WP44: semantic k-NN edges (5.67 M vectors, 56.7 M edges), figure layout fixes

Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>

Simon-Pierre Boucher committed 51 min ago (Nov 4, 2026) parent 7446797 Browse files

49 changed files +1,392 −7

added data/metadata/semantic_edges_allenai__specter2_base.json +9 −0

++{
++ "model": "allenai/specter2_base",
++ "k": 10,
++ "min_sim": 0.8,
++ "n_vectors": 5668933,
++ "n_edges": 56703396,
++ "index": "HNSW32 efC80 efS64 inner-product",
++ "seconds": 523
++}
+ \ No newline at end of file

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modified figures/make_figures.py +6 −6

				`@@ -271,7 +271,7 @@ def fig_flow_matrix(R, out):`
271	p = f"{R}/results/graph/area_flows_yearly.csv"	271	p = f"{R}/results/graph/area_flows_yearly.csv"
272	if not os.path.exists(p):	272	if not os.path.exists(p):
273	return	273	return
274	− d = pd.read_csv(p); fig, axes = plt.subplots(1, 2, figsize=(7.4, 3.6))	274	+ d = pd.read_csv(p); fig, axes = plt.subplots(1, 2, figsize=(7.6, 4.6))
275	for ax, (lo, hi), title in [(axes[0], (2018, 2022), "Baseline 2018–2022"), (axes[1], (2023, 2026), "Post 2023–2026")]:	275	for ax, (lo, hi), title in [(axes[0], (2018, 2022), "Baseline 2018–2022"), (axes[1], (2023, 2026), "Post 2023–2026")]:
276	g = d[(d.citing_year >= lo) & (d.citing_year <= hi)].groupby(["citing_area", "cited_area"]).n.sum().unstack(fill_value=0)	276	g = d[(d.citing_year >= lo) & (d.citing_year <= hi)].groupby(["citing_area", "cited_area"]).n.sum().unstack(fill_value=0)
277	g = g.reindex(index=sorted(set(g.index) \| set(g.columns)), columns=sorted(set(g.index) \| set(g.columns)), fill_value=0); rs = g.div(g.sum(1), axis=0) * 100	277	g = g.reindex(index=sorted(set(g.index) \| set(g.columns)), columns=sorted(set(g.index) \| set(g.columns)), fill_value=0); rs = g.div(g.sum(1), axis=0) * 100
				`@@ -281,7 +281,7 @@ def fig_flow_matrix(R, out):`
281	for j in range(len(rs.columns)):	281	for j in range(len(rs.columns)):
282	v = rs.values[i, j]	282	v = rs.values[i, j]
283	if v >= 5: ax.text(j, i, f"{v:.0f}", ha="center", va="center", fontsize=5.5, color="white" if v > 0.6 * rs.values.max() else style.INK)	283	if v >= 5: ax.text(j, i, f"{v:.0f}", ha="center", va="center", fontsize=5.5, color="white" if v > 0.6 * rs.values.max() else style.INK)
284	− fig.text(0.5, -0.02, "Row shares (%): references from the citing area (rows) to the cited area (columns), internal citations only", ha="center", fontsize=7, color=style.INK2)	284	+ fig.subplots_adjust(wspace=0.55, bottom=0.3); fig.text(0.5, 0.01, "Row shares (%): references from the citing area (rows) to the cited area (columns), internal citations only", ha="center", fontsize=7, color=style.INK2)
285	style.save(fig, f"{out}/fig_area_flow_matrix.pdf"); reg("fig_area_flow_matrix", "Citation-flow matrices between areas (row shares) for the baseline and post periods.", [p])	285	style.save(fig, f"{out}/fig_area_flow_matrix.pdf"); reg("fig_area_flow_matrix", "Citation-flow matrices between areas (row shares) for the baseline and post periods.", [p])
286		286
287		287
				`@@ -289,14 +289,14 @@ def fig_cross_area(R, out):`
289	p = f"{R}/results/graph/cross_area_share_yearly.csv"; q = f"{R}/results/graph/modularity_yearly.csv"	289	p = f"{R}/results/graph/cross_area_share_yearly.csv"; q = f"{R}/results/graph/modularity_yearly.csv"
290	if not os.path.exists(p):	290	if not os.path.exists(p):
291	return	291	return
292	− d = pd.read_csv(p); fig, axes = plt.subplots(1, 2, figsize=(7.4, 3.2))	292	+ d = pd.read_csv(p); fig, axes = plt.subplots(1, 2, figsize=(7.6, 3.4))
293	for i, a in enumerate(sorted(d.citing_area.unique())):	293	for i, a in enumerate(sorted(d.citing_area.unique())):
294	g = d[d.citing_area == a]; axes[0].plot(g.citing_year, 100 * g.share_cross, marker="o", ms=3, color=style.CATEGORICAL[i % 8], label=a, lw=1.1)	294	g = d[d.citing_area == a]; axes[0].plot(g.citing_year, 100 * g.share_cross, marker="o", ms=3, color=style.CATEGORICAL[i % 8], label=a, lw=1.1)
295	− axes[0].set_title("Share of references leaving the citing area (%)", loc="left"); axes[0].legend(fontsize=6, ncol=2)	295	+ axes[0].set_title("References leaving the citing area (%)", loc="left", fontsize=8); axes[0].legend(fontsize=5.5, ncol=2)
296	if os.path.exists(q):	296	if os.path.exists(q):
297	m = pd.read_csv(q); axes[1].plot(m.year, m.Q_areas, marker="o", color=style.CATEGORICAL[0], label="Modularity Q (areas)"); axes[1].plot(m.year, m.Q_categories, marker="o", color=style.CATEGORICAL[1], label="Modularity Q (categories)"); axes[1].plot(m.year, m.self_share, marker="o", color=style.CATEGORICAL[2], label="Within-category share")	297	m = pd.read_csv(q); axes[1].plot(m.year, m.Q_areas, marker="o", color=style.CATEGORICAL[0], label="Modularity Q (areas)"); axes[1].plot(m.year, m.Q_categories, marker="o", color=style.CATEGORICAL[1], label="Modularity Q (categories)"); axes[1].plot(m.year, m.self_share, marker="o", color=style.CATEGORICAL[2], label="Within-category share")
298	− axes[1].set_title("Modularity of the yearly category citation network", loc="left"); axes[1].legend(fontsize=6)	298	+ axes[1].set_title("Modularity of the category citation network", loc="left", fontsize=8); axes[1].legend(fontsize=6)
299	− style.save(fig, f"{out}/fig_cross_area.pdf"); reg("fig_cross_area", "Cross-area reference shares by citing area and modularity of the category citation network by year.", [p, q])	299	+ fig.subplots_adjust(wspace=0.3); style.save(fig, f"{out}/fig_cross_area.pdf"); reg("fig_cross_area", "Cross-area reference shares by citing area and modularity of the category citation network by year.", [p, q])
300		300
301		301
302	def fig_emerging(R, out):	302	def fig_emerging(R, out):

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modified manuscript/sections/part05_ai.tex +394 −0

+ research; the correct conclusion is that field normalisation inside a
+ multi-label category measures discourse, and that the measurement is
+ informative about diffusion rather than about invention.
++
++\section{AI agents and autonomous systems}
++\label{sec:ai-agents}
++
++\subsection{From tool use to agents}
++
++An LLM-based agent, in the definition shared by the surveys of the window
++\citep{wang2024agentsurvey, xi2023rise, guo2024multiagent,
++cheng2026agentsdefinitions}, is a language model wrapped in a loop of
++perception, planning, memory and action, in which the model's outputs are
++parsed into calls to tools or to other agents and the results are fed back as
++context. The architectures that fixed the vocabulary are generative agents in
++a simulated town \citep{park2023generativeagents}, a peer-reviewed UIST paper;
++the multi-agent conversation framework AutoGen \citep{wu2023autogen}; virtual
++context management in MemGPT \citep{packer2023memgpt}; and the ``LLM as
++operating system'' proposals \citep{mei2024aios, ge2023llmos}, all of the
++latter preprints. The memory component is surveyed by
++\citet{zhang2025agentmemory}, whose abstract calls self-evolution through
++agent--environment interaction the feature that distinguishes agents from the
++models they wrap. Evaluation lags design. The benchmarks that the community
++uses---SWE-bench for repository-level issue resolution
++\citep{jimenez2024swebench}, WebArena for web tasks \citep{zhou2023webarena},
++AgentBench across environments \citep{liu2023agentbench}---are preprints, and
++the only peer-reviewed agent benchmark in the landmark set is a clinical
++one, an evaluation of an open-source and a proprietary agent system on three
++families of medical decision tasks \citep{liu2026clinicalagents}. % [E-32]
++The detector flags \emph{LLM agents} in \KnownLlmAgentsDetectionMonth{},
++\KnownLlmAgentsLeadMonths{} months before peak growth in
++\KnownLlmAgentsPeakGrowthMonth{}, on \KnownLlmAgentsTotalWorks{} matching
++works; the concept arrived and peaked within a year.
++
++The strongest peer-reviewed evidence for agents is scientific. The Virtual Lab
++\citep{swanson2025virtuallab}, a Nature paper whose record carries no abstract
++in the corpus file, reports agents designing SARS-CoV-2 nanobodies that were
++validated experimentally; Robin \citep{ghareeb2026robin} is described in its
++abstract as the first multi-agent system automating both hypothesis generation
++and data analysis in experimental biology; DIVE \citep{zhang2026dive} extracts
++experimental data from the figures of the literature for hydrogen-storage
++materials; AgentSociety \citep{piao2026agentsociety} simulates large
++populations of LLM-driven generative agents for social science. % [E-33]
++These are single demonstrations with experimental or observational
++validation, which is more than the ``AI scientist'' preprints
++\citep{lu2024aiscientist, si2024novelideas} offer; whether autonomous research
++agents produce validated discoveries at a rate that justifies their cost is a
++question for preregistered multi-laboratory trials, not for case reports, and
++it is listed in Part~X.
++
++\subsection{``Agentic AI'': a definitional literature}
++
++The landmark set of the category contains six papers from 2025--2026 whose
++purpose is to define agentic AI and distinguish it from AI agents, generative
++AI and classical multi-agent systems \citep{acharya2025agenticsurvey,
++sapkota2026agentsvsagentic, bandi2025agenticreview, abouali2025agenticparadigms,
++allmendinger2026multiagentai, collaco2026agentichealthcare}, most of them
++reviews of reviews, one of them a PRISMA-based review of ninety studies that
++proposes a symbolic-versus-neural dual-paradigm framework, and one benchmark.
++% [E-34]
++A literature that produces six taxonomies and one benchmark in eighteen
++months is a literature whose object is not yet stabilised, and the question
++of whether ``agentic AI'' denotes a distinct class of systems or a relabelling
++of tool-augmented LLM pipelines is treated here as open. The trust, risk and
++security management framework for multi-agent LLM systems
++\citep{raza2026trism} belongs to the same genre: a structure without an
++empirical test.
++
++The corpus series of the category must be read with its composition in mind.
++Four of its landmark records are control-theoretic papers on consensus and
++formation control of nonlinear multi-agent systems, and its pre-2022 volume
++is dominated by classical multi-agent systems, distributed artificial
++intelligence and robotics, which explains why the category directs a large
++and growing share of its references to robotics (Table~\ref{tab:ai_flows}).
++The negative baseline growth rate, the negative level change at the reference
++month and the second Bai--Perron break of 2024 (Table~\ref{tab:ai_breaks})
++are the signature of a category in which an older literature was flat or
++declining while a new one, LLM agents, grew from a small base; the level
++effect at the o1 date, \ItsSeriesCatAiAgentsItsOOneLevelChange{}
++($p = \ItsSeriesCatAiAgentsItsOOneLevelP$), is where the new literature
++becomes visible in the aggregate. % [E-35]
++The explanation is inferential; separating the two literatures would require a
++sub-classification that the taxonomy does not provide. Embodied agents, from
++vision-language-action models \citep{brohan2023rt2, kim2024openvla,
++black2024pi0} to the driving model in the landmark set, are classified under
++robotics and are reviewed there.
++
++\section{Reinforcement learning}
++\label{sec:ai-rl}
++
++The reinforcement-learning landmarks are, almost without exception, the canon
++of the baseline period. Rainbow's combination of six extensions to deep
++Q-learning with an ablation of each \citep{hessel2018rainbow}; the
++maximum-entropy actor--critic \citep{haarnoja2018sac}, whose record is the
++arXiv deposit; clipped double-Q learning and delayed policy updates against
++overestimation \citep{fujimoto2018td3}; self-play without domain knowledge
++beyond the rules in AlphaZero \citep{silver2018alphazero}; grandmaster-level
++StarCraft~II \citep{vinyals2019alphastar}, a Nature record without an abstract
++in the file; the finding that proximal policy optimisation is, in the words of
++its abstract, surprisingly strong in cooperative multi-agent benchmarks
++\citep{yu2021mappo}; the large-scale study of curiosity-driven learning without
++extrinsic rewards \citep{burda2018curiosity}; and the reproducibility warning
++of \citet{henderson2018drlmatters}, whose abstract notes that non-determinism
++and intrinsic variance make reported results hard to interpret. The theoretical
++side is represented by an overview of multi-agent reinforcement learning
++\citep{zhang2021marloverview} and a survey of the synthesis of model-predictive
++control and reinforcement learning (record 7128436456 of the landmark file).
++The single post-2022 method landmark is DreamerV3 \citep{hafner2025dreamerv3},
++published in Nature, whose abstract reports a world-model algorithm that
++outperforms specialised methods across more than 150 tasks with a single
++configuration. % [E-36]
++Several records are misdated---a 2008 inverse-reinforcement-learning paper
++and a 2007 Bayesian one appear as 2018 and 2025 works---and four are
++control-theory papers; the category's citation top is therefore a mixture of
++classics, control theory and one new result.
++
++The corpus places reinforcement learning among the modestly accelerating
++categories: a post-period growth rate only slightly above the baseline
++(Table~\ref{tab:ai_growth}), a slope change at the reference month of
++\ItsSeriesCatReinforcementLearningItsChatgptSlopeChange{} log points per
++month after a negative level change, and a citation profile that moves towards
++robotics. % [E-15]
++It would be a mistake to read this as the field's eclipse. Reinforcement
++learning's most consequential role in the window---reward modelling and policy
++optimisation for language models, from PPO through DPO to the group-relative
++method of DeepSeek-R1---is invisible in the category because the papers that
++carry it are classified as LLM work, and the flow of references from the RL
++category to the LLM category stays below one per cent. Any statement that
++reinforcement learning declined after 2022 would be an artefact of the
++taxonomy, and the correct statement is that its methods migrated into another
++category's literature. % [E-37]
++
++\section{Machine-learning theory, architectures and efficiency}
++\label{sec:ai-theory}
++
++\subsection{What ``machine-learning theory'' contains in the corpus}
++
++The category named machine-learning theory does not, in its citation-ranked
++top, contain a learning-theory result from the window. Its landmarks are
++textbooks and textbook chapters, the case for the Matthews correlation
++coefficient over accuracy and F$_1$ on imbalanced binary problems
++\citep{chicco2020mcc}, the argument that high-stakes decisions should use
++inherently interpretable models rather than post-hoc explanations of black
++boxes \citep{rudin2019stopexplaining}, the conceptual analysis of
++interpretability \citep{lipton2018mythos}, exact Shapley attributions for tree
++ensembles \citep{lundberg2020treeexplainer}, the review of stochastic-gradient
++optimisation for large-scale learning \citep{bottou2018optimization}, a
++transfer-learning survey \citep{zhuang2021transfersurvey}, a reprinted chapter
++by Verma and Pearl on the equivalence of causal models, and the misdated
++bandit papers of Section~\ref{sec:ai-landmarks}. % [E-38]
++The category as the rules define it is general machine learning, and what the
++citation ranking rewards within it is pedagogy and measurement convention.
++Theoretical work on in-context learning, on the expressivity of Transformers
++or on the statistical basis of scaling laws exists inside the window, but it is
++classified under LLM or deep-learning architectures, or is cited too little
++within thirty-six months to surface. The corpus therefore cannot be used to
++say that theory lagged practice in the AI transformation; it can only say that
++the theory which is cited quickly is the theory of evaluation and
++interpretation.
++
++\subsection{Architectures: proposals, refutations and the efficiency turn}
++
++The deep-learning-architectures landmarks are the mirror image: surveys of
++graph neural networks \citep{wu2021gnnsurvey, zhou2020gnnreview} and of
++Transformers for time series \citep{wen2023tstransformers}, the
++long-sequence forecasting Transformer Informer \citep{zhou2021informer}, and
++its refutation---the finding, reported in the abstract of
++\citet{zeng2023transformerstsf}, that embarrassingly simple linear models
++outperform the Transformer-based forecasters on the standard long-term
++benchmarks because the permutation-invariant attention discards the temporal
++ordering that forecasting depends on. The disagreement is still open within
++the field, and the LLM-reprogramming approach to the same task
++\citep{jin2023timellm} reopens it from a third side. % [E-39]
++Mixup \citep{zhang2017mixup}, the convex-combination regulariser, is in the
++set with a wrong year; the tabular foundation model \citep{hollmann2025tabpfn}
++is its one peer-reviewed post-2022 architectural result. The two architectural
++proposals of the window that the set contains are both unreviewed: Mamba
++\citep{gu2023mamba}, whose abstract identifies the inability of earlier
++subquadratic models to perform content-based reasoning as their weakness and
++makes the state-space parameters input-dependent to obtain linear-time
++sequence modelling, and Kolmogorov--Arnold networks \citep{liu2024kan}, which
++replace fixed node activations by learnable spline functions on edges and
++claim accuracy and interpretability advantages over multilayer perceptrons on
++small scientific problems. Neither claim has been tested at language-model
++scale in a peer-reviewed venue within the corpus.
++
++Where architecture research demonstrably moved is efficiency. Exact attention
++with IO-aware tiling \citep{dao2022flashattention}; sparse mixture of experts
++at trillion-parameter scale \citep{fedus2022switch}; eight-bit matrix
++multiplication with outlier decomposition \citep{dettmers2022int8} and
++post-training weight quantisation to three or four bits \citep{frantar2023gptq},
++with the quantisation survey in the landmark set \citep{gholami2022quantization};
++speculative decoding, which samples from a small draft model and verifies in
++parallel with the large one \citep{leviathan2023speculative}; and the
++efficient-LLM and inference surveys \citep{wan2023efficientllm,
++zhou2024inferencesurvey}. The known-set detector gives these topics the
++shortest leads in the area---\KnownStateSpaceSequenceModelsLeadMonths{} months
++for state-space models, \KnownMixtureOfExpertsLeadMonths{} for mixture of
++experts, \KnownSpeculativeDecodingLeadMonths{} for speculative
++decoding---because each was driven by one widely adopted preprint and grew
++from near zero to its peak within a year. % [E-40]
++Deep-learning architectures is also the one AI category without a significant
++post-2022 slope change (Table~\ref{tab:ai_breaks}). Two readings are
++compatible with the counts: that architecture research plateaued once the
++Transformer became the default, or that papers which would have been
++classified as architecture work in 2021 are classified as LLM or
++foundation-model work in 2024 because their vocabulary changed. The
++multi-label counts cannot separate relabelling from decline, and the test
++proposed in Part~X---re-estimating the series on works whose only AI category
++is architectures, with a classifier vocabulary frozen at 2022---has not been
++run.
++
++\section{Evaluation}
++\label{sec:ai-eval}
++
++The evaluation literature of the window is a cycle of benchmark creation,
++saturation and replacement that the landmark set documents from its first
++turn. The SuperGLUE abstract \citep{wang2019superglue} records, already in
++2019, that performance on GLUE \citep{wang2018glue} had ``surpassed the level
++of non-expert humans'' and that a harder suite was needed; the same sentence
++could be written about MMLU \citep{hendrycks2021mmlu} and BIG-bench
++\citep{srivastava2022bigbench} by 2024. % [E-41]
++Three responses coexist. Holistic evaluation across scenarios and metrics
++\citep{liang2022helm} multiplies the axes; crowd-sourced pairwise human
++preference in an arena \citep{chiang2024arena}, a preprint, replaces fixed
++test sets by an Elo-style ranking; and LLM-as-judge \citep{zheng2023judge},
++also a preprint, replaces the human by a model, with the agreement rates
++reported there as its justification. The QLoRA abstract's ``99.3 per cent of
++ChatGPT'' is an instance of the third: a number produced by GPT-4 judging
++Vicuna-benchmark answers, whose meaning depends on the judge's biases, which
++the LLM-as-judge paper itself catalogues. Against all three stand the
++documented problems of contamination of test sets by training data
++\citep{sainz2023contamination}, the dependence of rankings on the choice of
++tasks \citep{dehghani2021lottery}, benchmark saturation and the
++sociology of benchmark use \citep{ott2022benchmarks, raji2021everything}, the
++case for dynamic, adversarially collected benchmarks \citep{kiela2021dynabench}
++and the earlier demonstration that even a faithfully re-collected test set
++shifts accuracies \citep{recht2019imagenet}. The evaluation survey of
++\citet{chang2024evalsurvey} maps the space.
++
++The corpus adds two facts. The landmark set contains four benchmark
++contributions in 234---SuperGLUE, LAION-5B, MultiMedQA and the clinical agent
++benchmark---so that evaluation work is under-rewarded by thirty-six-month
++citation impact relative to the role the field assigns it, a known property
++of citation measures for infrastructure. % [E-05]
++And no landmark abstract reports a contamination-controlled evaluation:
++the models that the abstracts compare are compared on benchmarks whose
++overlap with their training data is not stated. For a mapping study this is
++the central methodological caveat of the whole area. Every ``state of the art''
++claim read in an abstract of this part is a claim about a benchmark whose
++validity for the model in question is unknown, and the manuscript's
++distinction between genuine breakthroughs and incremental benchmark
++improvements (RQ7) cannot be drawn from abstracts alone; Part~IX draws it, to
++the extent that it can be drawn, from the citation and adoption structure
++instead.
++
++\section{Safety, alignment, fairness and societal evidence}
++\label{sec:ai-safety}
++
++\subsection{Risk framing}
++
++The safety literature entered the window with a concrete-problems agenda
++\citep{amodei2016concrete, hendrycks2021unsolved} and a critique of scale
++\citep{bender2021parrots}, and left it with a catalogue. The AI Risk
++Repository, published in \emph{Patterns} and in the landmark set, is
++described in its abstract as a living database of 1,725 risks extracted from
++74 taxonomies and organised by a causal taxonomy of origin and a domain
++taxonomy \citep{slattery2026riskrepository}; the International AI Safety
++Report \citep{bengio2025safetyreport} and the \emph{Science} perspective on
++managing extreme risks \citep{bengio2024extremerisks} are the consensus
++documents, and the alignment survey of \citet{ji2023alignment} and the
++foundational-challenges agenda of \citet{anwar2024foundational}, both
++preprints, the research agendas. % [E-42]
++Catastrophic-risk framing \citep{hendrycks2023catastrophic,
++shevlane2023extremerisks}, both preprints, and the ``stochastic parrots''
++critique disagree about whether scale produces dangerous capabilities at all;
++the corpus shows the AI-safety category re-orienting towards LLM work, which
++it cited at a negligible rate in 2019 and at a tenth of its references in
++2025 (Table~\ref{tab:ai_flows}), but no landmark abstract reports a validated
++measurement of a catastrophic capability, so the disagreement is recorded, not
++resolved. % [E-14]
++The sociology of the category's citation top is the same as that of ML
++theory: it is the pre-LLM ``responsible AI'' literature---fairness
++\citep{mehrabi2021biasfairness}, explainable AI \citep{barredoarrieta2020xai,
++ali2023xaitrust}, explanation as the social sciences understand it
++\citep{miller2019explanation}---rather than the alignment literature of the
++window, which is young and mostly unreviewed.
++
++\subsection{Empirical harms with peer review}
++
++Where the landmark set is strongest is in peer-reviewed empirical harm. The
++data-poisoning threat assessment in \emph{Nature Medicine} reports, in its
++abstract, that replacing 0.001 per cent of the tokens of a common pretraining
++corpus with medical misinformation produces models more likely to propagate
++medical errors \citep{alber2025poisoning}. A physician-led red-teaming of four
++public chatbots on 222 patient-posed primary-care questions found
++statistically significant differences in the safety of their answers
++\citep{draelos2026unsafeanswers}. Experiments on current models as
++replacement therapists report expressions of stigma and inappropriate
++responses contrary to clinical guidelines \citep{moore2025stigma}. % [E-43]
++These three, together with the jailbreak results of Section~\ref{sec:ai-align},
++are what the corpus can offer as established, replicable harm; the
++over-reliance review \citep{zhai2024overreliance}, the healthcare bias
++review \citep{hasanzadeh2025biashealthcare} and the governance frameworks
++\citep{papagiannidis2025raigovernance} are syntheses and proposals. Energy is
++the one harm with a measurement standard: the inference-cost comparison in
++the landmark set reports, in its abstract, the first systematic comparison of
++task-specific and general-purpose models and finds the latter more expensive
++by a wide margin \citep{luccioni2024powerhungry}, in line with the
++electricity-footprint estimates of \citet{devries2023energy}. % [E-44]
++
++\subsection{Labour, adoption and education}
++
++The social-science records of the set carry the window's evidence on who uses
++the models and to what effect. Occupational exposure to language models is
++estimated from task decompositions in \citet{eloundou2024gpts}, published in
++\emph{Science}, whose landmark record, as noted, carries the wrong abstract;
++the preregistered field experiment with 758 consultants, whose
++\emph{Organization Science} version is in the set, reports that assistance
++improved performance on some tasks and worsened it on others of similar
++apparent difficulty, the ``jagged frontier'' \citep{dellacqua2023jagged}; and
++the adoption surveys report use by nearly half of United States adults within
++two years of the first product \citep{bick2026rapidadoption}. % [E-45]
++In education, the category where the ChatGPT commentary concentrated, the
++abstracts move from perception surveys \citep{chan2023students,
++kasneci2023chatgptgood} to policy analysis \citep{mcdonald2025hepolicies,
++chan2023policyframework} and to one preregistered experiment reporting harm
++to unassisted performance \citep{bastani2024harmlearning}, a preprint. The
++direction of the effect of generative-AI use on learning is thus contested
++between survey evidence of perceived benefit and experimental evidence of
++harm, and the manuscript lists it among the open problems rather than taking
++a side.
++
++\section{Synthesis: established, contested, speculative}
++\label{sec:ai-synthesis}
++
++Four reconciliations summarise what this part can say and what it cannot.
++
++\emph{Growth versus discontinuity.} The nine AI categories accelerated after
++2022; the acceleration is a slope change that the data-driven procedures date
++to the middle of 2022 or later and that steepened in 2023--2024, not a
++level jump in December 2022 (Tables~\ref{tab:ai_breaks} and
++\ref{tab:ai_altdates}). The design cannot attribute it to one product, and
++sentences of the form ``ChatGPT caused a surge in AI research'' are not
++supported by anything in this manuscript. What is supported is weaker and
++more useful: the trend of the LLM literature changed, the change propagated
++through citation to generative AI, safety and agents within three years, and
++it did not propagate to reinforcement learning, theory or architectures.
++% [E-09--E-16]
++
++\emph{Impact versus invention.} The citation-ranked landmarks of the LLM and
++generative-AI categories are commentary and education papers; the technical
++lineage is reached through arXiv records that the bibliographic linkage
++sometimes attached to the wrong work. A list of the most influential AI papers
++of the window built from this corpus without the screening of
++Section~\ref{sec:ai-landmarks} would be wrong in about one record in five, and
++would rank the first wave of ChatGPT commentary above the papers that
++introduced the models. % [E-02, E-06]
++
++\emph{Taxonomy versus field.} The apparent stagnation of reinforcement
++learning and of deep-learning architectures is at least partly a consequence
++of classification: the methods of the first migrated into LLM post-training,
++and the vocabulary of the second migrated into the LLM and foundation-model
++categories. % [E-37, P-08]
++
++\emph{Evidence tiers.} Twenty-eight of the 234 landmark candidates are
++preprints and seventy-two are dated 2026 with twelve-month citation windows;
++many of the window's defining technical papers---LLaMA, BLIP-2, Mamba, the
++agent benchmarks, the emergent-abilities debate, the catastrophic-risk
++agenda---have not passed peer review. The established results of this part
++are therefore of two kinds: corpus statistics, which are reproducible from
++the files named in the tables, and peer-reviewed empirical findings, of which
++the landmark set contains a modest number, concentrated in medicine
++(foundation models, poisoning, unsafe answers), science (MatterGen, the
++Virtual Lab, Robin, DreamerV3) and the social sciences (adoption, the jagged
++frontier). The contested results---emergence, sparks, pure-RL reasoning,
++linear versus Transformer forecasting, fusion versus text-conditioning,
++agentic AI as a category, the sign of the learning effect---are contested in
++the literature itself. The speculative ones---domain foundation models
++displacing pipelines, autonomous science at scale, trust frameworks for
++multi-agent systems---are speculative because no landmark abstract tests
++them.
++
++The open problems that this reading leaves are collected in
++\texttt{literature\_synthesis/agent\_E/open\_problems.csv} and taken up in
++Part~X: whether capabilities are discontinuous in scale; whether pure-RL
++reasoning generalises beyond verifiable rewards; whether alignment brittleness
++is intrinsic; how to measure hallucination without references; whether
++vision-language models fuse or condition; what ``agentic'' denotes; whether
++autonomous science agents pay; whether architecture research plateaued or was
++relabelled; how to evaluate on benchmarks that may be in the training data;
++whether catastrophic capabilities can be measured before deployment; whether
++domain foundation models generalise across sites; whether Transformers suit
++long-horizon time series; whether generative-AI use helps or harms learning;
++and, for this manuscript's own method, whether citation-based landmark
++identification can be made reliable for a field whose most-cited identifiers
++are the ones most often attached to the wrong record.

modified manuscript/sections/part07a_theory.tex +622 −1

++% part07a_theory.tex — Part VII (theory half): algorithms and data structures, computational complexity, programming
++% languages, compilers, formal verification, software engineering. Agent G (Theory Specialist), 2026-11-03.
++% The director merges this file with part07b_security.tex into Part VII. Every corpus number is a macro of
++% tables/numbers.tex (prefix \Thy..., appended by tables/make_numbers.py, Agent G block) or appears in a generated table
++% (tables/make_tables.py, tables_part07a). Sources: results/graph/category_counts_yearly.csv, category_flows_yearly.csv,
++% category_cooccurrence_yearly.csv; results/econometrics/breaks.csv; literature_synthesis/landmarks/{theory,software_pl}_*;
++% literature_synthesis/agent_G/{claims,contributions,open_problems}.csv and review.md. Bibliography: references.bib and
++% references_part07a.bib (65 entries, each resolved by one live lookup; references_verification_part07a.csv).
++
++\section{Scope, evidence and reading rules for the theory half}
++\label{sec:thy-scope}
++
++This half of Part~VII reviews six categories of the taxonomy: algorithms and data structures, computational
++complexity, programming languages, compilers, formal verification and software engineering (the areas
++\emph{theory} and \emph{software\_pl} of the ontology, Section~\ref{sec:p2-ontology}). It has two objects. The
++first is the research itself: what the baseline period established in these categories, what the window added,
++and where the additions are contested. The second is the instrument: the corpus statistics for the six
++categories and, above all, the automatically selected landmark candidates, which in these areas turn out to
++measure the bibliographic record more than the research it records. The two objects cannot be separated,
++because the landmark list is the protocol's channel from corpus to synthesis, and in the theory and software
++areas that channel is obstructed in ways that are themselves findings.
++
++Three reading rules apply. First, no full text was read. Every statement about a work rests on its metadata
++(title, venue, type, year, identifiers) or on the abstract recorded in OpenAlex, and the text says which; where
++an abstract is the source, the phrase ``the abstract reports'' is used deliberately. Second, works available
++only as arXiv postings are preprints and are described as such, whatever their later fate. Third, the
++structural-change statistics are read as the protocol prescribes (Section~\ref{sec:m-structural}): the November
++2022 boundary is a reference date, the placebo distribution is the test, and nothing below attributes a change
++to any single event. The categories are small relative to the AI categories of Part~V (their combined primary
++share in 2025 is under ten per cent of the corpus), so the statistics are those of minorities inside a
++corpus whose growth is set elsewhere.
++
++The section proceeds from the instrument to the field. Section~\ref{sec:thy-landmarks} audits the landmark
++candidates; Section~\ref{sec:thy-statistics} reports the six categories' volume, structural-change and
++composition statistics; Sections~\ref{sec:thy-algorithms} to~\ref{sec:thy-se} review the categories; and
++Section~\ref{sec:thy-synthesis} draws the conclusions that Part~X and Part~XI inherit.
++
++\section{The landmark candidates: an audit}
++\label{sec:thy-landmarks}
++
++The protocol selects landmarks as the works with the highest field-normalised 36-month citation score
++(NCS36, Section~\ref{sec:m-citations}) within each primary category, with a floor of fifteen per category, plus
++the 200 most cited works overall; for 2025--2026 works the 12-month window replaces the 36-month one
++(Section~\ref{sec:data-core}). The theory area contributed \ThyLandTheoryN{} candidates and the software area
++\ThyLandSoftwarePlN{}, twenty-five per category, of the \SelectionLandmarks{} landmarks of the whole corpus.
++Before any of the \ThyAuditN{} can be called a landmark, the list has to be audited, and the audit is the first
++result of this section (Table~\ref{tab:theory_landmark_audit}). Each candidate received one of eight verdicts
++from its metadata and, where available, its abstract: an in-scope research contribution
++(\ThyAuditVerdictInScopeResearch{} candidates), an in-scope survey or report (\ThyAuditVerdictInScopeSurvey{}),
++a misclassified work outside the category and outside the theory and software scope
++(\ThyAuditVerdictMisclassified{}), a thesis (\ThyAuditVerdictDissertation{}), a classic re-deposited under a
++recent record date (\ThyAuditVerdictReprintMisdated{}), a textbook or chapter (\ThyAuditVerdictTextbook{}), a
++metaphor-based metaheuristic (\ThyAuditVerdictMetaheuristic{}) or a duplicate record
++(\ThyAuditVerdictDuplicate{}). Fewer than one candidate in five is a research contribution inside its
++category, and only \ThyAuditInScopeResearchPost{} of those are post-period works. Four mechanisms account
++for the rest.
++
++\input{tables/tab_theory_landmark_audit}
++
++\paragraph{Identifier collisions.}
++\ThyAuditIdCollision{} candidates carry OpenAlex identifiers that, resolved against the live database on
++3~November 2026, return a different work. The record titled ``Exploiting Generative AI to Scale up Intelligent
++Tutoring Systems'', with the DOI of an ITP~2023 paper, resolves to \emph{Attention Is All You Need}; the abstract
++stored for it in the corpus is that of \emph{MizAR~60 for Mizar~50}, which is what the DOI denotes
++\citep{jakubuv2023mizar}; its 36-month citation count in the corpus is that of the Transformer paper, which is
++why it ranks first in the whole software area. The ECOOP~2025 paper on functionality-specific
++vulnerabilities \citep{chen2025apiss} resolves to the graph-convolutional-network preprint; the SEA~2024
++paper on learned branching for maximum independent set \citep{langedal2024mis} to GraphSAGE; the WADS~2025
++Kd-tree paper \citep{pham2025kdtree} to the variational-autoencoder preprint; the SAIA~2024 robustness
++framework \citep{awadid2025robust} to the adversarial-training preprint of Madry and co-authors; an
++ESEM~2026 record \citep{naqvi2026bigger} to the Codex paper; and a DX~2024 answer-set-programming paper
++\citep{klampfl2024asp} to an unrelated preprint. In every case the recorded citation window matches the other
++work's count.\footnote{Raw responses in \texttt{literature\_synthesis/agent\_G/verification\_lookups\_raw.txt}.
++The mechanism (identifier merging or reuse between the snapshot of 14~October 2026 and the live database) is
++inferred; the consequence, that the citation windows of these records belong to other works, is observed.} The
++four top-ranked candidates of the software area and the top-ranked candidate of the theory area owe their rank
++to citations that are not theirs. The same check should be run on the other areas' lists before Part~IX uses
++them.
++
++\paragraph{Generic-title chapters.}
++Chapters titled ``Introduction to Algorithms'' (2020, 2021, 2023), ``Particle Swarm Optimization'' (2019),
++``Introduction to Software Testing'' (2021), ``Introduction to Graph Theory'' (2023), and Royce's 1970 paper on
++managing large software developments reprinted in a 2021 anthology, carry hundreds to thousands of citations in
++their windows, and live lookups confirm the counts. The plausible reading, offered as an interpretation, is
++that citations intended for the canonical work are attributed to a same-titled chapter by the indexer's
++reference matching. Whatever the mechanism, a chapter of a 2023 undergraduate textbook is not a landmark of
++2023 research.
++
++\paragraph{Re-deposited classics.}
++\ThyAuditVerdictReprintMisdated{} records are works whose record date is the date of a deposit, reprint or
++re-posting rather than of the work. Dijkstra's 1959 note on two problems in connexion with graphs appears as a
++2022 ACM Books chapter; Turing's 1936 paper as a 2021 MIT Press chapter; the 1994 survey of decidability for
++Petri nets as a 2024 arXiv posting; Lynch and Tuttle's introduction to input/output automata as a 2026 journal
++article \citep{lynch2026ioautomata}; the \emph{egg} equality-saturation paper \citep{willsey2021egg} as a 2026
++research highlight \citep{willsey2026eggcacm}; doctoral theses of the early 2000s as 2026 repository records;
++three theses of the L4/seL4 verification school as 2022 records; and the 2018 conference paper on online
++algorithms with machine-learned predictions as a 2024 arXiv posting. Because the year field is the deposit year,
++these records enter the post period: \ThyLandTheoryDatedTwentysix{} of the \ThyLandTheoryN{} theory candidates
++and \ThyLandSoftwarePlDatedTwentysix{} of the \ThyLandSoftwarePlN{} software candidates are dated 2026, a year
++of which the corpus holds ten months.
++
++\paragraph{Lexical misclassification and small cells.}
++\ThyAuditVerdictMisclassified{} candidates are outside the theory and software scope altogether. The
++computational-complexity list contains \ThyAuditComputationalComplexityMisclassified{} such works of its
++\ThyAuditComputationalComplexityN{}: technology-acceptance models of mobile learning, PLS-SEM studies of
++small enterprises and of students' well-being, a Routledge volume on complexity theory in the social sciences,
++a review of fringe-projection profilometry, captured by the word ``complexity''. The compilers list contains
++\ThyAuditCompilersMisclassified{}, captured by ``just-in-time'' (adaptive interventions in mental health),
++``compiling'' (a guide to literature reviews) and ``polyhedral'' (power-system dispatch). The
++programming-languages list includes a Nature paper on Calabi--Yau manifolds in black-hole scattering. The
++classifier's per-category precision, estimated on the validation sample (Section~\ref{sec:m-validation}), is
++the right place to quantify this; what the landmark list shows is the interaction between imprecision and
++normalisation. NCS36 divides a work's citations by the expectation for its category, year and type
++(Section~\ref{sec:m-citations}); cells of theses or of non-computing articles are small and lightly cited, so a
++thesis with a handful of citations, or a social-science article with a hundred, obtains a normalised score that
++no STOC paper of the same year can match. \ThyAuditVerdictDissertation{} of the \ThyAuditN{} candidates are
++theses.
++
++What survives is a set of \ThyAuditVerdictInScopeResearch{} research works and \ThyAuditVerdictInScopeSurvey{}
++surveys or reports, listed in Table~\ref{tab:theory_landmarks_discussed}. They are reviewed below with the
++pre-2022 lineage of Part~II and with the window's results that the ranking does not see: the major theoretical
++results of 2022--2025 are absent from the candidate list and enter this review through verified bibliographic
++records, not through the corpus ranking. Open problem~G-P1 of Part~X states the correction: type filters,
++live re-resolution of identifiers and venue priors at the selection stage, evaluated against a
++specialist-labelled sample.
++
++\input{tables/tab_theory_landmarks_discussed}
++
++\section{Volume, structural change and composition}
++\label{sec:thy-statistics}
++
++\subsection{Growth in absolute terms, decline in relative terms}
++
++Table~\ref{tab:theory_counts} gives the yearly counts of works carrying each category (multi-label) and of
++works whose primary category it is; Table~\ref{tab:theory_summary} summarises growth and structural change.
++The multi-label counts grew in every category after 2022. Between 2023 and 2025 the compound annual growth was
++\ThyCagrMultilabelSoftwareEngineeringPostPct\% for software engineering,
++\ThyCagrMultilabelComputationalComplexityPostPct\% for computational complexity,
++\ThyCagrMultilabelCompilersPostPct\% for compilers, \ThyCagrMultilabelAlgorithmsPostPct\% for algorithms,
++\ThyCagrMultilabelFormalVerificationPostPct\% for formal verification and
++\ThyCagrMultilabelProgrammingLanguagesPostPct\% for programming languages, against
++\ThyCagrMultilabelSoftwareEngineeringBaselinePct\%, \ThyCagrMultilabelComputationalComplexityBaselinePct\%,
++\ThyCagrMultilabelCompilersBaselinePct\%, \ThyCagrMultilabelAlgorithmsBaselinePct\%,
++\ThyCagrMultilabelFormalVerificationBaselinePct\% and \ThyCagrMultilabelProgrammingLanguagesBaselinePct\%
++between 2018 and 2022. The acceleration is real but not specific: the corpus-wide multi-label growth rose from
++\ThyCagrMultilabelTotalBaselinePct\% to \ThyCagrMultilabelTotalPostPct\% over the same periods, so the six
++categories accelerated with the corpus rather than within it. Measured by primary-label share, every category
++lost ground between 2018 and 2025: algorithms from \ThySharePrimaryAlgorithmsTwoZeroOneEightPct\% to
++\ThySharePrimaryAlgorithmsTwoZeroTwoFivePct\%, software engineering from
++\ThySharePrimarySoftwareEngineeringTwoZeroOneEightPct\% to \ThySharePrimarySoftwareEngineeringTwoZeroTwoFivePct\%,
++formal verification from \ThySharePrimaryFormalVerificationTwoZeroOneEightPct\% to
++\ThySharePrimaryFormalVerificationTwoZeroTwoFivePct\%, programming languages from
++\ThySharePrimaryProgrammingLanguagesTwoZeroOneEightPct\% to \ThySharePrimaryProgrammingLanguagesTwoZeroTwoFivePct\%,
++computational complexity from \ThySharePrimaryComputationalComplexityTwoZeroOneEightPct\% to
++\ThySharePrimaryComputationalComplexityTwoZeroTwoFivePct\% and compilers from
++\ThySharePrimaryCompilersTwoZeroOneEightPct\% to \ThySharePrimaryCompilersTwoZeroTwoFivePct\%. The theory and
++software categories are growing and shrinking at once: more works, a smaller fraction of a corpus whose growth
++is set by the AI categories of Part~V.
++
++\input{tables/tab_theory_counts}
++
++Two cautions attach to these numbers. The post-period growth rate spans two years and is exposed to the
++indexing lag of the most recent year (Section~\ref{sec:m-truncation}); the primary-label counts for 2025 are
++the last complete ones. And the file \texttt{results/bibliometrics/cagr\_by\_category.csv}, from which
++Table~\ref{tab:cagr} is generated, reports primary counts for these categories about three times smaller than
++those of the graph counts file (algorithms in 2018: \ThyCagrfileAlgorithmsNTwoZeroOneEight{} against
++\ThyCountAlgorithmsTwoZeroOneEightPrimary{}), so it predates the current classification; its growth rates are
++not used here, and the discrepancy is recorded for the reproducibility audit.\footnote{The generator
++\texttt{bibliometrics/04\_diversity\_cagr.py} reads \texttt{category\_counts\_yearly.csv}; re-running it on the
++current counts resolves the discrepancy. Until then, Table~\ref{tab:cagr} and Table~\ref{tab:theory_summary}
++disagree for these six rows.}
++
++\subsection{Structural change at the reference date}
++
++The interrupted-time-series slope change at December 2022 is positive for all six primary-label series and
++significant with HAC standard errors (Section~\ref{sec:m-its}): \ThyBreakAlgorithmsChatgptSlopePct\% per month
++for algorithms, \ThyBreakComputationalComplexityChatgptSlopePct\% for computational complexity,
++\ThyBreakProgrammingLanguagesChatgptSlopePct\% for programming languages,
++\ThyBreakCompilersChatgptSlopePct\% for compilers, \ThyBreakFormalVerificationChatgptSlopePct\% for formal
++verification and \ThyBreakSoftwareEngineeringChatgptSlopePct\% for software engineering. The placebo test
++(Section~\ref{sec:m-placebo}) does not confirm any of them at the five per cent level: the share of
++baseline-period pseudo-events whose slope statistic exceeds the observed one is
++\ThyBreakAlgorithmsPlaceboSlopeP{} for algorithms, \ThyBreakComputationalComplexityPlaceboSlopeP{} for
++computational complexity, \ThyBreakSoftwareEngineeringPlaceboSlopeP{} for software engineering,
++\ThyBreakCompilersPlaceboSlopeP{} for compilers, \ThyBreakFormalVerificationPlaceboSlopeP{} for formal
++verification and \ThyBreakProgrammingLanguagesPlaceboSlopeP{} for programming languages. The two theory
++categories are closest to the threshold; none crosses it. A slope change that pseudo-events of the baseline
++reproduce at these rates is a feature of series that were already accelerating, not of the reference date.
++
++\input{tables/tab_theory_summary}
++
++The level changes point the other way in five categories. At December 2022 the level fell by
++\ThyBreakFormalVerificationChatgptLevelPct\% for formal verification ($p = \ThyBreakFormalVerificationChatgptLevelP$),
++\ThyBreakComputationalComplexityChatgptLevelPct\% for computational complexity
++($p = \ThyBreakComputationalComplexityChatgptLevelP$), \ThyBreakAlgorithmsChatgptLevelPct\% for algorithms
++($p = \ThyBreakAlgorithmsChatgptLevelP$), \ThyBreakProgrammingLanguagesChatgptLevelPct\% for programming
++languages and \ThyBreakCompilersChatgptLevelPct\% for compilers (both not significant); only software
++engineering shows a positive level change (\ThyBreakSoftwareEngineeringChatgptLevelPct\%,
++$p = \ThyBreakSoftwareEngineeringChatgptLevelP$), and it is also the only category with a significant positive
++level change at the GPT-4 date (\ThyBreakSoftwareEngineeringGptFourLevel{},
++$p = \ThyBreakSoftwareEngineeringGptFourLevelP$; Section~\ref{sec:m-altdates}). The negative level changes
++are at least partly the seasonality of proceedings dates in categories dominated by conferences, and the
++alternative-date estimates for the o1 date (\ThyBreakComputationalComplexityOOneLevel{} for computational
++complexity, $p = \ThyBreakComputationalComplexityOOneLevelP$) show how sensitive a level estimate is to where
++the window is cut. The Bai--Perron procedure (Section~\ref{sec:m-bp}) dates a single break at
++\ThyBreakAlgorithmsBpBreaksBic{} for algorithms, computational complexity and compilers, two breaks for
++programming languages (\ThyBreakProgrammingLanguagesBpBreaksBic{}) and formal verification
++(\ThyBreakFormalVerificationBpBreaksBic{}), and one at \ThyBreakSoftwareEngineeringBpBreaksBic{} for software
++engineering: the data-driven dates fall in spring 2023 for the three categories with a single break, which is
++after the reference date and before the GPT-4 release had been indexed, and they say nothing about cause.
++
++\subsection{Vocabulary moves, citations do not}
++
++Composition is where the window is visible. Table~\ref{tab:theory_llm_cooccurrence} reports, for each
++category and year, the number of works that the multi-label classifier also assigns to the LLM category and
++their share of the category. Between 2022 and 2025 that share rose from
++\ThyCoocSoftwareEngineeringLlmTwoZeroTwoTwoPct\% to \ThyCoocSoftwareEngineeringLlmTwoZeroTwoFivePct\% in
++software engineering, from \ThyCoocProgrammingLanguagesLlmTwoZeroTwoTwoPct\% to
++\ThyCoocProgrammingLanguagesLlmTwoZeroTwoFivePct\% in programming languages, from
++\ThyCoocCompilersLlmTwoZeroTwoTwoPct\% to \ThyCoocCompilersLlmTwoZeroTwoFivePct\% in compilers, from
++\ThyCoocFormalVerificationLlmTwoZeroTwoTwoPct\% to \ThyCoocFormalVerificationLlmTwoZeroTwoFivePct\% in formal
++verification, and from \ThyCoocAlgorithmsLlmTwoZeroTwoTwoPct\% to \ThyCoocAlgorithmsLlmTwoZeroTwoFivePct\% in
++both algorithms and computational complexity. The gradient runs from the code-facing categories to the
++mathematical ones, and it is a gradient of vocabulary: co-classification is a lexical and topical signal
++(Section~\ref{sec:m-classification}), not a measure of method adoption.
++
++\input{tables/tab_theory_llm_cooccurrence}
++
++Citation flows are more conservative. In the post period, software-engineering works direct
++\ThyFlowSoftwareEngineeringToLlmPostPct\% of their categorised references to works of the LLM category,
++computational complexity \ThyFlowComputationalComplexityToLlmPostPct\%, compilers
++\ThyFlowCompilersToLlmPostPct\%, programming languages \ThyFlowProgrammingLanguagesToLlmPostPct\%, formal
++verification \ThyFlowFormalVerificationToLlmPostPct\% and algorithms \ThyFlowAlgorithmsToLlmPostPct\%; the
++within-category shares barely moved (algorithms \ThyFlowAlgorithmsToAlgorithmsBaselinePct\% to
++\ThyFlowAlgorithmsToAlgorithmsPostPct\%, software engineering
++\ThyFlowSoftwareEngineeringToSoftwareEngineeringBaselinePct\% to
++\ThyFlowSoftwareEngineeringToSoftwareEngineeringPostPct\%, formal verification
++\ThyFlowFormalVerificationToFormalVerificationBaselinePct\% to
++\ThyFlowFormalVerificationToFormalVerificationPostPct\%).\footnote{Flows are aggregated from
++\texttt{results/graph/category\_flows\_yearly.csv} over citing years 2018--2022 (baseline) and 2023--2026
++(post) and are shares of the references that fall inside the categorised corpus (Section~\ref{sec:m-citgraph}).
++The LLM category is young, which depresses its share of cited works mechanically; the comparison with the
++co-classification shares is therefore indicative, not a test.} Two flow changes deserve notice. The
++self-citation share of computational complexity fell from
++\ThyFlowComputationalComplexityToComputationalComplexityBaselinePct\% to
++\ThyFlowComputationalComplexityToComputationalComplexityPostPct\% while its references to human--computer
++interaction rose from \ThyFlowComputationalComplexityToHciBaselinePct\% to
++\ThyFlowComputationalComplexityToHciPostPct\%; given the candidate list of
++Section~\ref{sec:thy-landmarks}, this is more plausibly the category's contamination by social-science works
++than a change in theoretical research, and it is recorded as a contested reading. Inbound citations to formal
++verification from computer vision doubled, from \ThyInflowFormalVerificationFromComputerVisionBaselinePct\% to
++\ThyInflowFormalVerificationFromComputerVisionPostPct\% of the category's inbound references, which is
++consistent with the neural-network verification literature reviewed in Section~\ref{sec:thy-fv} being cited by
++robustness work; no paper-level breakdown tests this.
++
++\section{Algorithms and data structures}
++\label{sec:thy-algorithms}
++
++\subsection{What the window established}
++
++The category's largest results are not in the candidate list, and the first task of the review is to say what
++they are, from verified records. Maximum flow and minimum-cost flow were brought to almost-linear time
++\citep{chen2022maxflow}, closing a line of work that had run through interior-point methods and dynamic graph
++data structures for a decade. Single-source shortest paths with negative weights were brought to near-linear
++time by a combinatorial algorithm \citep{bernstein2022negative}, a result republished in the \emph{Journal of
++the ACM} and \emph{Communications of the ACM} in 2025. The sorting barrier for directed single-source shortest
++paths, the $\Omega(m + n \log n)$ cost that Dijkstra's algorithm with a priority queue cannot avoid, was
++broken by a deterministic $O(m \log^{2/3} n)$ algorithm \citep{duan2025sorting}, the best paper of STOC~2025.
++In data structures, the open-addressing bounds conjectured by Yao in 1985 were shown to be beatable: without
++reordering, a greedy scheme achieves expected amortised probe complexity $O(\log^2 \delta^{-1})$ at load
++$1 - \delta$, against the conjectured $\Theta(\delta^{-1})$ \citep{farachcolton2024openaddressing}. These
++four results, from 2022 to 2025, are what a specialist would list first for the window; none has the citation
++profile that the ranking rewards, because theoretical results are cited by a small community over long
++horizons, and the 36-month window and the field normalisation are both calibrated on the behaviour of
++applied categories.
++
++What the corpus does record is the programme of \emph{algorithms with predictions}, which the baseline period
++established and the window continued. The candidate list carries it through a 2024 arXiv re-posting of the 2018
++conference paper on ski rental and non-clairvoyant scheduling with machine-learned predictions, whose
++abstract describes algorithms that are ``oblivious to the performance of the predictor, improve with better
++predictions, but do not degrade much if the predictions are poor''; the programme's canonical references are
++the journal version of competitive caching with learned advice \citep{lykouris2021caching} and the overview in
++\emph{Communications of the ACM} \citep{mitzenmacher2022predictions}. The formal object is a pair of
++guarantees, consistency (the competitive ratio when the prediction is exact) and robustness (the ratio under
++arbitrary error), with a trade-off between them; it is the point at which online algorithms and machine
++learning meet without either giving up its guarantees, and it is the one place in the category where the
++``learning'' vocabulary of the window names a theory rather than a tool. The fine-grained complexity programme
++that organised algorithms research at the baseline (Section~\ref{sec:p2-theory}) is represented in the
++bibliography by the ICM survey \citep{vassilevskawilliams2019finegrained} and by the parameterized-algorithms
++monograph \citep{cygan2015parameterized}.
++
++\subsection{What the candidate list contains}
++
++Of the \ThyAuditAlgorithmsN{} algorithms candidates, \ThyAuditAlgorithmsInScopeResearch{} are research
++contributions and none is misclassified; the rest are textbook chapters, re-deposited classics, theses and
++metaheuristics. The two post-period research candidates are modest and both carry citation counts that belong
++to other works: the WADS~2025 paper extends Kd-trees to Bregman divergences, shows that the triangle inequality
++is not needed for correct pruning, and reports two orders of magnitude over linear search for
++Kullback--Leibler nearest-neighbour queries in up to a hundred dimensions \citep{pham2025kdtree}; the SEA~2024
++paper replaces the highest-degree branching rule of branch-and-reduce for maximum independent set with a graph
++neural network \citep{langedal2024mis}. Both abstracts describe engineering contributions with experimental
++evaluation, which is what algorithm engineering is; neither is the kind of result that defines the window.
++
++The other research genre in the list is the metaphor-based metaheuristic: the Arithmetic Optimization Algorithm
++\citep{abualigah2021aoa} and the Aquila Optimizer \citep{abualigah2021aquila} in 2021, and the artificial
++lemming, glider snake, gray langur and starfish optimisers in 2025--2026, published in engineering and
++AI-review journals and evaluated on CEC benchmark functions and constrained design problems. The abstracts share
++a template, a natural metaphor mathematically modelled into exploration and exploitation phases and
++``outperforming or matching'' a dozen earlier metaheuristics. The operational-research literature has argued
++since \citet{sorensen2013metaphor} that the genre re-describes existing methods under new names and that its
++benchmarks do not discriminate; the position was restated in a ``call for action'' \citep{aranha2021metaphor}
++and substantiated for six widely cited algorithms, shown to be equivalent to or special cases of earlier
++methods \citep{camachovillalon2022exposing}. The corpus does not adjudicate this dispute; what it shows is
++the genre's weight in the highly cited stratum of the algorithms category, where six of twenty-five candidates
++belong to it and where the post-2022 research articles with the highest normalised scores are of this kind.
++Whether the genre grows faster than the rest of the category and absorbs a growing share of its citations is an
++open problem of Part~X (G-P7), answerable with a keyword-defined subseries.
++
++\subsection{Algorithms discovered by learning systems}
++
++Three Nature papers and one preprint report algorithms found by reinforcement learning or program search:
++faster matrix-multiplication tensor decompositions for small sizes \citep{fawzi2022alphatensor}, shorter
++sorting networks for short sequences, merged into a standard library \citep{mankowitz2023alphadev}, new
++constructions for the cap-set problem and bin packing from evolved programs \citep{romeraparedes2024funsearch},
++and, as a 2025 preprint, a coding agent that reports improvements on a collection of open mathematical and
++algorithmic problems \citep{novikov2025alphaevolve}. All four are reports by the developers; the improvements
++are on specific instances; and no independent reproduction or extension by the theory community appears in the
++verified bibliography. The question these works pose to the category, whether search over programs changes how
++algorithms are designed or only how constants are tuned, is one the corpus cannot answer at this granularity;
++it is stated as open problem G-P9. The honest position is that the window's algorithmic breakthroughs were
++obtained by people, and that the learned-discovery results are promising, contested in scope, and awaiting
++replication.
++
++\section{Computational complexity}
++\label{sec:thy-complexity}
++
++The category's statistics are the least trustworthy of the six: \ThyAuditComputationalComplexityMisclassified{}
++of its \ThyAuditComputationalComplexityN{} candidates are outside computing, and the flow and co-occurrence
++changes of Section~\ref{sec:thy-statistics} are to that extent artefacts. The two in-scope research candidates
++are a STOC~2019 paper and a 2026 \emph{Theoretical Computer Science} article on SAT in spiking neural P systems
++(no abstract). The STOC paper is a genuine landmark of the baseline: its abstract describes the reachability
++problem for Petri nets as open since the 1960s, with Lipton's 1976 exponential-space lower bound unimproved, and
++establishes a non-elementary lower bound \citep{czerwinski2019petri}. The story was completed within the
++baseline period, when reachability in vector addition systems was shown Ackermann-complete
++\citep{czerwinski2021ackermann,leroux2021notprimitive}, matching the upper bound of Leroux and Schmitz; the
++window inherits a closed problem whose closure the corpus ranks below a technology-acceptance survey.
++
++The window's own contribution to complexity theory that a specialist would name first is the simulation of
++time $t$ in space $O(\sqrt{t \log t})$ \citep{williams2025sqrtspace}, which improves the Hopcroft--Paul--Valiant
++simulation of 1975 after fifty years and implies, through a known argument, that there are problems solvable
++in linear space that need nearly quadratic time on multitape machines; the paper is a STOC~2025 result
++republished in the \emph{Journal of the ACM} in 2026. In additive combinatorics, the strong bounds for
++three-term arithmetic progressions \citep{kelley2023threeap} brought the Roth-type exponent to a form conjectured
++since Behrend. Neither appears in the candidate list; both enter through verified records, and the general
++point is the one made for algorithms: results of this kind are cited slowly and by few, and a 36-month
++field-normalised window does not see them.
++
++The boundary with language models is where the category's co-classification share grew, yet the relevant
++works are mostly classified elsewhere. The proof that transformers with logarithmic precision lie in uniform
++$\mathrm{TC}^0$ \citep{merrill2023parallelism}, and the survey of what formal languages transformers can
++express \citep{strobl2024survey}, bound what a single forward pass can compute and make the case that
++chain-of-thought computation is where additional expressive power must come from; they are, in the verified
++bibliography, the window's main contribution of complexity theory to the study of the models that dominate
++Part~V. Whether such works are classified under computational complexity or under natural language processing
++is an empirical question about the taxonomy (open problem G-P8). The remainder of the candidate list is
++teaching material, including the fourth edition of the Kolmogorov-complexity monograph \citep{li2019kolmogorov}
++and undergraduate chapters on complexity and on parameterized complexity, and re-deposited classics, including
++Turing's 1936 paper and Wolfram's cellular-automata chapters.
++
++\section{Programming languages}
++\label{sec:thy-pl}
++
++The baseline lineage was set out in Section~\ref{sec:p2-pl}: Rust and its semantic foundation
++\citep{matsakis2014rust,jung2017rustbelt,jung2021saferust}, Lean~4 as a prover that is also a programming
++language \citep{demoura2021lean4} with its mathematical library \citep{mathlib2020}, and the verified
++artefacts CompCert \citep{leroy2009compcert} and seL4 \citep{klein2009sel4} that showed production software
++can be proved correct. The candidate list records this lineage only obliquely, through three theses of the
++L4/seL4 school re-deposited in 2022 (separation logic for programs in virtual memory, formal memory models for
++C systems code, a verified multiprocessor kernel design), which the ranking places in the post period. In the
++window the lineage continued with Verus \citep{lattuada2024verus}, a verification framework for Rust programs
++presented at SOSP~2024, which treats the ownership discipline of the language as the foundation for
++specification and proof rather than as an obstacle to them; the content of Verus is known here from its verified
++title and venue only.
++
++Of the \ThyAuditProgrammingLanguagesN{} candidates, \ThyAuditProgrammingLanguagesInScopeResearch{} are
++research contributions. Two of them mark the entry of language models into the community's top venue in
++2024. LLift \citep{li2024llift} combines static analysis with an LLM to find use-before-initialisation bugs in
++the Linux kernel; its abstract reports post-constraint guidance of the path analysis, procedures for handling
++``the unpredictable nature of LLMs'', and four previously unknown bugs acknowledged by the kernel community.
++WhiteFox \citep{yang2024whitefox} is described by its abstract as the first white-box compiler fuzzer that uses
++an LLM reading the source code of optimisations to generate test programs that exercise them, against
++black- and grey-box fuzzing that lacks that information and symbolic execution that does not scale to a
++compiler. Both papers report bugs found; neither reports a guarantee, and the abstracts do not claim one. The
++natural comparison is with the two instruments that the compiler community built for correctness before the
++window, Csmith's random differential testing, which found hundreds of bugs in production C compilers
++\citep{yang2011csmith}, and Alive2's bounded translation validation of LLVM optimisations
++\citep{lopes2021alive2}; whether LLM-generated tests can be combined with validation of that kind, so that the
++share of reported bugs that are confirmed can be measured per optimisation pass, is open problem G-P4. The
++third research candidate, ZEUS \citep{kalra2018zeus}, is the baseline representative of program analysis for a
++new target: abstract interpretation and constrained Horn clauses applied to smart contracts, with an abstract
++reporting that about 95\% of 22,400 contracts analysed were vulnerable. The universal fuzzer of
++\citet{xia2024fuzz4all} and the LLM-based bug-reproduction study of \citet{kang2023fewshottesters} are the
++software-engineering counterparts of the same move.
++
++The category's vocabulary moved more than any except software engineering's: co-classification with the LLM
++category reached \ThyCoocProgrammingLanguagesLlmTwoZeroTwoFivePct\% in 2025. Its references to LLM works are
++\ThyFlowProgrammingLanguagesToLlmPostPct\%, and its structural ties are unchanged, with
++\ThyFlowProgrammingLanguagesToFormalVerificationPostPct\% of its references going to formal verification and
++\ThyFlowProgrammingLanguagesToSoftwareEngineeringPostPct\% to software engineering in the post period, against
++\ThyFlowProgrammingLanguagesToFormalVerificationBaselinePct\% and
++\ThyFlowProgrammingLanguagesToSoftwareEngineeringBaselinePct\% at the baseline. Programming languages is, in
++the citation graph, the hinge between verification and engineering, and the window did not move the hinge.
++
++\section{Compilers}
++\label{sec:thy-compilers}
++
++Compilers is the smallest of the six categories, \ThySharePrimaryCompilersTwoZeroTwoFivePct\% of primary labels
++in 2025, and the most lexically contaminated after complexity. Its in-scope candidates nevertheless trace a
++coherent line. At the baseline, MLIR \citep{lattner2021mlir} is the infrastructure whose abstract promises to
++reduce ``the cost of building domain specific compilers'' and to connect existing ones; Tiramisu
++\citep{baghdadi2019tiramisu} is a polyhedral compiler whose scheduling language separates algorithm, loop
++transformation, data layout and communication so that one algorithm can target multicores, GPUs and
++distributed machines; ExaStencils is a four-layer domain-specific language for multigrid solvers. Two surveys
++establish that learned compiler heuristics are a programme older than the window: the \emph{Proceedings of the
++IEEE} survey of machine learning in compiler optimisation \citep{wang2018mlcompiler} and the \emph{Computing
++Surveys} review of autotuning, optimisation selection and phase ordering \citep{ashouri2018autotuning}, which
++dates the first attempts to the mid-1990s. Three MIT and Delaware theses on learned optimisation selection and
++on sparse tensor-algebra compilation complete the baseline picture.
++
++The post-period candidates show the programme's change of scale. LLM4VV \citep{munley2024llm4vv} uses language
++models to generate a validation test suite for directive-based parallel programming compilers (metadata only;
++no abstract). LLM Compiler \citep{cummins2025llmcompiler} is a suite of foundation models built on Code Llama
++and trained, per its abstract, on 546 billion tokens of LLVM intermediate representation and assembly, then
++instruction-tuned to predict and emulate compiler behaviour; fine-tuned versions reach 77\% of the optimising
++potential of an autotuning search for code size and disassemble x86-64 and ARM assembly back to LLVM-IR. The
++preprint that preceded it \citep{cummins2023llmcompiler} framed the task as predicting optimisation-pass
++sequences. The shift is from learning a heuristic inside a compiler, with features chosen by compiler writers,
++to pre-training a model on the compiler's own artefacts; what the shift leaves open is the question that
++compiler research has always had to answer last, correctness, which the field handled through differential
++testing \citep{yang2011csmith} and translation validation \citep{lopes2021alive2} rather than through model
++evaluation. Co-classification with the LLM category reached \ThyCoocCompilersLlmTwoZeroTwoFivePct\% in 2025;
++references to LLM works are \ThyFlowCompilersToLlmPostPct\%, and the category's citations continue to go
++first to computer architecture (\ThyFlowCompilersToComputerArchitecturePostPct\% of references in the post
++period) and to itself. The deep-learning compiler stack recorded in the main bibliography, TVM, Triton and
++PyTorch~2 \citep{chen2018tvm,tillet2019triton,ansel2024pytorch2}, and the image-processing DSL that preceded
++it \citep{ragankelley2013halide}, belong to Part~VI and are only noted here as the other post-2018 development
++of the category.
++
++\section{Formal verification}
++\label{sec:thy-fv}
++
++Formal verification has the most in-scope research candidates of the six
++(\ThyAuditFormalVerificationInScopeResearch{} of \ThyAuditFormalVerificationN{}) and the clearest internal
++structure, which the review follows: solvers and competitions, neural-network verification, verification of new
++targets, learned theorem proving, and the theory of verification.
++
++\paragraph{Solvers and competitions.}
++cvc5 \citep{barbosa2022cvc5} is the system description of the SMT solver that succeeded CVC4, evaluated on all
++SMT-LIB benchmarks against CVC4 and Z3; the Handbook of Satisfiability in its second edition
++\citep{biere2021handbook} is the reference work, present twice in the candidate list under two records. The
++SV-COMP~2024 report \citep{beyer2024svcomp} describes the largest edition of the software-verification
++competition to that date, 76 tools from 34 teams on 30,300 verification tasks for C programs, with a second
++track for witness validation and a new witness format. Competitions of this kind are the category's benchmark
++culture, and they are a point of contrast with the benchmark culture of Part~V: the tasks are fixed programs
++with fixed specifications, a tool's result is a proof or a counterexample that a validator checks, and the
++score is not self-reported.
++
++\paragraph{Neural-network verification.}
++The baseline built a sub-field: Reluplex \citep{katz2017reluplex} extended the simplex method to ReLU
++constraints; the 2018 symbolic-interval preprint in the candidate list argued, per its abstract, that existing
++analyses ``struggle to scale beyond very small networks'' and proposed interval propagation that does;
++DeepPoly \citep{singh2019deeppoly} introduced an abstract domain of floating-point polyhedra with intervals and
++transformers for ReLU, sigmoid, tanh and maxpool, and reported the first proofs of robustness to rotations
++with interpolation; Marabou \citep{katz2019marabou} packaged SMT-based verification as a framework accepting
++TensorFlow models. The VNN-COMP report \citep{brix2023vnncomp} summarises the first three years of the
++competition that organised the sub-field. The doubling of formal verification's inbound citations from
++computer vision in the post period (Section~\ref{sec:thy-statistics}) is consistent with this literature being
++cited by robustness work, and the sub-field is the main channel through which the category meets the
++machine-learning categories; whether it has guarantees that survive the scale of the models of Part~V is not a
++question these abstracts address.
++
++\paragraph{New targets.}
++VerX \citep{permenev2020verx} is described by its abstract as the first automated verifier of functional
++properties of Ethereum smart contracts, combining reduction of temporal properties to reachability, a symbolic
++execution engine for the Ethereum virtual machine, and delayed predicate abstraction, evaluated on 83
++properties of 12 projects. The answer-set-programming monitor for automated driving \citep{klampfl2024asp}
++and the theses on parametric and multi-objective Markov models represent runtime and probabilistic
++verification; the Warwick thesis on model checking quantum protocols, re-deposited with a 2026 date, is a
++baseline work of the previous decade.
++
++\paragraph{Learned theorem proving.}
++The abstract attached in the corpus to the ITP~2023 paper, under a wrong title (Section~\ref{sec:thy-landmarks}),
++is the clearest statement in the candidate set of what machine learning has done for automated reasoning:
++MizAR \citep{jakubuv2023mizar} proves about 60\% of the theorems of the Mizar Mathematical Library in the hammer
++setting and 75\% when given the premises of the human proofs, through the E and Vampire provers with the ENIGMA
++and Deepire learned guidance and an incremental loop that grows a corpus of millions of proofs while training
++stronger systems on it. This is the saturation-prover line. The language-model line is represented by Baldur
++\citep{first2023baldur}, which generates and repairs whole Isabelle proofs; by LeanDojo
++\citep{yang2023leandojo} and DeepSeek-Prover \citep{xin2024deepseekprover}, both preprints, which report
++retrieval-augmented and synthetic-data-trained provers on Lean benchmarks; and by the position paper on formal
++mathematical reasoning as a frontier \citep{yang2024formalmath}, also a preprint. The figures these works
++report are on different benchmarks, under different compute budgets, with the possibility of overlap between
++training corpora and test theorems that the abstracts do not exclude; a benchmark of theorems posted after the
++training cut-off, with proof checking rather than self-reported success, is the direction stated as open
++problem G-P5. The machine-learned mathematics of Part~V, olympiad geometry \citep{trinh2024alphageometry} and
++program search \citep{romeraparedes2024funsearch}, is adjacent but distinct: it produces objects that a human or
++a checker then verifies, where the works above produce the proofs.
++
++\paragraph{Theory of verification.}
++Equality saturation as implemented in \emph{egg} \citep{willsey2021egg}, whose abstract describes e-graph
++rewriting that ``only adds information'' and extraction by a user-supplied cost function, is a baseline result
++republished as a research highlight in 2026; the quantitative interface automata of the CSL~2026 paper on reward
++interfaces, whose abstract introduces multi-valued correctness requirements and best-effort satisfaction into
++interface theories, is the one post-period research candidate of this kind.
++
++The category's primary share halved between 2018 and 2025 while its multi-label count grew by eleven per cent
++(Table~\ref{tab:theory_counts}). The arithmetic of the argmax assignment (Section~\ref{sec:m-score}) suggests
++why: a work that carries a verification label and an AI or security label loses the primary label to the
++larger category when the scores are close, so the primary series understates a category whose work is
++increasingly done in combination. Measuring the share of works whose primary label switched, by co-assigned
++category and year, is open problem G-P10.
++
++\section{Software engineering}
++\label{sec:thy-se}
++
++Software engineering is where the window's change is largest, best documented, and most contested. Its
++baseline was not AI-free (Section~\ref{sec:p2-pl}): the Codex models \citep{chen2021codex}, the
++code-generation benchmarks \citep{austin2021mbpp}, the systematic evaluation of code models
++\citep{xu2022codelms} and the first productivity assessment of neural code completion
++\citep{ziegler2022copilot} precede the reference date, and the survey of machine-learning testing
++\citep{zhang2022mltesting}, 144 papers on testing properties, components and workflows of learned systems,
++shows the category already organised around learned software as an object of test. The window's surveys
++reverse the direction of the relation. The survey of software testing with large language models
++\citep{wang2024llmtesting} analyses 102 studies and reports, per its abstract, that test-case preparation and
++program repair are the most common uses; the two systematic reviews of language models for software
++engineering \citep{fan2023llm4se,hou2024llm4se} and the code-generation survey \citep{jiang2024codegensurvey}
++map the same territory. Method papers from the main bibliography and the Part~VII-a bibliography give the
++texture: program repair in the era of pre-trained models \citep{xia2023apr}, bug reproduction from reports
++\citep{kang2023fewshottesters}, universal fuzzing \citep{xia2024fuzz4all}, and the agentic setting defined by
++SWE-bench \citep{jimenez2024swebench} and SWE-agent \citep{yang2024sweagent}, both preprints, in which a model
++is asked to resolve a GitHub issue end to end. Among the candidates, APISS \citep{chen2025apiss} retrieves
++functionality-equivalent APIs through LLM embeddings to migrate proof-of-concept exploits, and a 2026 paper
++proposes a multi-scale temporal model for just-in-time defect prediction; both are method contributions judged
++from their abstracts.
++
++\paragraph{Productivity: two findings that need reconciling.}
++The most cited post-period empirical candidate reports three randomised field experiments at Microsoft,
++Accenture and an unnamed Fortune~100 company, run by the firms in the course of business, in which a random
++subset of 4,867 developers received an AI coding assistant; the pooled estimate is a 26.08\% increase in
++completed tasks with a standard error of 10.3\%, each experiment being ``noisy'' on its own, and less
++experienced developers adopting more and gaining more \citep{cui2026genaiwork}. The earlier controlled
++experiment on a single task reported a larger speed-up \citep{peng2023copilot}, and the field studies of
++generative AI at work outside software \citep{brynjolfsson2023genaiwork,noy2023productivity,dellacqua2023jagged}
++report the same pattern of larger gains for less experienced workers. Against these stands a 2025 randomised
++trial preprint in which experienced open-source developers working on repositories they maintain took longer
++to complete issues with AI tools than without \citep{becker2025metr}. The two findings are not contradictory
++once population and outcome are distinguished: enterprise developers completing assigned tasks versus expert
++maintainers resolving issues in mature codebases they know well, and tasks completed versus time per issue.
++But the reconciliation is a reading of two abstracts, the second of a preprint, and the design that would
++settle it, a preregistered multi-site trial stratified by experience and repository maturity with time-to-merge
++and defect rate as joint outcomes, has not been run (open problem G-P3). The user studies of how programmers
++interact with code-generating models \citep{barke2023groundedcopilot,vaithilingam2022expectation,liang2024usability}
++supply the mechanism, acceleration mode against exploration mode, that any such trial would need to measure.
++
++\paragraph{Security of generated code.}
++The security evaluations agree with one another. Copilot's suggestions were found vulnerable at a substantial
++rate in 2022 \citep{pearce2022asleep}; the CCS~2023 user study found that participants with an AI assistant
++wrote less secure code and believed it more secure \citep{perry2023insecure}; and the ICPEC~2025 study in the
++candidate list, evaluating three open models on a filtered Big-Vul subset with eight CWE categories, reports
++``a sharp contrast between high detection rates and markedly poor classification accuracy''
++\citep{gasparlopes2025vuln}. High detection or completion paired with poor fine-grained security reasoning is
++the consistent pattern across three designs, and it is the category's clearest negative result about the
++models.
++
++\paragraph{The statistics.}
++Software engineering has the highest co-classification with the LLM category of the six
++(\ThyCoocSoftwareEngineeringLlmTwoZeroTwoFivePct\% of its works in 2025, from
++\ThyCoocSoftwareEngineeringLlmTwoZeroTwoTwoPct\% in 2022), the largest post-period growth
++(\ThyCagrMultilabelSoftwareEngineeringPostPct\% per year), the only positive level change at the reference
++date and the only significant positive level change at the GPT-4 date (Section~\ref{sec:thy-statistics}).
++Its references to LLM works are \ThyFlowSoftwareEngineeringToLlmPostPct\% in the post period, from
++\ThyFlowSoftwareEngineeringToLlmBaselinePct\%, and inbound citations from the LLM category are
++\ThyInflowSoftwareEngineeringFromLlmPostPct\% of its inbound references. Whether a vocabulary shift of this
++size is a method shift, that is, whether the works co-classified with LLM cite LLM method papers at a higher
++rate than the category's baseline, can be tested on the knowledge graph of Part~III once method nodes are
++extracted for the high-priority set; it is open problem G-P2. The research-software case of the Astropy
++project \citep[in the candidate list;][]{collberg2016repeatability} and the reproducibility literature of
++Part~IX are the other side of the same category, and the only part of it where the window changed nothing
++visible.
++
++\section{Synthesis}
++\label{sec:thy-synthesis}
++
++Three conclusions follow, in decreasing order of evidential strength.
++
++The first is about the instrument. In the theory and software areas the automatically selected landmark
++candidates do not identify landmarks: \ThyAuditVerdictInScopeResearch{} of \ThyAuditN{} are in-scope research
++works, \ThyAuditIdCollision{} carry citation windows that belong to other works, and the rest are chapters with
++generic titles, re-deposited classics, theses in small normalisation cells and lexical misclassifications. The
++defects are identifiable and partly correctable at the selection stage, and the audit table
++(Table~\ref{tab:theory_landmark_audit}) should accompany any use of the list; the major theoretical results of
++the window, almost-linear flow, near-linear negative-weight shortest paths, the directed sorting barrier,
++optimal open addressing, square-root-space simulation and the 3-progression bounds, are invisible to a
++36-month field-normalised ranking and were brought into this review through verified records. The same
++blindness will affect any bibliometric answer to RQ3 and RQ7 for these categories.
++
++The second is about the statistics. The six categories grew in absolute terms and declined in relative terms
++after 2022; their slope changes at the reference date are positive but not distinguishable from pseudo-events
++in the baseline; their vocabulary moved toward language models in proportion to their distance from
++mathematics; and their citation behaviour moved little. For RQ2 the answer is that none of the six is among
++the categories with structural change that survives the placebo test, and for RQ5 that the relationship with
++the foundation-model categories changed in what these works say far more than in what they cite.
++
++The third is about the research. The substantive developments of the window are of two kinds that the corpus
++treats very differently. Theoretical results were obtained by people, at the rate and in the venues of the
++previous decade, and are cited accordingly. The entry of language models into verification, program analysis,
++compiler testing and software engineering is visible in the co-classification statistics and in the abstracts
++of the in-scope candidates, and it is where the open problems lie: guarantees for learned program analysis and
++compiler testing (G-P4), comparability of learned provers (G-P5), the productivity effect on experienced
++developers (G-P3), and the distinction between a vocabulary and a method (G-P2). What this half of Part~VII
++can say with confidence is which of these questions the corpus can answer with its present instruments, and
++which it cannot.

added results/graph/semantic_crossing_yearly.csv +361 −0

++year,primary_category,n_edges,share_cross_category
++2018,ai_accelerators,6157,0.5181
++2018,ai_agents,30233,0.548
++2018,ai_safety,5267,0.569
++2018,algorithms,170250,0.3159
++2018,applied_cryptography,60756,0.4146
++2018,autonomous_scientific_discovery,495,0.9253
++2018,blockchain,35962,0.2207
++2018,cloud_computing,58539,0.4688
++2018,compilers,4635,0.8194
++2018,computational_biology,92838,0.1982
++2018,computational_complexity,20738,0.6304
++2018,computer_architecture,140938,0.3222
++2018,computer_graphics,63877,0.4237
++2018,computer_networks,446365,0.1723
++2018,computer_vision,463491,0.1823
++2018,cybersecurity,183347,0.3546
++2018,database_systems,102960,0.4521
++2018,deep_learning_architectures,74595,0.4948
++2018,distributed_computing,75532,0.5767
++2018,edge_computing,34523,0.5445
++2018,federated_learning,726,0.8209
++2018,formal_verification,33962,0.465
++2018,foundation_models,245,0.9061
++2018,generative_ai,5206,0.6498
++2018,hci,216031,0.2699
++2018,information_retrieval,71592,0.3751
++2018,llm,4817,0.8337
++2018,ml_theory,91278,0.4192
++2018,multimodal_ai,2851,0.7555
++2018,nlp,158887,0.2678
++2018,operating_systems,9487,0.8001
++2018,privacy_preserving_computation,19705,0.4446
++2018,programming_languages,27407,0.6148
++2018,quantum_computing,39415,0.1159
++2018,reinforcement_learning,22774,0.351
++2018,robotics,134764,0.242
++2018,scientific_computing,86496,0.4539
++2018,software_engineering,179454,0.2979
++2018,speech_audio,77812,0.1873
++2018,sustainable_computing,3256,0.8068
++2019,ai_accelerators,7263,0.531
++2019,ai_agents,30144,0.5601
++2019,ai_safety,8150,0.5583
++2019,algorithms,179415,0.3109
++2019,applied_cryptography,68116,0.4176
++2019,autonomous_scientific_discovery,715,0.9105
++2019,blockchain,52593,0.2132
++2019,cloud_computing,57803,0.4702
++2019,compilers,4164,0.8343
++2019,computational_biology,109708,0.2013
++2019,computational_complexity,22012,0.6335
++2019,computer_architecture,137403,0.3238
++2019,computer_graphics,60635,0.4342
++2019,computer_networks,447623,0.1791
++2019,computer_vision,554061,0.1766
++2019,cybersecurity,202702,0.3475
++2019,database_systems,107527,0.4559
++2019,deep_learning_architectures,97754,0.4835
++2019,distributed_computing,76991,0.5673
++2019,edge_computing,45246,0.5424
++2019,federated_learning,1161,0.8243
++2019,formal_verification,29780,0.4782
++2019,foundation_models,506,0.8972
++2019,generative_ai,8293,0.6549
++2019,hci,232616,0.266
++2019,information_retrieval,74631,0.3586
++2019,llm,6560,0.8125
++2019,ml_theory,118641,0.4323
++2019,multimodal_ai,4264,0.7207
++2019,nlp,179279,0.2567
++2019,operating_systems,10082,0.7343
++2019,privacy_preserving_computation,21380,0.4506
++2019,programming_languages,28291,0.6236
++2019,quantum_computing,48912,0.1143
++2019,reinforcement_learning,34163,0.3233
++2019,robotics,151045,0.2428
++2019,scientific_computing,96671,0.4379
++2019,software_engineering,181111,0.3012
++2019,speech_audio,85969,0.1821
++2019,sustainable_computing,3832,0.7985
++2020,ai_accelerators,8555,0.5239
++2020,ai_agents,29472,0.5535
++2020,ai_safety,12179,0.538
++2020,algorithms,178532,0.3103
++2020,applied_cryptography,66281,0.408
++2020,autonomous_scientific_discovery,1271,0.8607
++2020,blockchain,59231,0.2082
++2020,cloud_computing,52776,0.4858
++2020,compilers,4503,0.8368
++2020,computational_biology,135533,0.1698
++2020,computational_complexity,21487,0.6388
++2020,computer_architecture,127745,0.3222
++2020,computer_graphics,60112,0.4218
++2020,computer_networks,417940,0.1881
++2020,computer_vision,577782,0.1761
++2020,cybersecurity,210760,0.3413
++2020,database_systems,98900,0.4658
++2020,deep_learning_architectures,120600,0.4763
++2020,distributed_computing,72448,0.5751
++2020,edge_computing,47740,0.5417
++2020,federated_learning,1722,0.7706
++2020,formal_verification,29104,0.4773
++2020,foundation_models,822,0.9051
++2020,generative_ai,10311,0.6379
++2020,hci,229085,0.2661
++2020,information_retrieval,73086,0.3568
++2020,llm,11303,0.7639
++2020,ml_theory,136518,0.4254
++2020,multimodal_ai,5280,0.7193
++2020,nlp,199186,0.2716
++2020,operating_systems,8975,0.7552
++2020,privacy_preserving_computation,26886,0.4459
++2020,programming_languages,25835,0.6379
++2020,quantum_computing,54478,0.1196
++2020,reinforcement_learning,41970,0.3203
++2020,robotics,159762,0.2454
++2020,scientific_computing,105432,0.4301
++2020,software_engineering,178500,0.2989
++2020,speech_audio,86196,0.1777
++2020,sustainable_computing,3712,0.7702
++2021,ai_accelerators,10185,0.4986
++2021,ai_agents,29211,0.5379
++2021,ai_safety,15348,0.5426
++2021,algorithms,185099,0.3075
++2021,applied_cryptography,65691,0.4139
++2021,autonomous_scientific_discovery,1520,0.8138
++2021,blockchain,68145,0.2024
++2021,cloud_computing,53243,0.4808
++2021,compilers,4166,0.8346
++2021,computational_biology,145854,0.1767
++2021,computational_complexity,21138,0.6401
++2021,computer_architecture,126775,0.3227
++2021,computer_graphics,59871,0.432
++2021,computer_networks,410121,0.1944
++2021,computer_vision,681479,0.1697
++2021,cybersecurity,221529,0.3434
++2021,database_systems,98155,0.4755
++2021,deep_learning_architectures,148163,0.4828
++2021,distributed_computing,69103,0.5739
++2021,edge_computing,55656,0.5292
++2021,federated_learning,4389,0.7471
++2021,formal_verification,30190,0.4816
++2021,foundation_models,1619,0.9036
++2021,generative_ai,13848,0.6419
++2021,hci,230614,0.2602
++2021,information_retrieval,75040,0.3477
++2021,llm,16560,0.7556
++2021,ml_theory,145324,0.4186
++2021,multimodal_ai,6653,0.7089
++2021,nlp,220164,0.2669
++2021,operating_systems,8703,0.7807
++2021,privacy_preserving_computation,29902,0.4454
++2021,programming_languages,25656,0.6097
++2021,quantum_computing,64423,0.1201
++2021,reinforcement_learning,50682,0.3234
++2021,robotics,174798,0.2535
++2021,scientific_computing,112748,0.4206
++2021,software_engineering,169864,0.3095
++2021,speech_audio,93912,0.1818
++2021,sustainable_computing,3960,0.7631
++2022,ai_accelerators,11638,0.4852
++2022,ai_agents,27771,0.5497
++2022,ai_safety,19034,0.5405
++2022,algorithms,181230,0.311
++2022,applied_cryptography,67510,0.4226
++2022,autonomous_scientific_discovery,1523,0.8772
++2022,blockchain,80994,0.2108
++2022,cloud_computing,51091,0.4913
++2022,compilers,3954,0.8462
++2022,computational_biology,150600,0.1813
++2022,computational_complexity,19809,0.6552
++2022,computer_architecture,120398,0.3229
++2022,computer_graphics,58221,0.4419
++2022,computer_networks,401999,0.199
++2022,computer_vision,718729,0.1678
++2022,cybersecurity,229807,0.3474
++2022,database_systems,96781,0.4772
++2022,deep_learning_architectures,159287,0.4821
++2022,distributed_computing,65426,0.589
++2022,edge_computing,62183,0.5375
++2022,federated_learning,6353,0.7244
++2022,formal_verification,26817,0.4724
++2022,foundation_models,2359,0.8978
++2022,generative_ai,15112,0.6619
++2022,hci,246850,0.2561
++2022,information_retrieval,77390,0.3335
++2022,llm,21803,0.7023
++2022,ml_theory,147928,0.4129
++2022,multimodal_ai,9216,0.6969
++2022,nlp,224621,0.2853
++2022,operating_systems,7330,0.7935
++2022,privacy_preserving_computation,32588,0.4538
++2022,programming_languages,24406,0.6179
++2022,quantum_computing,68971,0.1223
++2022,reinforcement_learning,54682,0.3177
++2022,robotics,184921,0.2512
++2022,scientific_computing,113486,0.4277
++2022,software_engineering,171531,0.3128
++2022,speech_audio,97128,0.1778
++2022,sustainable_computing,4700,0.7532
++2023,ai_accelerators,13046,0.4962
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++2023,autonomous_scientific_discovery,2873,0.8173
++2023,blockchain,101857,0.2044
++2023,cloud_computing,56168,0.4943
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++2023,computational_complexity,20196,0.6614
++2023,computer_architecture,131865,0.3229
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++2023,computer_networks,433815,0.2031
++2023,computer_vision,861207,0.1686
++2023,cybersecurity,274554,0.335
++2023,database_systems,103963,0.4807
++2023,deep_learning_architectures,184269,0.4815
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++2023,edge_computing,68563,0.5329
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++2023,hci,288325,0.2557
++2023,information_retrieval,87299,0.328
++2023,llm,77315,0.51
++2023,ml_theory,171365,0.419
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++2023,privacy_preserving_computation,38950,0.4841
++2023,programming_languages,25578,0.63
++2023,quantum_computing,82434,0.1331
++2023,reinforcement_learning,65006,0.3221
++2023,robotics,209839,0.2523
++2023,scientific_computing,120013,0.4333
++2023,software_engineering,191926,0.3164
++2023,speech_audio,111431,0.1851
++2023,sustainable_computing,6610,0.7452
++2024,ai_accelerators,14257,0.5063
++2024,ai_agents,37986,0.5616
++2024,ai_safety,41126,0.5402
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++2024,applied_cryptography,84262,0.4261
++2024,autonomous_scientific_discovery,3282,0.848
++2024,blockchain,111052,0.2038
++2024,cloud_computing,64842,0.49
++2024,compilers,4755,0.8307
++2024,computational_biology,179730,0.1943
++2024,computational_complexity,22786,0.6901
++2024,computer_architecture,141510,0.322
++2024,computer_graphics,75793,0.4667
++2024,computer_networks,455866,0.2086
++2024,computer_vision,990283,0.1717
++2024,cybersecurity,344043,0.3253
++2024,database_systems,108454,0.4992
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++2024,sustainable_computing,7770,0.739
++2025,ai_accelerators,15217,0.5092
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++2026,cybersecurity,311429,0.3431
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++2026,formal_verification,32567,0.5272
++2026,foundation_models,19258,0.8476
++2026,generative_ai,84628,0.6465
++2026,hci,313063,0.303
++2026,information_retrieval,109436,0.4269
++2026,llm,372690,0.4594
++2026,ml_theory,208330,0.4467
++2026,multimodal_ai,55739,0.7366
++2026,nlp,242621,0.4251
++2026,operating_systems,6866,0.8504
++2026,privacy_preserving_computation,48458,0.5405
++2026,programming_languages,23414,0.635
++2026,quantum_computing,140037,0.129
++2026,reinforcement_learning,86233,0.4415
++2026,robotics,194894,0.2596
++2026,scientific_computing,125821,0.4213
++2026,software_engineering,230827,0.3578
++2026,speech_audio,109792,0.2175
++2026,sustainable_computing,6619,0.7892

spb/wp44_uqo

WP44: semantic k-NN edges (5.67 M vectors, 56.7 M edges), figure layout fixes

Theme