1claim_id,claim,claim_type,evidence_type,supporting_keys,contradicting_keys,corpus_artifact,confidence,verification_status,notes2F-01,"In the interrupted time-series model, all eleven systems categories show a positive change in the monthly trend of log-volume at 2022-12 with HAC p-values below 0.01; edge computing has the smallest change (0.004, p=0.006) and information retrieval the largest (0.023).",established,corpus_statistic,,,"results/econometrics/breaks.csv columns chatgpt_slope, chatgpt_slope_p (rows cat_*)",0.9,machine-verified,Statistical description only; no causal reading.3F-02,"The trend change at 2022-12 is not specific to that date: for every systems category the placebo slope p-value is at least 0.062 (computer architecture), and reaches 0.477 for operating systems.",established,corpus_statistic,,,results/econometrics/breaks.csv column placebo_slope_p,0.9,machine-verified,Undermines any attribution of the steepening to ChatGPT.4F-03,"The level change at 2022-12 is negative and significant at 5% for information retrieval (-0.094), AI accelerators (-0.113) and quantum computing (-0.087); positive and significant for computer networks (+0.043) and sustainable computing (+0.049); not significant for the six other categories.",established,corpus_statistic,,,"results/econometrics/breaks.csv columns chatgpt_level, chatgpt_level_p",0.9,machine-verified,"Placebo level p-values are all above 0.47, so the level shifts are also not date-specific."5F-04,"Primary-category output of computer architecture, operating systems, distributed computing, cloud computing, computer networks, database systems and information retrieval declined between 2018 and 2022 (negative baseline CAGR) and grew between 2023 and 2025 (positive post CAGR); edge computing, quantum computing, sustainable computing and AI accelerators grew in both periods.",established,corpus_statistic,,,"results/bibliometrics/cagr_by_category.csv columns cagr_baseline, cagr_post",0.9,machine-verified,Counts are eligible works by primary category; 2026 excluded as incomplete.6F-05,"The share of total primary output held by computer networks fell from 0.118 in 2018 to 0.067 in 2025, and the shares of architecture, databases, distributed, cloud and operating systems also fell; quantum computing (0.0125 to 0.0196) and sustainable computing rose.",established,corpus_statistic,,,"results/bibliometrics/cagr_by_category.csv columns share_2018, share_2025",0.9,machine-verified,Relative decline predates November 2022.7F-06,"Bai-Perron (BIC) break dates for the systems categories are scattered between 2022-01 and 2025-04; cloud computing is the only category with a selected break at 2022-11, and computer networks' single break (2022-02) precedes ChatGPT.",established,corpus_statistic,,,results/econometrics/breaks.csv column bp_breaks_bic,0.85,machine-verified,Break detection on monthly log-volume series (Part IV).8F-07,"Citing works of the systems categories send most of their resolved references to their own category; in citing years 2023-2026 the self-share is 84.9% for quantum computing, 72.4% for networks, 59.2% for IR, 57.7% for architecture, 40.6% for databases, 34.7% for accelerators, 33.1% for cloud, 29.8% for distributed, 24.4% for edge, 16.0% for operating systems and 11.3% for sustainable computing.",established,corpus_statistic,,,"results/graph/category_flows_yearly.csv aggregated by Agent F over citing_year>=2023 (citing_cat, cited_cat, n)",0.85,machine-verified,"Aggregation uses calendar citing years (2023+), not the 30 Nov 2022 boundary."9F-08,Self-citation shares of the systems categories moved by less than four percentage points between citing years 2018-2022 and 2023-2026; the composition of destinations is stable.,established,corpus_statistic,,,results/graph/category_flows_yearly.csv aggregated by Agent F (baseline vs post citing years),0.8,machine-verified,"Largest moves: operating systems 20.5% to 16.0%, quantum 88.3% to 84.9%."10F-09,"In citing years 2023-2026 the llm category enters the seven largest destinations of information-retrieval references (3.0%), which it did not in 2018-2022; operating systems' largest destination becomes computer architecture (18.8%) rather than itself; edge computing and sustainable computing cite computer networks more than their own category in both periods.",established,corpus_statistic,,,results/graph/category_flows_yearly.csv aggregated by Agent F,0.8,machine-verified,Descriptive; no causal reading.11F-10,"The landmark candidate lists for the two areas contain 152 (data_distributed) and 125 (hardware_emerging) records, 25 per category after the floor rule; between 10 (distributed, operating systems) and 20 (accelerators, sustainable) per category are post-period; several records are evidently misclassified or are re-publications of older works.",established,metadata_only,,,"literature_synthesis/landmarks/data_distributed_landmarks.csv, hardware_emerging_landmarks.csv (counts by Agent F)",0.95,human-verified,Misclassified examples listed in review.md section 1.2.12F-11,"Because the landmark score for 2025-2026 is c12 relative to the year mean, several 2026 records with 9-30 citations in total are listed as landmarks; their status is provisional.",established,metadata_only,,,"literature_synthesis/landmarks/hardware_emerging_landmarks.csv (c36 column), literature_synthesis/select_landmarks.py",0.9,human-verified,"Affects accelerators, sustainable computing, networks, cloud."13F-12,"Within the post-period accelerators landmarks, at least ten of twenty records are memristive or chaotic neural-network circuit papers (Integration, Neural Networks, IEEE TCYB, Chaos Solitons & Fractals), a nonlinear-dynamics literature rather than AI hardware; the category's landmark list therefore overstates its coverage of accelerator research.",established,metadata_only,,,literature_synthesis/landmarks/hardware_emerging_landmarks.csv rows ai_accelerators 2025-2026,0.85,human-verified,Judged from titles and venues only.14F-13,"LLM serving systems treat KV-cache memory management as the central bottleneck; vLLM's PagedAttention reports 2-4x throughput at equal latency, while vAttention argues that non-contiguous KV layouts impose programming and performance overheads and proposes CUDA virtual memory instead.",contested,abstract,kwon2023vllm;prabhu2025vattention,,"literature_synthesis/landmarks/data_distributed_abstracts.jsonl (4387321091), hardware_emerging_abstracts.jsonl (4407217670)",0.8,human-verified,Competing designs evaluated by their own authors.15F-14,Phase splitting of LLM inference across machine pools (Splitwise) and activation-locality-based GPU-CPU hybrid inference (PowerInfer) are engineering demonstrations whose reported gains are specific to the hardware and models used.,established,abstract,patel2024splitwise;song2024powerinfer,,"literature_synthesis/landmarks/hardware_emerging_abstracts.jsonl (4401211704, 4404401018)",0.8,human-verified,Abstracts only; no independent replication reviewed.16F-15,"The 'memory wall' argument holds that peak server FLOPS scaled at 3.0x per two years against 1.6x for DRAM bandwidth and 1.4x for interconnect bandwidth over twenty years, making bandwidth the binding constraint for decoder inference; the figures are the authors' and the article is a perspective.",established,abstract,gholami2024memorywall,,literature_synthesis/landmarks/hardware_emerging_abstracts.jsonl (4393145114),0.7,human-verified,Widely cited; the trend rates are not re-derived here.17F-16,PyTorch 2's TorchDynamo/TorchInductor report geometric-mean speedups of 2.27x (inference) and 1.41x (training) over eager execution on the authors' model suite.,established,abstract,ansel2024pytorch2,,literature_synthesis/landmarks/hardware_emerging_abstracts.jsonl (4394998532),0.8,human-verified,Vendor-authored evaluation.18F-17,"Photonic accelerator papers of 2024-2026 report device-level efficiency figures (e.g., 160 TOPS/W for Taichi) and task demonstrations on small benchmarks; whether these translate into end-to-end system gains is not established.",speculative,abstract,xu2024taichi;ahmed2025photonic;fu2024onn;lam2026neuromorphicphotonic,,"literature_synthesis/landmarks/hardware_emerging_abstracts.jsonl (4394727812, 7128293306, 4402694963)",0.7,human-verified,ahmed2025photonic abstract unavailable (metadata only).19F-18,"Quantum error-correction milestones fall inside the window: surface-code scaling from distance 3 to 5 (2023), a 280-physical-qubit neutral-atom logical processor (2024) and below-threshold distance-7 memories with logical error suppression factor 2.14 (2024).",established,abstract,googlequantum2023suppressing;bluvstein2024logical;googlequantum2025belowthreshold,,"literature_synthesis/landmarks/hardware_emerging_abstracts.jsonl (4321610982, 4389373471, 4405183524)",0.85,human-verified,Peer-reviewed Nature articles; figures as stated in abstracts.20F-19,The claim of 'utility before fault tolerance' (127-qubit expectation values beyond brute-force classical simulation) is contested; the abstract restricts the claim to brute-force simulation and subsequent classical-simulation responses are not in the landmark set.,contested,abstract,kim2023utility,,literature_synthesis/landmarks/hardware_emerging_abstracts.jsonl (4380627387),0.7,human-verified,Rebuttals are not cited because they are not in the verified bibliography.21F-20,Barren plateaus are treated by the quantum-computing literature as a structural obstacle to variational algorithms; the 2025 review and the earlier NISQ/VQA reviews agree on this.,established,metadata_only,larocca2025barren;cerezo2021vqa;bharti2022nisq,,literature_synthesis/landmarks/hardware_emerging_landmarks.csv (4408847956),0.75,human-verified,larocca2025barren abstract unavailable.22F-21,"Modular quantum computing demonstrations of 2025 (two trapped-ion modules linked by an optical fibre; a 35-chip photonic scale model) are feasibility demonstrations, described by their authors as deterministic gate teleportation and as a sub-performant scale model respectively.",established,abstract,main2025distributedqc;aghaee2025aurora,,"literature_synthesis/landmarks/hardware_emerging_abstracts.jsonl (4407158326, 4406693713)",0.8,human-verified,23F-22,"Quantum computing has the highest post-period CAGR among the systems categories (0.287), the highest self-citation share, and a negative level change at 2022-12; its growth dynamics are internal to the field rather than linked to language models.",established,corpus_statistic,,,results/bibliometrics/cagr_by_category.csv row quantum_computing; results/econometrics/breaks.csv row cat_quantum_computing; results/graph/category_flows_yearly.csv,0.8,machine-verified,Interpretation ('internal') is inferential; statistics are descriptive.24F-23,"Among database landmarks, the only vector-search item is the Faiss library (GPU paper 2019; library paper 2025/26); the other post-period database landmarks are a data-centric AI survey and a data-quality study, not DBMS engine papers.",established,metadata_only,douze2026faiss;johnson2021faiss;zha2025datacentric;mohammed2025dataquality,,literature_synthesis/landmarks/data_distributed_landmarks.csv rows database_systems,0.85,human-verified,"Vector DBMS surveys (pan2024vectordb, han2023vectordb preprint, taipalus2024vectordb) are in the bibliography but not in the landmark set."25F-24,"The most cited post-2022 information-retrieval works in the corpus are RAG surveys, LLM-for-recommendation surveys and an embedding resource (C-Pack/BGE), whereas the most cited 2018-2022 works were GNN-based recommendation models and surveys.",established,metadata_only,fan2024ragsurvey;huang2026ragsurvey;zhao2026ragaigc;wu2024llmrec;zhao2024recsysllm;xiao2024cpack;he2020lightgcn;wu2023gnnrec;gao2023gnnrecsurvey,,literature_synthesis/landmarks/data_distributed_landmarks.csv rows information_retrieval,0.85,human-verified,"Shift in what is cited, observable from metadata."26F-25,"Dense retrieval and retrieval-augmented generation are methodologically continuous with 2020 work (DPR, RAG, ColBERT); the post-2022 landmark set contains surveys and resources rather than new retrieval architectures.",speculative,metadata_only,karpukhin2020dpr;lewis2020rag;khattab2020colbert;zhao2024denseretrieval,,literature_synthesis/landmarks/data_distributed_landmarks.csv rows information_retrieval,0.6,human-verified,Could reflect citation lag for 2024-2026 model papers.27F-26,"Information retrieval has the largest trend change at 2022-12 (0.023) and a negative level change (-0.094, p=0.019), with BIC breaks at 2022-07 and 2024-07.",established,corpus_statistic,,,results/econometrics/breaks.csv row cat_information_retrieval,0.9,machine-verified,28F-27,"The 2026 slice of cloud-computing landmarks consists almost entirely of LLM-inference scheduling papers at smaller IEEE conferences (token-level GPU pooling, serverless multi-tenant scheduling, carbon-aware prompt scheduling), each evaluated in simulation or on single testbeds.",established,abstract,liao2026caps;li2026deepservecb,,"literature_synthesis/landmarks/data_distributed_abstracts.jsonl (7160823269, 7161556515, 7160863705)",0.8,human-verified,Provisional landmarks (see F-11).29F-28,RCACopilot (EuroSys 2024) reports root-cause category accuracy up to 0.766 on a year of Microsoft incidents; the evaluation is on proprietary data.,established,abstract,chen2024rcacopilot,,literature_synthesis/landmarks/data_distributed_abstracts.jsonl (4394946189),0.8,human-verified,30F-29,"Cloud computing's ITS level change at 2022-12 is indistinguishable from zero (0.003, p=0.936) while its trend change is 0.019 (p<0.001, placebo p=0.231); its BIC breaks are 2022-11 and 2025-01.",established,corpus_statistic,,,results/econometrics/breaks.csv row cat_cloud_computing,0.9,machine-verified,31F-30,"Edge computing's baseline landmarks are blockchain-IoT and COVID-era IoT application papers; its post-period landmarks are surveys on LLMs at the mobile edge, agentic edge intelligence and edge-cloud collaboration; its trend change at 2022-12 is the smallest of the eleven (0.004) after a high baseline CAGR (0.133).",established,metadata_only,qu2025meillm;zhang2026edgegeneral;liu2026eccc;an2026aiflow;zhou2019edgeintelligence,,literature_synthesis/landmarks/data_distributed_landmarks.csv rows edge_computing; results/econometrics/breaks.csv row cat_edge_computing,0.85,human-verified,32F-31,"Computer networks' most cited post-2022 works are 6G vision and ISAC overviews and, in 2026, tutorials on large AI models and agentic AI for communications; the category's positive level change at 2022-12 has placebo p=0.538 and its single BIC break is 2022-02.",established,metadata_only,wang2023road6g;liu2022isac;jiang2026lamagentic;zhang2026edgegeneral,,literature_synthesis/landmarks/data_distributed_landmarks.csv rows computer_networks; results/econometrics/breaks.csv row cat_computer_networks,0.85,human-verified,33F-32,"Sustainable-computing landmarks of 2021-2026 form a measurement literature on AI's energy, carbon and water footprints (training carbon, inference carbon, water, grid demand, rebound effects, inference power benchmark); several foundational items are preprints.",established,abstract,patterson2021carbon;luccioni2023bloom;dodge2022carbonintensity;chien2023carbongenai;li2023thirsty;li2025thirsty;luccioni2025jevons;chen2026aielectricity;niu2026tokenpowerbench;devriesgao2026datacenters,,literature_synthesis/landmarks/hardware_emerging_abstracts.jsonl rows sustainable_computing,0.85,human-verified,"patterson2021carbon, luccioni2023bloom, li2023thirsty are preprints."34F-33,"Industry figures quoted in abstracts (e.g., inference accounting for more than 90% of LLM power) are not independently verifiable from the corpus.",speculative,abstract,niu2026tokenpowerbench,,literature_synthesis/landmarks/hardware_emerging_abstracts.jsonl (7138433923),0.6,human-verified,35F-34,"Sustainable computing's level change at 2022-12 is positive (+0.049, p=0.048) with placebo p=0.754, its post CAGR is 0.233 and it cites computer networks (14.2%) more than itself (11.3%).",established,corpus_statistic,,,results/econometrics/breaks.csv row cat_sustainable_computing; results/bibliometrics/cagr_by_category.csv; results/graph/category_flows_yearly.csv,0.9,machine-verified,36F-35,"Operating systems is the smallest systems category (1,363 primary works in 2025), declined at -0.087 per year in 2018-2022 and grew at 0.078 in 2023-2025; its post-period landmarks are GPU-sharing and LLM-serving systems papers.",established,metadata_only,strati2024orion;song2024powerinfer;prabhu2025vattention,,results/bibliometrics/cagr_by_category.csv row operating_systems; literature_synthesis/landmarks/hardware_emerging_landmarks.csv rows operating_systems,0.85,human-verified,List also contains misclassified health/education records.37F-36,"Record-quality anomalies affect the systems landmark lists: canonical distributed-systems papers (MapReduce, Lamport clocks, Byzantine generals) appear as 2018-2019 re-publications; an OpenMP taskwait paper carries c36=12,891; an eBPF preprint dated 2020 carries a 2026 OASIcs DOI; NumPy is classified as computer architecture.",established,metadata_only,dean2008mapreduce,,"literature_synthesis/landmarks/data_distributed_landmarks.csv (2122465391, 1973501242, 2120510885, 4381304672, 3036601975); hardware_emerging_landmarks.csv (3099878876)",0.9,human-verified,Feeds the data-quality discussion of Part III/XI.38F-37,"The most cited post-2022 computer-architecture works are ML-systems papers (PyTorch 2, Splitwise, AWQ, memory wall) and an accelerator survey; the baseline list mixes architecture with scientific software (NAMD, Quantum ESPRESSO).",established,metadata_only,ansel2024pytorch2;patel2024splitwise;lin2025awq;gholami2024memorywall;silvano2025accelerators,,literature_synthesis/landmarks/hardware_emerging_landmarks.csv rows computer_architecture,0.85,human-verified,39F-38,"Across the eleven categories, the most cited post-2022 works are predominantly surveys and tutorials (IEEE COMST, ACM CSUR, TKDE) and LLM-serving systems papers; new hardware or kernel artefacts are rare in the landmark lists.",established,metadata_only,,,"literature_synthesis/landmarks/data_distributed_landmarks.csv, hardware_emerging_landmarks.csv (type and venue columns)",0.75,human-verified,Short citation windows favour surveys.40F-39,"The post-2022 steepening of systems-category volume cannot be attributed to ChatGPT: placebo p-values are not small, break dates are scattered, and most classical categories were declining in 2018-2022 so that reversion is a competing explanation.",established,corpus_statistic,,,results/econometrics/breaks.csv; results/bibliometrics/cagr_by_category.csv,0.85,machine-verified,"Methodological conclusion, consistent with CLAUDE.md 6.5."41F-40,"Data-centric AI and data quality are the themes of the post-period database landmarks; related database-community positions on LLMs and data management (fernandez2023llmdata, stonebraker2024goesaround) are outside the landmark set.",speculative,metadata_only,zha2025datacentric;mohammed2025dataquality;fernandez2023llmdata;stonebraker2024goesaround;li2021ai4db,,literature_synthesis/landmarks/data_distributed_landmarks.csv rows database_systems,0.6,human-verified,42F-41,"Federated learning appears in the systems landmarks as surveys (green federated learning; an MDPI systematic review of 30 studies on FL for cloud/edge security) rather than as systems artefacts; the latter's quantitative claims (25% privacy-risk reduction, 40% better threat detection) are aggregate statements from a small review.",contested,abstract,thakur2025greenfl;mcmahan2017fl;kairouz2021fl,,literature_synthesis/landmarks/data_distributed_abstracts.jsonl (4408156900),0.6,human-verified,4408156900 is not cited in the manuscript.43F-42,"BPF standardisation (RFC 9669, 2024) and in-kernel aggregation (AggBox) indicate that kernel-extension technology is an infrastructure theme of the period; the evidence is one RFC and one abstract.",speculative,metadata_only,ietf2024rfc9669;vieira2020ebpf;hoiland2018xdp,,literature_synthesis/landmarks/hardware_emerging_landmarks.csv (4403931219); data_distributed_abstracts.jsonl (3036601975),0.5,human-verified,AggBox DOI could not be verified via Crossref; not cited.44F-43,"Neuromorphic hardware (Loihi 2018) and neuromorphic-computing perspectives (2022) are the baseline accelerators landmarks; the post period adds photonic processors rather than digital accelerators, which are absent from the landmark list despite TPU v4 and GPU papers in the bibliography.",established,metadata_only,davies2018loihi;schuman2022neuromorphic;jouppi2023tpuv4;dally2021gpu;reuther2022accelerators,,literature_synthesis/landmarks/hardware_emerging_landmarks.csv rows ai_accelerators,0.8,human-verified,Digital accelerator papers may be classified elsewhere (architecture).45