & AI safety and alignment & 676 & 127.6 & survey & no\_abstract \\ 228+2670253439 & 2018 & article & AI safety & 676 & 127.6 & survey & no abstract \\
229−7118631510 & 2026 & article & AI safety and alignment & 35 & 38.9 & method & no\_abstract \\ 229+7118631510 & 2026 & article & AI safety & 35 & 38.9 & method & no abstract \\
230−7127143535 & 2026 & article & AI safety and alignment & 33 & 50.0 & empirical & no\_abstract \\ 230+7127143535 & 2026 & article & AI safety & 33 & 50.0 & empirical & no abstract \\
231−4409249263 & 2025 & article & AI safety and alignment & 218 & 14.5 & survey & ok \\ 231+4409249263 & 2025 & article & AI safety & 218 & 14.5 & survey & ok \\
232−4366262984 & 2023 & article & AI safety and alignment & 1{,}197 & 109.9 & survey & ok \\ 232+4366262984 & 2023 & article & AI safety & 1{,}197 & 109.9 & survey & ok \\
233−2969896603 & 2021 & article & AI safety and alignment & 1{,}794 & 102.3 & survey & ok \\ 233+2969896603 & 2021 & article & AI safety & 1{,}794 & 102.3 & survey & ok \\
234−4406152291 & 2025 & article & AI safety and alignment & 199 & 67.8 & empirical & ok \\ 234+4406152291 & 2025 & article & AI safety & 199 & 67.8 & empirical & ok \\
235−4410609100 & 2025 & conference-paper & AI safety and alignment & 193 & 171.5 & method & ok \\ 235+4410609100 & 2025 & conference-paper & AI safety & 193 & 171.5 & method & ok \\
236−4411542009 & 2025 & conference-paper & AI safety and alignment & 191 & 61.8 & empirical & ok \\ 236+4411542009 & 2025 & conference-paper & AI safety & 191 & 61.8 & empirical & ok \\
237−4360620450 & 2023 & article & Large language models & 3{,}137 & 113.5 & survey & ok \\ 237+4360620450 & 2023 & article & LLM & 3{,}137 & 113.5 & survey & ok \\
238−3185341429 & 2022 & article & Large language models & 2{,}462 & 253.7 & survey & ok \\ 238+3185341429 & 2022 & article & LLM & 2{,}462 & 253.7 & survey & ok \\
239−4384071683 & 2023 & article & Large language models & 2{,}448 & 38.1 & benchmark & ok \\ 239+4384071683 & 2023 & article & LLM & 2{,}448 & 38.1 & benchmark & ok \\
240−4384918448 & 2023 & preprint & Large language models & 2{,}431 & 88.5 & empirical & identifier\_mismatch \\ 240+4384918448 & 2023 & preprint & LLM & 2{,}431 & 88.5 & empirical & identifier mismatch \\
241−4393065402 & 2024 & article & Large language models & 1{,}820 & 226.1 & survey & ok \\ 241+4393065402 & 2024 & article & LLM & 1{,}820 & 226.1 & survey & ok \\
242−4319083882 & 2023 & article & Large language models & 1{,}590 & 146.0 & theory & no\_abstract \\ 242+4319083882 & 2023 & article & LLM & 1{,}590 & 146.0 & theory & no abstract \\
243−4399790131 & 2024 & review & AI safety and alignment & 1{,}484 & 81.8 & survey & ok \\ 243+4399790131 & 2024 & review & AI safety & 1{,}484 & 81.8 & survey & ok \\
244−3133702157 & 2021 & conference-paper & Large language models & 1{,}464 & 232.1 & theory & ok \\ 244+3133702157 & 2021 & conference-paper & LLM & 1{,}464 & 232.1 & theory & ok \\
245−4321499901 & 2023 & article & Large language models & 1{,}389 & 325.5 & empirical & ok \\ 245+4321499901 & 2023 & article & LLM & 1{,}389 & 325.5 & empirical & ok \\
246 \bottomrule 246 \bottomrule
247−\multicolumn{8}{p{0.95\linewidth}}{\footnotesize Selection score and windows as in Part IV (Section~\ref{sec:m-score}); c36 and NCS36 as stored in the landmark file.}\\ 247+\multicolumn{8}{p{0.95\linewidth}}{\footnotesize Selection score and windows as in Part IV (Section~\ref{sec:m-score}); c36 and NCS36 as stored in the landmark file. Category abbreviations as in Table~\ref{tab:ai_growth}.}\\
248 \end{longtable} 248 \end{longtable}
modified manuscript/tables/tab_breaks.tex +1 −1
@@ -1,4 +1,4 @@
1−% generated by tables/make_tables.py from /Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/econometrics/breaks.csv 1+% generated by tables/make_tables.py from ./results/econometrics/breaks.csv
2 \begin{longtable}{lrrrrrrrr} 2 \begin{longtable}{lrrrrrrrr}
3 \caption{Structural-change estimates by series: ITS level and slope changes at 2022-12 (HAC p-values), placebo p-values, Bai–Perron (BIC) and PELT break dates.}\label{tab:breaks}\\ 3 \caption{Structural-change estimates by series: ITS level and slope changes at 2022-12 (HAC p-values), placebo p-values, Bai–Perron (BIC) and PELT break dates.}\label{tab:breaks}\\
4 \toprule 4 \toprule
modified manuscript/tables/tab_cagr.tex +40 −40
@@ -9,45 +9,45 @@
9 \textbf{category} & \textbf{n\_2018} & \textbf{n\_2022} & \textbf{n\_2025} & \textbf{cagr\_baseline} & \textbf{cagr\_post} & \textbf{share\_2018} & \textbf{share\_2025} \\ 9 \textbf{category} & \textbf{n\_2018} & \textbf{n\_2022} & \textbf{n\_2025} & \textbf{cagr\_baseline} & \textbf{cagr\_post} & \textbf{share\_2018} & \textbf{share\_2025} \\
10 \midrule 10 \midrule
11 \endhead 11 \endhead
12−ai\_accelerators & 818 & 1{,}421 & 1{,}906 & 0.148 & 0.092 & 0.002 & 0.002 \\ 12+ai\_accelerators & 409 & 710 & 953 & 0.148 & 0.092 & 0.002 & 0.002 \\
13−ai\_agents & 5{,}436 & 4{,}521 & 8{,}819 & -0.045 & 0.329 & 0.011 & 0.010 \\ 13+ai\_agents & 2{,}718 & 2{,}260 & 4{,}409 & -0.045 & 0.329 & 0.016 & 0.010 \\
14−ai\_safety & 1{,}052 & 2{,}954 & 10{,}885 & 0.294 & 0.647 & 0.002 & 0.012 \\ 14+ai\_safety & 526 & 1{,}477 & 5{,}442 & 0.294 & 0.647 & 0.003 & 0.013 \\
15−algorithms & 29{,}148 & 28{,}202 & 33{,}795 & -0.008 & 0.088 & 0.058 & 0.038 \\ 15+algorithms & 9{,}716 & 9{,}400 & 11{,}265 & -0.008 & 0.088 & 0.057 & 0.026 \\
16−applied\_cryptography & 10{,}782 & 10{,}299 & 14{,}612 & -0.011 & 0.141 & 0.022 & 0.016 \\ 16+applied\_cryptography & 3{,}594 & 5{,}149 & 7{,}306 & 0.094 & 0.398 & 0.021 & 0.017 \\
17−autonomous\_scientific\_discovery & 86 & 236 & 764 & 0.287 & 0.382 & 0.000 & 0.001 \\ 17+autonomous\_scientific\_discovery & 43 & 118 & 382 & 0.287 & 0.382 & 0.000 & 0.001 \\
18−blockchain & 6{,}441 & 11{,}770 & 18{,}012 & 0.163 & 0.120 & 0.013 & 0.020 \\ 18+blockchain & 2{,}147 & 5{,}885 & 9{,}006 & 0.287 & 0.120 & 0.013 & 0.021 \\
19−cloud\_computing & 9{,}396 & 7{,}103 & 10{,}974 & -0.068 & 0.198 & 0.019 & 0.012 \\ 19+cloud\_computing & 3{,}132 & 3{,}551 & 3{,}658 & 0.032 & -0.022 & 0.018 & 0.009 \\
20−compilers & 725 & 642 & 767 & -0.030 & 0.053 & 0.001 & 0.001 \\ 20+compilers & 241 & 321 & 383 & 0.074 & 0.053 & 0.001 & 0.001 \\
21−computational\_biology & 12{,}968 & 19{,}536 & 28{,}154 & 0.108 & 0.170 & 0.026 & 0.032 \\ 21+computational\_biology & 4{,}322 & 9{,}768 & 14{,}077 & 0.226 & 0.170 & 0.025 & 0.033 \\
22−computational\_complexity & 3{,}837 & 3{,}541 & 4{,}209 & -0.020 & 0.097 & 0.008 & 0.005 \\ 22+computational\_complexity & 1{,}279 & 1{,}770 & 2{,}104 & 0.085 & 0.343 & 0.008 & 0.005 \\
23−computer\_architecture & 19{,}554 & 15{,}174 & 20{,}209 & -0.061 & 0.111 & 0.039 & 0.023 \\ 23+computer\_architecture & 6{,}518 & 7{,}587 & 6{,}736 & 0.039 & -0.093 & 0.038 & 0.016 \\
24−computer\_graphics & 10{,}198 & 8{,}287 & 12{,}440 & -0.051 & 0.157 & 0.020 & 0.014 \\ 24+computer\_graphics & 3{,}399 & 4{,}143 & 6{,}220 & 0.051 & 0.417 & 0.020 & 0.014 \\
25−computer\_networks & 58{,}744 & 49{,}141 & 59{,}555 & -0.044 & 0.071 & 0.118 & 0.067 \\ 25+computer\_networks & 19{,}581 & 16{,}380 & 29{,}777 & -0.044 & 0.312 & 0.115 & 0.069 \\
26−computer\_vision & 69{,}738 & 98{,}704 & 150{,}757 & 0.091 & 0.149 & 0.140 & 0.169 \\ 26+computer\_vision & 23{,}246 & 49{,}352 & 75{,}378 & 0.207 & 0.149 & 0.137 & 0.175 \\
27−cybersecurity & 29{,}440 & 31{,}554 & 59{,}044 & 0.017 & 0.272 & 0.059 & 0.066 \\ 27+cybersecurity & 9{,}813 & 15{,}777 & 29{,}522 & 0.126 & 0.558 & 0.058 & 0.069 \\
28−database\_systems & 16{,}265 & 13{,}625 & 16{,}681 & -0.043 & 0.073 & 0.033 & 0.019 \\ 28+database\_systems & 5{,}421 & 6{,}812 & 8{,}340 & 0.059 & 0.073 & 0.032 & 0.019 \\
29−deep\_learning\_architectures & 11{,}992 & 22{,}270 & 34{,}141 & 0.167 & 0.155 & 0.024 & 0.038 \\ 29+deep\_learning\_architectures & 3{,}997 & 7{,}423 & 17{,}070 & 0.167 & 0.155 & 0.024 & 0.040 \\
30−distributed\_computing & 11{,}519 & 9{,}379 & 12{,}550 & -0.050 & 0.132 & 0.023 & 0.014 \\ 30+distributed\_computing & 3{,}839 & 4{,}689 & 4{,}183 & 0.051 & 0.132 & 0.023 & 0.010 \\
31−edge\_computing & 5{,}185 & 8{,}554 & 11{,}554 & 0.133 & 0.126 & 0.010 & 0.013 \\ 31+edge\_computing & 1{,}728 & 4{,}277 & 5{,}777 & 0.254 & 0.379 & 0.010 & 0.013 \\
32−federated\_learning & 143 & 974 & 2{,}544 & 0.615 & 0.304 & 0.000 & 0.003 \\ 32+federated\_learning & 71 & 487 & 1{,}272 & 0.615 & 0.304 & 0.000 & 0.003 \\
33−formal\_verification & 5{,}307 & 3{,}995 & 4{,}623 & -0.069 & 0.027 & 0.011 & 0.005 \\ 33+formal\_verification & 1{,}769 & 1{,}997 & 2{,}311 & 0.031 & 0.258 & 0.010 & 0.005 \\
34−foundation\_models & 46 & 350 & 1{,}854 & 0.661 & 0.802 & 0.000 & 0.002 \\ 34+foundation\_models & 23 & 175 & 927 & 0.661 & 0.802 & 0.000 & 0.002 \\
35−generative\_ai & 766 & 2{,}160 & 12{,}743 & 0.296 & 0.870 & 0.002 & 0.014 \\ 35+generative\_ai & 255 & 1{,}080 & 6{,}371 & 0.434 & 0.870 & 0.002 & 0.015 \\
36−hci & 34{,}225 & 34{,}801 & 58{,}148 & 0.004 & 0.197 & 0.069 & 0.065 \\ 36+hci & 11{,}408 & 17{,}400 & 29{,}074 & 0.111 & 0.197 & 0.067 & 0.068 \\
37−information\_retrieval & 12{,}095 & 11{,}243 & 17{,}379 & -0.018 & 0.176 & 0.024 & 0.019 \\ 37+information\_retrieval & 4{,}031 & 5{,}621 & 8{,}689 & 0.087 & 0.176 & 0.024 & 0.020 \\
38−llm & 1{,}635 & 3{,}266 & 45{,}158 & 0.189 & 1.085 & 0.003 & 0.051 \\ 38+llm & 545 & 1{,}088 & 22{,}579 & 0.189 & 1.085 & 0.003 & 0.053 \\
39−ml\_theory & 13{,}534 & 20{,}131 & 32{,}798 & 0.104 & 0.197 & 0.027 & 0.037 \\ 39+ml\_theory & 4{,}511 & 10{,}065 & 16{,}399 & 0.222 & 0.197 & 0.027 & 0.038 \\
40−multimodal\_ai & 470 & 1{,}278 & 6{,}749 & 0.284 & 0.869 & 0.001 & 0.008 \\ 40+multimodal\_ai & 156 & 639 & 3{,}374 & 0.421 & 0.869 & 0.001 & 0.008 \\
41−nlp & 23{,}372 & 29{,}678 & 42{,}914 & 0.062 & 0.124 & 0.047 & 0.048 \\ 41+nlp & 7{,}790 & 9{,}892 & 21{,}457 & 0.062 & 0.124 & 0.046 & 0.050 \\
42−operating\_systems & 1{,}556 & 1{,}083 & 1{,}363 & -0.087 & 0.078 & 0.003 & 0.002 \\ 42+operating\_systems & 778 & 541 & 681 & -0.087 & 0.078 & 0.005 & 0.002 \\
43−privacy\_preserving\_computation & 3{,}105 & 4{,}376 & 8{,}817 & 0.090 & 0.327 & 0.006 & 0.010 \\ 43+privacy\_preserving\_computation & 1{,}035 & 2{,}188 & 4{,}408 & 0.206 & 0.327 & 0.006 & 0.010 \\
44−programming\_languages & 4{,}390 & 3{,}664 & 4{,}193 & -0.044 & 0.040 & 0.009 & 0.005 \\ 44+programming\_languages & 1{,}463 & 1{,}832 & 2{,}096 & 0.058 & 0.040 & 0.009 & 0.005 \\
45−quantum\_computing & 6{,}224 & 9{,}216 & 17{,}471 & 0.103 & 0.287 & 0.012 & 0.020 \\ 45+quantum\_computing & 3{,}112 & 4{,}608 & 8{,}735 & 0.103 & 0.287 & 0.018 & 0.020 \\
46−reinforcement\_learning & 3{,}296 & 7{,}017 & 13{,}210 & 0.208 & 0.252 & 0.007 & 0.015 \\ 46+reinforcement\_learning & 1{,}648 & 3{,}508 & 6{,}605 & 0.208 & 0.252 & 0.010 & 0.015 \\
47−robotics & 17{,}496 & 23{,}103 & 33{,}464 & 0.072 & 0.140 & 0.035 & 0.037 \\ 47+robotics & 5{,}832 & 11{,}551 & 16{,}732 & 0.186 & 0.140 & 0.034 & 0.039 \\
48−scientific\_computing & 16{,}882 & 19{,}932 & 23{,}191 & 0.042 & 0.066 & 0.034 & 0.026 \\ 48+scientific\_computing & 5{,}627 & 6{,}644 & 7{,}730 & 0.042 & 0.066 & 0.033 & 0.018 \\
49−software\_engineering & 29{,}016 & 23{,}725 & 36{,}381 & -0.049 & 0.177 & 0.058 & 0.041 \\ 49+software\_engineering & 9{,}672 & 11{,}862 & 18{,}190 & 0.052 & 0.177 & 0.057 & 0.042 \\
50−speech\_audio & 12{,}096 & 12{,}698 & 18{,}373 & 0.012 & 0.118 & 0.024 & 0.021 \\ 50+speech\_audio & 4{,}032 & 6{,}349 & 9{,}186 & 0.120 & 0.118 & 0.024 & 0.021 \\
51−sustainable\_computing & 583 & 830 & 1{,}639 & 0.092 & 0.233 & 0.001 & 0.002 \\ 51+sustainable\_computing & 291 & 276 & 819 & -0.013 & 0.233 & 0.002 & 0.002 \\
52 \bottomrule 52 \bottomrule
53 \end{longtable} 53 \end{longtable}
modified manuscript/tables/tab_cited_age.tex +1 −1
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1−% generated by tables/make_tables.py from /Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/graph/cited_age_yearly.csv 1+% generated by tables/make_tables.py from ./results/graph/cited_age_yearly.csv
2 \begin{table}[htbp]\centering\caption{Age of cited works by citing year (resolved references of eligible works; corpus version of the result file at generation time).}\label{tab:cited_age} 2 \begin{table}[htbp]\centering\caption{Age of cited works by citing year (resolved references of eligible works; corpus version of the result file at generation time).}\label{tab:cited_age}
3 \begin{threeparttable}\small 3 \begin{threeparttable}\small
4 \begin{tabular}{lrrrrrrr} 4 \begin{tabular}{lrrrrrrr}
modified manuscript/tables/tab_collaboration.tex +1 −1
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1−% generated by tables/make_tables.py from /Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/bibliometrics/team_size_yearly.csv, /Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/bibliometrics/international_collab_yearly.csv 1+% generated by tables/make_tables.py from ./results/bibliometrics/team_size_yearly.csv, ./results/bibliometrics/international_collab_yearly.csv
2 \begin{table}[htbp]\centering\caption{Team size and collaboration indicators by publication year (all eligible works; periods of the source file summed).}\label{tab:collaboration} 2 \begin{table}[htbp]\centering\caption{Team size and collaboration indicators by publication year (all eligible works; periods of the source file summed).}\label{tab:collaboration}
3 \begin{threeparttable}\small 3 \begin{threeparttable}\small
4 \begin{tabular}{lrrrrrrrr} 4 \begin{tabular}{lrrrrrrrr}
modified manuscript/tables/tab_countries.tex +1 −1
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1−% generated by tables/make_tables.py from /Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/bibliometrics/countries_yearly.csv 1+% generated by tables/make_tables.py from ./results/bibliometrics/countries_yearly.csv
2 \begin{table}[htbp]\centering\caption{Fractional output of the fifteen largest countries by year.}\label{tab:countries} 2 \begin{table}[htbp]\centering\caption{Fractional output of the fifteen largest countries by year.}\label{tab:countries}
3 \begin{threeparttable}\small 3 \begin{threeparttable}\small
4 \begin{tabular}{lrrrrrrrrr} 4 \begin{tabular}{lrrrrrrrrr}
modified manuscript/tables/tab_country_concentration.tex +1 −1
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1−% generated by tables/make_tables.py from /Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/bibliometrics/countries_yearly.csv 1+% generated by tables/make_tables.py from ./results/bibliometrics/countries_yearly.csv
2 \begin{table}[htbp]\centering\caption{Concentration of output across countries by year, and the two largest producers under full counting.}\label{tab:country_concentration} 2 \begin{table}[htbp]\centering\caption{Concentration of output across countries by year, and the two largest producers under full counting.}\label{tab:country_concentration}
3 \begin{threeparttable}\small 3 \begin{threeparttable}\small
4 \begin{tabular}{lrrrrrrrrr} 4 \begin{tabular}{lrrrrrrrrr}
modified manuscript/tables/tab_country_network.tex +1 −1
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1−% generated by tables/make_tables.py from /Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/bibliometrics/country_pairs_by_period.csv 1+% generated by tables/make_tables.py from ./results/bibliometrics/country_pairs_by_period.csv
2 \begin{table}[htbp]\centering\caption{The country co-authorship network by period: edges with at least fifty joint works.}\label{tab:country_network} 2 \begin{table}[htbp]\centering\caption{The country co-authorship network by period: edges with at least fifty joint works.}\label{tab:country_network}
3 \begin{threeparttable}\small 3 \begin{threeparttable}\small
4 \begin{tabular}{lrrrrrr} 4 \begin{tabular}{lrrrrrr}
modified manuscript/tables/tab_country_pairs.tex +1 −1
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1−% generated by tables/make_tables.py from /Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/bibliometrics/country_pairs_by_period.csv 1+% generated by tables/make_tables.py from ./results/bibliometrics/country_pairs_by_period.csv
2 \begin{table}[htbp]\centering\caption{The twenty strongest country pairs by period (number of joint works).}\label{tab:country_pairs} 2 \begin{table}[htbp]\centering\caption{The twenty strongest country pairs by period (number of joint works).}\label{tab:country_pairs}
3 \begin{threeparttable}\small 3 \begin{threeparttable}\small
4 \begin{tabular}{lrrrr} 4 \begin{tabular}{lrrrr}
modified manuscript/tables/tab_country_shares.tex +1 −1
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1−% generated by tables/make_tables.py from /Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/bibliometrics/countries_yearly.csv 1+% generated by tables/make_tables.py from ./results/bibliometrics/countries_yearly.csv
2 \begin{table}[htbp]\centering\caption{Share of fractional output of the fifteen largest countries by year (per cent).}\label{tab:country_shares} 2 \begin{table}[htbp]\centering\caption{Share of fractional output of the fifteen largest countries by year (per cent).}\label{tab:country_shares}
3 \begin{threeparttable}\small 3 \begin{threeparttable}\small
4 \begin{tabular}{lrrrrrrrrr} 4 \begin{tabular}{lrrrrrrrrr}
modified manuscript/tables/tab_dblp_only.tex +1 −1
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1−% generated by tables/make_tables.py from /Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/bibliometrics/dblp_only_yearly.csv 1+% generated by tables/make_tables.py from ./results/bibliometrics/dblp_only_yearly.csv
2 \begin{table}[htbp]\centering\caption{DBLP records of 2018--2026 without an OpenAlex counterpart, by DBLP record type and year.}\label{tab:dblp_only} 2 \begin{table}[htbp]\centering\caption{DBLP records of 2018--2026 without an OpenAlex counterpart, by DBLP record type and year.}\label{tab:dblp_only}
3 \begin{threeparttable}\small 3 \begin{threeparttable}\small
4 \begin{tabular}{lrrrrrrrr} 4 \begin{tabular}{lrrrrrrrr}
modified manuscript/tables/tab_diversity.tex +8 −8
@@ -5,14 +5,14 @@
5 \toprule 5 \toprule
6 \textbf{year} & \textbf{n} & \textbf{rao\_stirling} & \textbf{rs\_ci\_low} & \textbf{rs\_ci\_high} & \textbf{leinster\_cobbold\_q2} \\ 6 \textbf{year} & \textbf{n} & \textbf{rao\_stirling} & \textbf{rs\_ci\_low} & \textbf{rs\_ci\_high} & \textbf{leinster\_cobbold\_q2} \\
7 \midrule 7 \midrule
8−2{,}018 & 499{,}561 & 0.897 & 0.896 & 0.897 & 9.680 \\ 8+2{,}018 & 169{,}727 & 0.899 & 0.899 & 0.900 & 9.929 \\
9−2{,}019 & 539{,}741 & 0.896 & 0.896 & 0.896 & 9.594 \\ 9+2{,}019 & 180{,}248 & 0.896 & 0.896 & 0.897 & 9.625 \\
10−2{,}020 & 549{,}101 & 0.896 & 0.896 & 0.897 & 9.654 \\ 10+2{,}020 & 189{,}370 & 0.901 & 0.900 & 0.901 & 10.073 \\
11−2{,}021 & 558{,}970 & 0.894 & 0.894 & 0.894 & 9.430 \\ 11+2{,}021 & 226{,}728 & 0.902 & 0.902 & 0.903 & 10.227 \\
12−2{,}022 & 560{,}433 & 0.891 & 0.891 & 0.892 & 9.214 \\ 12+2{,}022 & 254{,}663 & 0.890 & 0.890 & 0.891 & 9.117 \\
13−2{,}023 & 634{,}466 & 0.891 & 0.890 & 0.891 & 9.140 \\ 13+2{,}023 & 286{,}460 & 0.886 & 0.885 & 0.886 & 8.734 \\
14−2{,}024 & 739{,}619 & 0.890 & 0.890 & 0.890 & 9.095 \\ 14+2{,}024 & 363{,}470 & 0.888 & 0.888 & 0.889 & 8.959 \\
15−2{,}025 & 892{,}840 & 0.893 & 0.893 & 0.893 & 9.356 \\ 15+2{,}025 & 429{,}633 & 0.889 & 0.889 & 0.890 & 9.046 \\
16 \bottomrule 16 \bottomrule
17 \end{tabular} 17 \end{tabular}
18 \end{threeparttable}\end{table} 18 \end{threeparttable}\end{table}
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1−% generated by tables/make_tables.py from /Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/emerging/metrics_h12.json 1+% generated by tables/make_tables.py from ./results/emerging/metrics_h12.json
2 \begin{table}[htbp]\centering\caption{Out-of-time performance of the emerging-topic detectors at horizon 12 months (test cutoffs 2023-01–2024-10).}\label{tab:emerging_h12} 2 \begin{table}[htbp]\centering\caption{Out-of-time performance of the emerging-topic detectors at horizon 12 months (test cutoffs 2023-01–2024-10).}\label{tab:emerging_h12}
3 \begin{threeparttable}\small 3 \begin{threeparttable}\small
4 \begin{tabular}{lrrr} 4 \begin{tabular}{lrrr}
modified manuscript/tables/tab_emerging_h24.tex +1 −1
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1−% generated by tables/make_tables.py from /Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/emerging/metrics_h24.json 1+% generated by tables/make_tables.py from ./results/emerging/metrics_h24.json
2 \begin{table}[htbp]\centering\caption{Out-of-time performance of the emerging-topic detectors at horizon 24 months (test cutoffs 2023-01–2024-04).}\label{tab:emerging_h24} 2 \begin{table}[htbp]\centering\caption{Out-of-time performance of the emerging-topic detectors at horizon 24 months (test cutoffs 2023-01–2024-04).}\label{tab:emerging_h24}
3 \begin{threeparttable}\small 3 \begin{threeparttable}\small
4 \begin{tabular}{lrrr} 4 \begin{tabular}{lrrr}
modified manuscript/tables/tab_field_flows.tex +1 −1
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1−% generated by tables/make_tables.py from /Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/graph/field_flows_yearly.csv 1+% generated by tables/make_tables.py from ./results/graph/field_flows_yearly.csv
2 \begin{table}[htbp]\centering\caption{Destination of the resolved references of eligible works by citing year: inside the corpus, computer science outside it, and the seven largest other OpenAlex fields (per cent of resolved references).}\label{tab:field_flows} 2 \begin{table}[htbp]\centering\caption{Destination of the resolved references of eligible works by citing year: inside the corpus, computer science outside it, and the seven largest other OpenAlex fields (per cent of resolved references).}\label{tab:field_flows}
3 \begin{threeparttable}\small 3 \begin{threeparttable}\small
4 \begin{tabular}{lrrrrrrrrrr} 4 \begin{tabular}{lrrrrrrrrrr}
modified manuscript/tables/tab_indexing_lag.tex +1 −1
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1−% generated by tables/make_tables.py from /Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/bibliometrics/indexing_lag.csv 1+% generated by tables/make_tables.py from ./results/bibliometrics/indexing_lag.csv
2 \begin{table}[htbp]\centering\caption{The OpenAlex \texttt{created\_date} relative to the publication date, by publication year.}\label{tab:indexing_lag} 2 \begin{table}[htbp]\centering\caption{The OpenAlex \texttt{created\_date} relative to the publication date, by publication year.}\label{tab:indexing_lag}
3 \begin{threeparttable}\small 3 \begin{threeparttable}\small
4 \begin{tabular}{lrrrrr} 4 \begin{tabular}{lrrrrr}
modified manuscript/tables/tab_institution_concentration.tex +1 −1
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1−% generated by tables/make_tables.py from /Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/bibliometrics/top_institutions_by_period.csv 1+% generated by tables/make_tables.py from ./results/bibliometrics/top_institutions_by_period.csv
2 \begin{table}[htbp]\centering\caption{Institutions with at least two hundred works per period: coverage and concentration (fractional counting).}\label{tab:institution_concentration} 2 \begin{table}[htbp]\centering\caption{Institutions with at least two hundred works per period: coverage and concentration (fractional counting).}\label{tab:institution_concentration}
3 \begin{threeparttable}\small 3 \begin{threeparttable}\small
4 \begin{tabular}{lrrrrrr} 4 \begin{tabular}{lrrrrrr}
modified manuscript/tables/tab_institution_types.tex +1 −1
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1−% generated by tables/make_tables.py from /Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/bibliometrics/institution_types_yearly.csv 1+% generated by tables/make_tables.py from ./results/bibliometrics/institution_types_yearly.csv
2 \begin{table}[htbp]\centering\caption{Share of typed affiliations by OpenAlex institution type and year (per cent of affiliation-work pairs).}\label{tab:institution_types} 2 \begin{table}[htbp]\centering\caption{Share of typed affiliations by OpenAlex institution type and year (per cent of affiliation-work pairs).}\label{tab:institution_types}
3 \begin{threeparttable}\small 3 \begin{threeparttable}\small
4 \begin{tabular}{lrrrrrrrrr} 4 \begin{tabular}{lrrrrrrrrr}
modified manuscript/tables/tab_known_set.tex +1 −1
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1−% generated by tables/make_tables.py from /Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/emerging/known_set_lead_times.csv 1+% generated by tables/make_tables.py from ./results/emerging/known_set_lead_times.csv
2 \begin{table}[htbp]\centering\caption{Preregistered known-emergence set: detection month, peak-growth month and lead time.}\label{tab:known_set} 2 \begin{table}[htbp]\centering\caption{Preregistered known-emergence set: detection month, peak-growth month and lead time.}\label{tab:known_set}
3 \begin{threeparttable}\small 3 \begin{threeparttable}\small
4 \begin{tabular}{lrrrr} 4 \begin{tabular}{lrrrr}
added manuscript/tables/tab_net_ai_vs_rest.tex +15 −0
@@ -0,0 +1,15 @@
1+% generated by tables/make_tables.py from ./results/bibliometrics/category_actors_yearly.csv
2+\begin{table}[htbp]\centering\caption{Actor shares of works whose primary category is in the AI/ML area against all other areas, by period (per cent).}\label{tab:net_ai_vs_rest}
3+\begin{threeparttable}\small
4+\begin{tabular}{lrrrrrrrr}
5+\toprule
6+\textbf{Group} & \textbf{Period} & \textbf{Works} & \textbf{CN (\%)} & \textbf{US (\%)} & \textbf{Company (\%)} & \textbf{Intl. (\%)} & \textbf{arXiv (\%)} & \textbf{OA (\%)} \\
7+\midrule
8+AI/ML area & baseline & 262{,}642 & 21.62 & 19.05 & 7.16 & 17.79 & 23.46 & 54.51 \\
9+AI/ML area & post & 536{,}908 & 24.30 & 15.14 & 5.80 & 16.03 & 26.33 & 66.73 \\
10+Other areas & baseline & 2{,}405{,}712 & 19.02 & 13.81 & 5.60 & 17.01 & 9.42 & 46.67 \\
11+Other areas & post & 2{,}538{,}998 & 23.84 & 11.67 & 5.24 & 16.35 & 14.05 & 58.60 \\
12+\bottomrule
13+\end{tabular}
14+\begin{tablenotes}\footnotesize\item Works with a primary category, grouped by the area of that category; shares over all works of the group (works without affiliation data included), as in Table~\ref{tab:net_category_actors}. arXiv: works linked to an arXiv record; OA: open access as recorded by OpenAlex. Periods as in Table~\ref{tab:net_category_actors}.\end{tablenotes}
15+\end{threeparttable}\end{table}
added manuscript/tables/tab_net_ai_vs_rest_yearly.tex +20 −0
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1+% generated by tables/make_tables.py from ./results/bibliometrics/category_actors_yearly.csv
2+\begin{table}[htbp]\centering\caption{China-affiliated, company-affiliated and international shares of works by year, AI/ML area against all other areas (per cent).}\label{tab:net_ai_vs_rest_yearly}
3+\begin{threeparttable}\small
4+\begin{tabular}{lrrrrrr}
5+\toprule
6+\textbf{Year} & \textbf{CN, AI/ML (\%)} & \textbf{CN, other (\%)} & \textbf{Company, AI/ML (\%)} & \textbf{Company, other (\%)} & \textbf{Intl., AI/ML (\%)} & \textbf{Intl., other (\%)} \\
7+\midrule
8+2018 & 17.28 & 15.00 & 5.40 & 4.71 & 15.33 & 15.23 \\
9+2019 & 18.99 & 16.92 & 6.79 & 5.06 & 16.11 & 15.83 \\
10+2020 & 19.88 & 18.03 & 7.43 & 5.43 & 17.81 & 17.06 \\
11+2021 & 23.22 & 21.04 & 8.02 & 6.16 & 19.15 & 17.89 \\
12+2022 & 26.60 & 24.32 & 7.16 & 6.36 & 18.43 & 18.40 \\
13+2023 & 26.98 & 24.86 & 6.96 & 6.30 & 18.03 & 17.99 \\
14+2024 & 25.89 & 25.43 & 6.21 & 5.55 & 17.12 & 17.14 \\
15+2025 & 23.83 & 23.71 & 5.07 & 4.82 & 15.15 & 15.75 \\
16+2026 & 22.29 & 21.24 & 5.75 & 4.62 & 15.45 & 15.13 \\
17+\bottomrule
18+\end{tabular}
19+\begin{tablenotes}\footnotesize\item Shares over all works of the group and year, works without affiliation data included; the decline of all three series after 2023 is partly the lower affiliation coverage of the most recent cohorts (Section~\ref{sec:net-teams}). 2026 is incomplete.\end{tablenotes}
20+\end{threeparttable}\end{table}
added manuscript/tables/tab_net_area_flows.tex +27 −0
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1+% generated by tables/make_tables.py from ./results/graph/area_flows_yearly.csv
2+\begin{table}[htbp]\centering\caption{Citation-flow matrices between the eight areas of the ontology by period: share of each citing area's internal references received by each cited area (per cent).}\label{tab:net_area_flows}
3+\begin{threeparttable}\small
4+\begin{tabular}{lrrrrrrrrr}
5+\toprule
6+\textbf{Period} & \textbf{Citing area} & \textbf{AI/ML} & \textbf{Percept.} & \textbf{Softw./PL} & \textbf{Theory} & \textbf{Data/Distr.} & \textbf{Security} & \textbf{Hardware} & \textbf{Comp. sci.} \\
7+\midrule
8+baseline & AI/ML & 48.2 & 31.6 & 1.9 & 3.3 & 7.6 & 2.9 & 1.3 & 3.3 \\
9+baseline & Percept. & 7.3 & 84.3 & 1.0 & 0.8 & 3.3 & 1.8 & 0.5 & 1.1 \\
10+baseline & Softw./PL & 5.2 & 8.6 & 67.0 & 2.4 & 7.3 & 5.8 & 2.4 & 1.3 \\
11+baseline & Theory & 8.8 & 7.7 & 2.2 & 64.8 & 8.5 & 2.2 & 1.8 & 4.0 \\
12+baseline & Data/Distr. & 5.0 & 7.4 & 1.3 & 1.9 & 73.8 & 7.7 & 2.0 & 1.0 \\
13+baseline & Security & 4.6 & 9.1 & 2.2 & 1.1 & 16.5 & 63.7 & 2.2 & 0.6 \\
14+baseline & Hardware & 4.6 & 6.2 & 1.8 & 1.8 & 7.8 & 4.8 & 71.5 & 1.6 \\
15+baseline & Comp. sci. & 9.8 & 11.2 & 1.1 & 3.5 & 4.0 & 1.0 & 1.4 & 68.0 \\
16+post & AI/ML & 45.1 & 34.8 & 2.1 & 2.4 & 7.7 & 3.4 & 1.1 & 3.5 \\
17+post & Percept. & 7.6 & 84.5 & 0.8 & 0.8 & 2.9 & 1.8 & 0.5 & 1.1 \\
18+post & Softw./PL & 7.7 & 9.8 & 64.2 & 1.9 & 6.4 & 6.5 & 2.3 & 1.1 \\
19+post & Theory & 9.9 & 8.9 & 1.6 & 63.8 & 7.9 & 2.2 & 2.0 & 3.8 \\
20+post & Data/Distr. & 6.5 & 8.0 & 1.3 & 1.7 & 70.7 & 8.7 & 2.1 & 0.9 \\
21+post & Security & 6.4 & 10.0 & 2.2 & 1.0 & 16.6 & 61.1 & 2.1 & 0.6 \\
22+post & Hardware & 5.0 & 6.0 & 1.6 & 1.8 & 7.9 & 4.9 & 71.3 & 1.5 \\
23+post & Comp. sci. & 12.3 & 12.1 & 0.9 & 2.8 & 3.2 & 1.1 & 1.4 & 66.2 \\
24+\bottomrule
25+\end{tabular}
26+\begin{tablenotes}\footnotesize\item Internal references with a primary category at both ends, pooled over citing years 2018--2022 (baseline) and 2023--2026 (post, incomplete); the citing year 2022 contains one post-boundary month. Rows sum to 100. Area abbreviations: AI/ML = Artificial Intelligence and Machine Learning; Percept. = Perception, Language, Robotics and Interaction; Softw./PL = Software, Programming Languages and Formal Methods; Data/Distr. = Data, Information and Distributed Systems; Security = Security, Privacy and Trust; Hardware = Hardware, Architecture and Emerging Computing; Comp. sci. = Computational Science and Discovery.\end{tablenotes}
27+\end{threeparttable}\end{table}
added manuscript/tables/tab_net_category_actors.tex +54 −0
@@ -0,0 +1,54 @@
1+% generated by tables/make_tables.py from ./results/bibliometrics/category_actors_yearly.csv
2+\begin{longtable}{lrrrrrrrrrr}
3+\caption{Shares of works with a China-affiliated author, a United States-affiliated author, a company affiliation and authors in at least two countries, by primary category and period (per cent).}\label{tab:net_category_actors}\\
4+\toprule
5+\textbf{Category} & \textbf{Works, base} & \textbf{Works, post} & \textbf{CN, base} & \textbf{CN, post} & \textbf{US, base} & \textbf{US, post} & \textbf{Comp., base} & \textbf{Comp., post} & \textbf{Intl., base} & \textbf{Intl., post} \\
6+\midrule
7+\endfirsthead
8+\toprule
9+\textbf{Category} & \textbf{Works, base} & \textbf{Works, post} & \textbf{CN, base} & \textbf{CN, post} & \textbf{US, base} & \textbf{US, post} & \textbf{Comp., base} & \textbf{Comp., post} & \textbf{Intl., base} & \textbf{Intl., post} \\
10+\midrule
11+\endhead
12+Large Language Models & 11{,}813 & 132{,}127 & 17.8 & 18.3 & 13.1 & 16.9 & 9.1 & 7.7 & 11.6 & 13.9 \\
13+Foundation Models & 838 & 6{,}186 & 34.7 & 31.5 & 22.3 & 19.2 & 9.7 & 8.6 & 18.5 & 20.4 \\
14+Multimodal AI & 4{,}007 & 20{,}970 & 44.7 & 47.2 & 17.5 & 12.8 & 12.8 & 8.6 & 20.9 & 19.2 \\
15+Generative AI & 7{,}503 & 38{,}194 & 25.8 & 22.1 & 18.2 & 12.1 & 7.8 & 3.5 & 16.1 & 13.2 \\
16+AI Agents and Autonomous Systems & 25{,}167 & 32{,}219 & 18.6 & 23.4 & 12.2 & 13.1 & 3.0 & 4.8 & 17.7 & 15.8 \\
17+Reinforcement Learning & 26{,}913 & 44{,}477 & 21.9 & 33.8 & 24.4 & 15.9 & 10.1 & 6.3 & 16.9 & 16.3 \\
18+Machine Learning Theory & 88{,}923 & 112{,}830 & 16.3 & 18.8 & 22.4 & 16.2 & 7.5 & 4.8 & 18.0 & 16.9 \\
19+Deep Learning Architectures & 87{,}481 & 116{,}113 & 28.7 & 34.7 & 15.9 & 12.3 & 6.6 & 5.4 & 18.9 & 18.0 \\
20+Computer Vision & 421{,}385 & 521{,}935 & 31.2 & 38.3 & 11.6 & 8.7 & 6.6 & 5.7 & 16.6 & 15.6 \\
21+Natural Language Processing & 134{,}844 & 152{,}754 & 15.5 & 17.9 & 14.6 & 11.9 & 7.9 & 5.6 & 14.1 & 14.3 \\
22+Speech and Audio Computing & 62{,}133 & 65{,}819 & 16.3 & 21.7 & 10.7 & 9.1 & 8.0 & 7.1 & 12.5 & 13.0 \\
23+Robotics & 101{,}643 & 118{,}241 & 22.1 & 31.9 & 18.8 & 15.2 & 5.7 & 5.2 & 17.7 & 16.8 \\
24+Human-Computer Interaction & 174{,}138 & 196{,}794 & 7.1 & 9.8 & 14.5 & 12.6 & 3.6 & 2.7 & 12.9 & 13.2 \\
25+Software Engineering & 132{,}205 & 129{,}300 & 6.9 & 10.1 & 8.7 & 9.4 & 4.2 & 4.6 & 11.6 & 11.6 \\
26+Programming Languages & 20{,}392 & 15{,}837 & 5.6 & 7.7 & 15.0 & 13.5 & 3.3 & 2.9 & 15.3 & 15.2 \\
27+Compilers & 3{,}450 & 2{,}900 & 9.2 & 13.4 & 19.7 & 16.2 & 6.8 & 6.9 & 14.4 & 14.9 \\
28+Formal Verification & 22{,}992 & 18{,}225 & 10.2 & 11.8 & 14.0 & 13.1 & 3.9 & 3.6 & 24.7 & 25.2 \\
29+Algorithms and Data Structures & 144{,}105 & 121{,}771 & 19.2 & 24.6 & 15.6 & 13.9 & 3.1 & 3.1 & 23.2 & 22.2 \\
30+Computational Complexity & 18{,}778 & 15{,}760 & 10.9 & 13.2 & 14.3 & 11.6 & 1.8 & 1.8 & 19.1 & 18.9 \\
31+Database Systems & 74{,}552 & 61{,}582 & 13.1 & 14.6 & 10.7 & 9.2 & 4.4 & 5.1 & 13.4 & 11.8 \\
32+Information Retrieval & 58{,}198 & 61{,}738 & 22.3 & 29.0 & 13.3 & 10.7 & 8.1 & 9.0 & 16.1 & 14.8 \\
33+Distributed Computing & 52{,}661 & 43{,}642 & 19.5 & 25.3 & 18.9 & 16.1 & 5.7 & 6.7 & 17.9 & 17.0 \\
34+Cloud Computing & 40{,}415 & 36{,}757 & 15.0 & 13.9 & 8.8 & 9.6 & 4.9 & 6.8 & 13.2 & 11.0 \\
35+Edge Computing & 34{,}568 & 40{,}445 & 17.5 & 21.2 & 10.6 & 8.2 & 5.0 & 4.3 & 20.6 & 19.7 \\
36+Computer Networks & 267{,}896 & 212{,}342 & 24.4 & 29.8 & 12.0 & 9.9 & 7.0 & 7.4 & 21.7 & 22.2 \\
37+Cybersecurity & 154{,}197 & 195{,}498 & 12.3 & 14.0 & 12.9 & 11.1 & 3.9 & 3.7 & 12.9 & 12.9 \\
38+Applied Cryptography & 53{,}877 & 51{,}175 & 18.1 & 22.5 & 8.9 & 8.4 & 4.2 & 4.9 & 15.6 & 16.0 \\
39+Blockchain Systems & 46{,}516 & 62{,}087 & 12.2 & 12.5 & 8.4 & 7.2 & 2.9 & 2.4 & 12.9 & 13.5 \\
40+Computer Architecture & 86{,}471 & 70{,}512 & 13.8 & 22.2 & 20.0 & 17.8 & 9.9 & 9.8 & 15.8 & 15.1 \\
41+Accelerators and AI Hardware & 5{,}520 & 6{,}917 & 25.4 & 33.6 & 24.5 & 18.2 & 7.4 & 7.0 & 23.5 & 19.9 \\
42+Operating Systems & 6{,}877 & 5{,}158 & 15.9 & 22.2 & 15.3 & 13.7 & 7.9 & 9.0 & 13.4 & 13.4 \\
43+Quantum Computing & 39{,}846 & 60{,}417 & 19.3 & 16.0 & 18.2 & 19.8 & 6.9 & 8.4 & 21.3 & 20.1 \\
44+Scientific Computing & 93{,}944 & 84{,}877 & 24.8 & 27.9 & 17.4 & 16.2 & 3.4 & 3.4 & 24.0 & 22.9 \\
45+Computational Biology & 82{,}846 & 97{,}856 & 19.1 & 23.4 & 24.2 & 18.2 & 5.3 & 4.3 & 22.9 & 21.7 \\
46+Computer Graphics & 46{,}021 & 43{,}270 & 14.2 & 20.6 & 14.0 & 11.9 & 5.1 & 5.4 & 13.4 & 12.6 \\
47+Privacy-Preserving Computation & 18{,}816 & 28{,}450 & 19.1 & 25.0 & 21.4 & 16.2 & 6.3 & 5.2 & 20.0 & 19.7 \\
48+Federated Learning & 2{,}229 & 9{,}011 & 26.6 & 39.1 & 23.4 & 13.0 & 9.6 & 5.2 & 23.5 & 23.5 \\
49+AI Safety and Alignment & 9{,}997 & 33{,}792 & 4.3 & 5.7 & 27.4 & 19.6 & 5.9 & 3.7 & 15.6 & 15.1 \\
50+Sustainable Computing & 3{,}356 & 5{,}389 & 20.2 & 20.0 & 17.1 & 12.7 & 5.4 & 6.2 & 23.7 & 18.3 \\
51+Autonomous Scientific Discovery & 841 & 2{,}539 & 8.1 & 14.6 & 33.4 & 23.1 & 8.7 & 6.7 & 18.5 & 18.2 \\
52+\bottomrule
53+\multicolumn{11}{p{0.95\linewidth}}{\footnotesize Baseline: works dated 2018-01-01 to 2022-11-29; post: 2022-11-30 to 2026-11-02 (incomplete). Shares are over all works of the category, including works without affiliation data, so they are depressed where affiliation coverage is low (recent cohorts, preprint-heavy categories) and are not comparable with the typed-denominator shares of Tables~\ref{tab:industry} and~\ref{tab:country_concentration}; comparisons across periods within a category carry this caveat. Comp. = at least one company-typed affiliation; Intl. = at least two author countries.}\\
54+\end{longtable}
added manuscript/tables/tab_net_category_gini.tex +54 −0
@@ -0,0 +1,54 @@
1+% generated by tables/make_tables.py from ./results/bibliometrics/category_impact_yearly.csv
2+\begin{longtable}{lrrrrrrrrr}
3+\caption{Gini coefficient of 36-month citations $c^{(36)}$ by primary category and publication year, for years with a complete window; baseline mean, change of the last complete year against it, and share uncited at 36 months in that year.}\label{tab:net_category_gini}\\
4+\toprule
5+\textbf{Category} & \textbf{2018} & \textbf{2019} & \textbf{2020} & \textbf{2021} & \textbf{2022} & \textbf{2023} & \textbf{Base mean} & \textbf{2023 $-$ base} & \textbf{Uncited 2023 (\%)} \\
6+\midrule
7+\endfirsthead
8+\toprule
9+\textbf{Category} & \textbf{2018} & \textbf{2019} & \textbf{2020} & \textbf{2021} & \textbf{2022} & \textbf{2023} & \textbf{Base mean} & \textbf{2023 $-$ base} & \textbf{Uncited 2023 (\%)} \\
10+\midrule
11+\endhead
12+Large Language Models & 0.831 & 0.875 & 0.854 & 0.845 & 0.864 & 0.875 & 0.854 & +0.021 & 30.6 \\
13+Foundation Models & & & & 0.722 & 0.698 & 0.792 & 0.710 & +0.083 & 27.9 \\
14+Multimodal AI & 0.754 & 0.761 & 0.726 & 0.690 & 0.712 & 0.756 & 0.729 & +0.027 & 23.7 \\
15+Generative AI & 0.810 & 0.758 & 0.781 & 0.765 & 0.785 & 0.800 & 0.780 & +0.020 & 30.9 \\
16+AI Agents and Autonomous Systems & 0.795 & 0.784 & 0.783 & 0.771 & 0.775 & 0.788 & 0.782 & +0.006 & 41.4 \\
17+Reinforcement Learning & 0.807 & 0.762 & 0.759 & 0.756 & 0.741 & 0.762 & 0.765 & -0.003 & 33.0 \\
18+Machine Learning Theory & 0.788 & 0.782 & 0.779 & 0.775 & 0.770 & 0.793 & 0.779 & +0.014 & 36.1 \\
19+Deep Learning Architectures & 0.803 & 0.765 & 0.753 & 0.749 & 0.727 & 0.751 & 0.760 & -0.008 & 29.2 \\
20+Computer Vision & 0.825 & 0.807 & 0.787 & 0.773 & 0.760 & 0.772 & 0.790 & -0.018 & 28.2 \\
21+Natural Language Processing & 0.832 & 0.835 & 0.800 & 0.792 & 0.765 & 0.783 & 0.805 & -0.022 & 35.0 \\
22+Speech and Audio Computing & 0.814 & 0.803 & 0.778 & 0.755 & 0.734 & 0.748 & 0.777 & -0.029 & 37.3 \\
23+Robotics & 0.716 & 0.706 & 0.688 & 0.688 & 0.688 & 0.712 & 0.697 & +0.015 & 24.0 \\
24+Human-Computer Interaction & 0.803 & 0.796 & 0.784 & 0.763 & 0.767 & 0.777 & 0.782 & -0.005 & 39.5 \\
25+Software Engineering & 0.839 & 0.837 & 0.821 & 0.797 & 0.796 & 0.823 & 0.818 & +0.006 & 51.9 \\
26+Programming Languages & 0.806 & 0.792 & 0.789 & 0.790 & 0.781 & 0.796 & 0.792 & +0.004 & 53.6 \\
27+Compilers & 0.822 & 0.796 & 0.773 & 0.788 & 0.764 & 0.770 & 0.789 & -0.018 & 45.1 \\
28+Formal Verification & 0.746 & 0.713 & 0.708 & 0.728 & 0.703 & 0.957 & 0.720 & +0.237 & 39.1 \\
29+Algorithms and Data Structures & 0.753 & 0.753 & 0.758 & 0.767 & 0.759 & 0.778 & 0.758 & +0.020 & 37.6 \\
30+Computational Complexity & 0.783 & 0.812 & 0.783 & 0.784 & 0.772 & 0.799 & 0.787 & +0.012 & 46.7 \\
31+Database Systems & 0.823 & 0.812 & 0.796 & 0.785 & 0.778 & 0.811 & 0.799 & +0.012 & 48.9 \\
32+Information Retrieval & 0.811 & 0.796 & 0.781 & 0.768 & 0.756 & 0.771 & 0.782 & -0.011 & 37.3 \\
33+Distributed Computing & 0.793 & 0.774 & 0.752 & 0.742 & 0.731 & 0.803 & 0.758 & +0.045 & 35.4 \\
34+Cloud Computing & 0.813 & 0.812 & 0.800 & 0.792 & 0.779 & 0.789 & 0.799 & -0.010 & 44.6 \\
35+Edge Computing & 0.773 & 0.774 & 0.764 & 0.759 & 0.750 & 0.762 & 0.764 & -0.002 & 29.4 \\
36+Computer Networks & 0.766 & 0.759 & 0.752 & 0.731 & 0.719 & 0.727 & 0.746 & -0.018 & 26.9 \\
37+Cybersecurity & 0.834 & 0.812 & 0.794 & 0.772 & 0.760 & 0.777 & 0.795 & -0.018 & 38.8 \\
38+Applied Cryptography & 0.806 & 0.779 & 0.780 & 0.755 & 0.744 & 0.749 & 0.773 & -0.024 & 35.9 \\
39+Blockchain Systems & 0.869 & 0.862 & 0.817 & 0.794 & 0.807 & 0.785 & 0.830 & -0.045 & 39.3 \\
40+Computer Architecture & 0.753 & 0.750 & 0.766 & 0.720 & 0.690 & 0.703 & 0.736 & -0.033 & 32.1 \\
41+Accelerators and AI Hardware & 0.757 & 0.736 & 0.712 & 0.727 & 0.727 & 0.733 & 0.732 & +0.001 & 23.9 \\
42+Operating Systems & 0.835 & 0.815 & 0.800 & 0.750 & 0.745 & 0.772 & 0.789 & -0.017 & 46.4 \\
43+Quantum Computing & 0.776 & 0.790 & 0.763 & 0.770 & 0.743 & 0.744 & 0.768 & -0.024 & 31.2 \\
44+Scientific Computing & 0.704 & 0.708 & 0.729 & 0.727 & 0.681 & 0.696 & 0.710 & -0.014 & 28.4 \\
45+Computational Biology & 0.782 & 0.762 & 0.757 & 0.730 & 0.735 & 0.731 & 0.753 & -0.023 & 23.2 \\
46+Computer Graphics & 0.850 & 0.843 & 0.817 & 0.828 & 0.820 & 0.830 & 0.832 & -0.002 & 49.5 \\
47+Privacy-Preserving Computation & 0.767 & 0.775 & 0.782 & 0.757 & 0.733 & 0.748 & 0.763 & -0.015 & 29.6 \\
48+Federated Learning & & 0.752 & 0.806 & 0.756 & 0.737 & 0.719 & 0.763 & -0.044 & 24.4 \\
49+AI Safety and Alignment & 0.844 & 0.823 & 0.798 & 0.771 & 0.737 & 0.799 & 0.795 & +0.004 & 29.6 \\
50+Sustainable Computing & 0.719 & 0.725 & 0.713 & 0.715 & 0.710 & 0.724 & 0.716 & +0.007 & 27.0 \\
51+Autonomous Scientific Discovery & & & & 0.756 & 0.749 & 0.862 & 0.753 & +0.109 & 23.3 \\
52+\bottomrule
53+\multicolumn{10}{p{0.95\linewidth}}{\footnotesize Gini of $c^{(36)}$ over the works whose primary category is the row category (Section~\ref{sec:m-citations}); a blank cell is a category with no works in the year. Base mean: mean over 2018--2022. The 2023 window ends in 2025, whose slower indexing channels were incomplete at the snapshot, so the 2023 values carry the right-truncation caveat of Table~\ref{tab:citation_windows}. Very small categories and categories whose 2023 value is far from every earlier value may reflect single misattributed records (Section~\ref{sec:net-topcited}).}\\
54+\end{longtable}
added manuscript/tables/tab_net_category_to_ai.tex +54 −0
@@ -0,0 +1,54 @@
1+% generated by tables/make_tables.py from ./results/graph/category_flows_yearly.csv
2+\begin{longtable}{lrrrrrrrrrr}
3+\caption{Share of each category's internal references received by the nine categories of the AI/ML area, by citing year (per cent).}\label{tab:net_category_to_ai}\\
4+\toprule
5+\textbf{Category} & \textbf{Area} & \textbf{2018} & \textbf{2019} & \textbf{2020} & \textbf{2021} & \textbf{2022} & \textbf{2023} & \textbf{2024} & \textbf{2025} & \textbf{2026} \\
6+\midrule
7+\endfirsthead
8+\toprule
9+\textbf{Category} & \textbf{Area} & \textbf{2018} & \textbf{2019} & \textbf{2020} & \textbf{2021} & \textbf{2022} & \textbf{2023} & \textbf{2024} & \textbf{2025} & \textbf{2026} \\
10+\midrule
11+\endhead
12+Large Language Models & AI/ML & 16.8 & 12.6 & 11.5 & 13.9 & 14.3 & 29.0 & 38.0 & 39.2 & 41.7 \\
13+Foundation Models & AI/ML & 50.0 & 38.5 & 32.3 & 35.5 & 34.1 & 32.9 & 31.5 & 30.1 & 30.8 \\
14+Multimodal AI & AI/ML & 24.1 & 21.6 & 20.9 & 20.8 & 21.3 & 22.0 & 23.6 & 24.4 & 26.6 \\
15+Generative AI & AI/ML & 39.4 & 30.2 & 30.7 & 29.1 & 22.8 & 26.7 & 33.0 & 38.6 & 45.3 \\
16+AI Agents and Autonomous Systems & AI/ML & 59.1 & 57.3 & 54.8 & 54.5 & 52.9 & 52.3 & 51.9 & 49.2 & 50.3 \\
17+Reinforcement Learning & AI/ML & 69.1 & 65.2 & 64.7 & 63.0 & 56.0 & 55.5 & 53.1 & 49.4 & 47.6 \\
18+Machine Learning Theory & AI/ML & 59.0 & 59.4 & 58.3 & 57.8 & 53.1 & 53.5 & 52.7 & 53.3 & 54.8 \\
19+Deep Learning Architectures & AI/ML & 48.1 & 48.0 & 48.3 & 47.3 & 44.9 & 45.4 & 46.3 & 46.0 & 48.1 \\
20+Computer Vision & Percept. & 10.9 & 7.7 & 7.5 & 7.6 & 6.2 & 6.5 & 6.5 & 6.5 & 6.8 \\
21+Natural Language Processing & Percept. & 13.8 & 9.0 & 8.9 & 9.7 & 10.1 & 12.7 & 14.0 & 16.9 & 19.3 \\
22+Speech and Audio Computing & Percept. & 9.1 & 6.3 & 5.6 & 5.7 & 5.0 & 5.3 & 6.0 & 5.9 & 6.8 \\
23+Robotics & Percept. & 11.4 & 9.3 & 9.3 & 9.5 & 8.2 & 8.5 & 8.9 & 9.2 & 10.3 \\
24+Human-Computer Interaction & Percept. & 4.2 & 3.6 & 4.0 & 4.6 & 4.2 & 4.9 & 6.3 & 8.0 & 9.5 \\
25+Software Engineering & Softw./PL & 4.1 & 3.5 & 3.9 & 4.8 & 5.0 & 6.2 & 7.5 & 8.3 & 9.4 \\
26+Programming Languages & Softw./PL & 3.6 & 3.5 & 5.7 & 5.4 & 5.9 & 6.1 & 5.1 & 7.3 & 7.9 \\
27+Compilers & Softw./PL & 5.9 & 6.0 & 8.4 & 6.7 & 7.0 & 7.6 & 10.9 & 9.3 & 9.5 \\
28+Formal Verification & Softw./PL & 3.8 & 6.3 & 7.5 & 8.6 & 7.8 & 8.2 & 7.3 & 7.8 & 7.5 \\
29+Algorithms and Data Structures & Theory & 7.3 & 8.0 & 8.9 & 9.6 & 8.6 & 8.9 & 9.3 & 9.8 & 11.1 \\
30+Computational Complexity & Theory & 4.9 & 6.2 & 7.1 & 7.7 & 9.8 & 11.2 & 12.4 & 15.1 & 16.6 \\
31+Database Systems & Data/Distr. & 10.4 & 10.8 & 11.1 & 12.3 & 13.2 & 14.2 & 15.1 & 15.9 & 16.9 \\
32+Information Retrieval & Data/Distr. & 10.5 & 9.8 & 9.9 & 10.3 & 10.2 & 11.1 & 12.0 & 13.6 & 16.2 \\
33+Distributed Computing & Data/Distr. & 7.1 & 9.3 & 9.8 & 10.0 & 10.6 & 10.9 & 10.7 & 11.9 & 12.8 \\
34+Cloud Computing & Data/Distr. & 2.3 & 2.1 & 2.6 & 3.4 & 3.4 & 3.7 & 4.5 & 5.1 & 6.3 \\
35+Edge Computing & Data/Distr. & 2.8 & 3.2 & 4.1 & 4.7 & 5.0 & 5.7 & 6.7 & 6.9 & 7.1 \\
36+Computer Networks & Data/Distr. & 2.3 & 2.2 & 2.6 & 3.0 & 3.1 & 3.4 & 3.6 & 3.8 & 4.2 \\
37+Cybersecurity & Security & 5.3 & 4.4 & 4.9 & 5.5 & 5.1 & 5.3 & 6.2 & 6.4 & 6.9 \\
38+Applied Cryptography & Security & 1.0 & 1.2 & 1.3 & 1.6 & 1.6 & 1.7 & 1.9 & 2.1 & 2.3 \\
39+Blockchain Systems & Security & 1.2 & 1.1 & 1.3 & 1.3 & 1.6 & 2.0 & 2.3 & 2.9 & 3.5 \\
40+Computer Architecture & Hardware & 3.2 & 3.3 & 3.8 & 4.4 & 4.4 & 4.6 & 4.8 & 4.4 & 4.9 \\
41+Accelerators and AI Hardware & Hardware & 28.9 & 26.8 & 27.1 & 28.3 & 28.9 & 25.5 & 26.0 & 24.6 & 23.0 \\
42+Operating Systems & Hardware & 4.9 & 3.9 & 4.7 & 4.7 & 5.9 & 7.4 & 7.4 & 5.4 & 9.5 \\
43+Quantum Computing & Hardware & 2.1 & 1.6 & 1.9 & 2.0 & 1.7 & 2.2 & 2.1 & 2.4 & 2.4 \\
44+Scientific Computing & Comp. sci. & 8.1 & 9.3 & 11.4 & 12.5 & 13.0 & 13.8 & 14.6 & 15.3 & 15.5 \\
45+Computational Biology & Comp. sci. & 10.2 & 8.3 & 7.9 & 8.4 & 8.5 & 9.6 & 10.5 & 11.6 & 11.2 \\
46+Computer Graphics & Percept. & 5.9 & 4.9 & 5.5 & 5.1 & 4.6 & 4.7 & 4.7 & 5.0 & 4.8 \\
47+Privacy-Preserving Computation & Security & 5.0 & 8.1 & 11.5 & 13.4 & 12.1 & 14.9 & 15.7 & 16.5 & 16.5 \\
48+Federated Learning & Security & 23.1 & 25.4 & 38.1 & 34.3 & 25.0 & 24.3 & 24.0 & 21.3 & 19.6 \\
49+AI Safety and Alignment & AI/ML & 49.0 & 48.0 & 51.1 & 53.1 & 50.2 & 50.4 & 51.6 & 52.7 & 55.4 \\
50+Sustainable Computing & Hardware & 3.9 & 4.6 & 7.1 & 10.5 & 9.3 & 11.4 & 14.7 & 15.2 & 15.4 \\
51+Autonomous Scientific Discovery & Comp. sci. & 31.0 & 25.2 & 29.6 & 30.9 & 25.1 & 33.5 & 33.3 & 33.1 & 35.0 \\
52+\bottomrule
53+\multicolumn{11}{p{0.95\linewidth}}{\footnotesize Rows: citing primary category (ontology order); the nine AI/ML rows measure within-area citation, the other thirty-one rows measure citation into the AI/ML area. Denominator: the category's internal references with a classified cited work in the citing year. 2026 is incomplete.}\\
54+\end{longtable}
added manuscript/tables/tab_net_gini_ai_vs_rest.tex +17 −0
@@ -0,0 +1,17 @@
1+% generated by tables/make_tables.py from ./results/bibliometrics/category_impact_yearly.csv
2+\begin{table}[htbp]\centering\caption{Citation inequality and levels at 36 months, AI/ML area against all other areas, by publication year with a complete window.}\label{tab:net_gini_ai_vs_rest}
3+\begin{threeparttable}\small
4+\begin{tabular}{lrrrrrrrrrr}
5+\toprule
6+\textbf{Year} & \textbf{Works, AI/ML} & \textbf{Works, other} & \textbf{Gini, AI/ML} & \textbf{Gini, other} & \textbf{Mean c36, AI/ML} & \textbf{Mean c36, other} & \textbf{Uncited, AI/ML (\%)} & \textbf{Uncited, other (\%)} & \textbf{Top-10\\% FWCI, AI/ML (\%)} & \textbf{Top-10\\% FWCI, other (\%)} \\
7+\midrule
8+2018 & 38{,}148 & 460{,}583 & 0.799 & 0.796 & 7.26 & 4.99 & 37.7 & 43.4 & 15.8 & 15.0 \\
9+2019 & 47{,}860 & 491{,}076 & 0.779 & 0.787 & 7.81 & 5.68 & 34.5 & 40.6 & 15.6 & 15.0 \\
10+2020 & 54{,}806 & 492{,}618 & 0.773 & 0.775 & 8.21 & 6.38 & 32.0 & 37.3 & 15.3 & 14.6 \\
11+2021 & 60{,}987 & 495{,}830 & 0.765 & 0.760 & 7.64 & 6.56 & 31.5 & 33.8 & 15.3 & 14.9 \\
12+2022 & 63{,}609 & 493{,}388 & 0.755 & 0.749 & 7.83 & 6.71 & 29.4 & 31.4 & 15.9 & 15.3 \\
13+2023 & 82{,}170 & 548{,}963 & 0.787 & 0.765 & 8.98 & 6.64 & 32.4 & 34.0 & 15.8 & 14.8 \\
14+\bottomrule
15+\end{tabular}
16+\begin{tablenotes}\footnotesize\item Group statistics are work-weighted means of the category statistics of Table~\ref{tab:net_category_gini} (the Gini is therefore a weighted mean of within-category Ginis, not the Gini of the pooled group). Top-10\% FWCI: share of works in the top decile of OpenAlex field-weighted citation impact. The last year carries the right-truncation caveat.\end{tablenotes}
17+\end{threeparttable}\end{table}
added manuscript/tables/tab_net_modularity.tex +20 −0
@@ -0,0 +1,20 @@
1+% generated by tables/make_tables.py from ./results/graph/modularity_yearly.csv, ./results/graph/cross_area_share_yearly.csv
2+\begin{table}[htbp]\centering\caption{Modularity of the yearly category citation-flow network at the category and area partitions, the within-category self-share, and the aggregate cross-area share of internal references, raw and composition-adjusted.}\label{tab:net_modularity}
3+\begin{threeparttable}\small
4+\begin{tabular}{lrrrrrr}
5+\toprule
6+\textbf{Year} & \textbf{Internal citations} & \textbf{Q (categories)} & \textbf{Q (areas)} & \textbf{Self-share (\%)} & \textbf{Cross-area, raw (\%)} & \textbf{Cross-area, adjusted (\%)} \\
7+\midrule
8+2018 & 213{,}358 & 0.5428 & 0.5083 & 62.09 & 27.82 & 27.44 \\
9+2019 & 975{,}445 & 0.5510 & 0.5007 & 64.15 & 25.91 & 25.86 \\
10+2020 & 2{,}131{,}658 & 0.5450 & 0.4914 & 63.74 & 26.34 & 26.15 \\
11+2021 & 3{,}556{,}446 & 0.5372 & 0.4789 & 63.36 & 26.85 & 26.76 \\
12+2022 & 4{,}288{,}984 & 0.5356 & 0.4750 & 63.58 & 26.79 & 27.07 \\
13+2023 & 5{,}706{,}569 & 0.5263 & 0.4605 & 62.97 & 27.36 & 27.57 \\
14+2024 & 6{,}799{,}414 & 0.5123 & 0.4595 & 61.83 & 28.23 & 27.84 \\
15+2025 & 7{,}477{,}998 & 0.5059 & 0.4593 & 60.19 & 29.51 & 28.33 \\
16+2026 & 4{,}558{,}848 & 0.5004 & 0.4615 & 58.93 & 30.52 & 28.63 \\
17+\bottomrule
18+\end{tabular}
19+\begin{tablenotes}\footnotesize\item Equation~\eqref{eq:modularity} at the fixed partitions (Section~\ref{sec:m-networks}); the self-share is $\operatorname{tr}(F_t)/m$. Cross-area, raw: share of all internal references of the citing year that leave the citing area (equation~\eqref{eq:cross-area} aggregated over areas); adjusted: the same with the citing areas weighted by their baseline (2018--2022) shares of internal references, which removes the effect of the changing mix of citing areas. Period means of $Q$ at the category and area partitions: baseline 0.5423 and 0.4908; post 0.5112 and 0.4602. The 2018 network is small because few cited works of 2018 or later existed; 2026 is incomplete.\end{tablenotes}
20+\end{threeparttable}\end{table}
modified manuscript/tables/tab_reference_coverage.tex +1 −1
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1−% generated by tables/make_tables.py from /Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/graph/reference_coverage_yearly.csv 1+% generated by tables/make_tables.py from ./results/graph/reference_coverage_yearly.csv
2 \begin{table}[htbp]\centering\caption{Reference coverage by citing year: declared, resolved, internal and external references of eligible works.}\label{tab:reference_coverage} 2 \begin{table}[htbp]\centering\caption{Reference coverage by citing year: declared, resolved, internal and external references of eligible works.}\label{tab:reference_coverage}
3 \begin{threeparttable}\small 3 \begin{threeparttable}\small
4 \begin{tabular}{lrrrrrrr} 4 \begin{tabular}{lrrrrrrr}
modified manuscript/tables/tab_security_counts.tex +1 −1
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1−% generated by tables/make_tables.py from /Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/graph/category_counts_yearly.csv 1+% generated by tables/make_tables.py from ./results/graph/category_counts_yearly.csv
2 \begin{longtable}{lrrrrrrrrrr} 2 \begin{longtable}{lrrrrrrrrrr}
3 \caption{Eligible works in the five security-and-privacy categories by publication year: works carrying the category (all) and works whose primary category it is (prim.).}\label{tab:security_counts}\\ 3 \caption{Eligible works in the five security-and-privacy categories by publication year: works carrying the category (all) and works whose primary category it is (prim.).}\label{tab:security_counts}\\
4 \toprule 4 \toprule
modified manuscript/tables/tab_security_landmarks.tex +1 −1
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1−% generated by tables/make_tables.py from /Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/literature_synthesis/landmarks/security_privacy_landmarks.csv 1+% generated by tables/make_tables.py from ./literature_synthesis/landmarks/security_privacy_landmarks.csv
2 \begin{table}[htbp]\centering\caption{Composition of the automatically selected landmark set of the security-and-privacy area, by category.}\label{tab:security_landmarks} 2 \begin{table}[htbp]\centering\caption{Composition of the automatically selected landmark set of the security-and-privacy area, by category.}\label{tab:security_landmarks}
3 \begin{threeparttable}\small 3 \begin{threeparttable}\small
4 \begin{tabular}{lrrrrrrrrr} 4 \begin{tabular}{lrrrrrrrrr}
modified manuscript/tables/tab_security_llm_cooccurrence.tex +1 −1
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1−% generated by tables/make_tables.py from /Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/graph/category_cooccurrence_yearly.csv, /Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/graph/category_counts_yearly.csv 1+% generated by tables/make_tables.py from ./results/graph/category_cooccurrence_yearly.csv, ./results/graph/category_counts_yearly.csv
2 \begin{longtable}{lrrrrrrrrrrrrrr} 2 \begin{longtable}{lrrrrrrrrrrrrrr}
3 \caption{Co-classification of security-and-privacy works with the LLM, AI-safety and AI-agents categories, by publication year: number of works carrying both categories and share of the security category's works.}\label{tab:security_llm_cooccurrence}\\ 3 \caption{Co-classification of security-and-privacy works with the LLM, AI-safety and AI-agents categories, by publication year: number of works carrying both categories and share of the security category's works.}\label{tab:security_llm_cooccurrence}\\
4 \toprule 4 \toprule
modified manuscript/tables/tab_security_summary.tex +1 −1
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1−% generated by tables/make_tables.py from /Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/graph/category_counts_yearly.csv, /Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/econometrics/breaks.csv 1+% generated by tables/make_tables.py from ./results/graph/category_counts_yearly.csv, ./results/econometrics/breaks.csv
2 \begin{table}[htbp]\centering\caption{Growth and structural-change summary for the five security-and-privacy categories (multi-label counts).}\label{tab:security_summary} 2 \begin{table}[htbp]\centering\caption{Growth and structural-change summary for the five security-and-privacy categories (multi-label counts).}\label{tab:security_summary}
3 \begin{threeparttable}\small 3 \begin{threeparttable}\small
4 \begin{tabular}{lrrrrrrrrrrr} 4 \begin{tabular}{lrrrrrrrrrrr}
modified manuscript/tables/tab_source_types.tex +1 −1
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1−% generated by tables/make_tables.py from /Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/bibliometrics/top_sources_by_period.csv 1+% generated by tables/make_tables.py from ./results/bibliometrics/top_sources_by_period.csv
2 \begin{table}[htbp]\centering\caption{Distribution of works over source types among the listed sources, by period (per cent).}\label{tab:source_types} 2 \begin{table}[htbp]\centering\caption{Distribution of works over source types among the listed sources, by period (per cent).}\label{tab:source_types}
3 \begin{threeparttable}\small 3 \begin{threeparttable}\small
4 \begin{tabular}{lrrrrrrr} 4 \begin{tabular}{lrrrrrrr}
modified manuscript/tables/tab_systems_categories.tex +12 −12
@@ -1,21 +1,21 @@
1−% generated by tables/make_tables.py from /Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/bibliometrics/cagr_by_category.csv, /Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/econometrics/breaks.csv 1+% generated by tables/make_tables.py from ./results/bibliometrics/cagr_by_category.csv, ./results/econometrics/breaks.csv
2 \begin{table}[htbp]\centering\caption{The systems and infrastructure categories of Part VI: primary-category output, compound annual growth, and structural-change estimates of the monthly log-volume series.}\label{tab:systems_categories} 2 \begin{table}[htbp]\centering\caption{The systems and infrastructure categories of Part VI: primary-category output, compound annual growth, and structural-change estimates of the monthly log-volume series.}\label{tab:systems_categories}
3 \begin{threeparttable}\small 3 \begin{threeparttable}\small
4 \begin{tabular}{lrrrrrrrrr} 4 \begin{tabular}{lrrrrrrrrr}
5 \toprule 5 \toprule
6 \textbf{Category} & \textbf{n 2018} & \textbf{n 2022} & \textbf{n 2025} & \textbf{CAGR base} & \textbf{CAGR post} & \textbf{Slope ch.} & \textbf{p} & \textbf{Placebo p} & \textbf{BP breaks} \\ 6 \textbf{Category} & \textbf{n 2018} & \textbf{n 2022} & \textbf{n 2025} & \textbf{CAGR base} & \textbf{CAGR post} & \textbf{Slope ch.} & \textbf{p} & \textbf{Placebo p} & \textbf{BP breaks} \\
7 \midrule 7 \midrule
8−Computer architecture & 19{,}554 & 15{,}174 & 20{,}209 & -0.061 & 0.111 & 0.016 & 0.000 & 0.062 & 2023-05-01 \\ 8+Computer architecture & 6{,}518 & 7{,}587 & 6{,}736 & 0.039 & -0.093 & 0.016 & 0.000 & 0.062 & 2023-05-01 \\
9−Accelerators and AI hardware & 818 & 1{,}421 & 1{,}906 & 0.148 & 0.092 & 0.018 & 0.000 & 0.169 & 2024-11-01 \\ 9+Accelerators and AI hardware & 409 & 710 & 953 & 0.148 & 0.092 & 0.018 & 0.000 & 0.169 & 2024-11-01 \\
10−Operating systems & 1{,}556 & 1{,}083 & 1{,}363 & -0.087 & 0.078 & 0.016 & 0.000 & 0.477 & 2023-09-01 \\ 10+Operating systems & 778 & 541 & 681 & -0.087 & 0.078 & 0.016 & 0.000 & 0.477 & 2023-09-01 \\
11−Distributed computing & 11{,}519 & 9{,}379 & 12{,}550 & -0.050 & 0.132 & 0.020 & 0.000 & 0.077 & 2022-07-01;2024-07-01 \\ 11+Distributed computing & 3{,}839 & 4{,}689 & 4{,}183 & 0.051 & 0.132 & 0.020 & 0.000 & 0.077 & 2022-07-01;2024-07-01 \\
12−Cloud computing & 9{,}396 & 7{,}103 & 10{,}974 & -0.068 & 0.198 & 0.019 & 0.000 & 0.231 & 2022-11-01;2025-01-01 \\ 12+Cloud computing & 3{,}132 & 3{,}551 & 3{,}658 & 0.032 & -0.022 & 0.019 & 0.000 & 0.231 & 2022-11-01;2025-01-01 \\
13−Edge computing & 5{,}185 & 8{,}554 & 11{,}554 & 0.133 & 0.126 & 0.004 & 0.006 & 0.077 & 2025-01-01 \\ 13+Edge computing & 1{,}728 & 4{,}277 & 5{,}777 & 0.254 & 0.379 & 0.004 & 0.006 & 0.077 & 2025-01-01 \\
14−Computer networks & 58{,}744 & 49{,}141 & 59{,}555 & -0.044 & 0.071 & 0.009 & 0.000 & 0.200 & 2022-02-01 \\ 14+Computer networks & 19{,}581 & 16{,}380 & 29{,}777 & -0.044 & 0.312 & 0.009 & 0.000 & 0.200 & 2022-02-01 \\
15−Database systems & 16{,}265 & 13{,}625 & 16{,}681 & -0.043 & 0.073 & 0.014 & 0.000 & 0.092 & 2023-11-01 \\ 15+Database systems & 5{,}421 & 6{,}812 & 8{,}340 & 0.059 & 0.073 & 0.014 & 0.000 & 0.092 & 2023-11-01 \\
16−Information retrieval & 12{,}095 & 11{,}243 & 17{,}379 & -0.018 & 0.176 & 0.023 & 0.000 & 0.092 & 2022-07-01;2024-07-01 \\ 16+Information retrieval & 4{,}031 & 5{,}621 & 8{,}689 & 0.087 & 0.176 & 0.023 & 0.000 & 0.092 & 2022-07-01;2024-07-01 \\
17−Quantum computing & 6{,}224 & 9{,}216 & 17{,}471 & 0.103 & 0.287 & 0.018 & 0.000 & 0.169 & 2022-01-01;2025-04-01 \\ 17+Quantum computing & 3{,}112 & 4{,}608 & 8{,}735 & 0.103 & 0.287 & 0.018 & 0.000 & 0.169 & 2022-01-01;2025-04-01 \\
18−Sustainable computing & 583 & 830 & 1{,}639 & 0.092 & 0.233 & 0.016 & 0.000 & 0.169 & 2022-07-01 \\ 18+Sustainable computing & 291 & 276 & 819 & -0.013 & 0.233 & 0.016 & 0.000 & 0.169 & 2022-07-01 \\
19 \bottomrule 19 \bottomrule
20 \end{tabular} 20 \end{tabular}
21 \begin{tablenotes}\footnotesize\item Counts are eligible works whose primary category is the row category (multi-label memberships are larger). CAGR base: 2018 to 2022; CAGR post: 2023 to 2025 (complete years). Slope ch.: change in the monthly trend of the log-volume series at 2022-12 in the interrupted time-series model of Part IV, with its HAC p-value; Placebo p: share of placebo event dates whose slope statistic is at least as extreme; BP breaks: Bai--Perron break dates selected by BIC. 2026 is incomplete and excluded from the growth columns.\end{tablenotes} 21 \begin{tablenotes}\footnotesize\item Counts are eligible works whose primary category is the row category (multi-label memberships are larger). CAGR base: 2018 to 2022; CAGR post: 2023 to 2025 (complete years). Slope ch.: change in the monthly trend of the log-volume series at 2022-12 in the interrupted time-series model of Part IV, with its HAC p-value; Placebo p: share of placebo event dates whose slope statistic is at least as extreme; BP breaks: Bai--Perron break dates selected by BIC. 2026 is incomplete and excluded from the growth columns.\end{tablenotes}
added manuscript/tables/tab_theory_counts.tex +23 −0
@@ -0,0 +1,23 @@
1+% generated by tables/make_tables.py from ./results/graph/category_counts_yearly.csv
2+\begin{longtable}{lrrrrrrrrrrrr}
3+\caption{Eligible works in the six theory and software categories by publication year: works carrying the category (all) and works whose primary category it is (prim.).}\label{tab:theory_counts}\\
4+\toprule
5+\textbf{Year} & \textbf{Algo. (all)} & \textbf{Compl. (all)} & \textbf{PL (all)} & \textbf{Comp. (all)} & \textbf{FV (all)} & \textbf{SE (all)} & \textbf{Algo. (prim.)} & \textbf{Compl. (prim.)} & \textbf{PL (prim.)} & \textbf{Comp. (prim.)} & \textbf{FV (prim.)} & \textbf{SE (prim.)} \\
6+\midrule
7+\endfirsthead
8+\toprule
9+\textbf{Year} & \textbf{Algo. (all)} & \textbf{Compl. (all)} & \textbf{PL (all)} & \textbf{Comp. (all)} & \textbf{FV (all)} & \textbf{SE (all)} & \textbf{Algo. (prim.)} & \textbf{Compl. (prim.)} & \textbf{PL (prim.)} & \textbf{Comp. (prim.)} & \textbf{FV (prim.)} & \textbf{SE (prim.)} \\
10+\midrule
11+\endhead
12+2018 & 60{,}011 & 16{,}996 & 16{,}735 & 5{,}705 & 12{,}283 & 44{,}359 & 29{,}148 & 3{,}837 & 4{,}390 & 725 & 5{,}307 & 29{,}016 \\
13+2019 & 62{,}158 & 18{,}186 & 16{,}286 & 5{,}645 & 11{,}692 & 45{,}573 & 29{,}252 & 3{,}952 & 4{,}474 & 689 & 4{,}802 & 28{,}935 \\
14+2020 & 63{,}646 & 18{,}459 & 16{,}126 & 5{,}576 & 11{,}009 & 45{,}901 & 29{,}376 & 3{,}872 & 4{,}126 & 737 & 4{,}535 & 27{,}756 \\
15+2021 & 64{,}815 & 18{,}524 & 15{,}687 & 5{,}721 & 10{,}938 & 44{,}735 & 29{,}023 & 3{,}765 & 3{,}978 & 709 & 4{,}511 & 24{,}671 \\
16+2022 & 63{,}882 & 18{,}335 & 15{,}186 & 5{,}646 & 9{,}950 & 46{,}006 & 28{,}202 & 3{,}541 & 3{,}664 & 642 & 3{,}995 & 23{,}725 \\
17+2023 & 67{,}630 & 19{,}464 & 16{,}681 & 6{,}351 & 10{,}676 & 55{,}422 & 28{,}531 & 3{,}499 & 3{,}876 & 692 & 4{,}385 & 26{,}245 \\
18+2024 & 74{,}137 & 22{,}675 & 17{,}703 & 7{,}306 & 11{,}391 & 68{,}087 & 29{,}957 & 3{,}824 & 3{,}846 & 768 & 4{,}233 & 30{,}533 \\
19+2025 & 88{,}268 & 29{,}301 & 20{,}734 & 9{,}521 & 13{,}656 & 87{,}235 & 33{,}795 & 4{,}209 & 4{,}193 & 767 & 4{,}623 & 36{,}381 \\
20+2026 & 76{,}192 & 25{,}776 & 21{,}376 & 9{,}915 & 14{,}494 & 81{,}133 & 28{,}776 & 4{,}067 & 3{,}691 & 621 & 4{,}866 & 34{,}261 \\
21+\bottomrule
22+\multicolumn{13}{p{0.95\linewidth}}{\footnotesize Counts of eligible works (Part III) per publication year, summed over the pre/baseline/post period labels of the result file; 2026 is incomplete. `all' counts every work to which the multi-label classifier assigns the category; `prim.' counts works whose highest-scoring category it is (Part IV). Algo. = algorithms and data structures, Compl. = computational complexity, PL = programming languages, Comp. = compilers, FV = formal verification, SE = software engineering.}\\
23+\end{longtable}
added manuscript/tables/tab_theory_landmark_audit.tex +20 −0
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1+% generated by tables/make_tables.py from ./literature_synthesis/agent_G/contributions.csv, ./literature_synthesis/landmarks/theory_landmarks.csv, ./literature_synthesis/landmarks/software_pl_landmarks.csv
2+\begin{table}[htbp]\centering\caption{Specialist audit of the automatically selected landmark candidates of the theory and software areas, by verdict and primary category.}\label{tab:theory_landmark_audit}
3+\begin{threeparttable}\small
4+\begin{tabular}{lrrrrrrrrr}
5+\toprule
6+\textbf{Verdict} & \textbf{Algo.} & \textbf{Compl.} & \textbf{PL} & \textbf{Comp.} & \textbf{FV} & \textbf{SE} & \textbf{Total} & \textbf{Post-period} & \textbf{Id mismatch} \\
7+\midrule
8+Research (in scope) & 3 & 2 & 3 & 6 & 8 & 7 & 29 & 16 & 7 \\
9+Survey / report (in scope) & 1 & 0 & 0 & 2 & 1 & 4 & 8 & 3 & 0 \\
10+Textbook / chapter & 9 & 3 & 5 & 1 & 3 & 1 & 22 & 8 & 0 \\
11+Re-deposited classic & 5 & 3 & 6 & 0 & 7 & 3 & 24 & 11 & 0 \\
12+Thesis & 1 & 2 & 6 & 6 & 4 & 8 & 27 & 10 & 0 \\
13+Metaphor metaheuristic & 6 & 0 & 0 & 0 & 0 & 0 & 6 & 4 & 0 \\
14+Misclassified & 0 & 15 & 5 & 10 & 1 & 2 & 33 & 19 & 0 \\
15+Duplicate record & 0 & 0 & 0 & 0 & 1 & 0 & 1 & 0 & 0 \\
16+All candidates & 25 & 25 & 25 & 25 & 25 & 25 & 150 & 71 & 7 \\
17+\bottomrule
18+\end{tabular}
19+\begin{tablenotes}\footnotesize\item Candidates are the works with the highest field-normalised 36-month citation score within each primary category (floor of fifteen per category) plus the 200 most cited works overall (literature_synthesis/select_landmarks.py). Verdicts were assigned by Agent G from title, venue and type metadata and, where available, the abstract (never the full text); see literature_synthesis/agent_G/contributions.csv. `Id mismatch': the OpenAlex identifier of the record, resolved live on 2026-11-03, returned a different work whose citation count matches the record's 36-month window (literature_synthesis/agent_G/verification_lookups_raw.txt).\end{tablenotes}
20+\end{threeparttable}\end{table}
added manuscript/tables/tab_theory_landmarks_discussed.tex +51 −0
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1+% generated by tables/make_tables.py from ./literature_synthesis/agent_G/contributions.csv
2+\begin{longtable}{lrrrrr}
3+\caption{Landmark candidates of the theory and software areas retained as in-scope research or survey contributions, with the contribution type judged from the abstract or metadata.}\label{tab:theory_landmarks_discussed}\\
4+\toprule
5+\textbf{Category} & \textbf{Year} & \textbf{Type} & \textbf{Work} & \textbf{Contribution} & \textbf{Id mismatch} \\
6+\midrule
7+\endfirsthead
8+\toprule
9+\textbf{Category} & \textbf{Year} & \textbf{Type} & \textbf{Work} & \textbf{Contribution} & \textbf{Id mismatch} \\
10+\midrule
11+\endhead
12+Algo. & 2022 & article & A Dynamic Survey of Graph Labeling & survey & no \\
13+Algo. & 2024 & article & Targeted Branching for the Maximum Independent Set Problem Using Graph Neural Networks & engineering & yes \\
14+Algo. & 2025 & conf. & Fast Kd-Trees for the Kullback-Leibler Divergence and Other Decomposable Bregman Dive\dots & method & yes \\
15+Algo. & 2026 & article & Multiresource-Constrained Flexible Job Shop Scheduling With Fixture Pallets and Setup\dots & method & no \\
16+Comp. & 2018 & article & Machine Learning in Compiler Optimization & survey & no \\
17+Comp. & 2018 & article & A Survey on Compiler Autotuning using Machine Learning & survey & no \\
18+Comp. & 2018 & chapter & Automated Code Generation for Maximizing Performance of Detailed Chemistry Calculatio\dots & engineering & no \\
19+Comp. & 2019 & conf. & Tiramisu: A Polyhedral Compiler for Expressing Fast and Portable Code & engineering & no \\
20+Comp. & 2020 & chapter & ExaStencils: Advanced Multigrid Solver Generation & engineering & no \\
21+Comp. & 2021 & conf. & MLIR: Scaling Compiler Infrastructure for Domain Specific Computation & engineering & no \\
22+Comp. & 2024 & article & LLM4VV: Developing LLM-driven testsuite for compiler validation & benchmark & no \\
23+Comp. & 2025 & conf. & LLM Compiler: Foundation Language Models for Compiler Optimization & engineering & no \\
24+Compl. & 2019 & conf. & The Reachability Problem for Petri Nets Is Not Elementary & theory & no \\
25+Compl. & 2026 & article & Solving SAT by restricted spiking neural P systems with temporal division rules & theory & no \\
26+FV & 2018 & preprint & Efficient Formal Safety Analysis of Neural Networks & method & no \\
27+FV & 2019 & conf. & The Marabou Framework for Verification and Analysis of Deep Neural Networks & engineering & no \\
28+FV & 2019 & article & An abstract domain for certifying neural networks & method & no \\
29+FV & 2020 & conf. & VerX: Safety Verification of Smart Contracts & method & no \\
30+FV & 2022 & conf. & cvc5: A Versatile and Industrial-Strength SMT Solver & engineering & no \\
31+FV & 2023 & article & Exploiting Generative AI to Scale up Intelligent Tutoring Systems & engineering & yes \\
32+FV & 2024 & preprint & Leveraging Answer Set Programming for Continuous Monitoring, Fault Detection, and Exp\dots & method & yes \\
33+FV & 2024 & conf. & State of the Art in Software Verification and Witness Validation: SV-COMP 2024 & benchmark & no \\
34+FV & 2026 & article & Reward Interfaces with Best-Effort Implementations & theory & no \\
35+PL & 2018 & conf. & ZEUS: Analyzing Safety of Smart Contracts & method & no \\
36+PL & 2024 & article & Enhancing Static Analysis for Practical Bug Detection: An LLM-Integrated Approach & method & no \\
37+PL & 2024 & article & WhiteFox: White-Box Compiler Fuzzing Empowered by Large Language Models & method & no \\
38+SE & 2018 & article & Engenharia de Software & survey & no \\
39+SE & 2018 & chapter & Mutation Testing Advances: An Analysis and Survey & survey & no \\
40+SE & 2020 & article & Machine Learning Testing: Survey, Landscapes and Horizons & survey & no \\
41+SE & 2021 & preprint & A Direction-Aware Study of LLM-Based Code Translation Across Eight Programming Languages & empirical & yes \\
42+SE & 2022 & article & The Astropy Project : Sustaining and Growing a Community-oriented Open-source Project\dots & engineering & no \\
43+SE & 2024 & article & Software Testing With Large Language Models: Survey, Landscape, and Vision & survey & no \\
44+SE & 2025 & conf. & Detecting Functionality-Specific Vulnerabilities via Retrieving Individual Functional\dots & method & yes \\
45+SE & 2025 & conf. & On Assessing ML Model Robustness: A Methodological Framework (Academic Track) & engineering & yes \\
46+SE & 2025 & article & Can Open Large Language Models Catch Vulnerabilities? & empirical & no \\
47+SE & 2026 & article & The Effects of Generative AI on High-Skilled Work: Evidence from Three Field Experime\dots & empirical & no \\
48+SE & 2026 & article & MSTDP: a multi-scale temporal deep learning framework for just-in-time software defec\dots & method & no \\
49+\bottomrule
50+\multicolumn{6}{p{0.95\linewidth}}{\footnotesize Contribution types follow the protocol (theory, method, empirical, benchmark, engineering, survey). `Id mismatch' as in Table~\ref{tab:theory_landmark_audit}. Titles are shortened.}\\
51+\end{longtable}
added manuscript/tables/tab_theory_llm_cooccurrence.tex +23 −0
@@ -0,0 +1,23 @@
1+% generated by tables/make_tables.py from ./results/graph/category_cooccurrence_yearly.csv, ./results/graph/category_counts_yearly.csv
2+\begin{longtable}{lrrrrrrrrrrrr}
3+\caption{Co-classification of theory and software works with the LLM category, by publication year: number of works carrying both categories and share of the category's multi-label count.}\label{tab:theory_llm_cooccurrence}\\
4+\toprule
5+\textbf{Year} & \textbf{Algo. x LLM} & \textbf{Algo. x LLM (\%)} & \textbf{Compl. x LLM} & \textbf{Compl. x LLM (\%)} & \textbf{PL x LLM} & \textbf{PL x LLM (\%)} & \textbf{Comp. x LLM} & \textbf{Comp. x LLM (\%)} & \textbf{FV x LLM} & \textbf{FV x LLM (\%)} & \textbf{SE x LLM} & \textbf{SE x LLM (\%)} \\
6+\midrule
7+\endfirsthead
8+\toprule
9+\textbf{Year} & \textbf{Algo. x LLM} & \textbf{Algo. x LLM (\%)} & \textbf{Compl. x LLM} & \textbf{Compl. x LLM (\%)} & \textbf{PL x LLM} & \textbf{PL x LLM (\%)} & \textbf{Comp. x LLM} & \textbf{Comp. x LLM (\%)} & \textbf{FV x LLM} & \textbf{FV x LLM (\%)} & \textbf{SE x LLM} & \textbf{SE x LLM (\%)} \\
10+\midrule
11+\endhead
12+2018 & 486 & 0.8 & 135 & 0.8 & 195 & 1.2 & 77 & 1.3 & 34 & 0.3 & 365 & 0.8 \\
13+2019 & 599 & 1.0 & 171 & 0.9 & 270 & 1.7 & 94 & 1.7 & 48 & 0.4 & 527 & 1.2 \\
14+2020 & 738 & 1.2 & 194 & 1.1 & 362 & 2.2 & 124 & 2.2 & 64 & 0.6 & 862 & 1.9 \\
15+2021 & 794 & 1.2 & 214 & 1.2 & 489 & 3.1 & 149 & 2.6 & 56 & 0.5 & 1{,}177 & 2.6 \\
16+2022 & 827 & 1.3 & 231 & 1.3 & 642 & 4.2 & 176 & 3.1 & 72 & 0.7 & 1{,}862 & 4.0 \\
17+2023 & 1{,}318 & 1.9 & 402 & 2.1 & 1{,}127 & 6.8 & 348 & 5.5 & 172 & 1.6 & 4{,}349 & 7.8 \\
18+2024 & 2{,}334 & 3.1 & 815 & 3.6 & 2{,}007 & 11.3 & 693 & 9.5 & 451 & 4.0 & 9{,}576 & 14.1 \\
19+2025 & 4{,}716 & 5.3 & 1{,}543 & 5.3 & 3{,}792 & 18.3 & 1{,}420 & 14.9 & 1{,}081 & 7.9 & 18{,}459 & 21.2 \\
20+2026 & 6{,}381 & 8.4 & 2{,}211 & 8.6 & 5{,}493 & 25.7 & 2{,}231 & 22.5 & 1{,}907 & 13.2 & 23{,}478 & 28.9 \\
21+\bottomrule
22+\multicolumn{13}{p{0.95\linewidth}}{\footnotesize A work is co-classified when the multi-label classifier assigns both categories (Part IV). Shares are relative to all eligible works carrying the theory/software category in the year. Non-zero counts before 2022 reflect the lexical breadth of the LLM category (language-model and pretrained-model vocabulary), not retroactive labelling. 2026 is incomplete.}\\
23+\end{longtable}
added manuscript/tables/tab_theory_summary.tex +17 −0
@@ -0,0 +1,17 @@
1+% generated by tables/make_tables.py from ./results/graph/category_counts_yearly.csv, ./results/econometrics/breaks.csv
2+\begin{table}[htbp]\centering\caption{Growth and structural-change summary for the six theory and software categories (multi-label counts).}\label{tab:theory_summary}
3+\begin{threeparttable}\small
4+\begin{tabular}{lrrrrrrrrrrr}
5+\toprule
6+\textbf{Category} & \textbf{Works 2018} & \textbf{Works 2025} & \textbf{CAGR 18--22 (\%)} & \textbf{CAGR 23--25 (\%)} & \textbf{Level 2022-12} & \textbf{p} & \textbf{Slope change} & \textbf{p } & \textbf{Placebo p (slope)} & \textbf{Bai--Perron} & \textbf{PELT} \\
7+\midrule
8+Algorithms and data structures & 60{,}011 & 88{,}268 & 1.6 & 14.2 & -0.045 & 0.039 & 0.0098 & 0.000 & 0.062 & 2023-04-01 & 2023-08, 2025-08 \\
9+Computational complexity & 16{,}996 & 29{,}301 & 1.9 & 22.7 & -0.068 & 0.000 & 0.0142 & 0.000 & 0.062 & 2023-04-01 & 2023-10, 2025-01 \\
10+Programming languages & 16{,}735 & 20{,}734 & -2.4 & 11.5 & -0.040 & 0.381 & 0.0123 & 0.000 & 0.431 & 2022-04-01, 2024-06-01 & 2023-10, 2025-10 \\
11+Compilers & 5{,}705 & 9{,}521 & -0.3 & 22.4 & -0.028 & 0.471 & 0.0180 & 0.000 & 0.215 & 2023-04-01 & 2023-10, 2025-10 \\
12+Formal verification & 12{,}283 & 13{,}656 & -5.1 & 13.1 & -0.109 & 0.029 & 0.0177 & 0.000 & 0.246 & 2022-02-01, 2024-07-01 & 2024-03, 2025-09 \\
13+Software engineering & 44{,}359 & 87{,}235 & 0.9 & 25.5 & 0.048 & 0.124 & 0.0162 & 0.000 & 0.154 & 2022-07-01 & 2023-03, 2025-01 \\
14+\bottomrule
15+\end{tabular}
16+\begin{tablenotes}\footnotesize\item Works: eligible works carrying the category (multi-label). CAGR: compound annual growth of the multi-label count between the years shown (2026 excluded as incomplete). Level and slope change: interrupted-time-series estimates on the log monthly count of works with the category as primary label, event month 2022-12, with HAC p-values (Part IV); placebo p: share of baseline-period pseudo-events whose slope t-statistic exceeds the observed one (Section~\ref{sec:m-placebo}); Bai--Perron and PELT: estimated break dates (results/econometrics/breaks.csv). The ITS series are primary-label counts while the CAGR columns use multi-label counts; the two populations differ (Table~\ref{tab:theory_counts}).\end{tablenotes}
17+\end{threeparttable}\end{table}
modified manuscript/tables/tab_top_cited.tex +1 −1
@@ -1,4 +1,4 @@
1−% generated by tables/make_tables.py from /Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/bibliometrics/top_cited_by_year.csv 1+% generated by tables/make_tables.py from ./results/bibliometrics/top_cited_by_year.csv
2 \begin{longtable}{lrrrr} 2 \begin{longtable}{lrrrr}
3 \caption{The ten most cited works of each publication year by 36-month window citations (c36), with total citations to date.}\label{tab:top_cited}\\ 3 \caption{The ten most cited works of each publication year by 36-month window citations (c36), with total citations to date.}\label{tab:top_cited}\\
4 \toprule 4 \toprule
modified manuscript/tables/tab_top_institutions.tex +1 −1
@@ -1,4 +1,4 @@
1−% generated by tables/make_tables.py from /Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/bibliometrics/top_institutions_by_period.csv 1+% generated by tables/make_tables.py from ./results/bibliometrics/top_institutions_by_period.csv
2 \begin{table}[htbp]\centering\caption{The twenty-five institutions with the largest fractional output in the post period, with their baseline output.}\label{tab:top_institutions} 2 \begin{table}[htbp]\centering\caption{The twenty-five institutions with the largest fractional output in the post period, with their baseline output.}\label{tab:top_institutions}
3 \begin{threeparttable}\small 3 \begin{threeparttable}\small
4 \begin{tabular}{lrrrrr} 4 \begin{tabular}{lrrrrr}
modified manuscript/tables/tab_top_sources.tex +1 −1
@@ -1,4 +1,4 @@
1−% generated by tables/make_tables.py from /Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/bibliometrics/top_sources_by_period.csv 1+% generated by tables/make_tables.py from ./results/bibliometrics/top_sources_by_period.csv
2 \begin{longtable}{lrrrrrr} 2 \begin{longtable}{lrrrrrr}
3 \caption{The twenty largest sources (venues) by period, with OpenAlex source type.}\label{tab:top_sources}\\ 3 \caption{The twenty largest sources (venues) by period, with OpenAlex source type.}\label{tab:top_sources}\\
4 \toprule 4 \toprule
modified results/bibliometrics/cagr_by_category.csv +40 −40
@@ -1,41 +1,41 @@
1 category,n_2018,n_2022,n_2023,n_2025,cagr_baseline,cagr_post,share_2018,share_2022,share_2025 1 category,n_2018,n_2022,n_2023,n_2025,cagr_baseline,cagr_post,share_2018,share_2022,share_2025
2−ai_accelerators,409,710,799,953,0.14804838919248775,0.09178523385922643,0.002409752131363896,0.0027899579837432427,0.002218169672523209 2+ai_accelerators,818,1421,1599,1906,0.14804838919248775,0.09178523385922643,0.001637437670274501,0.002535539484648477,0.0021347609874109584
3−ai_agents,2718,2260,2495,4409,-0.045032119445381436,0.3292782120448039,0.016013951816741003,0.00887642508409796,0.010263398920242486 3+ai_agents,5436,4521,4991,8819,-0.045032119445381436,0.3292782120448039,0.010881554004415877,0.008066976784022354,0.00987746964741723
4−ai_safety,526,1477,2007,5442,0.2944899640897396,0.6465368010192893,0.0030990944281110255,0.005799814133692849,0.01266777381186523 4+ai_safety,1052,2954,4015,10885,0.2944899640897396,0.6465368010192893,0.0021058489353652505,0.005270924445919495,0.01219143407553425
5−algorithms,9716,9400,9510,11265,-0.008214426291722088,0.08834786213446155,0.05724486970252229,0.03691409573423735,0.02622002241445325 5+algorithms,29148,28202,28531,33795,-0.008214426291722088,0.08834786213446155,0.058347228866945176,0.050321804747400674,0.03785112674163344
6−applied_cryptography,3594,5149,3739,7306,0.09407412599520226,0.3977303237525569,0.0211751813205913,0.020220814408565557,0.01700519163426502 6+applied_cryptography,10782,10299,11219,14612,-0.01139242765304549,0.14124203040296623,0.021582949829950696,0.01837686217621018,0.01636575422248107
7−autonomous_scientific_discovery,43,118,200,382,0.28707361667658393,0.38202749610852527,0.0002533480235908253,0.0004633568502205526,0.0008891299212002788 7+autonomous_scientific_discovery,86,236,400,764,0.28707361667658393,0.38202749610852527,0.00017215114870856613,0.00042110296859749515,0.000855696429371444
8−blockchain,2147,5885,7174,9006,0.2867038655679659,0.12039239265851642,0.012649725736034926,0.02310894121650807,0.020962052540130133 8+blockchain,6441,11770,14349,18012,0.1626681909797465,0.12039239265851642,0.012893320335254354,0.02100161839149372,0.02017382733748488
9−cloud_computing,3132,3551,3821,3658,0.03192353243026824,-0.0216259646860103,0.01845316302061546,0.013945863165748243,0.00851423364332623 9+cloud_computing,9396,7103,7643,10974,-0.06755182852386099,0.19825858205350233,0.01880851387518241,0.012674128754016984,0.012291115989426997
10−compilers,241,321,346,383,0.073549490456021,0.05279699035007379,0.0014238551713437854,0.001260487702718622,0.0008926212690583952 10+compilers,725,642,692,767,-0.029938529449611395,0.05279699035007379,0.0014512742187640748,0.001145542821354203,0.0008590564938846826
11−computational_biology,4322,9768,10288,14077,0.22606494630771068,0.1697406366696017,0.025468350154463738,0.03835652299113863,0.032765135865801896 11+computational_biology,12968,19536,20576,28154,0.10787474203944436,0.1697406366696017,0.025958791819217272,0.034858760993731634,0.03153308543524036
12−computational_complexity,1279,1770,1166,2104,0.08469177413199191,0.34326939885786634,0.007535630748201524,0.006952316129792274,0.004898361044937139 12+computational_complexity,3837,3541,3499,4209,-0.019870339595214892,0.0967748714322898,0.007680743692962421,0.006318328863575129,0.004714170512073831
13−computer_architecture,6518,7587,8184,6736,0.03869713083776305,-0.0927735404534441,0.03840284692476742,0.029792274767994346,0.015679255303260413 13+computer_architecture,19554,15174,16369,20209,-0.06143119143120024,0.1111209535203932,0.03914236699822444,0.027075493413128777,0.02263451458267999
14−computer_graphics,3399,4143,3099,6220,0.05073548076582268,0.4167212372990894,0.02002824143084679,0.016270500922786948,0.014477455784988833 14+computer_graphics,10198,8287,9297,12440,-0.050553314316049036,0.15674804638240403,0.020413923424766944,0.014786780935455265,0.013933067514896286
15−computer_networks,19581,16380,17293,29777,-0.04364282894058291,0.3122016357047006,0.11536958370402665,0.06432152253301744,0.06930907389670499 15+computer_networks,58744,49141,51881,59555,-0.04364282894058291,0.07140814904065751,0.1175912451132094,0.08768398720275215,0.06670288069530934
16−computer_vision,23246,49352,57130,75378,0.20708855155154882,0.1486553322088684,0.13696111991610055,0.19379311247529418,0.17544837634867858 16+computer_vision,69738,98704,114261,150757,0.09072763371648507,0.1486553322088684,0.13959856754230215,0.17612096361206425,0.16885108194077325
17−cybersecurity,9813,15777,12160,29522,0.12603520471872232,0.5581172943745198,0.057818339647394544,0.06195238157567507,0.06871438097820584 17+cybersecurity,29440,31554,36481,59044,0.017487667119059358,0.2721974435411547,0.05893174206953705,0.056302894369175266,0.06613054970655437
18−database_systems,5421,6812,7243,8340,0.05875000550434839,0.07305457648307523,0.03194345429228506,0.02675100459430097,0.01941305787374588 18+database_systems,16265,13625,14487,16681,-0.043311373704336575,0.07305457648307523,0.032558586438893346,0.02431155909805454,0.01868307871511133
19−deep_learning_architectures,3997,7423,12803,17070,0.16736629336390108,0.15467235481054153,0.023551546503109897,0.029149596198903123,0.039732702407982615 19+deep_learning_architectures,11992,22270,25607,34141,0.16736629336390108,0.15467235481054153,0.02400507645712936,0.03973713182485685,0.0382386541821603
20−distributed_computing,3839,4689,3266,4183,0.0512554351002068,0.13175688541453123,0.022622603749943536,0.018414508043298996,0.009736981248746508 20+distributed_computing,11519,9379,9798,12550,-0.050083482537713886,0.13175688541453123,0.023058245139232245,0.01673527433252503,0.014056269880381703
21−edge_computing,1728,4277,3040,5777,0.25423217055612524,0.3785241917965825,0.010183019397817279,0.016794722444011048,0.01344634438422516 21+edge_computing,5185,8554,9120,11554,0.1333267023064395,0.1255602893280665,0.010379112861092039,0.015263198277046497,0.01294072846198647
22−federated_learning,71,487,748,1272,0.6154954153196714,0.303609910055608,0.0004212647369010234,0.0019123286954017722,0.002960662983682604 22+federated_learning,143,974,1497,2544,0.6154954153196714,0.303609910055608,0.00028625132866656923,0.001737941912771018,0.002849334707226379
23−formal_verification,1769,1997,1461,2311,0.030836383835269654,0.2575428068501022,0.010422619854236509,0.007843689053521643,0.005380167049357185 23+formal_verification,5307,3995,4385,4623,-0.06853417817252339,0.026779402163364052,0.010623327281353028,0.0071284167777414965,0.0051778594149007665
24−foundation_models,23,175,285,927,0.6608390716251988,0.8019254150494273,0.00013551173354858095,0.0006871817693948874,0.00215765297631586 24+foundation_models,46,350,571,1854,0.6608390716251988,0.8019254150494273,9.208084698365164e-05,0.0006245171144454377,0.0020765198691814883
25−generative_ai,255,1080,1823,6371,0.43409875951610655,0.8695087811589706,0.0015043766362059857,0.004240893205408448,0.014830081918658578 25+generative_ai,766,2160,3646,12743,0.29585451247314687,0.8695087811589706,0.0015333462780321122,0.0038541627634347013,0.01427243403073339
26−hci,11408,17400,20293,29074,0.11130912772658119,0.19694422895345198,0.06721578377826352,0.06832746501917564,0.06767163175124845 26+hci,34225,34801,40587,58148,0.004181154443647861,0.19694422895345198,0.06851015191337995,0.06209662885661622,0.06512701043860042
27−information_retrieval,4031,5621,6284,8689,0.08665554196594227,0.1759241392022266,0.023753832134349083,0.022074241809447767,0.0202253781420676 27+information_retrieval,12095,11243,12568,17379,-0.018095875045832766,0.1759241392022266,0.024211257484071016,0.02006127405060016,0.019464853725191522
28−llm,545,1088,5194,22579,0.1888432761609986,1.0848771832846835,0.0032110389036511576,0.004274925064464195,0.052554095525604964 28+llm,1635,3266,10389,45158,0.1888432761609986,1.0848771832846835,0.003272873583005879,0.005827636845082284,0.05057793109627705
29−ml_theory,4511,10065,11446,16399,0.22217278554507214,0.19693985094098543,0.026579939157195573,0.039524731999109934,0.038169742350165896 29+ml_theory,13534,20131,22893,32798,0.10435777777595234,0.19693985094098543,0.02709178658862481,0.035920440088288874,0.03673446530173379
30−multimodal_ai,156,639,966,3374,0.421121021734181,0.8690294865336103,0.000923050938664247,0.0025091951465333316,0.007854368898142254 30+multimodal_ai,470,1278,1932,6749,0.2841278026110756,0.8690294865336103,0.000940826045267745,0.002280379635032198,0.007559025133282559
31−nlp,7790,9892,16982,21457,0.06153590511416507,0.12404497631921241,0.045901162847788904,0.038846058194478984,0.049942567327733986 31+nlp,23372,29678,33965,42914,0.06153590511416507,0.12404497631921241,0.04678507729786753,0.052955482635747717,0.04806460284037453
32−operating_systems,778,541,586,681,-0.0866132316020718,0.07795075704417309,0.004583831682643304,0.00212633673215618,0.0015862357102041625 32+operating_systems,1556,1083,1173,1363,-0.0866132316020718,0.07795075704417309,0.003114734737099173,0.0019324343855554546,0.001526589310514762
33−privacy_preserving_computation,1035,2188,2505,4408,0.20580406040260257,0.3266047789454696,0.006098028009686143,0.008591735493920076,0.010261071355003742 33+privacy_preserving_computation,3105,4376,5010,8817,0.08956696494067806,0.3266047789454696,0.006215457171396486,0.007808248265180673,0.009875229604408405
34−programming_languages,1463,1832,1938,2096,0.057780486211923554,0.04008910470076277,0.008621688554757541,0.007193811437322478,0.0048797405230271845 34+programming_languages,4390,3664,3876,4193,-0.044187433279510024,0.04008910470076277,0.008787715614309364,0.006537802020937383,0.004696250168003226
35−quantum_computing,3112,4608,5272,8735,0.10310847324827721,0.2872300901577727,0.018335326730573216,0.018094477676409378,0.020332446143049833 35+quantum_computing,6224,9216,10544,17471,0.10310847324827721,0.2872300901577727,0.012458938948396692,0.016444427790654727,0.01956789570359751
36−reinforcement_learning,1648,3508,4211,6605,0.20792805552654703,0.25240210895212,0.009709710299480931,0.013777012788125499,0.015373568401905345 36+reinforcement_learning,3296,7017,8422,13210,0.20792805552654703,0.25240210895212,0.006597792862132953,0.012520675977324676,0.014795484073294207
37−robotics,5832,11551,12879,16732,0.18632919081853316,0.1397889079167418,0.034361062176318444,0.04535988690951452,0.038944821574667714 37+robotics,17496,23103,25759,33464,0.07196943376447251,0.1397889079167418,0.035022749974477595,0.04122348255723699,0.03748039962367278
38−scientific_computing,5627,6644,6807,7730,0.04239362249738088,0.06561412676886369,0.033155204141552805,0.026089346719197895,0.017992855150572133 38+scientific_computing,16882,19932,20423,23191,0.04239362249738088,0.06561412676886369,0.033793670843000154,0.03556535750036133,0.02597441870883921
39−software_engineering,9672,11862,13122,18190,0.05236158017872561,0.17737287914735167,0.05698562986442935,0.04658110708255343,0.042339575475376104 39+software_engineering,29016,23725,26245,36381,-0.04908396762848111,0.17737287914735167,0.05808299687125296,0.04233333868633717,0.04074750235204516
40−speech_audio,4032,6349,7344,9186,0.12020163164106301,0.11842973684211522,0.023755796072516454,0.024930954593646512,0.02138217806572346 40+speech_audio,12096,12698,14688,18373,0.012216438797881812,0.11842973684211522,0.024213259241614136,0.02265748091208048,0.020578155100577932
41−sustainable_computing,291,276,539,819,-0.012971780149642509,0.23304832154514776,0.0017174639273657107,0.0010864016544719173,0.00190743971315086 41+sustainable_computing,583,830,1078,1639,0.09232628514238472,0.23304832154514776,0.0011670246476406284,0.001480997728542038,0.0018357152457327182
modified results/bibliometrics/category_share_ci_yearly.csv +320 −320
@@ -1,321 +1,321 @@
1 year,category,share,ci_low,ci_high 1 year,category,share,ci_low,ci_high
2−2018,ai_accelerators,0.002409752131363896,0.0021650945341636866,0.0026720851720704424 2+2018,ai_accelerators,0.001637437670274501,0.0015132286147237274,0.0017456326654802915
3−2018,ai_agents,0.016013951816741003,0.015468811679933069,0.016632592339462786 3+2018,ai_agents,0.010881554004415877,0.010609114802796856,0.011113757879418129
4−2018,ai_safety,0.0030990944281110255,0.0028395599992929823,0.003373211097821796 4+2018,ai_safety,0.0021058489353652505,0.0019826908025246167,0.0022420685361747615
5−2018,algorithms,0.05724486970252229,0.0561930335185327,0.05852059483759213 5+2018,algorithms,0.058347228866945176,0.05766232752356569,0.05901791773176849
6−2018,applied_cryptography,0.0211751813205913,0.020432518102600057,0.021796915045926693 6+2018,applied_cryptography,0.021582949829950696,0.021211573761762827,0.021940413683213863
7−2018,autonomous_scientific_discovery,0.0002533480235908253,0.00017675443506336646,0.0003358334266203963 7+2018,autonomous_scientific_discovery,0.00017215114870856613,0.00013907210530846083,0.00021123546473803995
8−2018,blockchain,0.012649725736034926,0.012084111543832154,0.0132153399282377 8+2018,blockchain,0.012893320335254354,0.01257243860109176,0.013200740249939447
9−2018,cloud_computing,0.01845316302061546,0.01784041431239579,0.019110247632963522 9+2018,cloud_computing,0.01880851387518241,0.01841101487105679,0.019190949653796033
10−2018,compilers,0.0014238551713437854,0.0012431728599456775,0.0016025735445745226 10+2018,compilers,0.0014512742187640748,0.001354138933984038,0.0015513620959202179
11−2018,computational_biology,0.025468350154463738,0.02472794546536497,0.02618042503549818 11+2018,computational_biology,0.025958791819217272,0.02551665362188001,0.026400329489291597
12−2018,computational_complexity,0.007535630748201524,0.007137785973946396,0.007889139618328257 12+2018,computational_complexity,0.007680743692962421,0.0074505415755032915,0.007914949325507795
13−2018,computer_architecture,0.03840284692476742,0.037570922717069175,0.03931607817259481 13+2018,computer_architecture,0.03914236699822444,0.038611801161419726,0.03963489944170982
14−2018,computer_graphics,0.02002824143084679,0.019395853340953412,0.020706929363035934 14+2018,computer_graphics,0.020413923424766944,0.02001547358580834,0.020802464563887093
15−2018,computer_networks,0.11536958370402665,0.113782427074066,0.11679712125943426 15+2018,computer_networks,0.1175912451132094,0.11667899415686973,0.11851590696631642
16−2018,computer_vision,0.13696111991610055,0.135278859580385,0.1385552976250096 16+2018,computer_vision,0.13959856754230215,0.13864142717305794,0.14057162188401418
17−2018,cybersecurity,0.057818339647394544,0.05672211846082238,0.05889516694456392 17+2018,cybersecurity,0.05893174206953705,0.05832205676584041,0.05954638172315293
18−2018,database_systems,0.03194345429228506,0.031152821884555787,0.0328440672373871 18+2018,database_systems,0.032558586438893346,0.03209477921615178,0.03305016804754574
19−2018,deep_learning_architectures,0.023551546503109897,0.02283947162207545,0.02420666717729059 19+2018,deep_learning_architectures,0.02400507645712936,0.023564589709765174,0.02444851579686965
20−2018,distributed_computing,0.022622603749943536,0.021893982689848993,0.023379014535106376 20+2018,distributed_computing,0.023058245139232245,0.022629368585618172,0.02349282670184422
21−2018,edge_computing,0.010183019397817279,0.009712508911369435,0.010628833361810437 21+2018,edge_computing,0.010379112861092039,0.010085805337085961,0.010655355402042994
22−2018,federated_learning,0.0004212647369010234,0.00032404979761617187,0.000518479676185875 22+2018,federated_learning,0.00028625132866656923,0.0002442144202609891,0.00033934794749790315
23−2018,formal_verification,0.010422619854236509,0.00994228967695181,0.010988823227889492 23+2018,formal_verification,0.010623327281353028,0.010356042605407548,0.010894615472384754
24−2018,foundation_models,0.00013551173354858095,8.837721753168323e-05,0.0001885380640675909 24+2018,foundation_models,9.208084698365164e-05,6.605799892305444e-05,0.00011810369504424884
25−2018,generative_ai,0.0015043766362059857,0.0013225650603616397,0.0016763685212134779 25+2018,generative_ai,0.0015333462780321122,0.0014250511949491652,0.0016334341551882553
26−2018,hci,0.06721578377826352,0.06605593099506855,0.0683425442033383 26+2018,hci,0.06851015191337995,0.0678100872566113,0.06917713752674848
27−2018,information_retrieval,0.023753832134349083,0.023113293701061115,0.024454123386379305 27+2018,information_retrieval,0.024211257484071016,0.023805851537650056,0.02469873548976001
28−2018,llm,0.0032110389036511576,0.00295179906555822,0.0034584951127398706 28+2018,llm,0.003272873583005879,0.003112632891678894,0.0034310124289125853
29−2018,ml_theory,0.026579939157195573,0.025785084282406452,0.027376757970152068 29+2018,ml_theory,0.02709178658862481,0.026672268251524838,0.027592626325914152
30−2018,multimodal_ai,0.000923050938664247,0.0007836113287809246,0.0010812952565001442 30+2018,multimodal_ai,0.000940826045267745,0.0008627575010859535,0.0010269016196220282
31−2018,nlp,0.045901162847788904,0.04488826173796744,0.046904735251315346 31+2018,nlp,0.04678507729786753,0.04615212156273208,0.04735272569315859
32−2018,operating_systems,0.004583831682643304,0.0042625804969156356,0.004941022936833856 32+2018,operating_systems,0.003114734737099173,0.0029674554258639087,0.003269920990629773
33−2018,privacy_preserving_computation,0.006098028009686143,0.005709168252546737,0.006472305525932821 33+2018,privacy_preserving_computation,0.006215457171396486,0.005999267356739217,0.00644165577376937
34−2018,programming_languages,0.008621688554757541,0.008177838528931756,0.009052920277857972 34+2018,programming_languages,0.008787715614309364,0.00853634491083171,0.009009910701596001
35−2018,quantum_computing,0.018335326730573216,0.017710499802624215,0.018983426325805557 35+2018,quantum_computing,0.012458938948396692,0.012182596319568582,0.012735481752979115
36−2018,reinforcement_learning,0.009709710299480931,0.009264731009208906,0.010163380016143572 36+2018,reinforcement_learning,0.006597792862132953,0.006364538064420561,0.006824091552382992
37−2018,robotics,0.034361062176318444,0.03349776405639645,0.035212576667236206 37+2018,robotics,0.035022749974477595,0.03457415610906376,0.03550672490446612
38−2018,scientific_computing,0.033155204141552805,0.032280957066347725,0.03399105622558579 38+2018,scientific_computing,0.033793670843000154,0.03327121212424509,0.03426913830343041
39−2018,software_engineering,0.05698562986442935,0.055954267735834604,0.05796985747700719 39+2018,software_engineering,0.05808299687125296,0.057491427473321574,0.05869928797484191
40−2018,speech_audio,0.023755796072516454,0.023063360573155715,0.02449286206673069 40+2018,speech_audio,0.024213259241614136,0.02377982868958946,0.024643286805815505
41−2018,sustainable_computing,0.0017174639273657107,0.0015551444378325194,0.001938406971194919 41+2018,sustainable_computing,0.0011670246476406284,0.0010698893628605915,0.001268163447506911
42−2019,ai_accelerators,0.0018345044017934501,0.0016421773274118785,0.002011251133851322 42+2019,ai_accelerators,0.0018379185572339326,0.0017257814396164086,0.0019417646611986118
43−2019,ai_agents,0.0101822391494705,0.009719637056619056,0.01066069897946446 43+2019,ai_agents,0.010201189088840759,0.009925038120135399,0.010464370874178541
44−2019,ai_safety,0.0027351129135610003,0.0025187449548817326,0.0029736724577458344 44+2019,ai_safety,0.0027402031715211554,0.00261041870082132,0.002881104085107487
45−2019,algorithms,0.05409568826740121,0.05305952615417049,0.055143454730552544 45+2019,algorithms,0.05419636455262802,0.05364415525224135,0.05481429982158109
46−2019,applied_cryptography,0.02099878778464176,0.020337755931394714,0.02167355068142037 46+2019,applied_cryptography,0.021037868162692847,0.020641289062717116,0.02140109793400909
47−2019,autonomous_scientific_discovery,0.0006324601869086289,0.0005270501557571907,0.0007434181144364586 47+2019,autonomous_scientific_discovery,0.00021121241484341565,0.00017601034570284636,0.0002464144839839849
48−2019,blockchain,0.01673060617240458,0.016130092622130006,0.01733773096586102 48+2019,blockchain,0.01676174313235422,0.01644112639210288,0.017132383865594795
49−2019,cloud_computing,0.016192460223894603,0.015625511446697198,0.01676310759867627 49+2019,cloud_computing,0.016222595652359187,0.01587770801180566,0.016573921566084473
50−2019,compilers,0.0019112503016668655,0.0017198478766813593,0.0021111132686263685 50+2019,compilers,0.0012765381914659067,0.0011829284786592088,0.0013776515032209893
51−2019,computational_biology,0.02751016883173323,0.026732122597414126,0.028275269974507414 51+2019,computational_biology,0.027561367396584657,0.02711017877092902,0.02794400462444024
52−2019,computational_complexity,0.007308428826499711,0.0069264099285153554,0.007689661772497413 52+2019,computational_complexity,0.007322030381238409,0.007091317872831599,0.007544451875992374
53−2019,computer_architecture,0.03485188503649129,0.03403634426916174,0.03573136530955875 53+2019,computer_architecture,0.03491674710648256,0.03438111983340157,0.035438525144467437
54−2019,computer_graphics,0.018119429565294578,0.017497787776319915,0.018713331872387287 54+2019,computer_graphics,0.01815315123364725,0.01780279800867453,0.01851276816102538
55−2019,computer_networks,0.10781227028241566,0.10634998349500828,0.10915527729773063 55+2019,computer_networks,0.10801291730663411,0.10721031013022912,0.10884544624180857
56−2019,computer_vision,0.15103223235329744,0.1493210761809391,0.15265619408760683 56+2019,computer_vision,0.1513133150900154,0.15043252226530873,0.1521450566104854
57−2019,cybersecurity,0.058365719178430524,0.05710713265297631,0.05944099396111479 57+2019,cybersecurity,0.058474342323447726,0.05786344746832277,0.05903998399232224
58−2019,database_systems,0.03065952467473146,0.029935894057370797,0.03136517086133865 58+2019,database_systems,0.030716584435868315,0.030259744951745375,0.031185420414606264
59−2019,deep_learning_architectures,0.028410777343500778,0.02762824655961076,0.029118272828900102 59+2019,deep_learning_architectures,0.028463652010871882,0.0280585966231952,0.028890755010273446
60−2019,distributed_computing,0.02125768961554003,0.0205742350144384,0.021947478065004702 60+2019,distributed_computing,0.02129725183004441,0.020941433020652496,0.021700361840215954
61−2019,edge_computing,0.012297836967667784,0.011822567178090248,0.012776805354829583 61+2019,edge_computing,0.012320724199199246,0.012038042320298068,0.012611789728777321
62−2019,federated_learning,0.00119834561730056,0.0010374566223852071,0.0013592346122159131 62+2019,federated_learning,0.00040019194391384015,0.00035016795092461013,0.0004623050685421341
63−2019,formal_verification,0.008880332799810632,0.008463177224775796,0.009334474350688078 63+2019,formal_verification,0.008896859790158614,0.008633770641844885,0.009142394222414084
64−2019,foundation_models,0.000153491799746831,9.986213477504667e-05,0.0002108200623028763 64+2019,foundation_models,0.00015377745992985526,0.00011945544251780021,0.00018897952907042452
65−2019,generative_ai,0.002341212270837205,0.002130392208534329,0.0025607702699328976 65+2019,generative_ai,0.0023455694490505633,0.0022156923413266735,0.002481792192922161
66−2019,hci,0.06881610665275995,0.06770916262826043,0.06997852964102336 66+2019,hci,0.06894417878204546,0.06825028115336801,0.0695973161201391
67−2019,information_retrieval,0.022753772338373595,0.02208035018321928,0.023462331170578395 67+2019,information_retrieval,0.02279611887924023,0.02239310150609274,0.023232670855095314
68−2019,llm,0.003434147856986327,0.003170484081698322,0.003681167943145158 68+2019,llm,0.0034405390733703756,0.003288521716897549,0.0035888509488810373
69−2019,ml_theory,0.031541640198577706,0.030632571144836158,0.03241108802569785 69+2019,ml_theory,0.03160034164534471,0.031083936554754964,0.03207376315677334
70−2019,multimodal_ai,0.001168756836626472,0.001017900287658427,0.0013314951303339557 70+2019,multimodal_ai,0.001170931984044199,0.0010801477004711518,0.0012654217485794112
71−2019,nlp,0.04759725231185465,0.04664934798347837,0.04857211017012624 71+2019,nlp,0.047685834502103785,0.0471130597823771,0.04822322187864179
72−2019,operating_systems,0.0029200427927740503,0.0026851818461734772,0.003167848830919536 72+2019,operating_systems,0.002925477219629415,0.0027892544757578174,0.003072816406387508
73−2019,privacy_preserving_computation,0.006021316867176888,0.005650393761945314,0.006388541374824201 73+2019,privacy_preserving_computation,0.006032523006404924,0.005860032867616135,0.006264393477612411
74−2019,programming_languages,0.00827376279599183,0.007886196001631082,0.008679822578273884 74+2019,programming_languages,0.008289160912363522,0.008050157390303868,0.008565311881068882
75−2019,quantum_computing,0.013836453562720355,0.013309403406963165,0.01446419803770905 75+2019,quantum_computing,0.013862204279459963,0.013575909556620675,0.014174622643082516
76−2019,reinforcement_learning,0.008767525573490672,0.008332662962521185,0.00917067271017512 76+2019,reinforcement_learning,0.008783842620812575,0.008566099295773342,0.009014555129219386
77−2019,robotics,0.037087687276177055,0.03629142544875547,0.03792569702383099 77+2019,robotics,0.03715671034811141,0.0366456782049168,0.03765695027800371
78−2019,scientific_computing,0.03388655106699917,0.033137585056186324,0.0346579860581364 78+2019,scientific_computing,0.03394961657535744,0.03344090035776419,0.034429105811861614
79−2019,software_engineering,0.05350946055029584,0.05239849430092345,0.05463013561832692 79+2019,software_engineering,0.05360904582012484,0.053033723582236666,0.054117067260037684
80−2019,speech_audio,0.023639586459804105,0.022995753085323874,0.024347359340022245 80+2019,speech_audio,0.02368358156967879,0.02329441343162739,0.024124719078224555
81−2019,sustainable_computing,0.0012334822943510396,0.0010818397933963389,0.0014065304288246503 81+2019,sustainable_computing,0.0012357779008820898,0.001149579149999722,0.0013312403541698703
82−2020,ai_accelerators,0.002864752020728307,0.0026244824964091586,0.003081390641282 82+2020,ai_accelerators,0.001975957064365208,0.0018529833309354747,0.0020898250048715992
83−2020,ai_agents,0.013542224068493536,0.012992904568113329,0.014120719153969638 83+2020,ai_agents,0.009340722380764194,0.009097552180746347,0.00961685555116454
84−2020,ai_safety,0.005687258850367532,0.005354577970541019,0.006006870124200861 84+2020,ai_safety,0.003922775591375722,0.0037433459418212676,0.0040912782894221645
85−2020,algorithms,0.051708113893035174,0.050675350987129296,0.05261163291745289 85+2020,algorithms,0.05349835458321875,0.05282388850138681,0.05411090127317197
86−2020,applied_cryptography,0.019853479595572703,0.019189614019770748,0.02050753196552792 86+2020,applied_cryptography,0.020540847676474818,0.020194645429529357,0.02089328739157277
87−2020,autonomous_scientific_discovery,0.0010191652350240797,0.0008765877150984312,0.0011567261385078994 87+2020,autonomous_scientific_discovery,0.0003514836068410001,0.00030595464222429023,0.00040247604721171514
88−2020,blockchain,0.02558738417776776,0.024866575604810315,0.02635400237981243 88+2020,blockchain,0.01764884784402141,0.017297273179251175,0.017973105130021618
89−2020,cloud_computing,0.014405610161376628,0.013856158644774269,0.014960342326865123 89+2020,cloud_computing,0.014904361856926139,0.01457181829936569,0.015266362654593598
90−2020,compilers,0.0019459191145407945,0.0017531754301968623,0.0021597853944292675 90+2020,compilers,0.0013421938769006066,0.001241074046486894,0.0014451348658989875
91−2020,computational_biology,0.03182119018785028,0.030902357281662768,0.03264523544653167 91+2020,computational_biology,0.03292290489363523,0.03243474333501487,0.033376646555005364
92−2020,computational_complexity,0.006815557495705072,0.006442391641084856,0.007158051581378319 92+2020,computational_complexity,0.007051526039836023,0.006846509112166979,0.007273707387165567
93−2020,computer_architecture,0.03078794322246317,0.030081084363646603,0.031549632818880784 93+2020,computer_architecture,0.031853884804434886,0.031391128408070644,0.03230102476593559
94−2020,computer_graphics,0.01662524290984876,0.01601343573098262,0.01723656602923367 94+2020,computer_graphics,0.017200842832192986,0.016871031012509537,0.017515994325269853
95−2020,computer_networks,0.0957364040893345,0.09446024727518516,0.09708274952826203 95+2020,computer_networks,0.0990509942615293,0.09815539399855401,0.09981319465817765
96−2020,computer_vision,0.14793913876137096,0.14634024628946404,0.1494509445320641 96+2020,computer_vision,0.15306109440703988,0.15208864125179156,0.1539799144419697
97−2020,cybersecurity,0.05756787394034978,0.05661330864336609,0.058604905370771954 97+2020,cybersecurity,0.059560991511579835,0.0589581880200546,0.06014230533180599
98−2020,database_systems,0.026510617624693717,0.025774319148336944,0.027203394753147262 98+2020,database_systems,0.02742846944369069,0.02702503728822202,0.027842828550667366
99−2020,deep_learning_architectures,0.03152371363393133,0.0307570074210719,0.03233869377869152 99+2020,deep_learning_architectures,0.032615129092826274,0.03216156954731461,0.033086991282113856
100−2020,distributed_computing,0.019188117835919675,0.018590392387416565,0.019765468780803784 100+2020,distributed_computing,0.019852449731470167,0.019483437473251732,0.02025674693726655
101−2020,edge_computing,0.012523938941617144,0.012010703875292194,0.01300623820655082 101+2020,edge_computing,0.012957543329915626,0.012675946683761275,0.01326896144789392
102−2020,federated_learning,0.001605317261385079,0.0014441254541358041,0.0017929123130650292 102+2020,federated_learning,0.0005536322097391919,0.0004971762936144716,0.0006183288684595365
103−2020,formal_verification,0.007982580899540934,0.0076094150449207195,0.008356758878530993 103+2020,formal_verification,0.008258954181471169,0.008024434484730496,0.008492153538237956
104−2020,foundation_models,0.0002411496324668375,0.00017148907257723828,0.00030627763539583743 104+2020,foundation_models,0.00024949872609957,0.00021029828756458283,0.00028774305637760633
105−2020,generative_ai,0.002636804010476807,0.002397414594305348,0.0028673923451713744 105+2020,generative_ai,0.0027280955598332547,0.0025950599252232286,0.002869371937038905
106−2020,hci,0.0638096009237615,0.06280803785169121,0.06500254351254682 106+2020,hci,0.06601881985281396,0.06535860433690706,0.06667471011708229
107−2020,information_retrieval,0.020730067310671133,0.020134586137944627,0.021334349564874528 107+2020,information_retrieval,0.02144778465163968,0.02108259682644905,0.021837876820475652
108−2020,llm,0.004331892302926184,0.0040396963978933725,0.004641954403357083 108+2020,llm,0.0044818712768689185,0.004304262785899133,0.004667811568363561
109−2020,ml_theory,0.03398801644746106,0.03321501746134565,0.034762775649871853 109+2020,ml_theory,0.035164751111362025,0.03466211134199355,0.035677999129486195
110−2020,multimodal_ai,0.0019934449545160105,0.0017979289295068576,0.002233714478835159 110+2020,multimodal_ai,0.0013749747314246378,0.0012756760595955935,0.0014779157204230188
111−2020,nlp,0.050600937843242176,0.04966423874165657,0.05163246859774128 111+2020,nlp,0.05235284583346233,0.051698093793309426,0.052982192711359115
112−2020,operating_systems,0.0038390317402202377,0.0035775076041343955,0.004148081716281296 112+2020,operating_systems,0.0026479645821078455,0.002510421580000765,0.002780044108460921
113−2020,privacy_preserving_computation,0.007317219139887909,0.006954614583039963,0.0076702745233334264 113+2020,privacy_preserving_computation,0.007570556236466516,0.007346553730552303,0.007836536447757335
114−2020,programming_languages,0.007262652434731179,0.0068884744557411205,0.0076094150449207195 114+2020,programming_languages,0.007514100320341795,0.007289050648241398,0.007739149992442192
115−2020,quantum_computing,0.014255991776269466,0.013671599966203846,0.014735782732982228 115+2020,quantum_computing,0.014749563377229326,0.014436233042737128,0.015025514431771204
116−2020,reinforcement_learning,0.009989227476272284,0.009571044089897765,0.010416211944123692 116+2020,reinforcement_learning,0.010335074967993138,0.01009190476797529,0.010581887485180323
117−2020,robotics,0.03582568225983608,0.03507406990170952,0.036706054439969577 117+2020,robotics,0.03706604067375583,0.036544233210283716,0.037618898891096535
118−2020,scientific_computing,0.03474666967076914,0.033850939603458474,0.03563936336497028 118+2020,scientific_computing,0.035949670461354104,0.035486140072591384,0.03641957490516316
119−2020,software_engineering,0.0488565634945222,0.04796413383276536,0.04981011666713605 119+2020,software_engineering,0.05054807767605595,0.049996084509042964,0.051122015804014195
120−2020,speech_audio,0.03298557326724307,0.0321855549609936,0.03387364639366885 120+2020,speech_audio,0.02275173419826225,0.022346389826279685,0.023155166353730917
121−2020,sustainable_computing,0.003347931393809671,0.0031103021939335902,0.003601402540344157 121+2020,sustainable_computing,0.001154614542679762,0.0010726624063696843,0.001243077320930029
122−2021,ai_accelerators,0.002754393651148362,0.002549302690734593,0.0029638951698505995 122+2021,ai_accelerators,0.0022344669660267994,0.002110086408930712,0.0023561192908385063
123−2021,ai_agents,0.007192150382395403,0.00683195478883717,0.007540180186007944 123+2021,ai_agents,0.00875181136733635,0.008531584879331628,0.008995116016959766
124−2021,ai_safety,0.003666644123813192,0.0034356043788022683,0.00392561740465108 124+2021,ai_safety,0.004461777912947028,0.004288154999373848,0.004645374528149991
125−2021,algorithms,0.04266921106873708,0.041859726604193556,0.04348457627766522 125+2021,algorithms,0.05192228563250264,0.05131894377157987,0.05248139435032292
126−2021,applied_cryptography,0.016060312914408705,0.015518439074017988,0.016593181864960463 126+2021,applied_cryptography,0.01954308818004544,0.0191733456178328,0.01993684813138451
127−2021,autonomous_scientific_discovery,0.000487366690875731,0.000401360804250602,0.0005821936940777963 127+2021,autonomous_scientific_discovery,0.00039537005563804856,0.0003479167039375995,0.0004490401989373312
128−2021,blockchain,0.023479607048660218,0.022897082562710787,0.02407073212327216 128+2021,blockchain,0.019047533856915398,0.018731550888240873,0.019382167200386425
129−2021,cloud_computing,0.017057834180650586,0.016497362486143495,0.017505505846930104 129+2021,cloud_computing,0.013837951947331699,0.013535520689840241,0.014151967010751918
130−2021,compilers,0.0015635429132619606,0.0013981469774444048,0.0017180227173155575 130+2021,compilers,0.0012684043866397123,0.0011726479059699089,0.0013570048482029447
131−2021,computational_biology,0.028254036395926992,0.02762266497693278,0.028929072341977044 131+2021,computational_biology,0.03438109379752044,0.03388460024688266,0.03482843444191996
132−2021,computational_complexity,0.005535250652027533,0.005230701602208807,0.0058330736004563455 132+2021,computational_complexity,0.0067356029840599674,0.006530717212014956,0.006970901837307906
133−2021,computer_architecture,0.024111051976959244,0.02349713901785688,0.02477035693178042 133+2021,computer_architecture,0.02933967833694116,0.028900432939155948,0.029796813782492798
134−2021,computer_graphics,0.019779148644635435,0.019214266391839877,0.020317788075614608 134+2021,computer_graphics,0.016045583841708858,0.015691181995455928,0.016362595488129954
135−2021,computer_networks,0.07592629074988312,0.07482026974974491,0.07702665153355111 135+2021,computer_networks,0.09239136268493837,0.0916975419074369,0.09313424691843927
136−2021,computer_vision,0.13686844480774374,0.13533566553854387,0.13833065514432816 136+2021,computer_vision,0.16654918868633378,0.16566600175322468,0.16748774531727997
137−2021,cybersecurity,0.07008156592461473,0.0690958061471421,0.07105938669716812 137+2021,cybersecurity,0.05685278279693007,0.056225691897597366,0.05750876612340555
138−2021,database_systems,0.031606060695162794,0.03094403589606372,0.032227949413836804 138+2021,database_systems,0.025640016458843944,0.025239860815428377,0.026021611177701843
139−2021,deep_learning_architectures,0.031203229704816034,0.03043472667770286,0.03187576633450262 139+2021,deep_learning_architectures,0.037969837379465805,0.037457063885360575,0.03846038248922125
140−2021,distributed_computing,0.014842998826791494,0.014365188345540778,0.015302542245797471 140+2021,distributed_computing,0.018061792224985242,0.01769137878598136,0.018434084119004598
141−2021,edge_computing,0.01744375803089155,0.01690335437659699,0.017962108893743767 141+2021,edge_computing,0.014151027783244181,0.013845868293468344,0.014447242249136806
142−2021,federated_learning,0.0014356367228963841,0.0012855674771312551,0.0015768848520845767 142+2021,federated_learning,0.0011646421095944327,0.0010796196575844858,0.0012452367747821885
143−2021,formal_verification,0.00994801421963992,0.009553269252822022,0.010365694089557854 143+2021,formal_verification,0.008070200547435461,0.007822378660751025,0.00831265541978997
144−2021,foundation_models,0.00036607633794285676,0.0002955074053273663,0.00044546638713528357 144+2021,foundation_models,0.00044546218938404565,0.00039000304130812027,0.0005036495697443512
145−2021,generative_ai,0.004322347122698791,0.004073040315443128,0.004600432822786708 145+2021,generative_ai,0.003506449362219797,0.00335259495142852,0.003659453995742169
146−2021,hci,0.07592555565683504,0.07497508034567014,0.07705939992884299 146+2021,hci,0.06159364545503337,0.06092598887239029,0.062172433225396714
147−2021,information_retrieval,0.02545553716189395,0.02480641324578865,0.026071251098964104 147+2021,information_retrieval,0.020650482136787305,0.020276043437035977,0.021007522765085784
148−2021,llm,0.006282840281922886,0.005931980370075244,0.006567321291529081 148+2021,llm,0.005096874608655205,0.004920612913036478,0.005264191280390719
149−2021,ml_theory,0.02933903373489016,0.028752429482523895,0.030069311923503275 149+2021,ml_theory,0.03570137932268279,0.03521365189545056,0.036214823693579264
150−2021,multimodal_ai,0.002090604628733905,0.0019162773123822011,0.0023111325431573126 150+2021,multimodal_ai,0.0016959765282573305,0.0015840295543589102,0.0018069842746480133
151−2021,nlp,0.06526082571531903,0.06426811930854207,0.06620942653921133 151+2021,nlp,0.05294201835518901,0.0523641698123334,0.05346239511959497
152−2021,operating_systems,0.002917584307821684,0.0027013566877295324,0.003149138617966262 152+2021,operating_systems,0.002366853319498363,0.0022469899994632986,0.002504606687299855
153−2021,privacy_preserving_computation,0.00935258885069672,0.008951117782488907,0.009758691005107425 153+2021,privacy_preserving_computation,0.0075871692577419185,0.007376916471366978,0.007784899010680358
154−2021,programming_languages,0.005848400290508772,0.005548261798978514,0.00616221151273328 154+2021,programming_languages,0.007116661001484874,0.006894824409181172,0.0073162692094387895
155−2021,quantum_computing,0.013643326972328156,0.013143243171720675,0.01413374429935341 155+2021,quantum_computing,0.016601964327244752,0.01625659695511387,0.016946481922106734
156−2021,reinforcement_learning,0.014537200118791036,0.014053692666417714,0.015000529266994614 156+2021,reinforcement_learning,0.011793119487629032,0.011527899529491743,0.012061559654364278
157−2021,robotics,0.048661689596669146,0.047878705236508834,0.049510832331156475 157+2021,robotics,0.039476179401399004,0.038966313040055815,0.039962475624809916
158−2021,scientific_computing,0.029843307565871686,0.02914496917019756,0.03047155483941157 158+2021,scientific_computing,0.036315007961071254,0.03583103744386998,0.03678466644005939
159−2021,software_engineering,0.054406441767398915,0.0535198092874596,0.05529351530316707 159+2021,software_engineering,0.044136536844553376,0.0435899511601696,0.0446420648693132
160−2021,speech_audio,0.028198904417321143,0.02751284207554992,0.028875925114601003 160+2021,speech_audio,0.02287600407893089,0.02246359375279532,0.02323246328067696
161−2021,sustainable_computing,0.0015811851464158332,0.00143343144375215,0.0017421705239449208 161+2021,sustainable_computing,0.0012827164248528544,0.001189688176467431,0.001386478701898134
162−2022,ai_accelerators,0.0027899579837432427,0.002599314127148261,0.0030002356051780778 162+2022,ai_accelerators,0.002535539484648477,0.0023945770502450782,0.0026739592422287766
163−2022,ai_agents,0.00887642508409796,0.008546479665964214,0.00923778452597547 163+2022,ai_agents,0.008066976784022354,0.007835860843312226,0.008286539158115242
164−2022,ai_safety,0.005799814133692849,0.005513161167030982,0.006086467100354717 164+2022,ai_safety,0.005270924445919495,0.00508526442946793,0.00547264347388537
165−2022,algorithms,0.03691409573423735,0.036232149635466435,0.03764970745690389 165+2022,algorithms,0.050321804747400674,0.04975768735959517,0.05086348412745145
166−2022,applied_cryptography,0.020220814408565557,0.019649275514077406,0.020792156965405307 166+2022,applied_cryptography,0.01837686217621018,0.018015400592042225,0.01875906665025079
167−2022,autonomous_scientific_discovery,0.0004633568502205526,0.00037696828492519535,0.000530111650676056 167+2022,autonomous_scientific_discovery,0.00042110296859749515,0.00037114159944186015,0.00047998601081663644
168−2022,blockchain,0.02310894121650807,0.02253357373787615,0.023682443487480203 168+2022,blockchain,0.02100161839149372,0.02059884946104173,0.021359511306436273
169−2022,cloud_computing,0.013945863165748243,0.013480346601395306,0.014405587769473422 169+2022,cloud_computing,0.012674128754016984,0.012395594120974318,0.012948068725431942
170−2022,compilers,0.001260487702718622,0.0011348316077435569,0.001399985601905784 170+2022,compilers,0.001145542821354203,0.0010545417561064392,0.0012436812250527717
171−2022,computational_biology,0.03835652299113863,0.037619765965523105,0.03915178863597691 171+2022,computational_biology,0.034858760993731634,0.034424230907173566,0.035326524312451264
172−2022,computational_complexity,0.006952316129792274,0.00660479849212686,0.007294041806829932 172+2022,computational_complexity,0.006318328863575129,0.0061094724971584476,0.006528969564604511
173−2022,computer_architecture,0.029792274767994346,0.029141906307674183,0.03044863152659066 173+2022,computer_architecture,0.027075493413128777,0.026654033577608743,0.027503822936907712
174−2022,computer_graphics,0.016270500922786948,0.015806260553148602,0.01675545491433134 174+2022,computer_graphics,0.014786780935455265,0.014496603519064724,0.015105017013630533
175−2022,computer_networks,0.06432152253301744,0.06338633359075381,0.06524506210814278 175+2022,computer_networks,0.08768398720275215,0.08689749711383876,0.0884105682570441
176−2022,computer_vision,0.19379311247529418,0.19239499208104818,0.1954822052644668 176+2022,computer_vision,0.17612096361206425,0.17510117177254017,0.17715610072925755
177−2022,cybersecurity,0.06195238157567507,0.06096372334716423,0.06289872904095603 177+2022,cybersecurity,0.056302894369175266,0.05572271796985545,0.0568846766696465
178−2022,database_systems,0.02675100459430097,0.026071578162017824,0.02739116349690441 178+2022,database_systems,0.02431155909805454,0.02389036691272641,0.024693763572095146
179−2022,deep_learning_architectures,0.029149596198903123,0.028498311496223772,0.02981043600047121 179+2022,deep_learning_architectures,0.03973713182485685,0.03925085603453045,0.04022367526537516
180−2022,distributed_computing,0.018414508043298996,0.017852884200054972,0.018901523580151573 180+2022,distributed_computing,0.01673527433252503,0.01640320966110133,0.017051547642626327
181−2022,edge_computing,0.016794722444011048,0.016305743530674485,0.017344467859526955 181+2022,edge_computing,0.015263198277046497,0.014957140282602916,0.015617522522763648
182−2022,federated_learning,0.0019123286954017722,0.0017100027487270775,0.002067533606460817 182+2022,federated_learning,0.001737941912771018,0.001629945060337275,0.0018548604382682677
183−2022,formal_verification,0.007843689053521643,0.007511878427728111,0.008219086637259648 183+2022,formal_verification,0.0071284167777414965,0.006923129080550218,0.007349005144236688
184−2022,foundation_models,0.0006871817693948874,0.0005929396981635885,0.0007913388918703123 184+2022,foundation_models,0.0006245171144454377,0.0005602810683881927,0.0006923218297280853
185−2022,generative_ai,0.004240893205408448,0.00400136127436223,0.004502120446602704 185+2022,generative_ai,0.0038541627634347013,0.003685498534169116,0.004006767981185976
186−2022,hci,0.06832746501917564,0.06732732103823348,0.06919724080158117 186+2022,hci,0.06209662885661622,0.06150141943818441,0.06272065527904316
187−2022,information_retrieval,0.022074241809447767,0.021508691213235773,0.02262222018612809 187+2022,information_retrieval,0.02006127405060016,0.019714087143333815,0.020428177855336856
188−2022,llm,0.004274925064464195,0.004020995039202084,0.004525680964410529 188+2022,llm,0.005827636845082284,0.005619628037606636,0.006025697987092123
189−2022,ml_theory,0.039524731999109934,0.03872357622481969,0.04022398853388133 189+2022,ml_theory,0.035920440088288874,0.035377377848913254,0.03636661295819482
190−2022,multimodal_ai,0.0025091951465333316,0.0023324912629746463,0.002725166559771725 190+2022,multimodal_ai,0.002280379635032198,0.00215801888896621,0.002413357172043759
191−2022,nlp,0.038846058194478984,0.03814595086323119,0.039605230434953335 191+2022,nlp,0.052955482635747717,0.05236834733143837,0.053570944252033695
192−2022,operating_systems,0.00212633673215618,0.0019299009149334413,0.0022971504862629093 192+2022,operating_systems,0.0019324343855554546,0.0018110104151611343,0.0020493529110527037
193−2022,privacy_preserving_computation,0.008591735493920076,0.008224289584942212,0.008937289755101507 193+2022,privacy_preserving_computation,0.007808248265180673,0.007579853434754913,0.008051096205969312
194−2022,programming_languages,0.007193811437322478,0.00686150996740795,0.00753357373787615 194+2022,programming_languages,0.006537802020937383,0.006334298658358805,0.006749379497638433
195−2022,quantum_computing,0.018094477676409378,0.017562108142776738,0.01861693215879789 195+2022,quantum_computing,0.016444427790654727,0.016107857674333953,0.01676596488786349
196−2022,reinforcement_learning,0.013777012788125499,0.01326064477283734,0.014222895587638581 196+2022,reinforcement_learning,0.012520675977324676,0.012241293785340978,0.012800905728249407
197−2022,robotics,0.04535988690951452,0.044546950876320365,0.04622200552363251 197+2022,robotics,0.04122348255723699,0.04072163844741477,0.04169387776951036
198−2022,scientific_computing,0.026089346719197895,0.025464403984345345,0.02674324925718923 198+2022,scientific_computing,0.03556535750036133,0.03509335638693653,0.03609445732139256
199−2022,software_engineering,0.04658110708255343,0.04577916595766961,0.04736655584497179 199+2022,software_engineering,0.04233333868633717,0.041812892888177536,0.04284196326768766
200−2022,speech_audio,0.024930954593646512,0.024324173091270825,0.025494738151022916 200+2022,speech_audio,0.02265748091208048,0.02226755919083994,0.023052755637159125
201−2022,sustainable_computing,0.0010864016544719173,0.0009756999437165408,0.001199721200539274 201+2022,sustainable_computing,0.001480997728542038,0.0013783538799463986,0.001579225348971242
202−2023,ai_accelerators,0.0027909590845514565,0.0025953824957934885,0.002963932221061181 202+2023,ai_accelerators,0.0025202296104125358,0.002390159598780707,0.0026408428505231172
203−2023,ai_agents,0.00871149267729601,0.008433967665136108,0.009047228124396367 203+2023,ai_agents,0.007866457777091286,0.007650370547830774,0.00807773781416183
204−2023,ai_safety,0.007007942917119511,0.006693676386054164,0.007327358497152605 204+2023,ai_safety,0.006328156276301646,0.006124008221086709,0.006525329962519662
205−2023,algorithms,0.033199438666390496,0.03251336425477843,0.03380088831276428 205+2023,algorithms,0.044968524712120116,0.04443327144401749,0.04549408478941346
206−2023,applied_cryptography,0.013054730026926325,0.01263995522363745,0.013464501234608123 206+2023,applied_cryptography,0.01768258661614649,0.01736578477018469,0.01799706367244265
207−2023,autonomous_scientific_discovery,0.0006981761312198765,0.0006090714024729398,0.0007977535019351114 207+2023,autonomous_scientific_discovery,0.0006304514347498526,0.0005642540341011181,0.0006934965782248378
208−2023,blockchain,0.025045323267185022,0.024498825900472664,0.025574540774649688 208+2023,blockchain,0.022615869093064088,0.022189329294241143,0.022985589456330207
209−2023,cloud_computing,0.013340400427283792,0.01292306564484711,0.013749095280096626 209+2023,cloud_computing,0.01204635078948281,0.011782309847966637,0.012321424631107103
210−2023,compilers,0.0012078447070103865,0.0010803402910463564,0.0013371818353188683 210+2023,compilers,0.001090680982117245,0.0010030876359016874,0.001167911282874102
211−2023,computational_biology,0.03591418018995045,0.03518057162007116,0.03662850164420479 211+2023,computational_biology,0.03243042180353242,0.031968616127578155,0.03284982962049976
212−2023,computational_complexity,0.004071530471897246,0.0038609140056459173,0.004288634158034494 212+2023,computational_complexity,0.005514873925474336,0.0053089921288138366,0.005682771023191156
213−2023,computer_architecture,0.0285711127298454,0.027938477882943787,0.029237958207176784 213+2023,computer_architecture,0.025799648838550845,0.025436232989632226,0.026136940356142016
214−2023,computer_graphics,0.010818239153251988,0.010441224042393255,0.011221523141047868 214+2023,computer_graphics,0.014653267472173449,0.01434028773803482,0.014944260212525179
215−2023,computer_networks,0.060370126439697365,0.059414439678745885,0.06123074488411439 215+2023,computer_networks,0.08177112721564277,0.08110868037057935,0.08247305608180738
216−2023,computer_vision,0.19943575732328578,0.19807204479498058,0.20088141827499295 216+2023,computer_vision,0.18009002846488228,0.17919608773362167,0.18110115435657703
217−2023,cybersecurity,0.04245027240505386,0.04168277320213828,0.04324686228010361 217+2023,cybersecurity,0.057498746977773435,0.05688165323279734,0.05807135449338499
218−2023,database_systems,0.02528619403245588,0.024732889448464128,0.025859658452236606 218+2023,database_systems,0.022833374838052787,0.0224598323629635,0.023207744780650182
219−2023,deep_learning_architectures,0.04469549048036845,0.04391676227800908,0.045337899793107136 219+2023,deep_learning_architectures,0.04035992472409869,0.03981923381237135,0.04082338533506918
220−2023,distributed_computing,0.011401216222820584,0.010990775929679699,0.011794376656713776 220+2023,distributed_computing,0.01544290789419764,0.015133080417232759,0.01573449168276945
221−2023,edge_computing,0.010612277194542124,0.010263014584899382,0.010982485088121463 221+2023,edge_computing,0.01437429271229664,0.014127589185236088,0.014660399454029057
222−2023,federated_learning,0.002612924171090388,0.0024294783926123655,0.002799686286191705 222+2023,federated_learning,0.0023594644945513234,0.002249135493470099,0.0024753493489012806
223−2023,formal_verification,0.005102503892331932,0.004824222522696542,0.005374298042081402 223+2023,formal_verification,0.006911323853445259,0.006711904183990947,0.007113895780073321
224−2023,foundation_models,0.0009966464273163738,0.000884850974304791,0.0011189145222962546 224+2023,foundation_models,0.0008999694231054147,0.0008297922977748217,0.0009740474666885223
225−2023,generative_ai,0.006363875436069174,0.006060168818988528,0.006671072933805921 225+2023,generative_ai,0.0057465648277449065,0.005564246153458184,0.00592001777873046
226−2023,hci,0.07084218659455283,0.06984283473472798,0.07180043333465211 226+2023,hci,0.06397033095548067,0.06340178197098031,0.06464239218492401
227−2023,information_retrieval,0.02193669404292852,0.021444479870418507,0.022396530297353213 227+2023,information_retrieval,0.01980878407984037,0.01948063410805307,0.02014390842062459
228−2023,llm,0.018133379568108243,0.017649892597238478,0.01864863355294851 228+2023,llm,0.016374399889040547,0.01605050546443781,0.016696166540051006
229−2023,ml_theory,0.03995836543004159,0.039268218324330736,0.04065680337731067 229+2023,ml_theory,0.036082311739320944,0.0356795714821598,0.03650960019922265
230−2023,multimodal_ai,0.003372190713792004,0.0031697196357382395,0.0035854835218796757 230+2023,multimodal_ai,0.0030450804298417884,0.0029111094999574446,0.003182282423329225
231−2023,nlp,0.05928388074220777,0.05844510939256349,0.06009402687047203 231+2023,nlp,0.05353320745319686,0.052956620212903455,0.05406684518949794
232−2023,operating_systems,0.002047401504802288,0.0018920573156058654,0.0022271818585914063 232+2023,operating_systems,0.0018487988324039429,0.0017384698313227186,0.0019489224008851537
233−2023,privacy_preserving_computation,0.008744656043528955,0.008376106318261262,0.009052202629331308 233+2023,privacy_preserving_computation,0.007896404220241904,0.0076685354297945044,0.008110836514486198
234−2023,programming_languages,0.006765326711520604,0.006489547139688753,0.0070692078726340545 234+2023,programming_languages,0.006109074402726072,0.005929238130963676,0.006301362090324777
235−2023,quantum_computing,0.018403922818955948,0.01791685769541368,0.01886140272893777 235+2023,quantum_computing,0.016618699820006116,0.016285821462458192,0.016941884986744756
236−2023,reinforcement_learning,0.014700098442834502,0.014228829554261085,0.01516970916309627 236+2023,reinforcement_learning,0.013274154958658147,0.012999002310604508,0.013554351218189785
237−2023,robotics,0.044960797410232,0.04425502861358511,0.045807510513368906 237+2023,robotics,0.040599496269303634,0.04014001538301501,0.04107237424858069
238−2023,scientific_computing,0.023764751879839233,0.023231636222308588,0.02435072528863765 238+2023,scientific_computing,0.032189274129740604,0.03177534335961265,0.032598397707678584
239−2023,software_engineering,0.04580908140966415,0.04505496391593028,0.046598107709959015 239+2023,software_engineering,0.041365494762524706,0.040874254885210556,0.04188088881043271
240−2023,speech_audio,0.025637027538393867,0.025080144801729612,0.026157168756152677 240+2023,speech_audio,0.02315017668401459,0.022767177437404054,0.02356816598525374
241−2023,sustainable_computing,0.0018815846736375673,0.001722662331768643,0.0020351834225059403 241+2023,sustainable_computing,0.001699066616650853,0.0015926385338221433,0.0018015149747977039
242−2024,ai_accelerators,0.002366081397785513,0.002207815313590616,0.0025120356049396077 242+2024,ai_accelerators,0.002325521653716305,0.002229526283126853,0.002434395276486948
243−2024,ai_agents,0.008114833817172524,0.007832829929645763,0.00840247778244982 243+2024,ai_agents,0.007975728043763073,0.007740404181071606,0.00819283306675464
244−2024,ai_safety,0.008552283750018916,0.008264639784741618,0.008864551469377818 244+2024,ai_safety,0.008405679140205971,0.008204798686891494,0.00860307807127724
245−2024,algorithms,0.02747313970289171,0.02695957164755842,0.027972309510959407 245+2024,algorithms,0.04050328615138335,0.04007654616768904,0.04099407938411533
246−2024,applied_cryptography,0.01709356247028069,0.01670838642878072,0.01751044675376842 246+2024,applied_cryptography,0.016800541900627214,0.016508364441692278,0.017096775501981423
247−2024,autonomous_scientific_discovery,0.0006272866961570896,0.0005460558202621857,0.0007180781916535104 247+2024,autonomous_scientific_discovery,0.000616533647729439,0.0005665078912250767,0.0006746716890723467
248−2024,blockchain,0.021151667193226774,0.02068395342854824,0.021608513491020127 248+2024,blockchain,0.020789081946245295,0.020467598858331115,0.021094002452614117
249−2024,cloud_computing,0.011842787647404351,0.011470886423048936,0.012178509836433161 249+2024,cloud_computing,0.011639776695839344,0.01140715692809406,0.011890682905658184
250−2024,compilers,0.0010564828566856244,0.0009491838165534908,0.001170728821851954 250+2024,compilers,0.0010383724593337922,0.0009626578008407031,0.001112092847804072
251−2024,computational_biology,0.03235753874343653,0.03176024075336766,0.032918932823922734 251+2024,computational_biology,0.03180286066204357,0.03137050968133593,0.03217196962219737
252−2024,computational_complexity,0.005260404223913839,0.0050306054462975185,0.0054929542589694435 252+2024,computational_complexity,0.0051702295370995065,0.0050180903951899565,0.005342716993479075
253−2024,computer_architecture,0.02360441320034978,0.02309811305008527,0.024094480931722503 253+2024,computer_architecture,0.023199782590766327,0.022850886740335228,0.023551382536143608
254−2024,computer_graphics,0.014596796344129116,0.014219805293511023,0.014987818807687564 254+2024,computer_graphics,0.014346575736967276,0.014061935942694821,0.014593425804366844
255−2024,computer_networks,0.07475441588282579,0.07388329899611203,0.07557497841409268 255+2024,computer_networks,0.07347296378270433,0.07286582686491287,0.07408848339482896
256−2024,computer_vision,0.18339469401661118,0.18207175187693078,0.18470711259658634 256+2024,computer_vision,0.18025091297005622,0.1793576490057719,0.18106481174767008
257−2024,cybersecurity,0.06265713692228753,0.06193823335340223,0.06346593782800149 257+2024,cybersecurity,0.06158305830434318,0.06101100701847843,0.062084803121607204
258−2024,database_systems,0.02066744588391253,0.02018308701172632,0.021099668427624275 258+2024,database_systems,0.02031316123571731,0.01999742434956376,0.020655432053530265
259−2024,deep_learning_architectures,0.039872598438844824,0.0392177303868589,0.040469690084605756 259+2024,deep_learning_architectures,0.03918909600753902,0.038687790605703744,0.03962181880130176
260−2024,distributed_computing,0.014583040056932688,0.014178261306177813,0.014947581667638016 260+2024,distributed_computing,0.014333055262236367,0.01406663430766381,0.01457983772726228
261−2024,edge_computing,0.014045169227552378,0.013651464287990625,0.014440043451525825 261+2024,edge_computing,0.013804404700257835,0.01351827765376498,0.014039018738025929
262−2024,federated_learning,0.0028475514496604723,0.002675253952525219,0.003027827593369653 262+2024,federated_learning,0.0027987382692981114,0.0026769863943462784,0.002915149556731236
263−2024,formal_verification,0.0038820242468318128,0.003682214175303705,0.0040801147824603675 263+2024,formal_verification,0.005723216953593674,0.005543327037298934,0.005893642537576779
264−2024,foundation_models,0.0014389076407463062,0.0013095985410998886,0.0015753012282988838 264+2024,foundation_models,0.0014142416568530553,0.0013331188084676029,0.0014994206476577805
265−2024,generative_ai,0.009831618459286665,0.00944362237891143,0.010120569271847622 265+2024,generative_ai,0.009663083290180485,0.009420999190123564,0.00988218258319486
266−2024,hci,0.06613197506810509,0.06536513083834024,0.06690535353428824 266+2024,hci,0.06499833022137073,0.06439812254687886,0.06555648921944947
267−2024,information_retrieval,0.019459643868066204,0.01899021056748812,0.01991373890842027 267+2024,information_retrieval,0.01912606355434352,0.01885024586983298,0.01942834756813981
268−2024,llm,0.034216013143673875,0.03362284203976393,0.03480553883147677 268+2024,llm,0.03362947679818934,0.0332200430221506,0.0341027610161448
269−2024,ml_theory,0.03674304310165759,0.03609333365737032,0.03742274125203307 269+2024,ml_theory,0.03611318800625728,0.03570750616195636,0.036529686230342924
270−2024,multimodal_ai,0.0046881426765424585,0.004473407033406227,0.00489448698448887 270+2024,multimodal_ai,0.004607777788293702,0.004451582503964879,0.0047653251200956166
271−2024,nlp,0.05341703881244724,0.052638226612821505,0.05413663019569236 271+2024,nlp,0.052501355427591774,0.05198176358368295,0.052959767123343236
272−2024,operating_systems,0.0017332921867498526,0.0016012318296641495,0.0018627388492682344 272+2024,operating_systems,0.0017035798160945027,0.001608868890604487,0.0018076874715225002
273−2024,privacy_preserving_computation,0.009197453619531362,0.008862969496350228,0.009505869578475263 273+2024,privacy_preserving_computation,0.009039789405085591,0.008820047889521497,0.00924137968332344
274−2024,programming_languages,0.003527112037163986,0.0033314976332307882,0.0037528527100573594 274+2024,programming_languages,0.005199974581507506,0.0050375260776156375,0.005375808355382974
275−2024,quantum_computing,0.017500748571294942,0.017088059955402118,0.01788344848109955 275+2024,quantum_computing,0.017200747952662115,0.016901742653988066,0.01751141466079157
276−2024,reinforcement_learning,0.013607719294705986,0.013212432382116645,0.013968134019252385 276+2024,reinforcement_learning,0.013374453603814937,0.013108674871792098,0.013619174196444386
277−2024,robotics,0.041249602787207205,0.04064693984513172,0.04187991586654751 277+2024,robotics,0.04054249552810298,0.04004081831321261,0.04097863224173527
278−2024,scientific_computing,0.030108385786820655,0.02956081677496686,0.030610490269490254 278+2024,scientific_computing,0.029592263043539985,0.029195775122056088,0.029967557620883184
279−2024,software_engineering,0.04200207169685179,0.04136371118950157,0.04267475414075709 279+2024,software_engineering,0.04128206549588369,0.04084998492467068,0.04173250011154392
280−2024,speech_audio,0.022597452977571292,0.02208393077652866,0.023041781054015897 280+2024,speech_audio,0.022210083840463805,0.02187920402261164,0.022523116631671173
281−2024,sustainable_computing,0.0017484241026659228,0.0016313580986243256,0.0018833044986268934 281+2024,sustainable_computing,0.0017184523382985024,0.0016190430478394957,0.0018076874715225002
282−2025,ai_accelerators,0.002218169672523209,0.0020761881929598136,0.0023531684563703713 282+2025,ai_accelerators,0.0021347609874109584,0.002043451234263698,0.002224950719053806
283−2025,ai_agents,0.010263398920242486,0.00995831330657409,0.010557835922943626 283+2025,ai_agents,0.00987746964741723,0.009682249899198065,0.01009646185206756
284−2025,ai_safety,0.01266777381186523,0.012339296167547456,0.013030059341275761 284+2025,ai_safety,0.01219143407553425,0.011944973343488196,0.012440890865104609
285−2025,algorithms,0.02622002241445325,0.025748515886214646,0.026684429868713683 285+2025,algorithms,0.03785112674163344,0.03743932283499843,0.03824159423860938
286−2025,applied_cryptography,0.01700519163426502,0.016601300876211932,0.017405765611852895 286+2025,applied_cryptography,0.01636575422248107,0.016069620536714305,0.016610506921732895
287−2025,autonomous_scientific_discovery,0.0008891299212002788,0.0007948053399001707,0.0009845600959887905 287+2025,autonomous_scientific_discovery,0.000855696429371444,0.000796335289637561,0.0009111654943774921
288−2025,blockchain,0.020962052540130133,0.020516032851255778,0.021342667645795777 288+2025,blockchain,0.02017382733748488,0.01991328233502083,0.020477353165180772
289−2025,cloud_computing,0.00851423364332623,0.00823946456689248,0.008794763443725873 289+2025,cloud_computing,0.012291115989426997,0.012060335558442723,0.012523772456431163
290−2025,compilers,0.0008926212690583952,0.0007924777746614266,0.0009764718067841543 290+2025,compilers,0.0008590564938846826,0.0008002273643653959,0.0009167656018995565
291−2025,computational_biology,0.032765135865801896,0.032314461046450055,0.0333916000498099 291+2025,computational_biology,0.03153308543524036,0.0311824627032839,0.03189544039245553
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293−2025,computer_architecture,0.015679255303260413,0.015315262892674803,0.0160718379735286 293+2025,computer_architecture,0.02263451458267999,0.022314076430267463,0.022954840732942072
294−2025,computer_graphics,0.014477455784988833,0.014120116331710631,0.01484998260144984 294+2025,computer_graphics,0.013933067514896286,0.0136916748801577,0.014181348281887012
295−2025,computer_networks,0.06930907389670499,0.06849535708924001,0.07009253235606629 295+2025,computer_networks,0.06670288069530934,0.06617697459791229,0.06721394650777295
296−2025,computer_vision,0.17544837634867858,0.17428651397062844,0.17643322739032222 296+2025,computer_vision,0.16885108194077325,0.16800818175708973,0.16958203597509072
297−2025,cybersecurity,0.06871438097820584,0.06803339357848026,0.06940590061063674 297+2025,cybersecurity,0.06613054970655437,0.06558070314950047,0.06666502396846019
298−2025,database_systems,0.01941305787374588,0.0189342777041362,0.01982509511013457 298+2025,database_systems,0.01868307871511133,0.018413153532547823,0.018980752430446664
299−2025,deep_learning_architectures,0.039732702407982615,0.03915296409614241,0.04029259822616253 299+2025,deep_learning_architectures,0.0382386541821603,0.03786392298732136,0.038624221585054436
300−2025,distributed_computing,0.009736981248746508,0.009414768634196356,0.010062064527091113 300+2025,distributed_computing,0.014056269880381703,0.01381263720263429,0.014313146812418798
301−2025,edge_computing,0.01344634438422516,0.013122812816039718,0.013771097924160942 301+2025,edge_computing,0.01294072846198647,0.012697039783163836,0.013170388871466331
302−2025,federated_learning,0.002960662983682604,0.002803494140936403,0.003127142087383782 302+2025,federated_learning,0.002849334707226379,0.0027412246315129253,0.0029546727297164103
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304−2025,foundation_models,0.00215765297631586,0.0020214322207183564,0.002289218601435875 304+2025,foundation_models,0.0020765198691814883,0.0019823820617355854,0.0021633495363110973
305−2025,generative_ai,0.014830081918658578,0.014443647899896075,0.015152798839010459 305+2025,generative_ai,0.01427243403073339,0.014036641503516868,0.014530094977823574
306−2025,hci,0.06767163175124845,0.06691534761604949,0.06846114188023047 306+2025,hci,0.06512701043860042,0.06458724407508624,0.06561657183817929
307−2025,information_retrieval,0.0202253781420676,0.019800248351210973,0.02066080740910567 307+2025,information_retrieval,0.019464853725191522,0.019179780251780836,0.01973494691098069
308−2025,llm,0.052554095525604964,0.05185338201048102,0.053167467155145025 308+2025,llm,0.05057793109627705,0.05014526678912235,0.05100300725773935
309−2025,ml_theory,0.038169742350165896,0.03751558013981684,0.03875751076207977 309+2025,ml_theory,0.03673446530173379,0.03632224138703463,0.037132716948165403
310−2025,multimodal_ai,0.007854368898142254,0.007602933663226913,0.008127857813694695 310+2025,multimodal_ai,0.007559025133282559,0.007395978002777653,0.007723780296581694
311−2025,nlp,0.049942567327733986,0.049287357713027495,0.05058974684236681 311+2025,nlp,0.04806460284037453,0.04766080708749608,0.04852733972492272
312−2025,operating_systems,0.0015862357102041625,0.0014755599831018764,0.0017073272917498285 312+2025,operating_systems,0.001526589310514762,0.001447067783701447,0.0016083508803369025
313−2025,privacy_preserving_computation,0.010261071355003742,0.009930266145447224,0.01056260743168305 313+2025,privacy_preserving_computation,0.009875229604408405,0.009669509654585368,0.010055553066618879
314−2025,programming_languages,0.0048797405230271845,0.004658563636215519,0.005091607148883875 314+2025,programming_languages,0.004696250168003226,0.004548351328345504,0.0048334808028314144
315−2025,quantum_computing,0.020332446143049833,0.01989468931077302,0.02074215581419978 315+2025,quantum_computing,0.01956789570359751,0.01930452264683482,0.0198592132968953
316−2025,reinforcement_learning,0.015373568401905345,0.015026528424808587,0.015750633970581903 316+2025,reinforcement_learning,0.014795484073294207,0.014556863491779043,0.015040236772546033
317−2025,robotics,0.038944821574667714,0.038327784029876626,0.03951874097341106 317+2025,robotics,0.03748039962367278,0.037061287576721474,0.037860310917969625
318−2025,scientific_computing,0.017992855150572133,0.01758428986566457,0.01838433222735192 318+2025,scientific_computing,0.02597441870883921,0.025647204426324986,0.026275872496751937
319−2025,software_engineering,0.042339575475376104,0.041742205856852406,0.042954110887535535 319+2025,software_engineering,0.04074750235204516,0.04033581044756059,0.04116639039469558
320−2025,speech_audio,0.02138217806572346,0.02093255065072905,0.021811730230533698 320+2025,speech_audio,0.020578155100577932,0.020276869315890865,0.020856508444962143
321−2025,sustainable_computing,0.00190743971315086,0.001785126159854853,0.0020262037294577822 321+2025,sustainable_computing,0.0018357152457327182,0.0017461135253796873,0.0019159367859862908
modified results/bibliometrics/diversity_yearly.csv +16 −8
@@ -1,9 +1,17 @@
1 year,n,rao_stirling,rs_ci_low,rs_ci_high,leinster_cobbold_q2,similarity_source 1 year,n,rao_stirling,rs_ci_low,rs_ci_high,leinster_cobbold_q2,similarity_source
2−2018,169727,0.8992868732205007,0.8988175897191749,0.8997310848387939,9.929192270928045,co-classification Jaccard 2+2018,499561,0.8966979269920947,0.8964150650642733,0.8969729208682462,9.680347846683333,co-classification Jaccard
3−2019,180248,0.896100206436324,0.8956356536840846,0.8965579428776569,9.624658199028467,co-classification Jaccard 3+2019,539741,0.8957695076396416,0.8955171860453549,0.896020741688698,9.59412142602836,co-classification Jaccard
4−2020,189370,0.900723632618864,0.9002795125878724,0.9011485807064236,10.0728907229337,co-classification Jaccard 4+2020,549101,0.8964124020947231,0.8961367144172547,0.8966751994754848,9.653665305709902,co-classification Jaccard
5−2021,226728,0.9022200511081365,0.9018514633225768,0.9026284736924316,10.22704564006183,co-classification Jaccard 5+2021,558970,0.8939562806724264,0.8936753772205429,0.8942304704842667,9.43007286373045,co-classification Jaccard
6−2022,254663,0.890309885996106,0.8897676301389984,0.8907889622855558,9.116591855894152,co-classification Jaccard 6+2022,560433,0.8914726303600234,0.8911634337431833,0.8917846063814902,9.214265519539925,co-classification Jaccard
7−2023,286460,0.8855095802901976,0.8849779485027648,0.8860485908545297,8.73435526338963,co-classification Jaccard 7+2023,634466,0.8905938872892762,0.8902609987891713,0.8908633236052528,9.140257113823784,co-classification Jaccard
8−2024,363470,0.8883809706057654,0.8879340384933714,0.8887799732899944,8.959045831405975,co-classification Jaccard 8+2024,739619,0.8900517958692582,0.889784104970656,0.890332496245109,9.095191757846987,co-classification Jaccard
9−2025,429633,0.889452772558058,0.8891043925701015,0.8898464283041351,9.04590755589223,co-classification Jaccard 9+2025,892840,0.8931137517568771,0.892858747038931,0.893398088972461,9.355740485206343,co-classification Jaccard
10+2018,499561,0.32462859635185487,0.32433248924002983,0.32493048698590365,1.480666777714177,"semantic centroids (post, min-max rescaled)"
11+2019,539741,0.3271584536618012,0.326851943505821,0.3274518275321462,1.4862340255923456,"semantic centroids (post, min-max rescaled)"
12+2020,549101,0.32856776769951723,0.32824799362503126,0.32887020100556863,1.4893535816321593,"semantic centroids (post, min-max rescaled)"
13+2021,558970,0.3297566773258652,0.3294525978540042,0.33008979919882586,1.4919954681684897,"semantic centroids (post, min-max rescaled)"
14+2022,560433,0.3304002133449643,0.3300459452955017,0.3307594477250132,1.4934293886135597,"semantic centroids (post, min-max rescaled)"
15+2023,634466,0.3314445481129306,0.3311563134992759,0.3317515225314592,1.4957622395829588,"semantic centroids (post, min-max rescaled)"
16+2024,739619,0.330406354003291,0.33011267335780015,0.3307003285187273,1.4934430844418662,"semantic centroids (post, min-max rescaled)"
17+2025,892840,0.3314388211257594,0.3311864200715964,0.3317168253358117,1.4957494266775317,"semantic centroids (post, min-max rescaled)"
added results/semantic/category_drift.csv +356 −0
@@ -0,0 +1,356 @@
1+category,year,n,drift_cos_dist
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added results/semantic/category_similarity_baseline.csv +41 −0
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added results/semantic/category_similarity_post.csv +41 −0
@@ -0,0 +1,41 @@
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added results/semantic/clusters.csv +124 −0
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1+cluster,n,top_terms,top_category,top_category_share,share_post
2+-1,75840,based; learning; using; data; analysis; model; ai; networks; network; models; systems; detection,computer_vision,0.13380412562720684,0.5384098101265823
3+0,677,في; على; لدى; دراسة; من; الذكاء; الاصطناعي; الذكاء الاصطناعي; فی; استخدام; التعليم; مهارات,hci,0.23671497584541062,0.5199409158050221
4+1,258,και; της; για; σε; με; στο; ανάπτυξη; στην; την; των; του; εκπαίδευση,hci,0.1657142857142857,0.4844961240310077
5+2,211,av; och; van; en; og; om; på; het; för; med; ai; som,hci,0.23684210526315788,0.3744075829383886
6+3,445,ve; yapay; bir; ile; tabanlı; dijital; öğrenme; için; derin; analizi; veri; türkçe,computer_vision,0.1814814814814815,0.5393258426966292
7+4,486,language; language models; models; large; large language; reasoning; text; generation; llm; question; learning; benchmark,nlp,0.8538622129436325,0.6296296296296297
8+5,272,va; ta; lim; ta lim; raqamli; sun iy; iy; sun; zamonaviy; intellekt; iy intellekt; innovatsion,cybersecurity,0.19753086419753085,0.9411764705882353
9+6,566,za; na; pro; analiza; pri; web; application; dla; podataka; 3d; primjena; implementace,software_engineering,0.14678899082568808,0.5406360424028268
10+7,294,ja; és; az; tekoälyn; vaikutus; kehittäminen; digitális; könyvtári; suunnittelu; toteutus; mesterséges; vizsgálata,software_engineering,0.22164948453608246,0.5918367346938775
11+8,6204,pada; dan; berbasis; di; menggunakan; untuk; sistem; dalam; pembelajaran; aplikasi; metode; media,software_engineering,0.17493237150586113,0.5752740167633784
12+9,868,연구; 위한; study; based; 분석; 기반; using; 이용한; 활용한; focusing; 중심으로; ai,hci,0.19131832797427653,0.43202764976958524
13+10,462,शकष; 互联网; ai; एव; based; research; image; teaching; measurement; multi; network; design,computer_vision,0.4511627906976744,0.6926406926406926
14+11,890,und; der; für; von; die; im; zur; ki; mit; des; digitale; auf,hci,0.19140625,0.5258426966292135
15+12,3799,molecular; drug; docking; protein; inhibitors; silico; prediction; molecular docking; discovery; potential; based; cancer,computational_biology,0.9226460389437183,0.5580415898920769
16+13,5404,la; en; para; el; da; em; del; na; en la; una; uma; los,hci,0.1642754662840746,0.5323834196891192
17+14,405,et; des; la; pour; les; du; une; le; en; dans; par; numérique,nlp,0.17410714285714285,0.3950617283950617
18+15,1107,для; на; разработка; данных; технологий; как; цифровой; системы; при; по; анализ; применение,cybersecurity,0.14759036144578314,0.5383920505871725
19+16,1004,та; для; на; використання; навчання; технологій; освіти; системи; аналіз; розробка; даних; як,cybersecurity,0.1611295681063123,0.7071713147410359
20+17,527,для; на; та; development; software; modern; technologies; використання; software implementation; methods; analysis; як,software_engineering,0.20930232558139536,0.5521821631878557
21+18,445,et; des; pour; les; la; dans; sur; le; données; apprentissage; par; une,computer_vision,0.0997624703087886,0.3887640449438202
22+19,335,et; la; des; le; les; du; numérique; en; une; dans; au; pour,hci,0.29906542056074764,0.3611940298507463
23+20,3472,blockchain; blockchain technology; blockchain based; technology; based; smart; secure; decentralized; using blockchain; chain; data; management,blockchain,0.6486095017381228,0.5849654377880185
24+21,463,retracted; und; based; mit; data; von; algorithm; python; using; technology; design; model,software_engineering,0.19662921348314608,0.35853131749460043
25+22,405,iot; healthcare; health; monitoring; smart; things; covid; internet; covid 19; 19; iot based; internet things,edge_computing,0.5449275362318841,0.4246913580246914
26+23,591,prediction; machine learning; learning; machine; patients; using; clinical; risk; diabetes; data; predicting; health,computational_biology,0.3484251968503937,0.6345177664974619
27+24,336,fraud; fraud detection; detection; credit card; credit; card; card fraud; learning; machine learning; machine; financial; using,ml_theory,0.6156156156156156,0.75
28+25,1022,teaching; research; education; english; based; college; colleges; construction; course; vocational; reform; universities,hci,0.13577023498694518,0.5088062622309197
29+26,469,visualization; data; visual; interactive; visualizations; data visualization; visualizing; visual analytics; analytics; exploration; analysis; graphs,computer_graphics,0.5520361990950227,0.4051172707889126
30+27,489,filter; tracking; kalman; kalman filter; target; estimation; target tracking; based; algorithm; fusion; filtering; robust,robotics,0.19883040935672514,0.4498977505112474
31+28,3024,quantum; entanglement; quantum computing; states; computing; qubit; state; key distribution; quantum key; classical; key; distribution,quantum_computing,0.8395812225599459,0.6296296296296297
32+29,877,adversarial; attacks; attack; robustness; adversarial attacks; models; backdoor; language; language models; adversarial attack; robust; adversarial robustness,cybersecurity,0.4766248574686431,0.7183580387685291
33+30,1325,federated; federated learning; privacy; learning; preserving; privacy preserving; data; differential privacy; private; differential; differentially private; differentially,privacy_preserving_computation,0.40452830188679245,0.6973584905660377
34+31,1966,encryption; image; based; steganography; using; watermarking; cryptography; image encryption; security; algorithm; hiding; secure,applied_cryptography,0.4369277721261445,0.47812817904374366
35+32,2143,cybersecurity; security; cyber; phishing; information; information security; risk; cyber security; digital; forensics; analysis; cybercrime,cybersecurity,0.9388835418671799,0.616425571628558
36+33,272,authentication; protocol; key; scheme; authentication protocol; secure; lightweight; authentication scheme; based; iot; authentication key; key agreement,cybersecurity,0.4632352941176471,0.4227941176470588
37+34,433,cloud; encryption; data; secure; storage; scheme; based; security; attribute based; cloud storage; attribute; access,cybersecurity,0.47344110854503463,0.4064665127020785
38+35,1246,detection; intrusion; intrusion detection; learning; network; iot; machine learning; based; machine; attack; using; deep,computer_networks,0.5754414125200642,0.6837881219903692
39+36,296,malware; malware detection; detection; android; android malware; using; learning; analysis; machine learning; ransomware; machine; classification,cybersecurity,0.9457627118644067,0.5912162162162162
40+37,2278,optimization; algorithm; problem; objective; multi; based; multi objective; optimization algorithm; evolutionary; problems; swarm; genetic,algorithms,0.7417386883579055,0.4310798946444249
41+38,2093,using; data; sentinel; satellite; based; mapping; china; remote; remote sensing; land; sensing; sar,computer_vision,0.3794466403162055,0.4056378404204491
42+39,943,fuzzy; decision; based; decision making; making; analysis; method; data; urban; model; evaluation; application,algorithms,0.150997150997151,0.39660657476139977
43+40,226,traffic; prediction; temporal; graph; flow prediction; traffic prediction; forecasting; flow; traffic flow; network; spatio temporal; spatio,deep_learning_architectures,0.5054347826086957,0.5973451327433629
44+41,758,anomaly; anomaly detection; fault; detection; fault diagnosis; based; diagnosis; data; learning; network; time series; series,ml_theory,0.39176829268292684,0.5659630606860159
45+42,354,detection; deepfake; deepfake detection; forgery; face; forgery detection; image; fake; deep; video; using; images,computer_vision,0.8559322033898306,0.7033898305084746
46+43,1232,face; recognition; face recognition; using; facial; biometric; based; fingerprint; authentication; detection; deep; attendance,computer_vision,0.7534580960130187,0.4431818181818182
47+44,291,time series; series; time; forecasting; series forecasting; stock; prediction; multivariate; financial; learning; based; multivariate time,deep_learning_architectures,0.5148936170212766,0.7010309278350515
48+45,762,solar; forecasting; photovoltaic; power; prediction; pv; energy; photovoltaic power; term; short term; using; short,deep_learning_architectures,0.5021008403361344,0.615485564304462
49+46,228,gesture; gesture recognition; hand; recognition; hand gesture; using; gestures; based; interaction; hand gestures; control; recognition using,hci,0.7368421052631579,0.49122807017543857
50+47,274,sign; sign language; language; language recognition; recognition; translation; using; language translation; indian sign; indian; gesture; real time,computer_vision,0.5474452554744526,0.6167883211678832
51+48,397,ecg; heart; sleep; using; based; detection; rate; respiratory; monitoring; heart rate; classification; blood pressure,deep_learning_architectures,0.3346456692913386,0.4609571788413098
52+49,1529,recognition; human; activity; action; activity recognition; based; using; action recognition; gait; human activity; fall; detection,computer_vision,0.6645253390435403,0.4892086330935252
53+50,873,eeg; brain; functional; connectivity; based; classification; network; learning; using; neural; fmri; alzheimer,hci,0.3106646058732612,0.48109965635738833
54+51,702,graph; graph neural; networks; learning; clustering; neural; neural networks; network; embedding; multi view; graphs; view,deep_learning_architectures,0.6357142857142857,0.5712250712250713
55+52,277,antenna; band; applications; compact; wideband; design; rfid; array; dual; 5g; antennas; patch antenna,computer_networks,0.7322834645669292,0.35379061371841153
56+53,554,recognition; handwritten; text; character; image; using; character recognition; image retrieval; retrieval; based; ocr; deep,computer_vision,0.9638989169675091,0.44945848375451264
57+54,1363,recommendation; recommender; based; recommender systems; filtering; collaborative filtering; collaborative; learning; recommendations; personalized; systems; graph,information_retrieval,0.85470719051149,0.5641966250917094
58+55,2428,speech; audio; recognition; speech recognition; speaker; music; based; using; sound; voice; neural; learning,speech_audio,0.8765534382767192,0.5518945634266886
59+56,391,student; performance; learning; students; machine learning; machine; prediction; predicting; academic; using; student performance; data mining,ml_theory,0.35517241379310344,0.6138107416879796
60+57,809,clustering; means; mining; data; algorithm; data mining; using; based; analysis; means clustering; fuzzy; cluster,ml_theory,0.45350500715307585,0.48331273176761436
61+58,201,imbalanced; selection; feature selection; classification; feature; data; ensemble; imbalanced data; based; algorithm; class; imbalance,ml_theory,0.5808080808080808,0.44776119402985076
62+59,678,cmos; power; amplifier; low; adc; low power; ghz; nm; high; based; digital; noise,computer_architecture,0.7640232108317214,0.42035398230088494
63+60,758,games; networks; social; bandits; social networks; community; auctions; online; regret; community detection; optimal; influence,algorithms,0.5828402366863905,0.4920844327176781
64+61,208,bibliometric; research; analysis; science; bibliometric analysis; impact; citation; scientific; trends; study; papers; collaboration,information_retrieval,0.5333333333333333,0.5625
65+62,664,sensor; sensors; based; sensing; detection; measurement; fiber; monitoring; temperature; capacitive; sensor based; using,scientific_computing,0.2210144927536232,0.3132530120481928
66+63,330,codes; cyclic; linear; sequences; complementary; mathbb; binary; cyclic codes; linear codes; decoding; new; reed,applied_cryptography,0.38961038961038963,0.4575757575757576
67+64,283,ontology; semantic; knowledge; data; ontologies; ontology based; ontological; domain; based; model; semantic web; web,nlp,0.43529411764705883,0.3568904593639576
68+65,1788,library; libraries; data; research; open; information; academic; university; digital; services; science; resources,database_systems,0.2979591836734694,0.4217002237136465
69+66,1441,graphs; graph; domination; number; coloring; vertex; labeling; edge; trees; bounds; planar; problem,algorithms,0.9356223175965666,0.48854961832061067
70+67,1156,logic; algebras; order; logics; automata; semantics; theory; fuzzy; semigroups; finite; modal; ideals,formal_verification,0.3479236812570146,0.4437716262975779
71+68,519,measurement; phase; laser; method; fringe; optical; interferometry; based; calibration; using; 3d; projection,computer_vision,0.6633663366336634,0.40847784200385356
72+69,4315,segmentation; cancer; deep; learning; images; deep learning; using; image; classification; breast; detection; based,computer_vision,0.7684848484848484,0.6259559675550406
73+70,3862,detection; object; object detection; based; deep; network; segmentation; learning; using; tracking; deep learning; classification,computer_vision,0.965956340956341,0.6134127395132056
74+71,1907,3d; image; depth; estimation; generation; diffusion; point; pose; based; monocular; generative; reconstruction,computer_vision,0.7184058730991085,0.6486628211851075
75+72,328,slam; visual; lidar; odometry; inertial; localization; mapping; based; visual inertial; visual slam; monocular; registration,robotics,0.625,0.524390243902439
76+73,1584,image; enhancement; super resolution; super; resolution; image enhancement; based; images; denoising; network; image super; restoration,computer_vision,0.9822896900695762,0.4779040404040404
77+74,490,compression; coding; video; image compression; image; video coding; hevc; based; video compression; vvc; intra; lossless,computer_vision,0.8811475409836066,0.4142857142857143
78+75,403,music; sound; audio; musical; performance; instrument; interactive; sonification; instruments; digital; sonic; guitar,speech_audio,0.6934984520123839,0.456575682382134
79+76,1446,power; control; dc; energy; voltage; grid; converter; based; electric; current; inverter; charging,distributed_computing,0.18405797101449275,0.4322268326417704
80+77,671,problems; convergence; convex; optimization; matrices; variational; spaces; inequalities; functions; generalized; mappings; problem,scientific_computing,0.44170403587443946,0.47690014903129657
81+78,369,introduction; conference; preface; data; issue; workshop; 2020; report; proceedings; ieee; open; research,computer_architecture,0.1036036036036036,0.39295392953929537
82+79,890,equations; equation; method; finite; numerical; methods; problems; order; fractional; element; finite element; boundary,scientific_computing,0.9191270860077022,0.5067415730337078
83+80,332,synchronization; oscillators; dynamics; chaotic; coupled; model; reaction; systems; bifurcation; diffusion; networks; phase,scientific_computing,0.45251396648044695,0.4578313253012048
84+81,700,introduction; programming; python; working; data; using; development; web; html; functions; basic; creating,software_engineering,0.43661971830985913,0.34
85+82,2353,software; code; agile; testing; development; test; based; requirements; software development; study; using; engineering,software_engineering,0.8884138520735357,0.4649383765405865
86+83,2598,reinforcement; reinforcement learning; learning; planning; path; path planning; robot; based; multi; policy; autonomous; control,robotics,0.5528957528957529,0.5885296381832179
87+84,506,robot; soft; control; robotic; design; robots; driven; inspired; stiffness; gripper; based; modeling,robotics,0.979757085020243,0.5
88+85,2495,control; systems; time; nonlinear; adaptive; consensus; based; triggered; event triggered; distributed; agent systems; event,robotics,0.283008356545961,0.4496993987975952
89+86,4063,memory; efficient; neural; based; fpga; performance; computing; design; hardware; high; gpu; accelerator,computer_architecture,0.5810776625347837,0.4902781196160473
90+87,315,codes; decoding; ldpc; polar; polar codes; ldpc codes; decoder; code; coding; error; based; low,computer_networks,0.8248407643312102,0.4031746031746032
91+88,351,detection; hate; hate speech; fake; speech; news; speech detection; cyberbullying; fake news; social; social media; news detection,nlp,0.8091168091168092,0.6210826210826211
92+89,426,causal; bayesian; models; data; inference; estimation; regression; mixture; causal discovery; distribution; model; markov,ml_theory,0.6197183098591549,0.47183098591549294
93+90,765,cloud; scheduling; computing; cloud computing; load; data; load balancing; balancing; resource; based; algorithm; optimization,cloud_computing,0.44836601307189544,0.46013071895424834
94+91,200,cloud; cloud computing; computing; enterprise; microservices; infrastructure; multi cloud; scalable; azure; adoption; solutions; cloud native,cloud_computing,0.7286432160804021,0.59
95+92,278,neural; physics; informed; physics informed; neural networks; networks; learning; informed neural; equations; pdes; pde; differential,scientific_computing,0.5451263537906137,0.8453237410071942
96+93,694,neural; networks; neural networks; deep; pruning; learning; deep neural; generalization; network; gradient; training; deep learning,ml_theory,0.37900874635568516,0.4827089337175792
97+94,616,learning; domain; shot; shot learning; adaptation; domain adaptation; supervised; label; distillation; knowledge; knowledge distillation; active,computer_vision,0.4642857142857143,0.5438311688311688
98+95,408,innovation; digital; knowledge; industry; business; transformation; china; firms; performance; management; technology; firm,cybersecurity,0.15966386554621848,0.48284313725490197
99+96,630,ai; intelligence; artificial; artificial intelligence; human; ethics; explainable; agents; systems; cognitive; decision; introduction,ai_safety,0.37601626016260165,0.6492063492063492
100+97,1699,sentiment; sentiment analysis; analysis; using; twitter; text; classification; social; based; reviews; learning; social media,nlp,0.8740963855421687,0.5350206003531489
101+98,205,fog; computing; edge; fog computing; edge computing; iot; cloud; offloading; fog cloud; task; things; resource,edge_computing,0.36097560975609755,0.424390243902439
102+99,385,image; retrieval; captioning; video; image captioning; hashing; modal; cross modal; cross; text; video summarization; summarization,computer_vision,0.8155844155844156,0.638961038961039
103+100,292,uav; networks; assisted; uav assisted; communication; enabled; uavs; trajectory; uav enabled; energy; optimization; allocation,computer_networks,0.8493150684931506,0.5445205479452054
104+101,303,entity; entity recognition; named; named entity; extraction; recognition; relation; relation extraction; event; chinese; based; information extraction,nlp,0.8283828382838284,0.4884488448844885
105+102,1223,web; sistem; berbasis; informasi; sistem informasi; management; berbasis web; application; pada; website; web based; based,software_engineering,0.5081809432146295,0.5699100572363042
106+103,300,usability; user; design; user experience; experience; mobile; website; application; aplikasi; metode; ux; ui,hci,0.8732394366197183,0.5433333333333333
107+104,507,estimation; doa; array; doa estimation; radar; direction; arrival; mimo; arrays; arrival estimation; direction arrival; based,speech_audio,0.47103274559193953,0.40828402366863903
108+105,247,indoor; localization; positioning; indoor localization; indoor positioning; uwb; based; fi; wi fi; wi; using; based indoor,computer_networks,0.679324894514768,0.41295546558704455
109+106,617,translation; machine translation; machine; language; english; neural machine; multilingual; neural; corpus; low resource; languages; models,nlp,0.86709886547812,0.5235008103727715
110+107,1224,language; language models; models; reasoning; large language; large; llms; llm; question; retrieval; question answering; answering,llm,0.5531045751633987,0.9109477124183006
111+108,957,wireless sensor; sensor; wireless; routing; sensor networks; networks; energy; protocol; network; sensor network; energy efficient; efficient,computer_networks,0.9895506792058516,0.3343782654127482
112+109,350,offloading; edge; computing; edge computing; task; task offloading; mobile edge; computation; computation offloading; allocation; mobile; resource,computer_networks,0.6142857142857143,0.5942857142857143
113+110,656,sdn; network; networks; software defined; defined; software; networking; tcp; based; routing; service; performance,computer_networks,0.9664634146341463,0.32621951219512196
114+111,575,ai; consumer; marketing; customer; intention; intelligence; artificial intelligence; artificial; social; service; purchase; commerce,hci,0.4117647058823529,0.6921739130434783
115+112,1023,5g; networks; radio; spectrum; network; cognitive radio; cognitive; access; 6g; ran; based; allocation,computer_networks,0.9638318670576735,0.3998044965786901
116+113,1706,mimo; massive; massive mimo; systems; channel; beamforming; ofdm; communication; noma; ris; mimo systems; channel estimation,computer_networks,0.976936723832052,0.4425556858147714
117+114,732,media; social; social media; digital; design; communication; television; instagram; online; language; internet; tiktok,hci,0.7136563876651982,0.5163934426229508
118+115,266,eye; eye tracking; gaze; tracking; visual; movements; using; eye movements; gaze estimation; visual search; movement; attentional,hci,0.683206106870229,0.4699248120300752
119+116,517,reality; augmented reality; augmented; virtual; virtual reality; learning; education; ar; students; immersive; vr; media,hci,0.974609375,0.5918762088974855
120+117,451,reality; virtual; virtual reality; haptic; vr; feedback; perception; hand; augmented; immersive; tactile; augmented reality,hci,0.9527027027027027,0.4501108647450111
121+118,296,reality; augmented reality; virtual; augmented; virtual reality; vr; ar; immersive; anxiety; review; using; treatment,hci,0.9457627118644067,0.5574324324324325
122+119,1921,ai; education; artificial intelligence; intelligence; artificial; learning; students; teaching; generative; student; thinking; generative ai,llm,0.21476510067114093,0.7917751171264966
123+120,2345,students; english; learning; media; reading; efl; school; teaching; language; writing; pembelajaran; skills,hci,0.2277580071174377,0.5692963752665245
124+121,1092,education; digital; educational; technologies; teachers; learning; higher; higher education; pedagogical; students; training; future,hci,0.2647058823529412,0.48626373626373626
added results/semantic/stability.json +13 −0
@@ -0,0 +1,13 @@
1+{
2+ "n_sample": 200000,
3+ "n_clusters": 122,
4+ "noise_share": 0.3792,
5+ "ari_bootstrap": [
6+ 0.6449001969352965,
7+ 0.6824468886669448,
8+ 0.7098157543218491,
9+ 0.6249706273265837,
10+ 0.6919697620834717
11+ ],
12+ "ari_mean": 0.6708206458668292
13+}
\ No newline at end of file
added tables/MAKE_TABLES_INCIDENT_2026-11-03.md +14 −0
@@ -0,0 +1,14 @@
1+# make_tables.py — concurrent-edit incident, 2026-11-03 ~20:00–20:10 ET (reported by Agent E)
2+
3+Several writer agents appended generators to `tables/make_tables.py` at the same time by read–modify–write. Agent E's second
4+patch (20:06) searched for two anchor strings and replaced the slice between them; because another agent's block had been
5+inserted in the meantime, the slice crossed into `tables_part07b()` (Agent H) and removed (i) the last three security generators,
6+(ii) Agent E's own `tables_part05()` definition and (iii) the `tables_part09()` definition (21 `tab_p9_*.tex` tables).
7+
8+Repair done by Agent E at ~20:10: `tables_part07b()` generators rebuilt from the generated `.tex` files (regenerated output is
9+byte-identical to the 20:05 output for all three tables); `tables_part05()` restored; `tables_part09()` replaced by a
10+**placeholder that only prints a warning** — the Part IX writer must re-append the real function (the existing `tab_p9_*.tex`
11+files are untouched but are no longer listed in `TABLES.json` until then). Broken version kept at
12+`/tmp/wp44_tabs_before/make_tables.py.broken` (laptop, volatile).
13+
14+Rule proposed: append-only edits at the end of the file (insert before `if __name__`), never slice-replace by anchors.
modified tables/make_numbers.py +113 −0
@@ -90,6 +90,14 @@ if os.path.exists(p):
90 for (c, y), v in agg.items(): 90 for (c, y), v in agg.items():
91 (post if int(y) >= 2023 else base).setdefault(c, 0); (post if int(y) >= 2023 else base)[c] += v 91 (post if int(y) >= 2023 else base).setdefault(c, 0); (post if int(y) >= 2023 else base)[c] += v
92 for c in sorted(set(base) | set(post)): emit(f"catcount {c} baseline primary", base.get(c, 0)); emit(f"catcount {c} post primary", post.get(c, 0)) 92 for c in sorted(set(base) | set(post)): emit(f"catcount {c} baseline primary", base.get(c, 0)); emit(f"catcount {c} post primary", post.get(c, 0))
93+p = os.path.join(R, "results/graph/category_flows_yearly.csv")
94+if os.path.exists(p): # Agent F: citation flows between primary categories by citing period (calendar years: baseline <= 2022, post >= 2023)
95+ fl = {}; ft = {}
96+ for row in csv.DictReader(open(p)):
97+ per = "post" if int(row["citing_year"]) >= 2023 else "baseline"; k = (row["citing_cat"], per)
98+ fl[(k, row["cited_cat"])] = fl.get((k, row["cited_cat"]), 0) + int(float(row["n"])); ft[k] = ft.get(k, 0) + int(float(row["n"]))
99+ for k, n in sorted(ft.items()): emit(f"sysflow {k[0]} {k[1]} n", n)
100+ for (k, cited), n in sorted(fl.items()): emit(f"sysflow {k[0]} {k[1]} to {cited} share", f"{100.0 * n / ft[k]:.1f}")
93 # de-duplicate macro names (last definition dropped) so that independent appends cannot break the compile 101 # de-duplicate macro names (last definition dropped) so that independent appends cannot break the compile
94 _seen = set(); _out = [] 102 _seen = set(); _out = []
95 for line in out: 103 for line in out:
@@ -182,3 +190,108 @@ if os.path.exists(p):
182 emit2(f"sec land {c} conference", sum(r["type"] == "conference-paper" for r in g)); emit2(f"sec land {c} grey", sum(r["type"] in ("dissertation", "report", "book-chapter", "book") for r in g)) 190 emit2(f"sec land {c} conference", sum(r["type"] == "conference-paper" for r in g)); emit2(f"sec land {c} grey", sum(r["type"] in ("dissertation", "report", "book-chapter", "book") for r in g))
183 open(os.path.join(R, "manuscript/tables/numbers.tex"), "a").write("% ---- Part VII-b security macros (Agent H) ----\n" + "\n".join(extra) + "\n") 191 open(os.path.join(R, "manuscript/tables/numbers.tex"), "a").write("% ---- Part VII-b security macros (Agent H) ----\n" + "\n".join(extra) + "\n")
184 print(len(extra), "Part VII-b macros appended") 192 print(len(extra), "Part VII-b macros appended")
193+
194+# ---- Part VII-a (theory half of Part VII; Agent G, 2026-11-03): category-level macros for the six theory/software_pl ----
195+# categories (algorithms, computational_complexity, programming_languages, compilers, formal_verification,
196+# software_engineering), appended to numbers.tex with prefix "thy". Sources: results/graph/category_counts_yearly.csv,
197+# results/bibliometrics/cagr_by_category.csv, results/econometrics/breaks.csv, results/graph/category_flows_yearly.csv
198+# (aggregated by period: citing_year <= 2022 baseline, >= 2023 post), results/graph/category_cooccurrence_yearly.csv,
199+# literature_synthesis/landmarks/{theory,software_pl}_landmarks.csv and literature_synthesis/agent_G/contributions.csv
200+# (the specialist's audit of the candidate landmarks). Percentages are pre-formatted strings.
201+THY = ["algorithms", "computational_complexity", "programming_languages", "compilers", "formal_verification", "software_engineering"]
202+extraG = []
203+def emitG(k, v): extraG.append(f"\\newcommand{{\\{name(k)}}}{{{fmt(v)}}}")
204+aggG = {}; totG = {}; totmG = {}
205+p = os.path.join(R, "results/graph/category_counts_yearly.csv")
206+if os.path.exists(p):
207+ for row in csv.DictReader(open(p)):
208+ totG[row["year"]] = totG.get(row["year"], 0) + int(row["n_primary"]); totmG[row["year"]] = totmG.get(row["year"], 0) + int(row["n_multilabel"])
209+ if row["category_id"] in THY:
210+ a = aggG.setdefault((row["category_id"], row["year"]), [0, 0]); a[0] += int(row["n_multilabel"]); a[1] += int(row["n_primary"])
211+ for (c, y), (m, pr) in sorted(aggG.items()):
212+ emitG(f"thy count {c} {y} multilabel", m); emitG(f"thy count {c} {y} primary", pr)
213+ if totG.get(y): emitG(f"thy share primary {c} {y} pct", f"{100 * pr / totG[y]:.2f}")
214+ if totmG.get(y): emitG(f"thy share multilabel {c} {y} pct", f"{100 * m / totmG[y]:.2f}")
215+ for c in THY:
216+ for y0, y1, tag in [("2018", "2022", "baseline"), ("2023", "2025", "post"), ("2022", "2025", "span")]:
217+ m0, m1 = aggG.get((c, y0), [0, 0])[0], aggG.get((c, y1), [0, 0])[0]
218+ if m0 > 0: emitG(f"thy cagr multilabel {c} {tag} pct", f"{100 * ((m1 / m0) ** (1 / (int(y1) - int(y0))) - 1):.1f}"); emitG(f"thy ratio multilabel {c} {tag}", f"{m1 / m0:.2f}")
219+ p0, p1 = aggG.get((c, y0), [0, 0])[1], aggG.get((c, y1), [0, 0])[1]
220+ if p0 > 0: emitG(f"thy cagr primary {c} {tag} pct", f"{100 * ((p1 / p0) ** (1 / (int(y1) - int(y0))) - 1):.1f}"); emitG(f"thy ratio primary {c} {tag}", f"{p1 / p0:.2f}")
221+ emitG(f"thy count {c} all multilabel", sum(v[0] for (cc, y), v in aggG.items() if cc == c)); emitG(f"thy count {c} all primary", sum(v[1] for (cc, y), v in aggG.items() if cc == c))
222+ for y0, y1, tag in [("2018", "2022", "baseline"), ("2023", "2025", "post")]:
223+ if totG.get(y0): emitG(f"thy cagr primary total {tag} pct", f"{100 * ((totG[y1] / totG[y0]) ** (1 / (int(y1) - int(y0))) - 1):.1f}")
224+ if totmG.get(y0): emitG(f"thy cagr multilabel total {tag} pct", f"{100 * ((totmG[y1] / totmG[y0]) ** (1 / (int(y1) - int(y0))) - 1):.1f}")
225+p = os.path.join(R, "results/bibliometrics/cagr_by_category.csv")
226+if os.path.exists(p):
227+ for row in csv.DictReader(open(p)):
228+ if row["category"] in THY:
229+ c = row["category"]
230+ for k in ("n_2018", "n_2022", "n_2023", "n_2025"): emitG(f"thy cagrfile {c} {k}", int(row[k]))
231+ for k in ("cagr_baseline", "cagr_post"): emitG(f"thy cagrfile {c} {k} pct", f"{100 * float(row[k]):.1f}")
232+ for k in ("share_2018", "share_2022", "share_2025"): emitG(f"thy cagrfile {c} {k} pct", f"{100 * float(row[k]):.2f}")
233+p = os.path.join(R, "results/econometrics/breaks.csv")
234+if os.path.exists(p):
235+ for row in csv.DictReader(open(p)):
236+ c = row["series"].replace("cat_", "")
237+ if c in THY:
238+ for k in ("bp_breaks_bic", "pelt", "bocpd_resets"): emitG(f"thy break {c} {k}", (row[k] or "none").replace(";", ", "))
239+ for k in ("chatgpt_level", "gpt3_level", "dalle2_level", "gpt4_level", "o1_level", "chatgpt_slope"): emitG(f"thy break {c} {k}", float(row[k])); emitG(f"thy break {c} {k} p", float(row[k + "_p"]))
240+ for k in ("placebo_level_p", "placebo_slope_p"): emitG(f"thy break {c} {k}", float(row[k]))
241+ emitG(f"thy break {c} chatgpt slope pct", f"{100 * float(row['chatgpt_slope']):.1f}"); emitG(f"thy break {c} chatgpt level pct", f"{100 * float(row['chatgpt_level']):.1f}")
242+p = os.path.join(R, "results/graph/category_flows_yearly.csv")
243+if os.path.exists(p):
244+ rows = list(csv.DictReader(open(p))); per = lambda y: "baseline" if int(y) <= 2022 else "post"
245+ outf = {}; inf = {}; tout = {}; tin = {}
246+ for row in rows:
247+ pd_ = per(row["citing_year"]); n = int(row["n"])
248+ if row["citing_cat"] in THY:
249+ outf[(row["citing_cat"], row["cited_cat"], pd_)] = outf.get((row["citing_cat"], row["cited_cat"], pd_), 0) + n; tout[(row["citing_cat"], pd_)] = tout.get((row["citing_cat"], pd_), 0) + n
250+ if row["cited_cat"] in THY:
251+ inf[(row["cited_cat"], row["citing_cat"], pd_)] = inf.get((row["cited_cat"], row["citing_cat"], pd_), 0) + n; tin[(row["cited_cat"], pd_)] = tin.get((row["cited_cat"], pd_), 0) + n
252+ for (c, d, pd_), n in sorted(outf.items()):
253+ if tout[(c, pd_)] and (n / tout[(c, pd_)] >= 0.01 or d in ("llm", "ai_agents", "generative_ai", "foundation_models", "ai_safety", "quantum_computing")): emitG(f"thy flow {c} to {d} {pd_} pct", f"{100 * n / tout[(c, pd_)]:.1f}")
254+ for (c, s, pd_), n in sorted(inf.items()):
255+ if tin[(c, pd_)] and (n / tin[(c, pd_)] >= 0.01 or s in ("llm", "ai_agents", "generative_ai", "foundation_models", "ai_safety", "quantum_computing")): emitG(f"thy inflow {c} from {s} {pd_} pct", f"{100 * n / tin[(c, pd_)]:.1f}")
256+ for (c, pd_), n in sorted(tout.items()): emitG(f"thy flow {c} total {pd_}", n)
257+ for (c, pd_), n in sorted(tin.items()): emitG(f"thy inflow {c} total {pd_}", n)
258+p = os.path.join(R, "results/graph/category_cooccurrence_yearly.csv")
259+if os.path.exists(p):
260+ partners = ["llm", "ai_agents", "ai_safety", "generative_ai", "foundation_models", "ml_theory", "deep_learning_architectures", "quantum_computing", "cybersecurity"]
261+ for row in csv.DictReader(open(p)):
262+ a, b, y = row["cat_a"], row["cat_b"], row["year"]
263+ if a in THY and (b in partners or b in THY): c, q = a, b
264+ elif b in THY and (a in partners or a in THY): c, q = b, a
265+ else: continue
266+ n = int(row["n"]); emitG(f"thy cooc {c} {q} {y}", n)
267+ m = aggG.get((c, y), [0, 0])[0]
268+ if m: emitG(f"thy cooc {c} {q} {y} pct", f"{100 * n / m:.1f}")
269+ if q in THY:
270+ m2 = aggG.get((q, y), [0, 0])[0]
271+ if m2: emitG(f"thy cooc {q} {c} {y} pct", f"{100 * n / m2:.1f}")
272+from collections import Counter as _C
273+for ar in ("theory", "software_pl"):
274+ p = os.path.join(R, f"literature_synthesis/landmarks/{ar}_landmarks.csv")
275+ if os.path.exists(p):
276+ L = list(csv.DictReader(open(p))); emitG(f"thy land {ar} n", len(L))
277+ emitG(f"thy land {ar} post", sum(r["period"] == "post" for r in L)); emitG(f"thy land {ar} dated twentysix", sum(r["year"] == "2026" for r in L))
278+ emitG(f"thy land {ar} abstract", sum(r["has_abstract"] == "True" for r in L)); emitG(f"thy land {ar} oa", sum(r["is_oa"] == "True" for r in L))
279+ for t, n in _C(r["type"] for r in L).items(): emitG(f"thy land {ar} type {t}", n)
280+ for c, n in _C(r["primary_category"] for r in L).items(): emitG(f"thy land {ar} cat {c}", n)
281+p = os.path.join(R, "literature_synthesis/agent_G/contributions.csv")
282+if os.path.exists(p):
283+ L = list(csv.DictReader(open(p))); emitG("thy audit n", len(L))
284+ for v, n in _C(r["verdict"] for r in L).items(): emitG(f"thy audit verdict {v}", n)
285+ for v, n in _C(r["contribution_type"] for r in L).items(): emitG(f"thy audit type {v}", n)
286+ emitG("thy audit id collision", sum(r["id_resolves_to_other_work"] == "yes" for r in L))
287+ emitG("thy audit in scope research post", sum(r["verdict"] == "in_scope_research" and r["period"] == "post" for r in L))
288+ emitG("thy audit in scope research", sum(r["verdict"] == "in_scope_research" for r in L))
289+ for c in THY:
290+ g = [r for r in L if r["primary_category"] == c]; emitG(f"thy audit {c} n", len(g)); emitG(f"thy audit {c} in scope research", sum(r["verdict"] == "in_scope_research" for r in g)); emitG(f"thy audit {c} misclassified", sum(r["verdict"] == "misclassified" for r in g))
291+_seenG = set(); _outG = []
292+for line in extraG:
293+ n = line.split("}")[0]
294+ if n in _seenG: continue
295+ _seenG.add(n); _outG.append(line)
296+open(os.path.join(R, "manuscript/tables/numbers.tex"), "a").write("% ---- Part VII-a theory macros (Agent G) ----\n" + "\n".join(_outG) + "\n")
297+print(len(_outG), "Part VII-a macros appended")
modified tables/make_tables.py +194 −249
@@ -92,10 +92,12 @@ def main():
92 if os.path.exists(p): 92 if os.path.exists(p):
93 d = pd.read_csv(p)[["concept", "total_works", "detection_month", "peak_growth_month", "lead_months"]]; write("tab_known_set", d, "Preregistered known-emergence set: detection month, peak-growth month and lead time.", "tab:known_set", [p]) 93 d = pd.read_csv(p)[["concept", "total_works", "detection_month", "peak_growth_month", "lead_months"]]; write("tab_known_set", d, "Preregistered known-emergence set: detection month, peak-growth month and lead time.", "tab:known_set", [p])
94 tables_part08() # Part VIII tables (appended 2026-11-03) 94 tables_part08() # Part VIII tables (appended 2026-11-03)
95+ tables_part08_categories() # Part VIII category-level tables, corpus v3 (Agent L/I, 2026-11-03 20:15)
95 tables_part05() # Part V tables (Agent E, appended 2026-11-03) 96 tables_part05() # Part V tables (Agent E, appended 2026-11-03)
96 tables_part07b() # Part VII-b security tables (Agent H, appended 2026-11-03) 97 tables_part07b() # Part VII-b security tables (Agent H, appended 2026-11-03)
97 tables_part06() # Part VI systems table (appended by Agent F, 2026-11-03) 98 tables_part06() # Part VI systems table (appended by Agent F, 2026-11-03)
98 tables_part09() # Part IX results tables (appended by the Part IX writer, 2026-11-03) 99 tables_part09() # Part IX results tables (appended by the Part IX writer, 2026-11-03)
100+ tables_part07a() # Part VII-a theory tables (Agent G, appended 2026-11-03)
99 json.dump(INV, open(os.path.join(OUT, "TABLES.json"), "w"), indent=1); print(len(INV), "tables") 101 json.dump(INV, open(os.path.join(OUT, "TABLES.json"), "w"), indent=1); print(len(INV), "tables")
100 102
101 103
@@ -264,257 +266,45 @@ def tables_part07b():
264 for c in SEC: t[SHORT[c] + suffix] = piv[c].values 266 for c in SEC: t[SHORT[c] + suffix] = piv[c].values
265 write("tab_security_counts", t, "Eligible works in the five security-and-privacy categories by publication year: works carrying the category (all) and works whose primary category it is (prim.).", "tab:security_counts", [p], longtable=True, 267 write("tab_security_counts", t, "Eligible works in the five security-and-privacy categories by publication year: works carrying the category (all) and works whose primary category it is (prim.).", "tab:security_counts", [p], longtable=True,
266 note="Counts of eligible works (Part III) per publication year, summed over the pre/baseline/post period labels of the result file. `all' counts every work to which the multi-label classifier assigns the category (a work can carry several categories); `prim.' counts works for which the category is the argmax of the assignment score (Part IV). Cyber. = cybersecurity; Crypto. = applied cryptography; Blockch. = blockchain systems; Privacy = privacy-preserving computation; Fed. learn. = federated learning. The 2026 row is incomplete (the corpus ends on 2026-11-02 and recent months are under-indexed).") 268 note="Counts of eligible works (Part III) per publication year, summed over the pre/baseline/post period labels of the result file. `all' counts every work to which the multi-label classifier assigns the category (a work can carry several categories); `prim.' counts works for which the category is the argmax of the assignment score (Part IV). Cyber. = cybersecurity; Crypto. = applied cryptography; Blockch. = blockchain systems; Privacy = privacy-preserving computation; Fed. learn. = federated learning. The 2026 row is incomplete (the corpus ends on 2026-11-02 and recent months are under-indexed).")
267− p = f"{E}/breaks.csv"; q = f"{B}/cagr_by_category.csv" 269+ # --- The three generators below were lost on 2026-11-03 ~20:06 in a concurrent edit of this file (Agent E's patch replaced
270+ # --- the slice between the two anchors it searched for, which crossed into this function). Reconstructed by Agent E from the
271+ # --- generated tab_security_summary.tex / tab_security_llm_cooccurrence.tex / tab_security_landmarks.tex; Agent H to verify.
272+ p = f"{E}/breaks.csv"; q = f"{G}/category_counts_yearly.csv"
268 if os.path.exists(p) and agg is not None: 273 if os.path.exists(p) and agg is not None:
269− b = pd.read_csv(p).set_index("series"); rows = [] 274+ b = pd.read_csv(p).set_index("series"); piv = agg.pivot_table(index="year", columns="category_id", values="n_multilabel", aggfunc="sum"); rows = []
270 for c in SEC: 275 for c in SEC:
271− g = agg[agg.category_id == c].set_index("year") 276+ s_ = b.loc["cat_" + c] if ("cat_" + c) in b.index else None
272− m18, m22, m23, m25 = (int(g.loc[y, "n_multilabel"]) if y in g.index else 0 for y in (2018, 2022, 2023, 2025)) 277+ rows.append({"Category": NAMES[c], "Works 2018": int(piv.loc[2018, c]), "Works 2025": int(piv.loc[2025, c]), "CAGR 18--22 (%)": 100 * ((piv.loc[2022, c] / piv.loc[2018, c]) ** 0.25 - 1), "CAGR 23--25 (%)": 100 * ((piv.loc[2025, c] / piv.loc[2023, c]) ** 0.5 - 1),
273− r = b.loc[f"cat_{c}"] 278+ "Level 2022-12": float(s_.chatgpt_level) if s_ is not None else float("nan"), "p": float(s_.chatgpt_level_p) if s_ is not None else float("nan"), "Slope change": float(s_.chatgpt_slope) if s_ is not None else float("nan"), "p ": float(s_.chatgpt_slope_p) if s_ is not None else float("nan"),
274− rows.append({"Category": NAMES[c], "Works 2018": m18, "Works 2025": m25, "CAGR 18--22 (%)": 100 * ((m22 / m18) ** 0.25 - 1) if m18 else float("nan"), "CAGR 23--25 (%)": 100 * ((m25 / m23) ** 0.5 - 1) if m23 else float("nan"), 279+ "Placebo p (slope)": float(s_.placebo_slope_p) if s_ is not None else float("nan"), "Bai--Perron": str(s_.bp_breaks_bic) if s_ is not None else "", "PELT": str(s_.pelt).replace(";", ", ") if s_ is not None else ""})
275− "Level 2022-12": float(r.chatgpt_level), "p": float(r.chatgpt_level_p), "Slope change": float(r.chatgpt_slope), "p ": float(r.chatgpt_slope_p), "Placebo p (slope)": float(r.placebo_slope_p), "Bai--Perron": str(r.bp_breaks_bic).replace(";", ", "), "PELT": str(r.pelt).replace(";", ", ")}) 280+ write("tab_security_summary", pd.DataFrame(rows), "Growth and structural-change summary for the five security-and-privacy categories (multi-label counts).", "tab:security_summary", [q, p], fmt={"CAGR 18--22 (%)": "{:.1f}", "CAGR 23--25 (%)": "{:.1f}", "Level 2022-12": "{:.3f}", "p": "{:.3f}", "Slope change": "{:.3f}", "p ": "{:.3f}", "Placebo p (slope)": "{:.3f}"},
276− write("tab_security_summary", pd.DataFrame(rows), "Growth and structural-change summary for the five security-and-privacy categories (multi-label counts).", "tab:security_summary", [f"{G}/category_counts_yearly.csv", p], fmt={"CAGR 18--22 (%)": "{:.1f}", "CAGR 23--25 (%)": "{:.1f}", "Level 2022-12": "{:.3f}", "p": "{:.3f}", "Slope change": "{:.3f}", "p ": "{:.3f}", "Placebo p (slope)": "{:.3f}"},
277 note="Works: eligible works carrying the category (multi-label). CAGR: compound annual growth of the multi-label count between the years shown (2026 excluded as incomplete). Level and slope change: interrupted-time-series estimates on the log monthly series at the 2022-12 reference month with HAC p-values (Part IV, Section~\\ref{sec:m-its}); Placebo p: share of placebo event months whose slope t-statistic exceeds the one at 2022-12 (Section~\\ref{sec:m-placebo}); Bai--Perron: BIC-selected break months (Section~\\ref{sec:m-bp}); PELT: penalised change-point months. The ITS, placebo and break columns are those of Table~\\ref{tab:breaks}.") 281 note="Works: eligible works carrying the category (multi-label). CAGR: compound annual growth of the multi-label count between the years shown (2026 excluded as incomplete). Level and slope change: interrupted-time-series estimates on the log monthly series at the 2022-12 reference month with HAC p-values (Part IV, Section~\\ref{sec:m-its}); Placebo p: share of placebo event months whose slope t-statistic exceeds the one at 2022-12 (Section~\\ref{sec:m-placebo}); Bai--Perron: BIC-selected break months (Section~\\ref{sec:m-bp}); PELT: penalised change-point months. The ITS, placebo and break columns are those of Table~\\ref{tab:breaks}.")
278 p = f"{G}/category_cooccurrence_yearly.csv" 282 p = f"{G}/category_cooccurrence_yearly.csv"
279 if os.path.exists(p) and agg is not None: 283 if os.path.exists(p) and agg is not None:
280− d = pd.read_csv(p); pairs = [("cybersecurity", "llm"), ("cybersecurity", "ai_safety"), ("cybersecurity", "ai_agents"), ("applied_cryptography", "llm"), ("blockchain", "llm"), ("privacy_preserving_computation", "llm"), ("federated_learning", "llm")] 284+ co = pd.read_csv(p); piv = agg.pivot_table(index="year", columns="category_id", values="n_multilabel", aggfunc="sum")
281− LAB = {"llm": "LLM", "ai_safety": "AI safety", "ai_agents": "AI agents"} 285+ def pair(a_, b_):
282− rows = [] 286+ m = co[((co.cat_a == a_) & (co.cat_b == b_)) | ((co.cat_a == b_) & (co.cat_b == a_))].groupby("year").n.sum(); return m
283− for y in sorted(d.year.unique()): 287+ pairs = [("cybersecurity", "llm", "LLM"), ("cybersecurity", "ai_safety", "AI safety"), ("cybersecurity", "ai_agents", "AI agents"), ("applied_cryptography", "llm", "LLM"), ("blockchain", "llm", "LLM"), ("privacy_preserving_computation", "llm", "LLM"), ("federated_learning", "llm", "LLM")]
284− if y < 2018 or y > 2026: continue 288+ years = sorted(y for y in piv.index if y <= 2026); t = pd.DataFrame({"Year": [str(y) for y in years]})
285− row = {"Year": str(y)} 289+ for sc, ac, lab in pairs:
286− for a, bb in pairs: 290+ m = pair(sc, ac); t[f"{SHORT[sc]} x {lab}"] = [int(m.get(y, 0)) for y in years]; t[f"{SHORT[sc]} x {lab} (%)"] = [100 * m.get(y, 0) / piv.loc[y, sc] if piv.loc[y, sc] else 0.0 for y in years]
287− n = d[(d.year == y) & (((d.cat_a == a) & (d.cat_b == bb)) | ((d.cat_a == bb) & (d.cat_b == a)))].n.sum() 291+ write("tab_security_llm_cooccurrence", t, "Co-classification of security-and-privacy works with the LLM, AI-safety and AI-agents categories, by publication year: number of works carrying both categories and share of the security category's works.", "tab:security_llm_cooccurrence", [p, q], fmt={c: "{:.1f}" for c in t.columns if c.endswith("(%)")}, longtable=True,
288− m = agg[(agg.year == y) & (agg.category_id == a)].n_multilabel.sum()
289− row[f"{SHORT[a]} x {LAB[bb]}"] = int(n); row[f"{SHORT[a]} x {LAB[bb]} (%)"] = 100 * n / m if m else float("nan")
290− rows.append(row)
291− t = pd.DataFrame(rows); cols = ["Year"] + [c for c in t.columns if c != "Year"]
292− write("tab_security_llm_cooccurrence", t[cols], "Co-classification of security-and-privacy works with the LLM, AI-safety and AI-agents categories, by publication year: number of works carrying both categories and share of the security category's works.", "tab:security_llm_cooccurrence", [p, f"{G}/category_counts_yearly.csv"], longtable=True, fmt={c: "{:.1f}" for c in cols if c.endswith("(%)")},
293 note="A work is co-classified when the multi-label classifier assigns both categories (Part IV). Shares are relative to all eligible works carrying the security category in the year (multi-label count). Non-zero counts before 2022 reflect the breadth of the keyword patterns of the AI categories (for example `language model' matches pre-transformer work), not the use of LLMs; the table should be read for its trend, not its level. The 2026 row is incomplete.") 292 note="A work is co-classified when the multi-label classifier assigns both categories (Part IV). Shares are relative to all eligible works carrying the security category in the year (multi-label count). Non-zero counts before 2022 reflect the breadth of the keyword patterns of the AI categories (for example `language model' matches pre-transformer work), not the use of LLMs; the table should be read for its trend, not its level. The 2026 row is incomplete.")
294 p = f"{R}/literature_synthesis/landmarks/security_privacy_landmarks.csv" 293 p = f"{R}/literature_synthesis/landmarks/security_privacy_landmarks.csv"
295 if os.path.exists(p): 294 if os.path.exists(p):
296− d = pd.read_csv(p); rows = [] 295+ d = pd.read_csv(p, dtype={"doi": str}); rows = []
297 for c in SEC: 296 for c in SEC:
298− g = d[d.primary_category == c] 297+ x = d[d.primary_category == c]
299− rows.append({"Category": NAMES[c], "Works": int(len(g)), "Post-period": int((g.period == "post").sum()), "Dated 2026": int((g.year == 2026).sum()), "Articles": int(g.type.isin(["article", "review"]).sum()), "Conf.": int((g.type == "conference-paper").sum()), "Preprints": int((g.type == "preprint").sum()), "Grey": int(g.type.isin(["dissertation", "report", "book-chapter", "book"]).sum()), "Abstract": int((g.has_abstract == True).sum()), "No DOI": int(g.doi.isna().sum())}) 298+ rows.append({"Category": NAMES[c], "Works": len(x), "Post-period": int((x.period == "post").sum()), "Dated 2026": int((x.year == 2026).sum()), "Articles": int(x.type.isin(["article", "review"]).sum()), "Conf.": int((x.type == "conference-paper").sum()), "Preprints": int((x.type == "preprint").sum()),
299+ "Grey": int(x.type.isin(["dissertation", "report", "book-chapter", "book"]).sum()), "Abstract": int(x.has_abstract.astype(str).eq("True").sum()), "No DOI": int(x.doi.isna().sum() + (x.doi.astype(str).str.strip() == "").sum())})
300 write("tab_security_landmarks", pd.DataFrame(rows), "Composition of the automatically selected landmark set of the security-and-privacy area, by category.", "tab:security_landmarks", [p], 300 write("tab_security_landmarks", pd.DataFrame(rows), "Composition of the automatically selected landmark set of the security-and-privacy area, by category.", "tab:security_landmarks", [p],
301 note="Landmarks are the works with the highest field-normalised 36-month citation score (NCS36; 2025--2026 works ranked by 12-month citations) within each primary category, with a floor of fifteen per category, plus the 200 most cited works of the corpus overall (literature_synthesis/select_landmarks.py). Grey = dissertations, reports, book chapters and books as typed by OpenAlex. Abstract = works whose abstract is available in the metadata; the synthesis of Part VII reads these abstracts only, never full texts. The set is flagged for principal-investigator review; Part VII documents the records that are duplicates, misdated or misclassified.") 301 note="Landmarks are the works with the highest field-normalised 36-month citation score (NCS36; 2025--2026 works ranked by 12-month citations) within each primary category, with a floor of fifteen per category, plus the 200 most cited works of the corpus overall (literature_synthesis/select_landmarks.py). Grey = dissertations, reports, book chapters and books as typed by OpenAlex. Abstract = works whose abstract is available in the metadata; the synthesis of Part VII reads these abstracts only, never full texts. The set is flagged for principal-investigator review; Part VII documents the records that are duplicates, misdated or misclassified.")
302 302
303 303
304−# ---------------- Part IX (Empirical Results): tables appended by Agent L/I/J (Part IX writer), 2026-11-03 ----------------
305−def _p9_names():
306− import yaml
307− o = yaml.safe_load(open(f"{R}/taxonomy/ontology.yaml"))
308− return {c["id"]: c["name"] for c in o["categories"]}, {c["id"]: c["area"] for c in o["categories"]}, {a["id"]: a["name"] for a in o["areas"]}
309−
310−
311−_P9_AREA_SHORT = {"ai_ml": "AI & ML", "perception_interaction": "Perception & interaction", "software_pl": "Software & PL", "theory": "Theory", "data_distributed": "Data & distributed",
312− "security_privacy": "Security & privacy", "hardware_emerging": "Hardware & emerging", "computational_science": "Computational science"}
313−_P9_EVENTS = [("gpt3", "GPT-3 (2020-06)"), ("dalle2", "DALL$\\cdot$E 2 (2022-05)"), ("chatgpt", "ChatGPT (2022-12)"), ("gpt4", "GPT-4 (2023-04)"), ("o1", "o1 (2024-10)")]
314−
315−
316−def _p9_estp(e, p):
317− return f"{e:+.3f} ({p:.3f})" if pd.notna(e) and pd.notna(p) else ""
318−
319−
320−def tables_part09():
321− import numpy as np
322− names, area, area_names = _p9_names(); B = f"{R}/results/bibliometrics"; G = f"{R}/results/graph"; E = f"{R}/results/econometrics"; X = f"{R}/results/emerging"
323− # --- corpus by period (volume_monthly, baseline vs post) ---
324− p = f"{B}/volume_monthly.csv"
325− if os.path.exists(p):
326− m = pd.read_csv(p); m = m[m.period.isin(["baseline", "post"])]; rows = []
327− tot = m.groupby("period").n.sum()
328− def share(mask, label):
329− s = m[mask].groupby("period").n.sum().reindex(["baseline", "post"]).fillna(0) / tot * 100
330− rows.append({"Indicator": label, "Baseline": float(s["baseline"]), "Post": float(s["post"])})
331− rows.append({"Indicator": "Eligible works (count)", "Baseline": float(tot["baseline"]), "Post": float(tot["post"])})
332− share(m.date_precision.eq("month"), "Month-precision first date (%)"); share(m.in_dblp.astype(bool), "DBLP-indexed (%)"); share(m.cs_primary.astype(bool), "OpenAlex primary field CS (%)")
333− share(m.has_arxiv.astype(bool), "arXiv-linked (%)")
334− for c, lab in [("n_abstract", "With abstract (%)"), ("n_with_refs", "With at least one reference (%)"), ("n_oa", "Open access (%)")]:
335− s = m.groupby("period")[c].sum() / tot * 100; rows.append({"Indicator": lab, "Baseline": float(s["baseline"]), "Post": float(s["post"])})
336− for ty, lab in [("article", "Type: article (%)"), ("conference-paper", "Type: conference paper (%)"), ("preprint", "Type: preprint (%)"), ("book-chapter", "Type: book chapter (%)"), ("dissertation", "Type: dissertation (%)")]:
337− share(m.type.eq(ty), lab)
338− t = pd.DataFrame(rows)
339− for c in ("Baseline", "Post"): t[c] = [f"{v:,.0f}" if i == 0 else f"{v:.2f}" for i, v in enumerate(t[c])]
340− write("tab_p9_periods", t, "The eligible corpus by protocol period: completeness, source membership and document type.", "tab:p9-periods", [p], fmt={"Baseline": "{:,.2f}", "Post": "{:,.2f}"},
341− note="Baseline: first date 2018-01-01 to 2022-11-29; post: 2022-11-30 to 2026-11-02 (right-truncated, see Part III). Percentages are over all eligible works of the period. The first row is a count.")
342− # --- classification coverage by year ---
343− p = f"{G}/category_counts_yearly.csv"; q = f"{R}/data/metadata/prisma_counts.json"
344− if os.path.exists(p) and os.path.exists(q):
345− cc = pd.read_csv(p); cc = cc[(cc.year >= 2018) & (cc.year <= 2026)]; P = json.load(open(q))["eligible_by_year"]
346− y = cc.groupby("year")[["n_primary", "n_multilabel"]].sum(); y["eligible"] = [P[str(i)] for i in y.index]
347− t = pd.DataFrame({"Year": y.index.astype(str), "Eligible works": y.eligible, "With primary category": y.n_primary, "Coverage (%)": 100 * y.n_primary / y.eligible, "Category assignments": y.n_multilabel, "Labels per classified work": y.n_multilabel / y.n_primary})
348− write("tab_p9_classification", t, "Classification coverage by publication year: eligible works, works with a primary category, and multi-label assignments.", "tab:p9-classification", [p, q], fmt={"Coverage (%)": "{:.1f}", "Labels per classified work": "{:.2f}"},
349− note="A work receives a primary category when at least one category score passes the threshold of Part IV; works without any assignment (no abstract, unmapped venue and no arXiv category) have no primary category and enter no category series. Assignments count every (work, category) pair. 2026 is incomplete.")
350− cc["area"] = cc.category_id.map(area)
351− ya = cc.groupby(["year", "area"]).n_primary.sum().unstack().fillna(0); sh = ya.div(ya.sum(1), axis=0) * 100
352− per = cc[cc.period.isin(["baseline", "post"])].groupby(["period", "area"]).n_primary.sum().unstack().fillna(0); shp = per.div(per.sum(1), axis=0) * 100
353− t = pd.concat([sh, shp.loc[["baseline", "post"]]]); t.index = [str(i) for i in sh.index] + ["Baseline (2018-01 to 2022-11)", "Post (2022-12 to 2026-11)"]
354− t = t[[a for a in _P9_AREA_SHORT if a in t.columns]].rename(columns=_P9_AREA_SHORT).reset_index().rename(columns={"index": "Year / period"})
355− write("tab_p9_area_shares", t, "Share of primary-category assignments by top-level area and year (per cent), with the two protocol periods.", "tab:p9-area-shares", [p], fmt={c: "{:.2f}" for c in _P9_AREA_SHORT.values()},
356− note="Shares of works with a primary category (Table~\\ref{tab:p9-classification}); rows sum to 100. The period rows split at the reference date; the year rows split at the calendar year. 2026 is incomplete.")
357− # category-level periods joined with CAGR
358− cp = cc[cc.period.isin(["baseline", "post"])].groupby(["period", "category_id"])[["n_primary", "n_multilabel"]].sum().unstack(0)
359− cp.columns = ["_".join(c) for c in cp.columns]
360− r = f"{B}/cagr_by_category.csv"; cg = pd.read_csv(r).set_index("category") if os.path.exists(r) else None
361− rows = []
362− for k in cp.index:
363− rr = cp.loc[k]; rows.append({"Category": names.get(k, k), "Area": _P9_AREA_SHORT.get(area.get(k), area.get(k)), "Primary, base": int(rr.n_primary_baseline), "Primary, post": int(rr.n_primary_post),
364− "Share base (%)": 100 * rr.n_primary_baseline / cp.n_primary_baseline.sum(), "Share post (%)": 100 * rr.n_primary_post / cp.n_primary_post.sum(),
365− "Multi-label post/base": rr.n_multilabel_post / rr.n_multilabel_baseline if rr.n_multilabel_baseline > 0 else float("nan"),
366− "CAGR base": float(cg.loc[k, "cagr_baseline"]) if cg is not None and k in cg.index else float("nan"), "CAGR post": float(cg.loc[k, "cagr_post"]) if cg is not None and k in cg.index else float("nan")})
367− t = pd.DataFrame(rows).sort_values("Share post (%)", ascending=False)
368− write("tab_p9_category_periods", t, "The forty categories by protocol period: primary-category output, composition shares, multi-label growth and compound annual growth rates.", "tab:p9-category-periods", [p, r], longtable=True,
369− fmt={"Share base (%)": "{:.2f}", "Share post (%)": "{:.2f}", "Multi-label post/base": "{:.2f}", "CAGR base": "{:.3f}", "CAGR post": "{:.3f}"},
370− note="Primary counts and shares use the primary category; the multi-label ratio is the post-period count of all assignments to the category divided by the baseline count (periods split at the reference date). CAGR base: 2018 to 2022 (four years); CAGR post: 2023 to 2025 (two years), both on primary counts of complete calendar years (equation~\\eqref{eq:cagr}). Sorted by post-period share.")
371− # --- AI categories: share with bootstrap CI (H2) ---
372− p = f"{B}/category_share_ci_yearly.csv"
373− if os.path.exists(p):
374− s = pd.read_csv(p); ai = [k for k, a in area.items() if a == "ai_ml"]; rows = []
375− for k in ai:
376− r = {"Category": names[k]}
377− for yv in (2018, 2022, 2025):
378− z = s[(s.category == k) & (s.year == yv)]
379− r[str(yv)] = f"{100 * float(z.share.iloc[0]):.2f} [{100 * float(z.ci_low.iloc[0]):.2f}, {100 * float(z.ci_high.iloc[0]):.2f}]" if len(z) else ""
380− rows.append(r)
381− r = {"Category": "AI & ML area (sum)"}
382− for yv in (2018, 2022, 2025):
383− z = s[(s.category.isin(ai)) & (s.year == yv)]; r[str(yv)] = f"{100 * float(z.share.sum()):.2f}" if len(z) else ""
384− rows.append(r)
385− write("tab_p9_ai_shares", pd.DataFrame(rows), "Share of primary-category assignments held by the AI and machine-learning categories in 2018, 2022 and 2025 (per cent, bootstrap 95\\% intervals).", "tab:p9-ai-shares", [p],
386− note="Shares are of the year's works with a primary category; intervals from 500 multinomial resamples of the works (seed 44). The area row is the sum of the category shares (no interval). Categories 1--8 and 38 of the mandate.")
387− # --- ITS total: events, robustness ---
388− p = f"{E}/its_results.json"
389− if os.path.exists(p):
390− J = json.load(open(p)); S = J["series"]; T = S["total"]; rows = []
391− for key, lab in _P9_EVENTS:
392− e = T.get("its_" + key)
393− if e: rows.append({"Event (month)": lab, "Pre-slope (SE)": f"{e['pre_slope']:.4f} ({e['pre_slope_se']:.4f})", "Level change (SE)": f"{e['level_change']:+.3f} ({e['level_se']:.3f})", "HAC p (level)": e["level_p"], "Slope change (SE)": f"{e['slope_change']:+.4f} ({e['slope_se']:.4f})", "HAC p (slope)": e["slope_p"], "$R^2$": e["r2"]})
394− write("tab_p9_its_total_events", pd.DataFrame(rows), "Interrupted time series of the total monthly series at the reference date and at four alternative event dates (log counts, month-of-year dummies, Newey--West standard errors).", "tab:p9-its-total-events", [p],
395− fmt={"HAC p (level)": "{:.3f}", "HAC p (slope)": "{:.3g}", "$R^2$": "{:.3f}"}, note="Equation~\\eqref{eq:its}; each event is coded as the first full month after the public release. Slopes are monthly log growth rates. A HAC $p$ printed as 0.000 is below 0.0005.")
396− rows = [("Observations (months), first to last complete month", f"{T['n_months']} ({J['first']} to {J['last_complete']})"),
397− ("Placebo rank of the level $t$-statistic (all candidate months)", f"rank {T['placebo_chatgpt']['level_t_rank']} of {T['placebo_chatgpt']['n_placebo']}, placebo $p$ = {T['placebo_chatgpt']['level_t_placebo_p']:.3f}"),
398− ("Placebo rank of the slope $t$-statistic (all candidate months)", f"rank {T['placebo_chatgpt']['slope_t_rank']} of {T['placebo_chatgpt']['n_placebo']}, placebo $p$ = {T['placebo_chatgpt']['slope_t_placebo_p']:.3f}"),
399− ("Placebo $p$, baseline candidate months only (level; slope)", f"{T['placebo_chatgpt_baseline_only']['level_t_placebo_p']:.3f}; {T['placebo_chatgpt_baseline_only']['slope_t_placebo_p']:.3f} (n = {T['placebo_chatgpt_baseline_only']['n_placebo']}; zero is attainable)"),
400− ("Donut ITS, $\\pm$3 months removed: level change (HAC $p$)", f"{T['its_chatgpt_donut']['level_change']:+.3f} ({T['its_chatgpt_donut']['level_p']:.3f}), n = {T['its_chatgpt_donut']['n']}"),
401− ("Donut ITS: slope change (HAC $p$)", f"{T['its_chatgpt_donut']['slope_change']:+.4f} ({T['its_chatgpt_donut']['slope_p']:.2g})"),
402− ("Bai--Perron break dates, BIC (LWZ)", f"{', '.join(T['bai_perron']['break_dates_bic'])} ({', '.join(T['bai_perron']['break_dates_lwz'])}); m = {T['bai_perron']['m_bic']}, h = {T['bai_perron']['h']}"),
403− ("Bai--Perron BIC by number of breaks m = 0, 1, 2, 3", ", ".join(f"{T['bai_perron']['bic_by_m'][k]:.1f}" for k in ("0", "1", "2", "3"))),
404− ("PELT changepoints (piecewise-constant mean)", ", ".join(T["pelt"]) or "none"),
405− ("BOCPD: max P(run length $\\le$ 3) over 2022-12 to 2023-03; MAP resets", f"{T['bocpd_cp_prob_2022_12_to_2023_03']:.3f}; {', '.join(T['bocpd_resets']) or 'none'}")]
406− for lm, v in J.get("sensitivity_last_complete_total", {}).items():
407− e = v["its_chatgpt"]; rows.append((f"Last complete month {lm}: level (p); slope (p); Bai--Perron", f"{e['level_change']:+.3f} ({e['level_p']:.3f}); {e['slope_change']:+.4f} ({e['slope_p']:.2g}); {', '.join(v['bai_perron'])}"))
408− write("tab_p9_total_robustness", pd.DataFrame(rows, columns=["Diagnostic", "Total series"]), "Robustness diagnostics of the structural-change analysis of the total monthly series.", "tab:p9-total-robustness", [p],
409− note="Placebo ranks follow equation~\\eqref{eq:placebo}; the baseline-only rank excludes the true event from the candidate set, so a value of zero means that no baseline month yields a larger $t$-statistic. Bai--Perron on the seasonally adjusted log series with trimming $h$; PELT with the $\\ell_2$ cost; BOCPD with hazard 1/60 (Part IV).")
410− # categories: slopes
411− rows = []; rows2 = []; rows3 = []; rows4 = []
412− for sid, v in S.items():
413− if sid == "total": continue
414− k = sid[4:]; e = v["its_chatgpt"]; pl = v["placebo_chatgpt"]; plb = v["placebo_chatgpt_baseline_only"]; dn = v.get("its_chatgpt_donut", {})
415− rows.append({"Category": names.get(k, k), "Works": int(v["total"]), "Pre-slope": e["pre_slope"], "Slope change": e["slope_change"], "SE": e["slope_se"], "HAC p": e["slope_p"], "Placebo p (all)": pl["slope_t_placebo_p"], "Placebo p (base)": plb["slope_t_placebo_p"], "Post slope": e["pre_slope"] + e["slope_change"]})
416− rows2.append({"Category": names.get(k, k), "Level change": e["level_change"], "SE": e["level_se"], "HAC p": e["level_p"], "Placebo p (all)": pl["level_t_placebo_p"], "Placebo p (base)": plb["level_t_placebo_p"], "Donut level": dn.get("level_change", float("nan")), "Donut p": dn.get("level_p", float("nan")), "BOCPD P": v["bocpd_cp_prob_2022_12_to_2023_03"]})
417− bp = v["bai_perron"]; inw = any("2022-10-01" <= d <= "2023-06-01" for d in bp["break_dates_bic"]); pin = any("2022-10" <= d <= "2023-06" for d in v["pelt"])
418− rows3.append({"Category": names.get(k, k), "Bai--Perron (BIC)": ", ".join(d[:7] for d in bp["break_dates_bic"]) or "none", "Bai--Perron (LWZ)": ", ".join(d[:7] for d in bp["break_dates_lwz"]) or "none", "PELT": ", ".join(v["pelt"]) or "none", "BOCPD resets": ", ".join(v["bocpd_resets"]) or "none", "BIC break in window": "yes" if inw else "no", "PELT in window": "yes" if pin else "no"})
419− r4 = {"Category": names.get(k, k)}
420− for key, lab in _P9_EVENTS:
421− ev = v.get("its_" + key); r4[lab.split(" (")[0]] = _p9_estp(ev["level_change"], ev["level_p"]) if ev else ""
422− rows4.append(r4)
423− t = pd.DataFrame(rows).sort_values("Slope change", ascending=False)
424− write("tab_p9_its_slopes", t, "ITS slope change at December 2022 by category: pre-period slope, change, HAC and placebo $p$-values, and implied post-period slope (monthly log growth).", "tab:p9-its-slopes", [p], longtable=True,
425− fmt={"Pre-slope": "{:.4f}", "Slope change": "{:+.4f}", "SE": "{:.4f}", "HAC p": "{:.3g}", "Placebo p (all)": "{:.3f}", "Placebo p (base)": "{:.3f}", "Post slope": "{:.4f}"},
426− note="Multi-label monthly series of month-precision works (Part IV); Works: series total over the window. Placebo $p$ (all): rank of the slope $t$-statistic among all candidate event months; (base): among baseline months only, where zero is attainable. Sorted by slope change.")
427− t = pd.DataFrame(rows2).sort_values("Category")
428− write("tab_p9_its_levels", t, "ITS level change at December 2022 by category: full-sample and donut estimates, HAC and placebo $p$-values, and the BOCPD change probability over December 2022 to March 2023.", "tab:p9-its-levels", [p], longtable=True,
429− fmt={"Level change": "{:+.3f}", "SE": "{:.3f}", "HAC p": "{:.3f}", "Placebo p (all)": "{:.3f}", "Placebo p (base)": "{:.3f}", "Donut level": "{:+.3f}", "Donut p": "{:.3f}", "BOCPD P": "{:.3f}"},
430− note="Donut: the event month and three months on each side removed. BOCPD P: maximum posterior probability that a changepoint occurred within the last four months, taken over December 2022 to March 2023.")
431− write("tab_p9_unknown_breaks", pd.DataFrame(rows3).sort_values("Category"), "Data-driven break and changepoint dates by category, and whether any falls in the H3 window (October 2022 to June 2023).", "tab:p9-unknown-breaks", [p], longtable=True,
432− note="Bai--Perron on the seasonally adjusted log series with trimming $h = 15$ months (no break possible after April 2025); PELT with the $\\ell_2$ cost and a six-month minimum segment; BOCPD resets as defined in Part IV. The window test is the one pre-specified in H3; Bai--Perron dates carry no $p$-value.")
433− write("tab_p9_altdates", pd.DataFrame(rows4).sort_values("Category"), "ITS level change (HAC $p$) by category at the five pre-specified event dates.", "tab:p9-altdates", [p], longtable=True,
434− note="Each column re-estimates equation~\\eqref{eq:its} with the event at the stated month; entries are the log level change with the HAC $p$-value in parentheses. The five estimates of a row are not independent: they are the same series with the break moved.")
435− # H3 focus table
436− h3 = [("llm", "break expected"), ("generative_ai", "break expected"), ("ai_agents", "break expected"), ("ai_safety", "break expected"), ("formal_verification", "no break expected"), ("computational_complexity", "no break expected"), ("operating_systems", "no break expected"), ("computer_architecture", "no break expected")]
437− rows = []
438− for k, pred in h3:
439− v = S.get("cat_" + k)
440− if not v: continue
441− bp = v["bai_perron"]; e = v["its_chatgpt"]
442− rows.append({"Category": names[k], "H3 prediction": pred, "Bai--Perron (BIC)": ", ".join(d[:7] for d in bp["break_dates_bic"]) or "none", "PELT": ", ".join(v["pelt"]) or "none", "In window": "yes" if any("2022-10-01" <= d <= "2023-06-01" for d in bp["break_dates_bic"]) else "no",
443− "Level change (p)": _p9_estp(e["level_change"], e["level_p"]), "Slope change (p)": f"{e['slope_change']:+.4f} ({e['slope_p']:.2g})", "Placebo p (slope)": v["placebo_chatgpt"]["slope_t_placebo_p"], "BOCPD P": v["bocpd_cp_prob_2022_12_to_2023_03"]})
444− write("tab_p9_h3", pd.DataFrame(rows), "Hypothesis H3: break dates and ITS estimates for the eight categories named in the protocol.", "tab:p9-h3", [p], fmt={"Placebo p (slope)": "{:.3f}", "BOCPD P": "{:.3f}"},
445− note="In window: a Bai--Perron (BIC) break date between October 2022 and June 2023. The protocol predicted a break in that window for the first four categories and none for the last four.")
446− # --- cross-area shares, modularity, flows ---
447− p = f"{G}/cross_area_share_yearly.csv"
448− if os.path.exists(p):
449− cs = pd.read_csv(p); piv = cs.pivot(index="citing_year", columns="citing_area", values="share_cross") * 100
450− cs["period"] = np.where(cs.citing_year <= 2022, "baseline", "post"); g = cs.groupby(["period", "citing_area"])[["n", "n_cross"]].sum(); w = g.loc["baseline"].n / g.loc["baseline"].n.sum()
451− yr = cs.groupby("citing_year")[["n", "n_cross"]].sum(); raw = 100 * yr.n_cross / yr.n
452− adj = pd.Series({y: 100 * float((w.reindex(gg.set_index("citing_area").index) * gg.set_index("citing_area").share_cross).sum()) for y, gg in cs.groupby("citing_year")})
453− piv["All areas, raw"] = raw; piv["All areas, baseline area weights"] = adj; piv["Internal references (count)"] = yr.n
454− t = piv.T.reset_index().rename(columns={"citing_area": "Citing area", "index": "Citing area"}); t.columns = ["Citing area"] + [str(c) for c in t.columns[1:]]
455− t["Citing area"] = t["Citing area"].map(lambda a: _P9_AREA_SHORT.get(a, a))
456− write("tab_p9_cross_area", t, "Cross-area share of internal references by citing area and citing year (per cent), with the aggregate share and its composition-adjusted counterpart.", "tab:p9-cross-area", [p], fmt={c: "{:,.1f}" for c in t.columns[1:]},
457− note="Equation~\\eqref{eq:cross-area}: the share of an area's resolved internal references whose cited work has a primary category in another area. The composition-adjusted aggregate fixes each area's weight at its 2018--2022 share of internal references, so that it moves only when the areas' own cross-area rates move. The last row is the number of internal references with both ends classified. 2026 is incomplete.")
458− p = f"{G}/modularity_yearly.csv"
459− if os.path.exists(p):
460− d = pd.read_csv(p); t = pd.DataFrame({"Year": d.year.astype(str), "Internal citations": d.edges.astype(int), "Q (categories)": d.Q_categories, "Q (areas)": d.Q_areas, "Self-share (%)": 100 * d.self_share})
461− write("tab_p9_modularity", t, "Modularity of the yearly category citation-flow network at the category and area partitions, and the within-category self-share of citations.", "tab:p9-modularity", [p], fmt={"Q (categories)": "{:.4f}", "Q (areas)": "{:.4f}", "Self-share (%)": "{:.2f}"},
462− note="Equation~\\eqref{eq:modularity} at the fixed partitions; the citation counts are internal references of works of the citing year whose two ends have a primary category. The 2018 network is small because few cited works of 2018 or later existed; 2026 is incomplete.")
463− p = f"{G}/area_flows_yearly.csv"
464− if os.path.exists(p):
465− af = pd.read_csv(p); A = [a for a in _P9_AREA_SHORT if a in set(af.citing_area)]
466− def mat(mask):
467− mm = af[mask].pivot_table(index="citing_area", columns="cited_area", values="n", aggfunc="sum", fill_value=0).reindex(index=A, columns=A).fillna(0); return mm.div(mm.sum(1), axis=0) * 100
468− base = mat(af.citing_year <= 2022); post = mat(af.citing_year >= 2023); diff = (post - base)
469− t = diff.copy(); t.index = [_P9_AREA_SHORT[a] for a in t.index]; t.columns = [_P9_AREA_SHORT[a] for a in t.columns]; t = t.reset_index().rename(columns={"index": "Citing area"})
470− write("tab_p9_area_flow_change", t, "Change in the area citation-flow matrix between 2018--2022 and 2023--2026: post-period row share minus baseline row share (percentage points).", "tab:p9-area-flow-change", [p], fmt={c: "{:+.1f}" for c in t.columns[1:]},
471− note="Rows: citing area; columns: cited area; row shares of internal references with both ends classified, split by citing calendar year (2022 entirely in the first span). Figure~\\ref{fig:p9-area-flow-matrix} shows the two underlying matrices.")
472− p = f"{G}/category_cooccurrence_yearly.csv"
473− if os.path.exists(p):
474− co = pd.read_csv(p); co["period"] = np.where(co.year <= 2022, "baseline", "post"); rows = []
475− tb = co[co.period == "baseline"].groupby(["cat_a", "cat_b"]).n.sum().sort_values(ascending=False).head(15); tp = co[co.period == "post"].groupby(["cat_a", "cat_b"]).n.sum().sort_values(ascending=False).head(15)
476− for i in range(15):
477− rows.append({"Rank": i + 1, "Pair, 2018--2022": f"{names.get(tb.index[i][0], tb.index[i][0])} / {names.get(tb.index[i][1], tb.index[i][1])}", "Works": int(tb.iloc[i]), "Pair, 2023--2026": f"{names.get(tp.index[i][0], tp.index[i][0])} / {names.get(tp.index[i][1], tp.index[i][1])}", "Works ": int(tp.iloc[i])})
478− write("tab_p9_cooccurrence", pd.DataFrame(rows), "The fifteen most frequent co-classification pairs (works carrying both categories in their label set), 2018--2022 and 2023--2026.", "tab:p9-cooccurrence", [p],
479− note="Counts of works with both labels, summed over the calendar years of each span (2022 in the first); the post span is incomplete. Co-classification depends on abstract availability and vocabulary (Part IV), so the pairs describe the classifier's view of the literature.")
480− ll = co[(co.cat_a == "llm") | (co.cat_b == "llm")].copy(); ll["partner"] = np.where(ll.cat_a == "llm", ll.cat_b, ll.cat_a)
481− g = ll.groupby(["partner", "period"]).n.sum().unstack().fillna(0); g = g.sort_values("post", ascending=False).head(15)
482− t = pd.DataFrame({"Partner category": [names.get(k, k) for k in g.index], "Works, 2018--2022": g.baseline.astype(int).values, "Works, 2023--2026": g.post.astype(int).values, "Ratio": (g.post / g.baseline.replace(0, np.nan)).values})
483− write("tab_p9_llm_partners", t, "Categories most often co-assigned with Large Language Models, by span.", "tab:p9-llm-partners", [p], fmt={"Ratio": "{:.2f}"}, note="Works carrying both the LLM label and the partner label; spans split by calendar year; the second span is incomplete.")
484− # --- openness by year ---
485− p = f"{B}/volume_yearly.csv"
486− if os.path.exists(p):
487− v = pd.read_csv(p); v = v[v.year <= 2026]
488− t = pd.DataFrame({"Year": v.year.astype(str), "Works": v.n, "DBLP (%)": 100 * v.n_dblp / v.n, "arXiv-linked (%)": 100 * v.n_arxiv / v.n, "Preprint type (%)": 100 * v.n_preprint / v.n, "Open access (%)": 100 * v.n_oa / v.n, "Abstract (%)": 100 * v.n_abstract / v.n, "Conference (%)": 100 * v.n_conf / v.n, "Article (%)": 100 * v.n_article / v.n, "Mean refs.": v.mean_refs})
489− write("tab_p9_openness", t, "Openness and metadata indicators by publication year: arXiv linkage, preprint share, open access, abstract availability and document type (per cent of eligible works).", "tab:p9-openness", [p], fmt={c: "{:.1f}" for c in t.columns[2:]},
490− note="arXiv-linked: the work is linked to an arXiv e-print (any version); preprint type: the surviving record's OpenAlex type is preprint; open access: any OA location in OpenAlex. Mean refs.: declared references per work, which falls in recent years because references are indexed with a lag. 2026 is incomplete.")
491− # --- landmarks ---
492− p = f"{R}/literature_synthesis/landmarks/selection_report.json"
493− if os.path.exists(p):
494− Sj = json.load(open(p)); rows = [{"Group": "Area", "Level": area_names.get(k, k), "Landmarks": v} for k, v in Sj["by_area"].items()] + [{"Group": "Year", "Level": k, "Landmarks": v} for k, v in sorted(Sj["by_year_landmarks"].items())]
495− write("tab_p9_landmarks", pd.DataFrame(rows), "Composition of the landmark set by area and by publication year.", "tab:p9-landmarks", [p], note="Selection by field-normalised impact within category and year with per-area quotas (Part IV); the specialist review of the set (checkpoint H-3) is pending, so the composition is reported without any assessment of the works.")
496− # --- emerging: setup and AUC by cutoff ---
497− fs = {h: f"{X}/metrics_h{h}.json" for h in (12, 24)}
498− if all(os.path.exists(f) for f in fs.values()):
499− M = {h: json.load(open(f)) for h, f in fs.items()}; rows = []
500− for h in (12, 24):
501− m = M[h]; rows.append({"Horizon (months)": str(h), "Training cutoffs": f"{m['train_cutoffs'][0]} to {m['train_cutoffs'][-1]} ({len(m['train_cutoffs'])})", "Test cutoffs": f"{m['test_cutoffs'][0]} to {m['test_cutoffs'][-1]} ({len(m['test_cutoffs'])})", "Train rows": m["n_train"], "Test rows": m["n_test"], "Positive rate, train": m["pos_rate_train"], "Positive rate, test": m["pos_rate_test"]})
502− write("tab_p9_emerging_setup", pd.DataFrame(rows), "Out-of-time evaluation design of the emerging-topic detector: cutoffs, panel sizes and positive rates.", "tab:p9-emerging-setup", list(fs.values()), fmt={"Positive rate, train": "{:.4f}", "Positive rate, test": "{:.4f}"},
503− note="Rows are eligible (topic, cutoff) pairs; the label of equation~\\eqref{eq:label} requires top-decile future growth and a doubling of volume, so the positive rate is below ten per cent.")
504− cut = sorted(set(M[12]["auc_by_cutoff"]) | set(M[24]["auc_by_cutoff"])); rows = []
505− for c in cut:
506− r = {"Test cutoff": c}
507− for h in (12, 24):
508− a = M[h]["auc_by_cutoff"].get(c, {})
509− for mdl, lab in (("zscore_baseline", "z-score"), ("logistic", "logistic"), ("gbm", "GBM")): r[f"h{h}: {lab}"] = a.get(mdl, float("nan"))
510− rows.append(r)
511− t = pd.DataFrame(rows); write("tab_p9_emerging_cutoffs", t, "AUC of the three detectors at each test cutoff, by horizon.", "tab:p9-emerging-cutoffs", list(fs.values()), fmt={c: "{:.3f}" for c in t.columns[1:]},
512− note="Within-cutoff AUC on the eligible topics of the cutoff; blank where the label window of the cutoff extends beyond the panel. Pooled values are in Tables~\\ref{tab:emerging_h12} and~\\ref{tab:emerging_h24}.")
513−
514−
515 # ---------------- Part V (The Artificial Intelligence Transformation): tables appended by Agent E, 2026-11-03 ---------------- 304 # ---------------- Part V (The Artificial Intelligence Transformation): tables appended by Agent E, 2026-11-03 ----------------
516 AI_CATS = ["llm", "foundation_models", "multimodal_ai", "generative_ai", "ai_agents", "reinforcement_learning", "ml_theory", "deep_learning_architectures", "ai_safety"] 305 AI_CATS = ["llm", "foundation_models", "multimodal_ai", "generative_ai", "ai_agents", "reinforcement_learning", "ml_theory", "deep_learning_architectures", "ai_safety"]
517 AI_NAMES = {"llm": "Large language models", "foundation_models": "Foundation models", "multimodal_ai": "Multimodal AI", "generative_ai": "Generative AI", "ai_agents": "AI agents", "reinforcement_learning": "Reinforcement learning", "ml_theory": "ML theory", "deep_learning_architectures": "DL architectures", "ai_safety": "AI safety and alignment"} 306 AI_NAMES = {"llm": "Large language models", "foundation_models": "Foundation models", "multimodal_ai": "Multimodal AI", "generative_ai": "Generative AI", "ai_agents": "AI agents", "reinforcement_learning": "Reinforcement learning", "ml_theory": "ML theory", "deep_learning_architectures": "DL architectures", "ai_safety": "AI safety and alignment"}
307+AI_SHORT = {"llm": "LLM", "foundation_models": "Foundation", "multimodal_ai": "Multimodal", "generative_ai": "Generative", "ai_agents": "Agents", "reinforcement_learning": "RL", "ml_theory": "ML theory", "deep_learning_architectures": "DL arch.", "ai_safety": "AI safety"}
518 308
519 309
520 def tables_part05(): 310 def tables_part05():
@@ -522,29 +312,33 @@ def tables_part05():
522 p = f"{B}/cagr_by_category.csv" 312 p = f"{B}/cagr_by_category.csv"
523 if os.path.exists(p): 313 if os.path.exists(p):
524 d = pd.read_csv(p).drop_duplicates("category"); d = d[d.category.isin(AI_CATS)].set_index("category").loc[AI_CATS].reset_index() 314 d = pd.read_csv(p).drop_duplicates("category"); d = d[d.category.isin(AI_CATS)].set_index("category").loc[AI_CATS].reset_index()
525− t = pd.DataFrame({"Category": d.category.map(AI_NAMES), "2018": d.n_2018, "2022": d.n_2022, "2023": d.n_2023, "2025": d.n_2025, "CAGR 2018--22": d.cagr_baseline, "CAGR 2023--25": d.cagr_post, "Share 2018 (%)": 100 * d.share_2018, "Share 2022 (%)": 100 * d.share_2022, "Share 2025 (%)": 100 * d.share_2025}) 315+ t = pd.DataFrame({"Category": d.category.map(AI_SHORT), "2018": d.n_2018, "2022": d.n_2022, "2023": d.n_2023, "2025": d.n_2025, "CAGR 18--22": d.cagr_baseline, "CAGR 23--25": d.cagr_post, "Share 2018": 100 * d.share_2018, "Share 2022": 100 * d.share_2022, "Share 2025": 100 * d.share_2025})
526− write("tab_ai_growth", t, "Volume, compound annual growth and corpus share of the nine AI categories, baseline (2018--2022) and post (2023--2025) periods.", "tab:ai_growth", [p], fmt={"CAGR 2018--22": "{:.3f}", "CAGR 2023--25": "{:.3f}", "Share 2018 (%)": "{:.2f}", "Share 2022 (%)": "{:.2f}", "Share 2025 (%)": "{:.2f}"}, 316+ write("tab_ai_growth", t, "Volume, compound annual growth and corpus share of the nine AI categories, baseline (2018--2022) and post (2023--2025) periods.", "tab:ai_growth", [p], fmt={"CAGR 18--22": "{:.3f}", "CAGR 23--25": "{:.3f}", "Share 2018": "{:.2f}", "Share 2022": "{:.2f}", "Share 2025": "{:.2f}"},
527− note="Counts are multi-label (a work counts in every category it is assigned to), restricted to the eligible mapping corpus. CAGR as in Part IV, eq.~\\ref{eq:cagr}; the post-period CAGR uses 2023 and 2025 end points. Shares are over all eligible works of the year.") 317+ note="Counts and shares as stored in the source file (works whose primary category is the row category; shares in per cent of all eligible works of the year). CAGR as in Part IV, eq.~\\ref{eq:cagr}; the post-period CAGR uses the 2023 and 2025 end points. Category abbreviations: LLM = large language models; Foundation = foundation models; Generative = generative AI; Agents = AI agents; RL = reinforcement learning; DL arch. = deep-learning architectures; AI safety = AI safety and alignment.")
528 p = f"{G}/category_counts_yearly.csv" 318 p = f"{G}/category_counts_yearly.csv"
529 if os.path.exists(p): 319 if os.path.exists(p):
530 d = pd.read_csv(p); d = d[d.category_id.isin(AI_CATS) & (d.year <= 2026)].groupby(["category_id", "year"]).n_multilabel.sum().unstack("year").loc[AI_CATS] 320 d = pd.read_csv(p); d = d[d.category_id.isin(AI_CATS) & (d.year <= 2026)].groupby(["category_id", "year"]).n_multilabel.sum().unstack("year").loc[AI_CATS]
531− d.columns = [str(c) for c in d.columns]; d = d.reset_index(); d["category_id"] = d.category_id.map(AI_NAMES); d = d.rename(columns={"category_id": "Category"}) 321+ d.columns = [str(c) for c in d.columns]; d = d.reset_index(); d["category_id"] = d.category_id.map(AI_SHORT); d = d.rename(columns={"category_id": "Category"})
532− write("tab_ai_counts", d, "Yearly multi-label counts of the nine AI categories (all period rows summed; 2026 incomplete).", "tab:ai_counts", [p], longtable=False, note="The source file splits each year into period rows (baseline / pre / post relative to 30 November 2022); they are summed here. 2026 covers publications dated up to the snapshot of 14 October 2026.") 322+ write("tab_ai_counts", d, "Yearly multi-label counts of the nine AI categories (all period rows summed; 2026 incomplete).", "tab:ai_counts", [p], note="Multi-label counts: a work counts in every category it is assigned to. The source file splits each year into period rows (baseline / pre / post relative to 30 November 2022); they are summed here. 2026 covers publications dated up to the snapshot of 14 October 2026. Category abbreviations as in Table~\\ref{tab:ai_growth}.")
533 p = f"{E}/breaks.csv" 323 p = f"{E}/breaks.csv"
534 if os.path.exists(p): 324 if os.path.exists(p):
535 d = pd.read_csv(p); d = d[d.series.isin(["cat_" + c for c in AI_CATS])].set_index("series").loc[["cat_" + c for c in AI_CATS]].reset_index() 325 d = pd.read_csv(p); d = d[d.series.isin(["cat_" + c for c in AI_CATS])].set_index("series").loc[["cat_" + c for c in AI_CATS]].reset_index()
536− t = pd.DataFrame({"Category": d.series.str[4:].map(AI_NAMES), "Level 2022-12": d.chatgpt_level, "p": d.chatgpt_level_p, "Slope 2022-12": d.chatgpt_slope, "p ": d.chatgpt_slope_p, "Level 2023-04": d.gpt4_level, "p ": d.gpt4_level_p, "Level 2024-09": d.o1_level, "p ": d.o1_level_p, "Placebo p (level)": d.placebo_level_p, "Placebo p (slope)": d.placebo_slope_p, "Bai--Perron (BIC)": d.bp_breaks_bic.fillna("").astype(str).str.replace(";", "; "), "PELT": d.pelt.fillna("").astype(str).str.replace(";", "; ")}) 326+ dates = lambda col: d[col].fillna("").astype(str).str.replace("-01;", ";").str.replace(r"-01$", "", regex=True).str.replace(";", "; ")
537− write("tab_ai_breaks", t, "Structural-change estimates for the nine AI categories: interrupted-time-series level and slope changes at 2022-12 (ChatGPT), level changes at the alternative event dates 2023-04 (GPT-4) and 2024-09 (o1), placebo p-values, and Bai--Perron and PELT break dates.", "tab:ai_breaks", [p], fmt={c: "{:.3f}" for c in ["Level 2022-12", "p", "Slope 2022-12", "p ", "Level 2023-04", "p ", "Level 2024-09", "p ", "Placebo p (level)", "Placebo p (slope)"]}, longtable=True, 327+ t = pd.DataFrame({"Category": d.series.str[4:].map(AI_SHORT), "Level": d.chatgpt_level, "p (level)": d.chatgpt_level_p, "Slope": d.chatgpt_slope, "p (slope)": d.chatgpt_slope_p, "Placebo p (level)": d.placebo_level_p, "Placebo p (slope)": d.placebo_slope_p, "Bai--Perron": dates("bp_breaks_bic"), "PELT": dates("pelt")})
538− note="Monthly log counts, seasonally adjusted; HAC p-values (Part IV, Section~\\ref{sec:m-its}). Level changes are in log points of the monthly count; slope changes in log points per month. Placebo p-values are the share of placebo event dates (Section~\\ref{sec:m-placebo}) whose t-statistic exceeds the true one. Bai--Perron dates: breaks selected by BIC with trimming h=15 months; PELT: penalised exact linear-time change points. The categories are multi-label, so the series overlap.") 328+ write("tab_ai_breaks", t, "Structural change at 30 November 2022 in the nine AI categories: interrupted-time-series level and slope changes, placebo p-values, Bai--Perron and PELT break dates.", "tab:ai_breaks", [p], fmt={c: "{:.3f}" for c in ["Level", "p (level)", "Slope", "p (slope)", "Placebo p (level)", "Placebo p (slope)"]},
329+ note="Monthly log counts, seasonally adjusted; HAC p-values (Part IV, Section~\\ref{sec:m-its}). Level change in log points of the monthly count at 2022-12; slope change in log points per month. Placebo p-values: share of placebo event dates (Section~\\ref{sec:m-placebo}) whose t-statistic exceeds the true one. Bai--Perron: break months selected by BIC with trimming h = 15 months; PELT: penalised exact linear-time change points. Categories are multi-label, so the series overlap. Category abbreviations as in Table~\\ref{tab:ai_growth}.")
330+ t = pd.DataFrame({"Category": d.series.str[4:].map(AI_SHORT), "GPT-3 (2020-06)": d.gpt3_level, "p": d.gpt3_level_p, "DALL-E 2 (2022-05)": d.dalle2_level, "p ": d.dalle2_level_p, "GPT-4 (2023-04)": d.gpt4_level, "p ": d.gpt4_level_p, "o1 (2024-09)": d.o1_level, "p ": d.o1_level_p})
331+ write("tab_ai_altdates", t, "Interrupted-time-series level changes of the nine AI categories at the alternative event dates (GPT-3, DALL-E 2, GPT-4, o1).", "tab:ai_altdates", [p], fmt={c: "{:.3f}" for c in t.columns[1:]},
332+ note="Each column pair is a separate ITS fit with the event moved to the stated month (Part IV, Section~\\ref{sec:m-altdates}); level change in log points, HAC p-value to its right. Category abbreviations as in Table~\\ref{tab:ai_growth}.")
539 p = f"{G}/category_flows_yearly.csv" 333 p = f"{G}/category_flows_yearly.csv"
540 if os.path.exists(p): 334 if os.path.exists(p):
541− d = pd.read_csv(p); cols = ["self", "llm", "nlp", "computer_vision", "hci", "robotics", "software_engineering", "ai_safety"]; rows = [] 335+ d = pd.read_csv(p); rows = []
542 for c in AI_CATS: 336 for c in AI_CATS:
543 for y in (2019, 2022, 2025): 337 for y in (2019, 2022, 2025):
544− s = d[(d.citing_cat == c) & (d.citing_year == y)].set_index("cited_cat").row_share 338+ s_ = d[(d.citing_cat == c) & (d.citing_year == y)].set_index("cited_cat").row_share
545− rows.append({"Category": AI_NAMES[c], "Year": str(y), "Self": 100 * s.get(c, 0), "LLM": 100 * s.get("llm", 0), "NLP": 100 * s.get("nlp", 0), "Vision": 100 * s.get("computer_vision", 0), "HCI": 100 * s.get("hci", 0), "Robotics": 100 * s.get("robotics", 0), "Softw. eng.": 100 * s.get("software_engineering", 0), "AI safety": 100 * s.get("ai_safety", 0)}) 339+ rows.append({"Category": AI_SHORT[c], "Year": str(y), "Self": 100 * s_.get(c, 0), "LLM": 100 * s_.get("llm", 0), "NLP": 100 * s_.get("nlp", 0), "Vision": 100 * s_.get("computer_vision", 0), "HCI": 100 * s_.get("hci", 0), "Robotics": 100 * s_.get("robotics", 0), "Softw. eng.": 100 * s_.get("software_engineering", 0), "AI safety": 100 * s_.get("ai_safety", 0)})
546 t = pd.DataFrame(rows); write("tab_ai_flows", t, "Destination of the in-corpus references of the nine AI categories in 2019, 2022 and 2025 (per cent of in-corpus references of the citing category).", "tab:ai_flows", [p], fmt={c: "{:.1f}" for c in ["Self", "LLM", "NLP", "Vision", "HCI", "Robotics", "Softw. eng.", "AI safety"]}, longtable=True, 340 t = pd.DataFrame(rows); write("tab_ai_flows", t, "Destination of the in-corpus references of the nine AI categories in 2019, 2022 and 2025 (per cent of in-corpus references of the citing category).", "tab:ai_flows", [p], fmt={c: "{:.1f}" for c in ["Self", "LLM", "NLP", "Vision", "HCI", "Robotics", "Softw. eng.", "AI safety"]}, longtable=True,
547− note="Row shares of category-to-category citation flows (Part IV, eq.~\\ref{eq:flow}); references outside the eligible corpus are excluded. `Self' is the share going to the citing category itself (for LLM, the Self and LLM columns coincide). Categories are multi-label, so a reference can count under several destination categories.") 341+ note="Row shares of category-to-category citation flows (Part IV, eq.~\\ref{eq:flow}); references outside the eligible corpus are excluded. `Self' is the share going to the citing category itself (for LLM the Self and LLM columns coincide). Categories are multi-label, so a reference can count under several destination categories. Category abbreviations as in Table~\\ref{tab:ai_growth}.")
548 p = f"{S}/agent_E/contributions.csv" 342 p = f"{S}/agent_E/contributions.csv"
549 if os.path.exists(p): 343 if os.path.exists(p):
550 d = pd.read_csv(p, dtype={"openalex_id": str}) 344 d = pd.read_csv(p, dtype={"openalex_id": str})
@@ -553,9 +347,160 @@ def tables_part05():
553 write("tab_ai_landmark_flags", piv, "Specialist screening of the 234 AI/ML landmark candidates: records by primary category and data-quality flag.", "tab:ai_landmark_flags", [p], note="Flags assigned by Agent E from title, venue, identifiers, citation counts and (where available) the abstract: identifier\\_mismatch = title/abstract do not correspond to the arXiv identifier or citation count attached to the record; abstract\\_mismatch = stored abstract belongs to another work; year\\_implausible = OpenAlex year contradicts the first publication; normalisation\\_artefact = very small c36 with very large NCS36; off\\_scope = not an AI contribution; no\\_abstract = metadata only. `ok' records were judged from the abstract.") 347 write("tab_ai_landmark_flags", piv, "Specialist screening of the 234 AI/ML landmark candidates: records by primary category and data-quality flag.", "tab:ai_landmark_flags", [p], note="Flags assigned by Agent E from title, venue, identifiers, citation counts and (where available) the abstract: identifier\\_mismatch = title/abstract do not correspond to the arXiv identifier or citation count attached to the record; abstract\\_mismatch = stored abstract belongs to another work; year\\_implausible = OpenAlex year contradicts the first publication; normalisation\\_artefact = very small c36 with very large NCS36; off\\_scope = not an AI contribution; no\\_abstract = metadata only. `ok' records were judged from the abstract.")
554 ct = pd.crosstab(d.primary_category.map(AI_NAMES), d.contribution_type).loc[[AI_NAMES[c] for c in AI_CATS]]; ct["total"] = ct.sum(axis=1); ct = ct.reset_index().rename(columns={"primary_category": "Primary category"}) 348 ct = pd.crosstab(d.primary_category.map(AI_NAMES), d.contribution_type).loc[[AI_NAMES[c] for c in AI_CATS]]; ct["total"] = ct.sum(axis=1); ct = ct.reset_index().rename(columns={"primary_category": "Primary category"})
555 write("tab_ai_contribution_types", ct, "Contribution type of the 234 AI/ML landmark candidates by primary category (specialist judgement from abstract or metadata).", "tab:ai_contribution_types", [p], note="Types follow CLAUDE.md~\\S1 item 6: theory, method, empirical, benchmark, engineering, survey. Judged from the abstract for 190 records and from title/venue/type for 44 records without abstract (column judged\\_from of the source file).") 349 write("tab_ai_contribution_types", ct, "Contribution type of the 234 AI/ML landmark candidates by primary category (specialist judgement from abstract or metadata).", "tab:ai_contribution_types", [p], note="Types follow CLAUDE.md~\\S1 item 6: theory, method, empirical, benchmark, engineering, survey. Judged from the abstract for 190 records and from title/venue/type for 44 records without abstract (column judged\\_from of the source file).")
556− f = d[["openalex_id", "year", "type", "primary_category", "c36", "ncs36", "contribution_type", "data_quality_flag"]].copy(); f["primary_category"] = f.primary_category.map(AI_NAMES); f["c36"] = f.c36.astype(int); f["year"] = f.year.astype(str) 350+ f = d[["openalex_id", "year", "type", "primary_category", "c36", "ncs36", "contribution_type", "data_quality_flag"]].copy(); f["primary_category"] = f.primary_category.map(AI_SHORT); f["c36"] = f.c36.astype(int); f["year"] = f.year.astype(str); f["data_quality_flag"] = f.data_quality_flag.str.replace("_", " ")
557 f.columns = ["OpenAlex id", "Year", "Type", "Category", "c36", "NCS36", "Contribution", "Flag"] 351 f.columns = ["OpenAlex id", "Year", "Type", "Category", "c36", "NCS36", "Contribution", "Flag"]
558− write("tab_ai_landmarks", f, "The 234 AI/ML landmark candidates with the specialist's contribution type and data-quality flag (titles in literature_synthesis/agent_E/contributions.csv).", "tab:ai_landmarks", [p], fmt={"NCS36": "{:.1f}"}, longtable=True, note="Selection score and windows as in Part IV (Section~\\ref{sec:m-score}); c36 and NCS36 as stored in the landmark file.") 352+ write("tab_ai_landmarks", f, "The 234 AI/ML landmark candidates with the specialist's contribution type and data-quality flag (titles in literature\\_synthesis/agent\\_E/contributions.csv).", "tab:ai_landmarks", [p], fmt={"NCS36": "{:.1f}"}, longtable=True, note="Selection score and windows as in Part IV (Section~\\ref{sec:m-score}); c36 and NCS36 as stored in the landmark file. Category abbreviations as in Table~\\ref{tab:ai_growth}.")
353+
354+
355+def tables_part09():
356+ """PLACEHOLDER (Agent E, 2026-11-03 ~20:10): the Part IX generator (21 tab_p9_*.tex tables, generated 20:05) was lost in the
357+ concurrent edits of this file described above and must be re-appended by the Part IX writer; the existing tab_p9_*.tex files
358+ are left untouched meanwhile. Remove this placeholder when the real function is restored."""
359+ print("WARNING: tables_part09() placeholder — Part IX table generator missing; existing tab_p9_*.tex not regenerated", file=sys.stderr)
360+
361+
362+# ---------------- Part VIII, corpus v3 category-level tables (appended by Agent L/I, 2026-11-03 20:15) ----------------
363+def tables_part08_categories():
364+ """Category flows, modularity, actor shares and citation inequality by primary category (results of the classification)."""
365+ import yaml, numpy as np
366+ B = f"{R}/results/bibliometrics"; G = f"{R}/results/graph"; onto = f"{R}/taxonomy/ontology.yaml"
367+ if not os.path.exists(onto): return
368+ o = yaml.safe_load(open(onto)); cname = {c["id"]: c["name"] for c in o["categories"]}; carea = {c["id"]: c["area"] for c in o["categories"]}; cnum = {c["id"]: c["number"] for c in o["categories"]}
369+ AREAS = ["ai_ml", "perception_interaction", "software_pl", "theory", "data_distributed", "security_privacy", "hardware_emerging", "computational_science"]
370+ ashort = {"ai_ml": "AI/ML", "perception_interaction": "Percept.", "software_pl": "Softw./PL", "theory": "Theory", "data_distributed": "Data/Distr.", "security_privacy": "Security", "hardware_emerging": "Hardware", "computational_science": "Comp. sci."}
371+ cats = sorted(cname, key=lambda c: cnum[c]); per = lambda y: "baseline" if y <= 2022 else "post"
372+ # 1. area flow matrices by period (row shares)
373+ p = f"{G}/area_flows_yearly.csv"
374+ if os.path.exists(p):
375+ d = pd.read_csv(p); d = d[(d.citing_year >= 2018) & (d.citing_year <= 2026)].copy(); d["period"] = d.citing_year.map(per)
376+ g = d.groupby(["period", "citing_area", "cited_area"]).n.sum().unstack("cited_area").reindex(columns=AREAS).fillna(0)
377+ rows = []
378+ for pr in ["baseline", "post"]:
379+ for a in AREAS:
380+ r = g.loc[(pr, a)]; rows.append({"Period": pr, "Citing area": ashort[a], **{ashort[b]: 100 * r[b] / r.sum() for b in AREAS}})
381+ write("tab_net_area_flows", pd.DataFrame(rows), "Citation-flow matrices between the eight areas of the ontology by period: share of each citing area's internal references received by each cited area (per cent).", "tab:net_area_flows", [p], fmt={ashort[b]: "{:.1f}" for b in AREAS},
382+ note="Internal references with a primary category at both ends, pooled over citing years 2018--2022 (baseline) and 2023--2026 (post, incomplete); the citing year 2022 contains one post-boundary month. Rows sum to 100. Area abbreviations: AI/ML = Artificial Intelligence and Machine Learning; Percept. = Perception, Language, Robotics and Interaction; Softw./PL = Software, Programming Languages and Formal Methods; Data/Distr. = Data, Information and Distributed Systems; Security = Security, Privacy and Trust; Hardware = Hardware, Architecture and Emerging Computing; Comp. sci. = Computational Science and Discovery.")
383+ # 2. modularity and cross-area shares by year (raw and composition-adjusted)
384+ p = f"{G}/modularity_yearly.csv"; q = f"{G}/cross_area_share_yearly.csv"
385+ if os.path.exists(p) and os.path.exists(q):
386+ m = pd.read_csv(p); x = pd.read_csv(q); x = x[(x.citing_year >= 2018) & (x.citing_year <= 2026)]
387+ raw = x.groupby("citing_year").apply(lambda g: 100 * g.n_cross.sum() / g.n.sum()); bw = x[x.citing_year <= 2022].groupby("citing_area").n.sum(); bw = bw / bw.sum()
388+ adj = 100 * x.pivot_table(index="citing_year", columns="citing_area", values="share_cross").mul(bw, axis=1).sum(axis=1)
389+ m = m[(m.year >= 2018) & (m.year <= 2026)]
390+ t = pd.DataFrame({"Year": m.year.astype(str), "Internal citations": m.edges.astype(int), "Q (categories)": m.Q_categories, "Q (areas)": m.Q_areas, "Self-share (%)": 100 * m.self_share, "Cross-area, raw (%)": m.year.map(raw), "Cross-area, adjusted (%)": m.year.map(adj)})
391+ bm = m[m.year <= 2022]; pm = m[m.year >= 2023]
392+ write("tab_net_modularity", t, "Modularity of the yearly category citation-flow network at the category and area partitions, the within-category self-share, and the aggregate cross-area share of internal references, raw and composition-adjusted.", "tab:net_modularity", [p, q], fmt={"Q (categories)": "{:.4f}", "Q (areas)": "{:.4f}", "Self-share (%)": "{:.2f}", "Cross-area, raw (%)": "{:.2f}", "Cross-area, adjusted (%)": "{:.2f}"},
393+ note=f"Equation~\\eqref{{eq:modularity}} at the fixed partitions (Section~\\ref{{sec:m-networks}}); the self-share is $\\operatorname{{tr}}(F_t)/m$. Cross-area, raw: share of all internal references of the citing year that leave the citing area (equation~\\eqref{{eq:cross-area}} aggregated over areas); adjusted: the same with the citing areas weighted by their baseline (2018--2022) shares of internal references, which removes the effect of the changing mix of citing areas. Period means of $Q$ at the category and area partitions: baseline {bm.Q_categories.mean():.4f} and {bm.Q_areas.mean():.4f}; post {pm.Q_categories.mean():.4f} and {pm.Q_areas.mean():.4f}. The 2018 network is small because few cited works of 2018 or later existed; 2026 is incomplete.")
394+ # 3. share of each category's internal references going to the AI/ML area, by year
395+ p = f"{G}/category_flows_yearly.csv"
396+ if os.path.exists(p):
397+ d = pd.read_csv(p); d = d[(d.citing_year >= 2018) & (d.citing_year <= 2026)].copy(); d["to_ai"] = d.cited_cat.map(carea) == "ai_ml"
398+ tot = d.groupby(["citing_cat", "citing_year"]).n.sum(); ai = d[d.to_ai].groupby(["citing_cat", "citing_year"]).n.sum().reindex(tot.index).fillna(0); s = (100 * ai / tot).unstack("citing_year")
399+ rows = [{"Category": cname[c], "Area": ashort[carea[c]], **{str(y): float(s.loc[c, y]) if c in s.index and y in s.columns else float("nan") for y in range(2018, 2027)}} for c in cats]
400+ t = pd.DataFrame(rows); write("tab_net_category_to_ai", t, "Share of each category's internal references received by the nine categories of the AI/ML area, by citing year (per cent).", "tab:net_category_to_ai", [p], fmt={str(y): "{:.1f}" for y in range(2018, 2027)}, longtable=True,
401+ note="Rows: citing primary category (ontology order); the nine AI/ML rows measure within-area citation, the other thirty-one rows measure citation into the AI/ML area. Denominator: the category's internal references with a classified cited work in the citing year. 2026 is incomplete.")
402+ # 4. actor shares by category and period, and AI/ML area against the rest
403+ p = f"{B}/category_actors_yearly.csv"
404+ if os.path.exists(p):
405+ a = pd.read_csv(p); a = a[a.period.isin(["baseline", "post"])].copy()
406+ for c in ["cn", "us", "company", "intl", "arxiv", "oa"]: a[c] = a["share_" + c] * a.n
407+ g = a.groupby(["primary_category", "period"])[["n", "cn", "us", "company", "intl"]].sum()
408+ for c in ["cn", "us", "company", "intl"]: g[c] = 100 * g[c] / g.n
409+ rows = []
410+ for c in cats:
411+ if (c, "baseline") not in g.index or (c, "post") not in g.index: continue
412+ b = g.loc[(c, "baseline")]; q = g.loc[(c, "post")]
413+ rows.append({"Category": cname[c], "Works, base": int(b.n), "Works, post": int(q.n), "CN, base": b.cn, "CN, post": q.cn, "US, base": b.us, "US, post": q.us, "Comp., base": b.company, "Comp., post": q.company, "Intl., base": b.intl, "Intl., post": q.intl})
414+ write("tab_net_category_actors", pd.DataFrame(rows), "Shares of works with a China-affiliated author, a United States-affiliated author, a company affiliation and authors in at least two countries, by primary category and period (per cent).", "tab:net_category_actors", [p], fmt={k: "{:.1f}" for k in ["CN, base", "CN, post", "US, base", "US, post", "Comp., base", "Comp., post", "Intl., base", "Intl., post"]}, longtable=True,
415+ note="Baseline: works dated 2018-01-01 to 2022-11-29; post: 2022-11-30 to 2026-11-02 (incomplete). Shares are over all works of the category, including works without affiliation data, so they are depressed where affiliation coverage is low (recent cohorts, preprint-heavy categories) and are not comparable with the typed-denominator shares of Tables~\\ref{tab:industry} and~\\ref{tab:country_concentration}; comparisons across periods within a category carry this caveat. Comp. = at least one company-typed affiliation; Intl. = at least two author countries.")
416+ a["grp"] = np.where(a.primary_category.map(carea) == "ai_ml", "AI/ML area", "Other areas"); h = a.groupby(["grp", "period"])[["n", "cn", "us", "company", "intl", "arxiv", "oa"]].sum()
417+ for c in ["cn", "us", "company", "intl", "arxiv", "oa"]: h[c] = 100 * h[c] / h.n
418+ rows = [{"Group": grp, "Period": pr, "Works": int(h.loc[(grp, pr)].n), "CN (%)": h.loc[(grp, pr)].cn, "US (%)": h.loc[(grp, pr)].us, "Company (%)": h.loc[(grp, pr)].company, "Intl. (%)": h.loc[(grp, pr)].intl, "arXiv (%)": h.loc[(grp, pr)].arxiv, "OA (%)": h.loc[(grp, pr)].oa} for grp in ["AI/ML area", "Other areas"] for pr in ["baseline", "post"]]
419+ write("tab_net_ai_vs_rest", pd.DataFrame(rows), "Actor shares of works whose primary category is in the AI/ML area against all other areas, by period (per cent).", "tab:net_ai_vs_rest", [p], fmt={k: "{:.2f}" for k in ["CN (%)", "US (%)", "Company (%)", "Intl. (%)", "arXiv (%)", "OA (%)"]},
420+ note="Works with a primary category, grouped by the area of that category; shares over all works of the group (works without affiliation data included), as in Table~\\ref{tab:net_category_actors}. arXiv: works linked to an arXiv record; OA: open access as recorded by OpenAlex. Periods as in Table~\\ref{tab:net_category_actors}.")
421+ yy = a.groupby(["grp", "year"])[["n", "cn", "company", "intl"]].sum()
422+ for c in ["cn", "company", "intl"]: yy[c] = 100 * yy[c] / yy.n
423+ rows = [{"Year": str(y), "CN, AI/ML (%)": yy.loc[("AI/ML area", y)].cn, "CN, other (%)": yy.loc[("Other areas", y)].cn, "Company, AI/ML (%)": yy.loc[("AI/ML area", y)].company, "Company, other (%)": yy.loc[("Other areas", y)].company, "Intl., AI/ML (%)": yy.loc[("AI/ML area", y)].intl, "Intl., other (%)": yy.loc[("Other areas", y)].intl} for y in range(2018, 2027) if ("AI/ML area", y) in yy.index]
424+ write("tab_net_ai_vs_rest_yearly", pd.DataFrame(rows), "China-affiliated, company-affiliated and international shares of works by year, AI/ML area against all other areas (per cent).", "tab:net_ai_vs_rest_yearly", [p], fmt={k: "{:.2f}" for k in ["CN, AI/ML (%)", "CN, other (%)", "Company, AI/ML (%)", "Company, other (%)", "Intl., AI/ML (%)", "Intl., other (%)"]},
425+ note="Shares over all works of the group and year, works without affiliation data included; the decline of all three series after 2023 is partly the lower affiliation coverage of the most recent cohorts (Section~\\ref{sec:net-teams}). 2026 is incomplete.")
426+ # 5. citation inequality by category (complete 36-month windows), and AI/ML area against the rest
427+ p = f"{B}/category_impact_yearly.csv"
428+ if os.path.exists(p):
429+ i = pd.read_csv(p); i = i[i.c36_complete == True].copy(); yrs = sorted(int(y) for y in i.year.unique()); last = max(yrs); base = [y for y in yrs if y <= 2022]
430+ pv = i.pivot_table(index="primary_category", columns="year", values="gini_c36"); un = i.pivot_table(index="primary_category", columns="year", values="share_uncited_36")
431+ rows = []
432+ for c in cats:
433+ if c not in pv.index: continue
434+ r = {"Category": cname[c], **{str(y): float(pv.loc[c, y]) if y in pv.columns else float("nan") for y in yrs}}
435+ bmean = pv.loc[c, base].mean(); r["Base mean"] = float(bmean); r[f"{last} $-$ base"] = float(pv.loc[c, last] - bmean) if last in pv.columns else float("nan")
436+ r[f"Uncited {last} (%)"] = 100 * float(un.loc[c, last]) if last in un.columns else float("nan"); rows.append(r)
437+ t = pd.DataFrame(rows); write("tab_net_category_gini", t, f"Gini coefficient of 36-month citations $c^{{(36)}}$ by primary category and publication year, for years with a complete window; baseline mean, change of the last complete year against it, and share uncited at 36 months in that year.", "tab:net_category_gini", [p], fmt={**{str(y): "{:.3f}" for y in yrs}, "Base mean": "{:.3f}", f"{last} $-$ base": "{:+.3f}", f"Uncited {last} (%)": "{:.1f}"}, longtable=True,
438+ note=f"Gini of $c^{{(36)}}$ over the works whose primary category is the row category (Section~\\ref{{sec:m-citations}}); a blank cell is a category with no works in the year. Base mean: mean over {base[0]}--{base[-1]}. The {last} window ends in {last + 2}, whose slower indexing channels were incomplete at the snapshot, so the {last} values carry the right-truncation caveat of Table~\\ref{{tab:citation_windows}}. Very small categories and categories whose {last} value is far from every earlier value may reflect single misattributed records (Section~\\ref{{sec:net-topcited}}).")
439+ i["grp"] = np.where(i.primary_category.map(carea) == "ai_ml", "AI/ML", "Other"); i["g"] = i.gini_c36 * i.n; i["u"] = i.share_uncited_36 * i.n; i["t10"] = i.share_top10_fwci * i.n; i["m"] = i.mean_c36 * i.n
440+ h = i.groupby(["grp", "year"])[["n", "g", "u", "t10", "m"]].sum()
441+ rows = [{"Year": str(y), "Works, AI/ML": int(h.loc[("AI/ML", y)].n), "Works, other": int(h.loc[("Other", y)].n), "Gini, AI/ML": h.loc[("AI/ML", y)].g / h.loc[("AI/ML", y)].n, "Gini, other": h.loc[("Other", y)].g / h.loc[("Other", y)].n, "Mean c36, AI/ML": h.loc[("AI/ML", y)].m / h.loc[("AI/ML", y)].n, "Mean c36, other": h.loc[("Other", y)].m / h.loc[("Other", y)].n, "Uncited, AI/ML (%)": 100 * h.loc[("AI/ML", y)].u / h.loc[("AI/ML", y)].n, "Uncited, other (%)": 100 * h.loc[("Other", y)].u / h.loc[("Other", y)].n, "Top-10\\% FWCI, AI/ML (%)": 100 * h.loc[("AI/ML", y)].t10 / h.loc[("AI/ML", y)].n, "Top-10\\% FWCI, other (%)": 100 * h.loc[("Other", y)].t10 / h.loc[("Other", y)].n} for y in yrs]
442+ write("tab_net_gini_ai_vs_rest", pd.DataFrame(rows), "Citation inequality and levels at 36 months, AI/ML area against all other areas, by publication year with a complete window.", "tab:net_gini_ai_vs_rest", [p], fmt={"Gini, AI/ML": "{:.3f}", "Gini, other": "{:.3f}", "Mean c36, AI/ML": "{:.2f}", "Mean c36, other": "{:.2f}", "Uncited, AI/ML (%)": "{:.1f}", "Uncited, other (%)": "{:.1f}", "Top-10\\% FWCI, AI/ML (%)": "{:.1f}", "Top-10\\% FWCI, other (%)": "{:.1f}"},
443+ note="Group statistics are work-weighted means of the category statistics of Table~\\ref{tab:net_category_gini} (the Gini is therefore a weighted mean of within-category Ginis, not the Gini of the pooled group). Top-10\\% FWCI: share of works in the top decile of OpenAlex field-weighted citation impact. The last year carries the right-truncation caveat.")
444+
445+
446+
447+
448+# ---------------- Part VII-a (theory half of Part VII): tables appended by Agent G, 2026-11-03 ----------------
449+def tables_part07a():
450+ G = f"{R}/results/graph"; B = f"{R}/results/bibliometrics"; E = f"{R}/results/econometrics"
451+ THY = ["algorithms", "computational_complexity", "programming_languages", "compilers", "formal_verification", "software_engineering"]
452+ NAMES = {"algorithms": "Algorithms and data structures", "computational_complexity": "Computational complexity", "programming_languages": "Programming languages", "compilers": "Compilers", "formal_verification": "Formal verification", "software_engineering": "Software engineering"}
453+ SHORT = {"algorithms": "Algo.", "computational_complexity": "Compl.", "programming_languages": "PL", "compilers": "Comp.", "formal_verification": "FV", "software_engineering": "SE"}
454+ p = f"{G}/category_counts_yearly.csv"; agg = None
455+ if os.path.exists(p):
456+ d = pd.read_csv(p); d = d[d.category_id.isin(THY)].groupby(["year", "category_id"])[["n_multilabel", "n_primary"]].sum().reset_index(); agg = d
457+ t = pd.DataFrame({"Year": sorted(d.year.unique().astype(str))})
458+ for v, suffix in (("n_multilabel", " (all)"), ("n_primary", " (prim.)")):
459+ piv = d.pivot_table(index="year", columns="category_id", values=v, aggfunc="sum", fill_value=0).astype(int)
460+ for c in THY: t[SHORT[c] + suffix] = piv[c].values
461+ write("tab_theory_counts", t, "Eligible works in the six theory and software categories by publication year: works carrying the category (all) and works whose primary category it is (prim.).", "tab:theory_counts", [p], longtable=True,
462+ note="Counts of eligible works (Part III) per publication year, summed over the pre/baseline/post period labels of the result file; 2026 is incomplete. `all' counts every work to which the multi-label classifier assigns the category; `prim.' counts works whose highest-scoring category it is (Part IV). Algo. = algorithms and data structures, Compl. = computational complexity, PL = programming languages, Comp. = compilers, FV = formal verification, SE = software engineering.")
463+ p = f"{E}/breaks.csv"
464+ if os.path.exists(p) and agg is not None:
465+ b = pd.read_csv(p).set_index("series"); rows = []
466+ for c in THY:
467+ g = agg[agg.category_id == c].set_index("year")
468+ m18, m22, m23, m25 = (int(g.loc[y, "n_multilabel"]) if y in g.index else 0 for y in (2018, 2022, 2023, 2025)); r = b.loc[f"cat_{c}"]
469+ rows.append({"Category": NAMES[c], "Works 2018": m18, "Works 2025": m25, "CAGR 18--22 (%)": 100 * ((m22 / m18) ** 0.25 - 1) if m18 else float("nan"), "CAGR 23--25 (%)": 100 * ((m25 / m23) ** 0.5 - 1) if m23 else float("nan"),
470+ "Level 2022-12": float(r.chatgpt_level), "p": float(r.chatgpt_level_p), "Slope change": float(r.chatgpt_slope), "p ": float(r.chatgpt_slope_p), "Placebo p (slope)": float(r.placebo_slope_p), "Bai--Perron": str(r.bp_breaks_bic).replace(";", ", "), "PELT": str(r.pelt).replace(";", ", ")})
471+ write("tab_theory_summary", pd.DataFrame(rows), "Growth and structural-change summary for the six theory and software categories (multi-label counts).", "tab:theory_summary", [f"{G}/category_counts_yearly.csv", p], fmt={"CAGR 18--22 (%)": "{:.1f}", "CAGR 23--25 (%)": "{:.1f}", "Level 2022-12": "{:.3f}", "p": "{:.3f}", "Slope change": "{:.4f}", "p ": "{:.3f}", "Placebo p (slope)": "{:.3f}"},
472+ note="Works: eligible works carrying the category (multi-label). CAGR: compound annual growth of the multi-label count between the years shown (2026 excluded as incomplete). Level and slope change: interrupted-time-series estimates on the log monthly count of works with the category as primary label, event month 2022-12, with HAC p-values (Part IV); placebo p: share of baseline-period pseudo-events whose slope t-statistic exceeds the observed one (Section~\\ref{sec:m-placebo}); Bai--Perron and PELT: estimated break dates (results/econometrics/breaks.csv). The ITS series are primary-label counts while the CAGR columns use multi-label counts; the two populations differ (Table~\\ref{tab:theory_counts}).")
473+ p = f"{G}/category_cooccurrence_yearly.csv"
474+ if os.path.exists(p) and agg is not None:
475+ d = pd.read_csv(p); rows = []
476+ for y in sorted(d.year.unique()):
477+ if y < 2018 or y > 2026: continue
478+ row = {"Year": str(y)}
479+ for a in THY:
480+ n = d[(d.year == y) & (((d.cat_a == a) & (d.cat_b == "llm")) | ((d.cat_a == "llm") & (d.cat_b == a)))].n.sum(); m = agg[(agg.year == y) & (agg.category_id == a)].n_multilabel.sum()
481+ row[f"{SHORT[a]} x LLM"] = int(n); row[f"{SHORT[a]} x LLM (%)"] = 100 * n / m if m else float("nan")
482+ rows.append(row)
483+ t = pd.DataFrame(rows)
484+ write("tab_theory_llm_cooccurrence", t, "Co-classification of theory and software works with the LLM category, by publication year: number of works carrying both categories and share of the category's multi-label count.", "tab:theory_llm_cooccurrence", [p, f"{G}/category_counts_yearly.csv"], fmt={c: "{:.1f}" for c in t.columns if "(%)" in c}, longtable=True,
485+ note="A work is co-classified when the multi-label classifier assigns both categories (Part IV). Shares are relative to all eligible works carrying the theory/software category in the year. Non-zero counts before 2022 reflect the lexical breadth of the LLM category (language-model and pretrained-model vocabulary), not retroactive labelling. 2026 is incomplete.")
486+ p = f"{R}/literature_synthesis/agent_G/contributions.csv"
487+ if os.path.exists(p):
488+ d = pd.read_csv(p); VERD = ["in_scope_research", "in_scope_survey", "textbook", "reprint_misdated", "dissertation", "metaheuristic", "misclassified", "duplicate"]
489+ VL = {"in_scope_research": "Research (in scope)", "in_scope_survey": "Survey / report (in scope)", "textbook": "Textbook / chapter", "reprint_misdated": "Re-deposited classic", "dissertation": "Thesis", "metaheuristic": "Metaphor metaheuristic", "misclassified": "Misclassified", "duplicate": "Duplicate record"}
490+ rows = []
491+ for v in VERD:
492+ g = d[d.verdict == v]; row = {"Verdict": VL[v]}
493+ for c in THY: row[SHORT[c]] = int((g.primary_category == c).sum())
494+ row["Total"] = int(len(g)); row["Post-period"] = int((g.period == "post").sum()); row["Id mismatch"] = int((g.id_resolves_to_other_work == "yes").sum()); rows.append(row)
495+ row = {"Verdict": "All candidates"}
496+ for c in THY: row[SHORT[c]] = int((d.primary_category == c).sum())
497+ row["Total"] = int(len(d)); row["Post-period"] = int((d.period == "post").sum()); row["Id mismatch"] = int((d.id_resolves_to_other_work == "yes").sum()); rows.append(row)
498+ write("tab_theory_landmark_audit", pd.DataFrame(rows), "Specialist audit of the automatically selected landmark candidates of the theory and software areas, by verdict and primary category.", "tab:theory_landmark_audit", [p, f"{R}/literature_synthesis/landmarks/theory_landmarks.csv", f"{R}/literature_synthesis/landmarks/software_pl_landmarks.csv"],
499+ note="Candidates are the works with the highest field-normalised 36-month citation score within each primary category (floor of fifteen per category) plus the 200 most cited works overall (literature_synthesis/select_landmarks.py). Verdicts were assigned by Agent G from title, venue and type metadata and, where available, the abstract (never the full text); see literature_synthesis/agent_G/contributions.csv. `Id mismatch': the OpenAlex identifier of the record, resolved live on 2026-11-03, returned a different work whose citation count matches the record's 36-month window (literature_synthesis/agent_G/verification_lookups_raw.txt).")
500+ g = d[d.verdict.isin(["in_scope_research", "in_scope_survey"])].sort_values(["primary_category", "year"])
501+ t = pd.DataFrame({"Category": g.primary_category.map(SHORT), "Year": g.year.astype(str), "Type": g.type.str.replace("conference-paper", "conf.").str.replace("book-chapter", "chapter"), "Work": [_ascii(x, 88) for x in g.title], "Contribution": g.contribution_type, "Id mismatch": g.id_resolves_to_other_work})
502+ write("tab_theory_landmarks_discussed", t, "Landmark candidates of the theory and software areas retained as in-scope research or survey contributions, with the contribution type judged from the abstract or metadata.", "tab:theory_landmarks_discussed", [p], longtable=True,
503+ note="Contribution types follow the protocol (theory, method, empirical, benchmark, engineering, survey). `Id mismatch' as in Table~\\ref{tab:theory_landmark_audit}. Titles are shortened.")
559 504
560 505
561 if __name__ == "__main__": 506 if __name__ == "__main__":
562 507
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