WP44: Part VII-b security synthesis (Agent H, 53 refs); landmark selection v2 (dedup/venue/abstract filters) after audit

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

Simon-Pierre Boucher committed 51 min ago (Nov 4, 2026) parent 9decbb3 Browse files

25 changed files +2,691 −438

added literature_synthesis/landmarks_v1/ai_ml_landmarks.csv +235 −0

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added literature_synthesis/landmarks_v1/computational_science_landmarks.csv +101 −0

++work_id,doi,arxiv_id,title,source_name,year,type,primary_category,n_categories,c12,c36,ncs36,cited_by_count,fwci,has_abstract,is_oa,period,score,set,pi_review
++3029645440,10.1017/9781108993456.011,,Numerical optimization,Cambridge University Press eBooks,2021,book-chapter,scientific_computing,1,9332.0,9351.0,3148.4582677769677,13724,453.7174,True,False,baseline,3148.4582677769677,landmark,True
++3003257820,10.1038/s41592-019-0686-2,1907.10121,SciPy 1.0: fundamental algorithms for scientific computing in Python,Nature Methods,2020,article,scientific_computing,4,5667.0,10506.0,1314.046621797279,37925,1523.8871,True,True,baseline,1314.046621797279,landmark,True
++3035965352,10.1038/s41586-020-2649-2,2006.10256,Array programming with NumPy,Nature,2020,article,scientific_computing,4,2086.0,5137.0,642.5145151506398,23207,829.0931,True,True,baseline,642.5145151506398,landmark,True
++3201073812,10.1016/j.cpc.2021.108171,,"LAMMPS - a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales",Computer Physics Communications,2021,article,scientific_computing,8,826.0,2697.0,568.1403850199874,12382,907.8113,True,True,baseline,568.1403850199874,landmark,True
++2990427812,10.21105/joss.01686,,Welcome to the Tidyverse,The Journal of Open Source Software,2019,article,scientific_computing,2,697.0,2788.0,423.93060156353516,21504,642.5886,True,True,baseline,423.93060156353516,landmark,True
++90568776,,,PETSc Users Manual,,2019,article,scientific_computing,5,1066.0,1207.0,340.27902486121167,1896,114.6007,True,False,baseline,340.27902486121167,landmark,True
++3099929043,,,Bayesian probabilistic numerical methods,Warwick Research Archive Portal (University of Warwick),2019,dissertation,scientific_computing,3,61.0,81.0,294.96774193548384,85,,True,False,baseline,294.96774193548384,landmark,True
++3133457239,10.34726/hss.2010.001,,Numerical methods for topography simulation,reposiTUm (TU Wien),2019,dissertation,scientific_computing,2,9.0,10.0,222.03389830508473,13,,True,True,baseline,222.03389830508473,landmark,True
++2888532099,10.3847/1538-3881/aabc4f,,The Astropy Project: Building an Open-science Project and Status of the v2.0 Core Package *,The Astronomical Journal,2018,article,scientific_computing,1,459.0,1025.0,193.46314218749401,7687,234.9501,True,True,baseline,193.46314218749401,landmark,True
++1494474111,10.1515/9783110666991-007,,7 Linear algebra applications,De Gruyter eBooks,2020,book-chapter,scientific_computing,2,184.0,190.0,163.70790263421142,482,30.6361,True,False,baseline,163.70790263421142,landmark,True
++7154488692,10.1016/j.engappai.2026.114843,,Lightweight Kolmogorov-Arnold Network with dual-objective optimization for axial capacity prediction of square coal gangue concrete-filled steel tube stub columns based on finite element simulation,Engineering Applications of Artificial Intelligence,2026,article,scientific_computing,1,45.0,45.0,33.579175704989154,45,110.7417,False,False,post,159.98492606958206,landmark,True
++2913606428,10.3758/s13428-018-01193-y,,PsychoPy2: Experiments in behavior made easy,Behavior Research Methods,2019,article,scientific_computing,1,351.0,816.0,124.07724923810785,5166,170.3746,True,True,baseline,124.07724923810785,landmark,True
++4408339746,10.1007/s44379-025-00015-1,2410.13228,From PINNs to PIKANs: recent advances in physics-informed machine learning,Machine learning for computational science and engineering,2025,article,scientific_computing,3,239.0,239.0,51.33385214007782,239,102.1338,False,True,post,120.99586631516542,landmark,True
++4417522354,10.1038/s43588-026-01009-6,,NEP89: universal neuroevolution potential for inorganic and organic materials across 89 elements,Nature Computational Science,2026,article,scientific_computing,1,34.0,34.0,26.21830985915493,34,44.9009,False,True,post,120.87749969701756,landmark,True
++3038025793,10.1016/j.cpc.2021.108033,,VASPKIT: A user-friendly interface facilitating high-throughput computing and analysis using VASP code,Computer Physics Communications,2021,article,scientific_computing,1,947.0,2087.0,120.59877093864604,7856,267.5871,False,True,baseline,120.59877093864604,landmark,True
++3143496104,10.1201/9781315107042-14,,Numerical Optimization,CRC Press eBooks,2018,book-chapter,scientific_computing,2,225.0,235.0,109.14754610815879,335,14.0895,True,False,baseline,109.14754610815879,landmark,True
++4407925765,10.1016/j.cossms.2025.101214,2503.09814,A practical guide to machine learning interatomic potentials – Status and future,Current Opinion in Solid State and Materials Science,2025,review,scientific_computing,1,214.0,214.0,3.7709251101321586,214,9.2721,False,True,post,108.3393949432862,landmark,True
++4383218913,10.1038/s41586-023-06185-3,,Accurate medium-range global weather forecasting with 3D neural networks,Nature,2023,article,scientific_computing,1,511.0,1104.0,106.38333772739257,1634,181.7259,True,True,post,106.38333772739257,landmark,True
++4386373863,10.7717/peerj-cs.1516,,"PyMC: a modern, and comprehensive probabilistic programming framework in Python",PeerJ Computer Science,2023,article,scientific_computing,4,390.0,626.0,100.85192909280501,870,132.4344,True,True,post,100.85192909280501,landmark,True
++4242546620,10.1007/978-1-4842-6876-6_1,,DPLYR,Apress eBooks,2021,book-chapter,scientific_computing,1,185.0,286.0,96.29548332629801,367,143.0004,True,False,baseline,96.29548332629801,landmark,True
++2962853966,10.1007/s10915-018-0757-z,1511.06324,Global Convergence of ADMM in Nonconvex Nonsmooth Optimization,Journal of Scientific Computing,2018,article,scientific_computing,2,246.0,390.0,92.76320659281009,1268,89.8329,False,True,baseline,92.76320659281009,landmark,True
++4211084734,10.1017/9781108594967.028,,Positive Definite Matrices,Cambridge University Press eBooks,2019,book-chapter,scientific_computing,1,102.0,110.0,89.28544845552662,166,22.481,True,False,baseline,89.28544845552662,landmark,True
++4245687930,10.1201/9781003090601-06,,Chemical Reaction Engineering,CRC Press eBooks,2020,book-chapter,scientific_computing,2,90.0,102.0,81.77618941255305,322,24.1009,True,False,baseline,81.77618941255305,landmark,True
++7128919969,10.1016/j.advengsoft.2026.104124,,SA-PINN: A self-attention enhanced physics-informed neural network for multiaxial fatigue life prediction with small samples,Advances in Engineering Software,2026,article,scientific_computing,2,23.0,23.0,25.467432950191572,23,46.775,False,False,post,81.77007332445305,landmark,True
++4415338867,10.1137/25m1738863,2503.00768,Nonlinear Model Reduction by Probabilistic Manifold Decomposition,SIAM Journal on Scientific Computing,2026,article,scientific_computing,2,22.0,22.0,22.669565217391305,22,75.5825,True,True,post,78.21485274512901,landmark,True
++2944434778,10.1016/j.jbi.2019.103208,,The REDCap consortium: Building an international community of software platform partners,Journal of Biomedical Informatics,2019,article,computational_biology,1,1231.0,3835.0,1565.383789896379,23807,2212.4824,False,True,baseline,1565.383789896379,landmark,True
++4403324307,10.1101/2024.10.10.615955,,Chai-1: Decoding the molecular interactions of life,bioRxiv (Cold Spring Harbor Laboratory),2024,preprint,computational_biology,2,211.0,430.0,694.8057553956835,430,,True,True,post,694.8057553956835,landmark,True
++4416043407,10.1038/s41586-026-10644-y,2502.18864,Accelerating scientific discovery with Co-Scientist,Nature,2026,article,computational_biology,7,110.0,110.0,310.59696003922534,110,359.9514,True,True,post,391.07426372564504,landmark,True
++3097145107,10.1093/nar/gkaa971,,PubChem in 2021: new data content and improved web interfaces,Nucleic Acids Research,2020,article,computational_biology,1,405.0,1318.0,383.79097718298823,3926,538.5909,True,True,baseline,383.79097718298823,landmark,True
++4411463597,10.1101/2025.06.14.659707,,Boltz-2: Towards Accurate and Efficient Binding Affinity Prediction,bioRxiv (Cold Spring Harbor Laboratory),2025,preprint,computational_biology,3,665.0,665.0,2415.2006880733943,665,,True,True,post,336.6621384919875,landmark,True
++3008142620,10.1016/j.apsb.2020.02.008,,Analysis of therapeutic targets for SARS-CoV-2 and discovery of potential drugs by computational methods,Acta Pharmaceutica Sinica B,2020,article,computational_biology,2,1435.0,1826.0,268.8646986800856,2330,247.2738,True,True,baseline,268.8646986800856,landmark,True
++4395010444,10.1093/nar/gkae303,,ProTox 3.0: a webserver for the prediction of toxicity of chemicals,Nucleic Acids Research,2024,article,computational_biology,2,785.0,1601.0,266.05146653514754,1601,313.6638,True,True,post,266.05146653514754,landmark,True
++2951939904,10.4230/lipics.wabi.2019.10,,Jointly Embedding Multiple Single-Cell Omics Measurements,Leibniz international proceedings in informatics,2019,conference-paper,computational_biology,2,718.0,965.0,260.53544494720967,1141,577.848,True,True,baseline,260.53544494720967,landmark,True
++4409229284,10.1093/bioinformatics/btaf147,,CoverM: read alignment statistics for metagenomics,Bioinformatics,2025,article,computational_biology,2,511.0,511.0,110.40243438569799,511,130.364,True,True,post,258.6982748412114,landmark,True
++4406633314,10.1038/s41591-024-03434-4,,Artificial intelligence in drug development,Nature Medicine,2025,article,computational_biology,1,498.0,498.0,209.201336966164,498,241.4884,False,False,post,252.1169097278342,landmark,True
++4323304388,10.26434/chemrxiv-2022-jjm0j-v4,,Uni-Mol: A Universal 3D Molecular Representation Learning Framework,ChemRxiv,2023,preprint,computational_biology,2,71.0,196.0,249.05517241379312,324,,True,True,post,249.05517241379312,landmark,True
++3156458251,10.1093/nar/gkab301,,Interactive Tree Of Life (iTOL) v5: an online tool for phylogenetic tree display and annotation,Nucleic Acids Research,2021,article,computational_biology,2,1857.0,4291.0,249.05241916110123,11124,520.9954,True,True,baseline,249.05241916110123,landmark,True
++4307468223,10.1093/nar/gkac956,,PubChem 2023 update,Nucleic Acids Research,2022,article,computational_biology,1,388.0,1481.0,240.13678570600197,3385,333.84,True,True,baseline,240.13678570600197,landmark,True
++7119529911,10.1126/science.ads9530,,Deep contrastive learning enables genome-wide virtual screening,Science,2026,article,computational_biology,3,62.0,62.0,175.0637411130179,62,220.9602,True,True,post,220.42367591809082,landmark,True
++4393936599,10.1093/nar/gkae236,,"ADMETlab 3.0: an updated comprehensive online ADMET prediction platform enhanced with broader coverage, improved performance, API functionality and decision support",Nucleic Acids Research,2024,article,computational_biology,1,627.0,1260.0,209.38466448112797,1260,247.0103,True,True,post,209.38466448112797,landmark,True
++3168436232,10.1021/acs.jcim.1c00203,,"AutoDock Vina 1.2.0: New Docking Methods, Expanded Force Field, and Python Bindings",Journal of Chemical Information and Modeling,2021,article,computational_biology,2,340.0,1300.0,209.2664690527806,7546,373.4067,True,True,baseline,209.2664690527806,landmark,True
++2899070097,10.1093/nar/gky1033,,PubChem 2019 update: improved access to chemical data,Nucleic Acids Research,2018,article,computational_biology,1,200.0,819.0,194.80273384490118,3238,270.9191,True,True,baseline,194.80273384490118,landmark,True
++3011072970,10.1038/s41421-020-0153-3,,Network-based drug repurposing for novel coronavirus 2019-nCoV/SARS-CoV-2,Cell Discovery,2020,article,computational_biology,1,1057.0,1266.0,186.408931286412,1597,164.9683,True,True,baseline,186.408931286412,landmark,True
++3211795435,10.1093/nar/gkab1061,,AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models,Nucleic Acids Research,2021,article,computational_biology,3,944.0,3084.0,178.99735742084275,7770,355.1571,True,True,baseline,178.99735742084275,landmark,True
++3098269892,10.17863/cam.34134,,Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules.,Cambridge University Engineering Department Publications Database,2018,article,computational_biology,3,417.0,752.0,178.86649066100819,1703,200.0824,True,True,baseline,178.86649066100819,landmark,True
++4307425304,10.1093/nar/gkac963,,KEGG for taxonomy-based analysis of pathways and genomes,Nucleic Acids Research,2022,article,computational_biology,1,1600.0,3498.0,177.7903434574516,5752,391.9436,True,True,baseline,177.7903434574516,landmark,True
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++4308834893,10.1093/nar/gkac1000,,The STRING database in 2023: protein–protein association networks and functional enrichment analyses for any sequenced genome of interest,Nucleic Acids Research,2022,article,computational_biology,3,535.0,2781.0,141.34789741428614,9363,448.2907,True,True,baseline,141.34789741428614,landmark,True
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++3112376646,10.1093/nar/gkaa1100,,UniProt: the universal protein knowledgebase in 2021,Nucleic Acids Research,2020,article,computational_biology,1,679.0,2641.0,150.5152761457109,6342,306.9602,True,True,baseline,150.5152761457109,landmark,True
++2900569176,10.1093/nar/gky1131,,"STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets",Nucleic Acids Research,2018,article,computational_biology,3,421.0,2624.0,131.1704666044956,17675,509.3871,True,True,baseline,131.1704666044956,landmark,True
++2804822363,10.1093/nar/gky427,,SWISS-MODEL: homology modelling of protein structures and complexes,Nucleic Acids Research,2018,article,computational_biology,3,904.0,2454.0,122.67237997234459,13067,345.6079,True,True,baseline,122.67237997234459,landmark,True
++3208691473,10.1093/nar/gkab1038,,The PRIDE database resources in 2022: a hub for mass spectrometry-based proteomics evidences,Nucleic Acids Research,2021,article,computational_biology,2,743.0,2312.0,100.20101036958255,6103,381.4154,True,True,baseline,100.20101036958255,landmark,True
++2938574745,10.1093/nar/gkz268,,The EMBL-EBI search and sequence analysis tools APIs in 2019,Nucleic Acids Research,2019,article,computational_biology,2,1094.0,2262.0,149.0781904899552,4162,956.3441,True,True,baseline,149.0781904899552,landmark,True
++3136918052,10.1093/nar/gky1049,,UniProt: a worldwide hub of protein knowledge,Nucleic Acids Research,2018,article,computational_biology,1,457.0,2078.0,103.87661189182235,7679,309.1951,True,True,baseline,103.87661189182235,landmark,True
++3107527779,10.1093/nar/gkaa1074,,"The STRING database in 2021: customizable protein–protein networks, and functional characterization of user-uploaded gene/measurement sets",Nucleic Acids Research,2020,article,computational_biology,4,246.0,2031.0,115.75029377203289,8015,303.9867,True,True,baseline,115.75029377203289,landmark,True
++3159995035,10.1093/nar/gkab382,,MetaboAnalyst 5.0: narrowing the gap between raw spectra and functional insights,Nucleic Acids Research,2021,article,computational_biology,3,1102.0,2027.0,117.64839283140344,3958,207.4321,True,True,baseline,117.64839283140344,landmark,True
++3095583226,10.1093/nar/gkaa913,,Pfam: The protein families database in 2021,Nucleic Acids Research,2020,article,computational_biology,1,792.0,1991.0,113.47062279670975,7261,214.3673,True,True,baseline,113.47062279670975,landmark,True
++3096828292,10.1093/nar/gkaa970,,KEGG: integrating viruses and cellular organisms,Nucleic Acids Research,2020,article,computational_biology,1,541.0,1955.0,111.4189189189189,4022,222.942,True,True,baseline,111.4189189189189,landmark,True
++2899760200,10.1093/nar/gky1106,,The PRIDE database and related tools and resources in 2019: improving support for quantification data,Nucleic Acids Research,2018,article,computational_biology,3,507.0,1805.0,74.32352941176471,6693,384.4369,True,True,baseline,74.32352941176471,landmark,True
++4223552703,10.1093/nar/gkac240,,Search and sequence analysis tools services from EMBL-EBI in 2022,Nucleic Acids Research,2022,article,computational_biology,2,862.0,1760.0,89.45426085909514,2267,150.397,True,True,baseline,89.45426085909514,landmark,True
++4368358469,10.1093/nar/gkad344,,"antiSMASH 7.0: new and improved predictions for detection, regulation, chemical structures and visualisation",Nucleic Acids Research,2023,article,computational_biology,1,830.0,1741.0,32.99382988100485,2290,294.6456,True,True,post,32.99382988100485,landmark,True
++3027286012,10.1093/nar/gkaa407,,TIMER2.0 for analysis of tumor-infiltrating immune cells,Nucleic Acids Research,2020,article,computational_biology,2,587.0,1720.0,57.035267668961644,5209,230.0925,True,True,baseline,57.035267668961644,landmark,True
++2999044305,10.1038/s41586-019-1923-7,,Improved protein structure prediction using potentials from deep learning,Nature,2020,article,computational_biology,1,1072.0,1692.0,96.4300822561692,3363,151.7168,False,True,baseline,96.4300822561692,landmark,True
++2803664483,10.1093/nar/gky310,,MetaboAnalyst 4.0: towards more transparent and integrative metabolomics analysis,Nucleic Acids Research,2018,article,computational_biology,4,660.0,1555.0,77.73249831173425,3629,185.9094,True,True,baseline,77.73249831173425,landmark,True
++2942829220,10.1093/nar/gkz310,,antiSMASH 5.0: updates to the secondary metabolite genome mining pipeline,Nucleic Acids Research,2019,article,computational_biology,2,661.0,1520.0,18.891765924391507,2907,264.3601,True,True,baseline,18.891765924391507,landmark,True
++4384652177,10.1186/s12859-023-05385-y,,MitoHiFi: a python pipeline for mitochondrial genome assembly from PacBio high fidelity reads,BMC Bioinformatics,2023,article,computational_biology,5,839.0,1444.0,100.66579880594335,1858,138.9013,True,True,post,100.66579880594335,landmark,True
++2898402099,10.1093/nar/gky995,,The Pfam protein families database in 2019,Nucleic Acids Research,2018,article,computational_biology,2,366.0,1396.0,69.7842878734283,4595,208.2751,True,True,baseline,69.7842878734283,landmark,True
++3161596995,10.1093/nar/gkab335,,antiSMASH 6.0: improving cluster detection and comparison capabilities,Nucleic Acids Research,2021,article,computational_biology,1,649.0,1396.0,21.18855808523059,2562,230.4087,True,True,baseline,21.18855808523059,landmark,True
++4308616064,10.1093/nar/gkac993,,InterPro in 2022,Nucleic Acids Research,2022,article,computational_biology,2,418.0,1388.0,70.5468829956955,2399,150.466,True,True,baseline,70.5468829956955,landmark,True

added literature_synthesis/landmarks_v1/data_distributed_landmarks.csv +153 −0

++work_id,doi,arxiv_id,title,source_name,year,type,primary_category,n_categories,c12,c36,ncs36,cited_by_count,fwci,has_abstract,is_oa,period,score,set,pi_review
++2793823461,,,Endurable Transient Inconsistency in Byte-Addressable Persistent B+-Tree,Scholarworks@UNIST (Ulsan National Institute of Science and Technology),2018,dissertation,database_systems,3,17.0,42.0,377.7581573896353,89,,False,False,baseline,377.7581573896353,landmark,True
++3080729669,,,Pengembangan Sistem Pemetaan Otomatis EntityRelationship Diagram Ke Dalam Database,,2019,dissertation,database_systems,2,2.0,15.0,320.60747663551405,62,,True,False,baseline,320.60747663551405,landmark,True
++3204616723,,,Data Mining Concepts and Techniques Third Edition,Thammasat University Digital Collections,2021,article,database_systems,1,886.0,928.0,245.73952451819886,1176,66.8714,False,False,baseline,245.73952451819886,landmark,True
++4415124086,10.1109/tbdata.2025.3618474,,The Faiss Library,IEEE Transactions on Big Data,2025,article,database_systems,4,454.0,454.0,148.4520005233547,454,166.1231,True,True,post,229.8415201133268,landmark,True
++3014294420,10.1017/9781108635592.003,,Introduction to Data Mining,Cambridge University Press eBooks,2019,book-chapter,database_systems,2,163.0,235.0,228.82157331393535,439,233.5555,True,False,baseline,228.82157331393535,landmark,True
++4324129606,10.1371/journal.pone.0279720,,"Data quality in online human-subjects research: Comparisons between MTurk, Prolific, CloudResearch, Qualtrics, and SONA",PLoS ONE,2023,article,database_systems,1,403.0,973.0,228.63551401869157,1500,345.1684,True,True,post,228.63551401869157,landmark,True
++7161586974,10.5070/p9.62178,,Paleobiology Database User Guide Version 2.0,PaleoBios,2026,article,database_systems,1,61.0,61.0,433.3770927968851,61,109.834,True,True,post,216.86845533876678,landmark,True
++2963507231,10.1109/tkde.2017.2781227,,Untangling Blockchain: A Data Processing View of Blockchain Systems,IEEE Transactions on Knowledge and Data Engineering,2018,article,database_systems,5,201.0,405.0,214.72982183235243,1186,244.2498,True,True,baseline,214.72982183235243,landmark,True
++2795038878,10.1145/3458723,1803.09010,Datasheets for datasets,Communications of the ACM,2021,article,database_systems,1,481.0,794.0,210.25558455544171,2104,205.3426,True,True,baseline,210.25558455544171,landmark,True
++3032576892,,,Comparative study on Python web frameworks: Flask and Django,Theseus (Ammattikorkeakoulujen),2020,dissertation,database_systems,4,7.0,22.0,206.90719257540604,43,,True,True,baseline,206.90719257540604,landmark,True
++3148181069,10.38094/jastt20165,,Classification Based on Decision Tree Algorithm for Machine Learning,Journal of Applied Science and Technology Trends,2021,article,database_systems,4,257.0,704.0,186.42308756553018,1871,279.1938,True,True,baseline,186.42308756553018,landmark,True
++3037795158,10.1016/s2589-7500(20)30142-4,,Applications of digital technology in COVID-19 pandemic planning and response,The Lancet Digital Health,2020,article,database_systems,1,350.0,591.0,172.09443665792568,987,179.9177,True,True,baseline,172.09443665792568,landmark,True
++4406085118,10.1145/3711118,2303.10158,Data-centric Artificial Intelligence: A Survey,ACM Computing Surveys,2025,article,database_systems,3,334.0,334.0,113.77687020232747,334,110.6889,True,True,post,169.09045752830647,landmark,True
++4386450904,10.1145/3591366.3591378,,Linked Data - The Story So Far,ACM eBooks,2023,book-chapter,database_systems,2,285.0,301.0,166.41438539989264,319,42.7861,False,False,post,166.41438539989264,landmark,True
++4408444355,10.1016/j.is.2025.102549,2207.14529,The effects of data quality on machine learning performance on tabular data,Information Systems,2025,article,database_systems,3,322.0,322.0,80.50468595378078,322,192.6654,True,True,post,163.01535126980446,landmark,True
++4361200200,10.1201/9780429459016,,Spatial Data Science,Chapman & Hall/CRC eBooks,2023,book,database_systems,2,278.0,753.0,129.30007763975155,1250,222.9593,True,False,post,129.30007763975155,landmark,True
++3202749896,10.3758/s13428-021-01694-3,,Data quality of platforms and panels for online behavioral research,Behavior Research Methods,2021,article,database_systems,1,164.0,469.0,121.87616674723884,1687,188.3384,False,True,baseline,121.87616674723884,landmark,True
++4400494356,10.11606/t.82.2024.tde-05072024-092611,,Digital health research governance: from FAIR to RE-AIM,,2024,dissertation,database_systems,1,1.0,3.0,116.82222222222222,3,,True,True,post,116.82222222222222,landmark,True
++2593864460,10.1109/tbdata.2019.2921572,1702.08734,Billion-Scale Similarity Search with GPUs,IEEE Transactions on Big Data,2019,article,database_systems,6,384.0,765.0,115.95149891238218,3832,118.3163,True,True,pre,115.95149891238218,landmark,True
++4362632858,10.21941/kcss/2023/2,,Scalability Benchmarking of Cloud-Native Applications Applied to Event-Driven Microservices,,2023,dissertation,database_systems,4,2.0,4.0,115.45945945945945,4,,True,True,post,115.45945945945945,landmark,True
++3005599391,,,"Entangled Devices : An ethnographic study of students, mobile phones and capitalism",Työväentutkimus Vuosikirja,2020,dissertation,database_systems,1,11.0,12.0,112.8584686774942,15,,True,True,baseline,112.8584686774942,landmark,True
++4396219753,10.4018/979-8-3693-2909-2.ch002,,Machine Learning Mastery,"Advances in systems analysis, software engineering, and high performance computing book series",2024,book-chapter,database_systems,2,195.0,195.0,108.23837846298326,195,27.8969,True,False,post,108.23837846298326,landmark,True
++4408925021,10.3390/higheredu4020017,,The Impact of Artificial Intelligence on Personalized Learning in Higher Education: A Systematic Review,Trends in Higher Education,2025,review,database_systems,2,189.0,189.0,29.153309284818068,189,31.7339,True,True,post,95.68292357140696,landmark,True
++4402762189,10.3390/electronics13183762,,Leveraging AI in E-Learning: Personalized Learning and Adaptive Assessment through Cognitive Neuropsychology—A Systematic Analysis,Electronics,2024,article,database_systems,2,199.0,317.0,95.44078925847174,317,121.3104,True,True,post,95.44078925847174,landmark,True
++4380791906,10.31979/etd.qrrp-3equ,,NoSQL Databases in Kubernetes,,2023,dissertation,database_systems,4,3.0,3.0,86.5945945945946,3,,True,True,post,86.5945945945946,landmark,True
++7118085507,10.1007/s41019-025-00335-5,2402.19473,Retrieval-Augmented Generation for AI-Generated Content: A Survey,Data Science and Engineering,2026,article,information_retrieval,7,208.0,208.0,231.4627186325106,208,390.1628,True,True,post,739.4858804994016,landmark,True
++2274895364,10.1145/3591366.3591376,,The Semantic Web: A New Form of Web Content that is Meaningful to Computers will Unleash a Revolution of New Possibilities,ACM eBooks,2023,book-chapter,information_retrieval,2,578.0,606.0,335.0402576489533,1324,58.9755,False,False,post,335.0402576489533,landmark,True
++4394947112,10.1145/3805774,2404.10981,A Survey on Retrieval-Augmented Text Generation for Large Language Models,ACM Computing Surveys,2026,article,information_retrieval,5,70.0,70.0,77.8961072320949,70,184.2436,True,True,post,248.8654405526832,landmark,True
++3045200674,10.1145/3397271.3401063,,LightGCN,International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR),2020,conference-paper,information_retrieval,2,321.0,803.0,226.7421667675404,4430,311.5512,True,False,baseline,226.7421667675404,landmark,True
++4401857375,10.1145/3637528.3671470,2405.06211,A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models,ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD),2024,conference-paper,information_retrieval,8,401.0,795.0,220.97973411345592,795,241.8493,True,True,post,220.97973411345592,landmark,True
++2967467,10.26190/unsworks/23395,,Social networks and collaborative filtering for large-scale requirements elicitation,"UNSWorks (University of New South Wales, Sydney, Australia)",2022,dissertation,information_retrieval,2,22.0,23.0,199.5062656641604,34,,True,True,baseline,199.5062656641604,landmark,True
++3097300053,10.1145/3535101,2011.02260,Graph Neural Networks in Recommender Systems: A Survey,ACM Computing Surveys,2022,article,information_retrieval,4,336.0,711.0,174.743146538718,1326,268.2685,True,True,baseline,174.743146538718,landmark,True
++4288083766,10.1145/3285029,1707.07435,Deep Learning Based Recommender System,ACM Computing Surveys,2019,article,information_retrieval,3,224.0,396.0,161.64067295931318,1511,176.8236,True,False,baseline,161.64067295931318,landmark,True
++4395483946,10.1186/s41239-024-00453-6,,Generative AI and the future of higher education: a threat to academic integrity or reformation? Evidence from multicultural perspectives,International Journal of Educational Technology in Higher Education,2024,article,information_retrieval,2,263.0,529.0,159.26869879410583,529,202.4391,True,True,post,159.26869879410583,landmark,True
++4394867512,10.57784/1992/7835,,Privacy-enabled scalable recommender systems,HAL (Le Centre pour la Communication Scientifique Directe),2024,dissertation,information_retrieval,3,4.0,4.0,155.76296296296297,4,,True,True,post,155.76296296296297,landmark,True
++2408538552,10.1609/aaai.v26i1.8100,,Fused Matrix Factorization with Geographical and Social Influence in Location-Based Social Networks,Proceedings of the AAAI Conference on Artificial Intelligence,2021,conference-paper,information_retrieval,1,519.0,541.0,151.80700662176693,572,22.962,True,True,baseline,151.80700662176693,landmark,True
++4401834466,10.1007/s11280-024-01291-2,2305.19860,A survey on large language models for recommendation,World Wide Web,2024,article,information_retrieval,3,272.0,487.0,146.62354690497077,487,184.0703,False,True,post,146.62354690497077,landmark,True
++4315977496,10.1145/3568022,2109.12843,"A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions",ACM Transactions on Recommender Systems,2023,article,information_retrieval,3,326.0,612.0,143.4379748166452,756,176.9462,True,True,baseline,143.4379748166452,landmark,True
++4394994587,10.1109/tkde.2024.3392335,2307.02046,Recommender Systems in the Era of Large Language Models (LLMs),IEEE Transactions on Knowledge and Data Engineering,2024,article,information_retrieval,5,277.0,469.0,141.20419609534147,469,176.4167,True,True,post,141.20419609534147,landmark,True
++4400531953,10.1145/3626772.3657878,2309.07597,C-Pack: Packed Resources For General Chinese Embeddings,International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR),2024,conference-paper,information_retrieval,6,256.0,485.0,134.81153590569323,485,146.3264,True,True,post,134.81153590569323,landmark,True
++2945827670,10.1145/3331184.3331267,1905.08108,Neural Graph Collaborative Filtering,International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR),2019,conference-paper,information_retrieval,3,149.0,465.0,133.92825599565188,3237,200.9814,True,True,baseline,133.92825599565188,landmark,True
++2945623882,10.1145/3292500.3330989,1905.07854,KGAT,ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD),2019,conference-paper,information_retrieval,5,157.0,465.0,133.92825599565188,2147,160.7851,True,True,baseline,133.92825599565188,landmark,True
++4387316367,10.1134/s1995080223060471,,Ontological Approach to a Knowledge Graph Construction in a Semantic Library,Lobachevskii Journal of Mathematics,2023,dissertation,information_retrieval,4,10.0,16.0,133.0924024640657,18,,True,False,post,133.0924024640657,landmark,True
++7118165812,10.1007/s10115-025-02639-4,,Leveraging hierarchy-aware diffusion model and knowledge-enhanced contrastive learning for recommendation,Knowledge and Information Systems,2026,article,information_retrieval,2,36.0,36.0,255.7635301752109,36,329.5019,False,False,post,127.98794085566564,landmark,True
++2914721378,10.1145/3308558.3313488,1902.07243,Graph Neural Networks for Social Recommendation,,2019,conference-paper,information_retrieval,2,141.0,441.0,127.01582987974727,1984,153.1188,True,True,baseline,127.01582987974727,landmark,True
++2963707260,10.1109/tkde.2018.2833443,,Heterogeneous Information Network Embedding for Recommendation,IEEE Transactions on Knowledge and Data Engineering,2018,article,information_retrieval,5,72.0,234.0,124.06611928091473,1204,188.0081,True,True,baseline,124.06611928091473,landmark,True
++3094605801,10.1145/3404835.3462862,2010.10783,Self-supervised Graph Learning for Recommendation,International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR),2021,conference-paper,information_retrieval,3,141.0,434.0,121.78233063557643,1488,179.1038,True,True,baseline,121.78233063557643,landmark,True
++172918197,10.1007/978-3-030-63007-2,,Computational Collective Intelligence,Lecture notes in computer science,2020,conference-paper,information_retrieval,1,425.0,427.0,120.57148843056008,592,3.7704,False,False,baseline,120.57148843056008,landmark,True
++3092103025,10.1145/3564284,2010.03240,Bias and Debias in Recommender System: A Survey and Future Directions,ACM Transactions on Information Systems,2022,article,information_retrieval,3,255.0,475.0,116.74120197734325,733,146.66,True,True,baseline,116.74120197734325,landmark,True
++4323539384,10.5463/thesis.16,,Scientific dataset recommendation with semantic techniques,,2023,dissertation,information_retrieval,3,4.0,4.0,115.45945945945945,4,,True,True,post,115.45945945945945,landmark,True
++4381304672,10.1007/978-3-031-40744-4_5,,Suspending OpenMP Tasks on Asynchronous Events: Extending the Taskwait Construct,Lecture notes in computer science,2023,conference-paper,distributed_computing,3,12262.0,12891.0,2048.4305614736636,12935,942.1133,False,True,post,2048.4305614736636,landmark,True
++2122465391,10.21276/ijre.2018.5.5.4,,MapReduce: Simplified Data Processing on Large Cluster,INTERNATIONAL JOURNAL OF RESEARCH AND ENGINEERING,2018,article,distributed_computing,4,2098.0,2186.0,1159.010840803759,2948,215.3255,True,False,baseline,1159.010840803759,landmark,True
++4255535209,10.1504/ijwgs.2018.10016848,,Blockchain challenges and opportunities: a survey,International Journal of Web and Grid Services,2018,article,distributed_computing,4,357.0,804.0,426.27846111904034,1582,497.7393,True,False,baseline,426.27846111904034,landmark,True
++1973501242,10.1145/3335772.3335934,,"Time, clocks, and the ordering of events in a distributed system",ACM eBooks,2019,book-chapter,distributed_computing,2,386.0,424.0,331.9512682244857,1979,73.0762,True,False,baseline,331.9512682244857,landmark,True
++2897450989,10.1504/ijwgs.2018.095647,,Blockchain challenges and opportunities: a survey,International Journal of Web and Grid Services,2018,article,distributed_computing,4,251.0,625.0,331.37318184005005,3328,432.6596,True,False,baseline,331.37318184005005,landmark,True
++2120510885,10.1145/3335772.3335936,,The Byzantine generals problem,ACM eBooks,2019,book-chapter,distributed_computing,3,230.0,335.0,262.2728180547234,1033,180.7332,True,False,baseline,262.2728180547234,landmark,True
++2902511784,10.5555/3291656.3291738,,Dynamic data race detection for OpenMP programs,Rice Research Repository (Rice University),2018,dissertation,distributed_computing,5,1.0,12.0,219.90243902439025,20,,True,False,baseline,219.90243902439025,landmark,True
++4300092207,10.4230/oasics.nines.2026.5,,OrbitalBrain: A Distributed Framework for Training ML Models in Space,DROPS (Schloss Dagstuhl – Leibniz Center for Informatics),2026,article,distributed_computing,4,54.0,54.0,70.47771428571428,54,0.0,True,True,post,191.98191128349848,landmark,True
++3182706339,10.7717/peerj-cs.623,,"The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation",PeerJ Computer Science,2021,article,distributed_computing,1,409.0,1160.0,189.8515007898894,5310,437.5495,True,True,baseline,189.8515007898894,landmark,True
++4387321091,10.1145/3600006.3613165,2309.06180,Efficient Memory Management for Large Language Model Serving with PagedAttention,Symposium on Operating Systems Principles,2023,conference-paper,distributed_computing,9,173.0,787.0,181.49662514489972,1716,324.3817,True,True,post,181.49662514489972,landmark,True
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++2962883549,10.1109/comst.2019.2904897,,Deep Learning in Mobile and Wireless Networking: A Survey,IEEE Communications Surveys & Tutorials,2019,article,computer_networks,2,473.0,812.0,145.07207700570802,1768,138.2463,True,False,baseline,145.07207700570802,landmark,True
++4395064458,10.1017/9781009489843,,Wireless Communications,Cambridge University Press eBooks,2024,book,computer_networks,1,347.0,458.0,144.12803234501348,541,214.916,True,False,post,144.12803234501348,landmark,True
++153405511,10.57709/1059389,,"An Analysis of the MOS under Conditions of Delay, Jitter and Packet Loss and an Analysis of the Impact of Introducing Piggybacking and Reed Solomon FEC for VOIP",Digital Archive @ GSU,2022,dissertation,computer_networks,4,19.0,19.0,143.52450980392155,19,,True,True,baseline,143.52450980392155,landmark,True
++4416378800,10.1109/tcomm.2026.3658289,,Pinching-Antenna Systems (PASS): A Tutorial,IEEE Transactions on Communications,2026,article,computer_networks,2,40.0,40.0,52.205714285714286,40,111.2683,True,True,post,142.20882317296181,landmark,True
++4206510620,10.1007/978-3-030-87059-1_4,,The Role of Machine Learning Techniques in Internet of Things-Based Cloud Applications,Internet of things,2022,book-chapter,computer_networks,3,98.0,176.0,136.74967485796427,223,144.059,False,False,baseline,136.74967485796427,landmark,True
++4322576964,10.1109/comst.2023.3249835,,"On the Road to 6G: Visions, Requirements, Key Technologies, and Testbeds",IEEE Communications Surveys & Tutorials,2023,article,computer_networks,1,832.0,1763.0,133.06276599375022,2336,171.2254,True,True,post,133.06276599375022,landmark,True
++7123349468,10.1109/jiot.2026.3652582,,SMNet: A Novel Compositional Generalization Model for Industrial Robot Multijoint Fault Diagnosis,IEEE Internet of Things Journal,2026,article,computer_networks,4,37.0,37.0,40.9855601659751,37,100.0756,True,False,post,131.54316143498968,landmark,True
++7125196921,10.1109/jiot.2026.3655796,2505.03036,"Energy, Scalability, Data, and Security in Massive IoT: Current Landscape and Future Directions",IEEE Internet of Things Journal,2026,article,computer_networks,8,35.0,35.0,122.87423247935634,35,146.6674,True,True,post,124.4327202763416,landmark,True
++3104550706,10.1184/r1/6468842.v1,,Gossip Algorithms for Distributed Signal Processing,,2018,article,computer_networks,1,553.0,607.0,121.54403377438375,692,30.8092,True,True,baseline,121.54403377438375,landmark,True
++7125115033,10.1109/comst.2026.3655674,,Integrated Sensing and Communications Over the Years: An Evolution Perspective,IEEE Communications Surveys & Tutorials,2026,article,computer_networks,1,34.0,34.0,44.374857142857145,32,85.591,False,True,post,120.87749969701756,landmark,True
++1710433637,10.17487/rfc9260,,Stream Control Transmission Protocol,,2022,report,computer_networks,1,645.0,659.0,119.70282413350449,1922,,True,False,baseline,119.70282413350449,landmark,True
++4406207083,10.1109/comst.2025.3527641,,Mobile Edge Intelligence for Large Language Models: A Contemporary Survey,IEEE Communications Surveys & Tutorials,2025,article,computer_networks,6,236.0,236.0,89.76743566823257,235,102.2674,True,True,post,119.47708975053992,landmark,True
++3194620979,10.1109/jsac.2022.3156632,,Integrated Sensing and Communications: Toward Dual-Functional Wireless Networks for 6G and Beyond,IEEE Journal on Selected Areas in Communications,2022,article,computer_networks,2,639.0,1558.0,118.9064113669858,3786,312.6996,True,True,baseline,118.9064113669858,landmark,True
++3129486236,10.1109/tcomm.2021.3051897,2007.02759,Intelligent Reflecting Surface-Aided Wireless Communications: A Tutorial,IEEE Transactions on Communications,2021,article,computer_networks,2,774.0,1396.0,108.19285671504721,3002,145.9637,True,True,baseline,108.19285671504721,landmark,True

added literature_synthesis/landmarks_v1/hardware_emerging_landmarks.csv +126 −0

++work_id,doi,arxiv_id,title,source_name,year,type,primary_category,n_categories,c12,c36,ncs36,cited_by_count,fwci,has_abstract,is_oa,period,score,set,pi_review
++2187800381,10.48456/tr-579,,Practical lock-freedom,OpenGrey (Institut de l'Information Scientifique et Technique),2021,dissertation,computer_architecture,5,281.0,286.0,994.2153284671533,458,,True,True,baseline,994.2153284671533,landmark,True
++3099878876,,,Array programming with NumPy,RePEc: Research Papers in Economics,2020,article,computer_architecture,4,2343.0,5512.0,689.4179496808111,17096,889.1394,True,True,baseline,689.4179496808111,landmark,True
++4287393709,10.4230/lipics.ecrts.2026.17,,Uncertainty-Aware Resource Allocation for Multi-Path Programs with In-Kernel Predictions,DROPS (Schloss Dagstuhl – Leibniz Center for Informatics),2026,article,computer_architecture,3,71.0,71.0,330.9365269461078,71,0.0,True,True,post,252.42066113200724,landmark,True
++3046751266,10.1063/5.0014475,,Scalable molecular dynamics on CPU and GPU architectures with NAMD,The Journal of Chemical Physics,2020,article,computer_architecture,7,356.0,869.0,214.60681995758455,3144,304.3728,True,True,baseline,214.60681995758455,landmark,True
++4394998532,10.1145/3620665.3640366,,PyTorch 2: Faster Machine Learning Through Dynamic Python Bytecode Transformation and Graph Compilation,ACM International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS),2024,conference-paper,computer_architecture,6,434.0,690.0,204.38576349024112,690,218.9123,True,False,post,204.38576349024112,landmark,True
++4297813615,10.4230/lipics.ecrts.2024.13,,DeepTrust^RT: Confidential Deep Neural Inference Meets Real-Time!,DROPS (Schloss Dagstuhl – Leibniz Center for Informatics),2024,article,computer_architecture,5,674.0,707.0,157.60297582979868,708,51.0217,True,True,post,157.60297582979868,landmark,True
++3111721177,10.1007/978-981-16-7487-7_7,,A Modern Primer on Processing in Memory,Computer architecture and design methodologies,2022,book-chapter,computer_architecture,2,90.0,125.0,139.4308943089431,168,112.4027,False,False,baseline,139.4308943089431,landmark,True
++2273211146,10.17615/nyex-d314,,The multiprocessor real-time scheduling of general task systems,Carolina Digital Repository (University of North Carolina at Chapel Hill),2019,dissertation,computer_architecture,4,19.0,20.0,137.14285714285714,37,,True,True,baseline,137.14285714285714,landmark,True
++4406650295,10.1145/3714983.3714987,,AWQ: Activation-aware Weight Quantization for On-Device LLM Compression and Acceleration,GetMobile Mobile Computing and Communications,2025,article,computer_architecture,9,234.0,234.0,76.51490775873349,234,83.4275,True,False,post,118.46457204078958,landmark,True
++4286840061,10.7302/4622,,Algorithm-Architecture Co-Design for Domain-Specific Accelerators in Communication and Artificial Intelligence,Deep Blue (University of Michigan),2022,dissertation,computer_architecture,5,3.0,14.0,105.75490196078431,14,,True,True,baseline,105.75490196078431,landmark,True
++3016932526,10.1063/5.0005082,,Q uantum ESPRESSO toward the exascale,The Journal of Chemical Physics,2020,article,computer_architecture,4,175.0,425.0,104.9573055028463,2032,159.393,True,True,baseline,104.9573055028463,landmark,True
++4401211704,10.1109/isca59077.2024.00019,,Splitwise: Efficient Generative LLM Inference Using Phase Splitting,ACM/IEEE International Symposium on Computer Architecture (ISCA),2024,conference-paper,computer_architecture,6,125.0,292.0,86.49368541905856,286,90.7376,True,False,post,86.49368541905856,landmark,True
++5673758,10.7275/4018988,,A Novel Reconfiguration Scheme in Quantum-Dot Cellular Automata for Energy Efficient Nanocomputing,ScholarWorks@UMassAmherst (University of Massachusetts Amherst),2023,dissertation,computer_architecture,4,4.0,4.0,86.3030303030303,4,,True,True,post,86.3030303030303,landmark,True
++6922095860,10.1184/r1/14642340,,Practical Mechanisms for Reducing Processor–Memory Data Movement in Modern Workloads,Figshare,2021,dissertation,computer_architecture,9,9.0,11.0,81.43283582089553,11,,True,True,baseline,81.43283582089553,landmark,True
++7135078329,10.1109/tvlsi.2026.3670888,,Highly Reliable RRAM-Based Physical Unclonable Function With Auto-Write Technique,IEEE Transactions on Very Large Scale Integration (VLSI) Systems,2026,article,computer_architecture,5,22.0,22.0,102.5437125748503,22,154.5718,True,False,post,78.21485274512901,landmark,True
++4386978266,10.7302/8543,,Accelerated Systems for Portable DNA Sequencing,Deep Blue (University of Michigan),2023,dissertation,computer_architecture,5,11.0,11.0,75.89999999999999,11,,True,True,post,75.89999999999999,landmark,True
++4393145114,10.1109/mm.2024.3373763,2403.14123,AI and Memory Wall,IEEE Micro,2024,article,computer_architecture,8,201.0,336.0,74.90042415673601,336,105.8227,True,True,post,74.90042415673601,landmark,True
++1224547023,10.1201/9781315218823-12,,Model-Based Framework for Schedulability Analysis Using Uppaal 4.1,CRC Press eBooks,2018,book-chapter,computer_architecture,3,51.0,52.0,74.46337817638266,70,17.8443,True,True,baseline,74.46337817638266,landmark,True
++2972087877,10.1145/3320060,,Demystifying Parallel and Distributed Deep Learning,ACM Computing Surveys,2019,article,computer_architecture,6,159.0,263.0,74.14530533429881,577,81.2716,True,False,baseline,74.14530533429881,landmark,True
++2952332632,10.4230/oasics.asd.2020.1,,Towards a Reliable and Context-Based System Architecture for Autonomous Vehicles,OASIcs : OpenAccess Series in Informatics,2020,conference-paper,computer_architecture,3,395.0,516.0,73.13244814646819,797,52.3105,True,True,baseline,73.13244814646819,landmark,True
++4410153295,10.1109/tcad.2025.3567878,,Bursting Firings in Memristive Hopfield Neural Network With Image Encryption and Hardware Implementation,IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems,2025,article,computer_architecture,4,142.0,142.0,32.564312668463614,142,32.3565,True,False,post,71.88875739227402,landmark,True
++2921153466,10.48550/arxiv.1903.05714,1903.05714,Basic Performance Measurements of the Intel Optane DC Persistent Memory Module,arXiv (Cornell University),2019,preprint,computer_architecture,6,108.0,175.0,71.88172043010752,191,,True,True,baseline,71.88172043010752,landmark,True
++3014555390,10.4230/lipics.ecrts.2026.13,,CacheFlow: Using Maximum Flow to Bound Cache-Based Preemption Delays,Leibniz international proceedings in informatics,2026,conference-paper,computer_architecture,2,19.0,19.0,147.2590476190476,19,17.7143,True,True,post,67.54919100715686,landmark,True
++4409568286,10.1145/3729215,2306.15552,A Survey on Deep Learning Hardware Accelerators for Heterogeneous HPC Platforms,ACM Computing Surveys,2025,article,computer_architecture,7,133.0,133.0,76.14302694136292,133,81.5626,True,True,post,67.3324276983975,landmark,True
++4403931219,10.17487/rfc9669,,BPF Instruction Set Architecture (ISA),,2024,report,computer_architecture,4,4.0,9.0,66.0,9,,False,False,post,66.0,landmark,True
++2156094048,10.1145/3501714.3501727,,Reverend Bayes on Inference Engines: A Distributed Hierarchical Approach,ACM eBooks,2022,book-chapter,ai_accelerators,2,495.0,513.0,274.919235405545,727,46.4544,True,False,baseline,274.919235405545,landmark,True
++7130682590,10.1016/j.vlsi.2026.102688,,Extreme multistability in discrete memristive neuron maps and implications for dual-field applications,Integration,2026,article,ai_accelerators,2,37.0,37.0,38.12608695652174,37,127.1161,False,False,post,131.54316143498968,landmark,True
++3003967059,10.1038/s41586-020-03070-1,2002.00281,Parallel convolutional processing using an integrated photonic tensor core,Nature,2021,article,ai_accelerators,2,503.0,830.0,96.73020860110631,1874,105.2419,False,True,baseline,96.73020860110631,landmark,True
++7131231908,10.1109/tcyb.2026.3664460,,Reconfigurable Multiscroll Memristive Neural Network With Application to Telemedicine Privacy Protection,IEEE Transactions on Cybernetics,2026,article,ai_accelerators,5,25.0,25.0,67.00916730328495,25,77.9076,True,False,post,88.88051448310114,landmark,True
++7134974875,10.1109/icears67481.2026.11416637,,Neuromorphic Photonic Computing using Microring Resonator Arrays for Real-Time Edge AI Processing,International Conference on Electronics and Renewable Systems (ICEARS),2026,conference-paper,ai_accelerators,3,24.0,24.0,184.20139553974553,24,221.8777,False,False,post,85.3252939037771,landmark,True
++7135102590,10.1109/tii.2026.3668794,,Biologically Plausible Memristive Decision-Making Circuit for Adaptive Control in Industrial Autonomous Navigation,IEEE Transactions on Industrial Informatics,2026,article,ai_accelerators,2,24.0,24.0,35.86027014438752,24,41.1527,True,False,post,85.3252939037771,landmark,True
++4409283612,10.1038/s41586-025-08854-x,,Universal photonic artificial intelligence acceleration,Nature,2025,article,ai_accelerators,1,165.0,165.0,56.20713647719771,165,68.6604,False,False,post,83.53271105440291,landmark,True
++7128511721,10.1109/tce.2026.3663359,,A Memristor-Based Neural Network Circuit With Classical Conditioning and Fear Generalization,IEEE Transactions on Consumer Electronics,2026,article,ai_accelerators,3,23.0,23.0,34.366092221704704,23,39.438,True,False,post,81.77007332445305,landmark,True
++2783525259,10.1109/mm.2018.112130359,,Loihi: A Neuromorphic Manycore Processor with On-Chip Learning,IEEE Micro,2018,article,ai_accelerators,4,327.0,745.0,64.97395387432005,3897,115.1562,True,False,baseline,64.97395387432005,landmark,True
++2944119451,10.1038/s41586-019-1157-8,2102.09360,All-optical spiking neurosynaptic networks with self-learning capabilities,Nature,2019,article,ai_accelerators,3,162.0,384.0,58.38929375910958,1529,75.8628,False,True,baseline,58.38929375910958,landmark,True
++7141052859,10.1016/j.vlsi.2026.102731,,A dual-neuron second-order memristor-based Hopfield neural network with controllable extreme multistability and its application in image encryption,Integration,2026,article,ai_accelerators,4,15.0,15.0,40.20550038197097,15,46.7446,False,False,post,53.328308689860684,landmark,True
++7149961640,10.1016/j.neunet.2026.108953,,A discrete memristive cyclic Hopfield neural network with multi-cavity-like attractors and application in secure communication,Neural Networks,2026,article,ai_accelerators,3,14.0,14.0,14.426086956521738,14,48.098,False,False,post,49.77308811053664,landmark,True
++7140216027,10.1016/j.neunet.2026.108887,,Discrete bidirectional memristive neural network-based hyperchaotic system and its FPGA implementation,Neural Networks,2026,article,ai_accelerators,3,13.0,13.0,13.395652173913042,13,44.6624,False,False,post,46.2178675312126,landmark,True
++7130433466,10.1109/tcsi.2026.3663432,,A Second-Order Memristor Method to Construct Memristive Neural Networks With Multi-Butterfly and Multi-Scroll Dynamics,IEEE Transactions on Circuits and Systems I Regular Papers,2026,article,ai_accelerators,4,12.0,12.0,17.93013507219376,12,20.5764,True,False,post,42.66264695188855,landmark,True
++7130516837,10.1142/s0218127426500914,,Gaussian-Function-Driven Chaotic Memristive Dynamics with Application to Image Encryption,International Journal of Bifurcation and Chaos,2026,article,ai_accelerators,3,12.0,12.0,32.16440030557678,12,37.3956,True,False,post,42.66264695188855,landmark,True
++4210357113,10.1038/s43588-021-00184-y,,Opportunities for neuromorphic computing algorithms and applications,Nature Computational Science,2022,article,ai_accelerators,2,224.0,537.0,42.17948197363679,1107,60.0929,False,True,baseline,42.17948197363679,landmark,True
++4417057079,10.1038/s41467-026-68452-x,,Single-shot matrix-matrix photonic processor based on spatial-spectral hypermultiplexed parallel diffraction,Nature Communications,2026,article,ai_accelerators,8,11.0,11.0,12.24081685075777,11,39.7388,True,True,post,39.107426372564504,landmark,True
++4409363528,10.1186/s43593-025-00085-x,,Intelligent nanophotonics: when machine learning sheds light,eLight,2025,article,ai_accelerators,5,77.0,77.0,26.229997022692263,77,32.0415,True,True,post,38.981931825388024,landmark,True
++4394727812,10.1126/science.adl1203,,Large-scale photonic chiplet Taichi empowers 160-TOPS/W artificial general intelligence,Science,2024,article,ai_accelerators,4,198.0,308.0,38.256050750798984,308,45.8367,True,False,post,38.256050750798984,landmark,True
++4414574650,10.1016/j.chaos.2025.117292,,Field and delay effects on synchronization in a multilayer memristive neuronal network with a hub,Chaos Solitons & Fractals,2025,article,ai_accelerators,1,71.0,71.0,27.006304798493694,71,31.2974,False,False,post,35.94437869613701,landmark,True
++7129076810,10.1109/jiot.2026.3665333,,Dual Memristor-Coupled Unidirectional Ring Neural Network: Abundant Hidden Firings and Application in Hardware Image Encryption,IEEE Internet of Things Journal,2026,article,ai_accelerators,7,10.0,10.0,14.941779226828132,10,17.147,True,False,post,35.552205793240454,landmark,True
++7128293306,10.1038/s41467-026-69084-x,,Neuromorphic photonic computing with an electro-optic analog memory,Nature Communications,2026,article,ai_accelerators,3,10.0,10.0,11.1280153188707,9,32.5136,True,True,post,35.552205793240454,landmark,True
++4413052649,10.1016/j.vlsi.2025.102500,,An attractor-controllable memristive Hopfield neural network and its application on voice encryption,Integration,2025,article,ai_accelerators,4,66.0,66.0,14.175875486381322,66,28.5628,False,False,post,33.41308442176116,landmark,True
++4402694963,10.1038/s41377-024-01590-3,,Optical neural networks: progress and challenges,Light Science & Applications,2024,article,ai_accelerators,4,135.0,263.0,32.666692686558875,263,39.1398,True,True,post,32.666692686558875,landmark,True
++7127097285,10.1364/optica.578687,,Nonlinear photonic neuromorphic chips for spiking reinforcement learning,Optica,2026,article,ai_accelerators,7,9.0,9.0,10.015213786983631,9,32.5136,True,True,post,31.99698521391641,landmark,True
++2916516729,10.5555/3323298.3323317,,SLM-DB: Single-Level Key-Value Store with Persistent Memory,Scholarworks@UNIST (Ulsan National Institute of Science and Technology),2019,dissertation,operating_systems,7,19.0,42.0,328.8494623655914,75,,True,True,baseline,328.8494623655914,landmark,True
++4295093682,10.5281/zenodo.19901496,,fast16 as a Second-Order Subversion Attack,,2026,preprint,operating_systems,4,28.0,28.0,1089.5438596491229,28,,True,True,post,99.54617622107328,landmark,True
++7130515337,10.1186/s12912-026-04420-6,,Artificial intelligence literacy and academic resilience in undergraduate nursing students: the mediating role of self-efficacy and artificial intelligence anxiety,BMC Nursing,2026,article,operating_systems,1,13.0,13.0,92.3590525632706,13,118.9868,True,True,post,46.2178675312126,landmark,True
++25727798,10.48550/arxiv.1903.01955,1903.01955,The Lustre Storage Architecture,arXiv (Cornell University),2019,preprint,operating_systems,3,96.0,112.0,46.00430107526881,206,,True,True,baseline,46.00430107526881,landmark,True
++2930508541,10.4230/oasics.microservices.2020-2022.5,1902.03383,Cloud Programming Simplified: A Berkeley View on Serverless Computing,arXiv (Cornell University),2019,preprint,operating_systems,6,103.0,195.0,43.97867397390838,409,,True,True,baseline,43.97867397390838,landmark,True
++4404401018,10.1145/3694715.3695964,2312.12456,PowerInfer: Fast Large Language Model Serving with a Consumer-grade GPU,ACM SIGOPS Symposium on Operating Systems Principles (SOSP),2024,conference-paper,operating_systems,10,64.0,133.0,39.39609644087256,133,42.1961,True,True,post,39.39609644087256,landmark,True
++3182301081,,,Zico: Efficient {GPU} Memory Sharing for Concurrent {DNN} Training,Scholarworks@UNIST (Ulsan National Institute of Science and Technology),2021,dissertation,operating_systems,5,0.0,5.0,37.01492537313433,8,,True,False,baseline,37.01492537313433,landmark,True
++7162190207,10.1016/j.dss.2026.114696,,"AI-driven mental health therapy: A comparative study of emotional clarity and stigma reduction across journaling, human-guided therapy, and AI-guided therapy",Decision Support Systems,2026,article,operating_systems,1,10.0,10.0,5.225225225225225,10,63.1822,False,False,post,35.552205793240454,landmark,True
++2975712713,10.1145/3341301.3359642,,A generic communication scheduler for distributed DNN training acceleration,,2019,conference-paper,operating_systems,8,53.0,119.0,31.997431506849313,321,32.6252,True,False,baseline,31.997431506849313,landmark,True
++4384697207,10.22215/etd/2023-15552,,Methods and Techniques for Inter-cloud Data Transfers,,2023,dissertation,operating_systems,4,1.0,1.0,28.864864864864863,1,,True,True,post,28.864864864864863,landmark,True
++4394944658,10.1145/3627703.3629578,,"Orion: Interference-aware, Fine-grained GPU Sharing for ML Applications",,2024,conference-paper,operating_systems,7,56.0,97.0,28.73249138920781,97,30.7746,True,False,post,28.73249138920781,landmark,True
++3012028616,10.1145/3373376.3378512,,Catalyzer,International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS),2020,conference-paper,operating_systems,6,56.0,88.0,24.84845663205922,281,26.591,True,False,baseline,24.84845663205922,landmark,True
++4295682935,,,CodaLab Competitions An Open Source Platform to Organize Scientific Challenges,HAL (Le Centre pour la Communication Scientifique Directe),2022,report,operating_systems,4,28.0,68.0,22.289935364727608,75,,True,True,baseline,22.289935364727608,landmark,True
++3152809665,10.1145/3445814.3446701,,"Nightcore: efficient and scalable serverless computing for latency-sensitive, interactive microservices",,2021,conference-paper,operating_systems,5,33.0,78.0,21.88714698058747,215,30.1787,True,True,baseline,21.88714698058747,landmark,True
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++3023238978,10.1145/3342195.3387524,,Autopilot,,2020,conference-paper,operating_systems,5,53.0,76.0,21.46003072768751,303,26.591,True,True,baseline,21.46003072768751,landmark,True
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++4407091026,10.1016/s2589-7500(24)00267-x,,"Screening performance and characteristics of breast cancer detected in the Mammography Screening with Artificial Intelligence trial (MASAI): a randomised, controlled, parallel-group, non-inferiority, single-blinded, screening accuracy study",The Lancet Digital Health,2025,article,quantum_computing,1,164.0,164.0,55.86648716521469,164,67.8282,True,True,post,83.02645219952774,landmark,True
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++4294786246,,,Time-optimal holonomic quantum computation,KTH Publication Database DiVA (KTH Royal Institute of Technology),2022,dissertation,quantum_computing,1,7.0,10.0,74.89035087719299,13,,True,True,baseline,74.89035087719299,landmark,True
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added literature_synthesis/landmarks_v1/landmarks.csv +1094 −0

++work_id,doi,arxiv_id,title,source_name,year,type,primary_category,n_categories,c12,c36,ncs36,cited_by_count,fwci,has_abstract,is_oa,period,score,set,pi_review
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++7154011589,10.55905/rdelosv19.n79-020,2005.14165,Modelos pré-treinados de LLM e questões éticas face aos direitos autorais,DELOS Desarrollo Local Sostenible,2026,article,llm,3,2847.0,2847.0,3168.1459612824883,0,0.0,True,True,baseline,10121.712989335558,landmark,True
++4362515116,10.1007/s11704-026-60308-3,2303.18223,A Survey of Large Language Models,Frontiers of Computer Science,2026,article,llm,6,1549.0,1549.0,1723.7295728930715,1549,964.5692,True,True,post,5507.0366773729465,landmark,True
++4288253152,10.5281/zenodo.12935200,,Multilingual Classifier for Automatic Dewey Decimal Classification trained on Open Access Linguistics Abstracts,Repositorio Institucional UPTC,2024,dissertation,llm,3,286.0,364.0,3102.1223140495867,364,,True,True,post,3102.1223140495867,landmark,True
++4322718191,10.48550/arxiv.2302.13971,2302.13971,LLaMA: Open and Efficient Foundation Language Models,arXiv (Cornell University),2023,preprint,llm,4,2565.0,3629.0,1206.5430511141133,3837,,True,True,post,1206.5430511141133,landmark,True
++4323655724,10.1016/j.lindif.2023.102274,,ChatGPT for good? On opportunities and challenges of large language models for education,Learning and Individual Differences,2023,article,llm,1,2213.0,4227.0,990.7064044933976,7222,1695.9108,True,True,post,990.7064044933976,landmark,True
++4367369313,10.18260/jet.43.1.5,,"Prompt Engineering for ChatGPT: A Quick Guide to Techniques, Tips, and Best Practices",ASEE Journal of Engineering Technology,2026,article,llm,2,261.0,261.0,290.4411998225253,261,187.8562,True,True,post,927.9125712035759,landmark,True
++4226278401,10.48550/arxiv.2203.02155,2203.02155,Discernment and Social Learning as a Companion Training Layer,arXiv (Cornell University),2022,preprint,llm,5,1547.0,3077.0,894.2065022118338,4202,,True,True,baseline,894.2065022118338,landmark,True
++4396531244,10.22215/etd/2024-15938,,The Fusion of Multilingual Semantic Search and Large Language Models: A New Paradigm for Enhanced Topic Exploration and Contextual Search,,2024,dissertation,llm,6,20.0,20.0,778.8148148148148,20,,True,True,post,778.8148148148148,landmark,True
++4221143046,10.48550/arxiv.2201.11903,2201.11903,"BNAI, NO-TOKEN, and MIND-UNITY: Pillars of a Systemic Revolution in Artificial Intelligence",arXiv (Cornell University),2022,preprint,llm,3,962.0,2465.0,716.353275252574,4203,,True,True,baseline,716.353275252574,landmark,True
++4327810158,10.4230/lipics.cosit.2024.11,2303.08774,GPT-4 Technical Report,arXiv (Cornell University),2023,preprint,llm,3,1261.0,1968.0,654.30606905279,2391,,True,True,post,654.30606905279,landmark,True
++7135096569,10.1287/orsc.2025.21838,,Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of Artificial Intelligence on Knowledge Worker Productivity and Quality,Organization Science,2026,article,llm,1,183.0,183.0,203.64268033533384,183,650.2714,True,False,post,650.6053660163003,landmark,True
++4414281281,10.1038/s41586-025-09422-z,,DeepSeek-R1 incentivizes reasoning in LLMs through reinforcement learning,Nature,2025,article,llm,3,1099.0,1099.0,374.373593869335,1099,456.904,True,True,post,556.3784815078109,landmark,True
++4389437528,10.61969/jai.1337500,,Education in the Era of Generative Artificial Intelligence (AI): Understanding the Potential Benefits of ChatGPT in Promoting Teaching and Learning,Journal of AI,2023,article,llm,2,1417.0,2361.0,553.3612067681362,2953,693.6856,True,True,post,553.3612067681362,landmark,True
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++7110046239,10.4230/oasics.icpec.2025.4,,Can Open Large Language Models Catch Vulnerabilities?,Repositório do ISCTE-IUL,2025,article,software_engineering,3,540.0,540.0,468.828520182987,1,1.0925,True,True,post,273.37978163259135,landmark,True
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++2921681895,10.18154/rwth-2020-02348,1903.07993,Parameter synthesis in Markov models,RWTH Publications (RWTH Aachen),2020,dissertation,formal_verification,6,14.0,19.0,111.636815920398,26,,True,True,baseline,111.636815920398,landmark,True
++4225922019,10.1007/978-3-030-99524-9_24,,cvc5: A Versatile and Industrial-Strength SMT Solver,Lecture notes in computer science,2022,conference-paper,formal_verification,4,111.0,222.0,98.23784328546233,468,135.8061,True,True,baseline,98.23784328546233,landmark,True
++4318573502,10.5463/thesis.149,,Semantically informed methods in structural proof theory,,2022,dissertation,formal_verification,2,9.0,13.0,97.35745614035089,14,,True,True,baseline,97.35745614035089,landmark,True
++2154148696,10.1007/978-3-319-77452-7_9,,Supervisory Control of Timed Discrete-Event Systems,Communications and control engineering/Communications and control engineering series,2018,book-chapter,formal_verification,1,100.0,117.0,86.49113924050633,365,36.4666,False,False,baseline,86.49113924050633,landmark,True
++4297781990,10.4230/oasics.dx.2024.10,,"Leveraging Answer Set Programming for Continuous Monitoring, Fault Detection, and Explanation of Automated and Autonomous Driving Systems",HAL (Le Centre pour la Communication Scientifique Directe),2024,preprint,formal_verification,3,426.0,431.0,78.93772893772893,431,,True,True,post,78.93772893772893,landmark,True
++2789444712,10.1080/00207543.2018.1444806,,Industry 4.0: state of the art and future trends,International Journal of Production Research,2018,article,formal_verification,4,298.0,734.0,61.81756449008323,3221,267.6956,True,False,baseline,61.81756449008323,landmark,True
++1586677474,,,Handbook of Satisfiability,,2021,article,formal_verification,2,374.0,375.0,60.365327611379016,486,1.7496,True,False,baseline,60.365327611379016,landmark,True
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++4289665848,10.4230/lipics.csl.2026.30,,Reward Interfaces with Best-Effort Implementations,DROPS (Schloss Dagstuhl – Leibniz Center for Informatics),2026,article,formal_verification,4,15.0,15.0,42.354130914439814,15,0.0,True,True,post,53.328308689860684,landmark,True
++149631060,10.48550/arxiv.2411.01592,2411.01592,Decidability Issues for Petri Nets -- a survey,arXiv (Cornell University),2024,preprint,formal_verification,3,33.0,33.0,53.32230215827339,72,,True,True,post,53.32230215827339,landmark,True
++2890660842,10.48550/arxiv.1809.08098,1809.08098,Efficient Formal Safety Analysis of Neural Networks,arXiv (Cornell University),2018,preprint,formal_verification,6,41.0,98.0,52.040918390543304,167,,True,True,baseline,52.040918390543304,landmark,True
++1932627192,10.82444/warw.33484729,,Model checking quantum protocols,Warwick Research Archive Portal (University of Warwick),2026,dissertation,formal_verification,4,14.0,14.0,141.45283018867926,18,,True,True,post,49.77308811053664,landmark,True
++4393942723,10.1007/978-3-031-57256-2_15,,State of the Art in Software Verification and Witness Validation: SV-COMP 2024,Lecture notes in computer science,2024,conference-paper,formal_verification,4,66.0,78.0,48.594819515239784,78,53.289,True,True,post,48.594819515239784,landmark,True
++2957311447,10.1007/978-3-030-25540-4_26,,The Marabou Framework for Verification and Analysis of Deep Neural Networks,Lecture notes in computer science,2019,conference-paper,formal_verification,4,55.0,141.0,46.39818783571157,498,53.6017,True,True,baseline,46.39818783571157,landmark,True
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++4408444355,10.1016/j.is.2025.102549,2207.14529,The effects of data quality on machine learning performance on tabular data,Information Systems,2025,article,database_systems,3,322.0,322.0,80.50468595378078,322,192.6654,True,True,post,163.01535126980446,landmark,True
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++2593864460,10.1109/tbdata.2019.2921572,1702.08734,Billion-Scale Similarity Search with GPUs,IEEE Transactions on Big Data,2019,article,database_systems,6,384.0,765.0,115.95149891238218,3832,118.3163,True,True,pre,115.95149891238218,landmark,True
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++2945623882,10.1145/3292500.3330989,1905.07854,KGAT,ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD),2019,conference-paper,information_retrieval,5,157.0,465.0,133.92825599565188,2147,160.7851,True,True,baseline,133.92825599565188,landmark,True
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++3036601975,10.4230/oasics.nines.2026.13,2006.11477,In-Kernel Aggregation and Broadcast Acceleration for Distributed Communication,arXiv (Cornell University),2020,preprint,distributed_computing,10,200.0,699.0,157.87121576165305,2368,,True,True,baseline,157.87121576165305,landmark,True
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++2973502831,,,Performance Analysis of Proof-of-Elapsed-Time (PoET) Consensus in the Sawtooth Blockchain Framework,Scholars' Bank (University of Oregon),2019,dissertation,distributed_computing,5,3.0,14.0,140.31616982836496,28,,True,False,baseline,140.31616982836496,landmark,True
++1572499759,10.56726/irjmets66464,,Microservices Architecture,International Research Journal of Modernization in Engineering Technology and Science,2025,article,distributed_computing,4,245.0,245.0,93.19077007930923,245,0.8816,True,True,post,124.03341944441644,landmark,True
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++3012404397,10.5626/ktcp.2020.26.3.135,1801.10228,Santa Claus Algorithm: Agent-Based Consensus Preprocessing Method for Scalability Improvement in Private Blockchain Environment,정보과학회 컴퓨팅의 실제 논문지,2020,article,distributed_computing,3,328.0,400.0,116.47677607981433,1,0.2325,True,True,baseline,116.47677607981433,landmark,True
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++4229658977,10.1007/978-1-4842-4470-8_7,,Google Colaboratory,Apress eBooks,2019,book-chapter,cloud_computing,2,24.0,126.0,122.68731164917385,709,185.9225,True,False,baseline,122.68731164917385,landmark,True
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++2784822088,10.2312/2632114,,A microservice architecture for the processing of large geospatial data in the Cloud,Publikationsdatenbank der Fraunhofer-Gesellschaft (Fraunhofer-Gesellschaft),2018,dissertation,cloud_computing,3,3.0,8.0,81.83864915572232,15,,True,False,baseline,81.83864915572232,landmark,True
++7118010419,10.37547/tajet/volume08issue01-02,,"Optimizing Retail Application Performance: A Systematic Review of Monitoring Tools, Metrics, And Best Practices",The American Journal of Engineering And Technology,2026,review,cloud_computing,4,23.0,23.0,30.77027027027027,23,33.0364,True,True,post,81.77007332445305,landmark,True
++4394946189,10.1145/3627703.3629553,2305.15778,Automatic Root Cause Analysis via Large Language Models for Cloud Incidents,,2024,conference-paper,cloud_computing,5,101.0,187.0,81.27074434424932,187,84.6778,True,True,post,81.27074434424932,landmark,True
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++4395047770,10.1007/s10462-024-10756-9,2105.04086,Deep reinforcement learning-based methods for resource scheduling in cloud computing: a review and future directions,Artificial Intelligence Review,2024,article,cloud_computing,6,149.0,232.0,69.84941043522221,232,83.4248,True,True,post,69.84941043522221,landmark,True
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++7160823269,10.1109/cisce69494.2026.11504752,,TokenPool-Scheduler: Token-Level GPU Pooling and Resource Slicing for Multi-Model Co-Location,,2026,conference-paper,cloud_computing,6,19.0,19.0,179.3930434782609,19,217.1439,True,False,post,67.54919100715686,landmark,True
++7161556515,10.1109/gaiis69281.2026.11519206,,DeepServeCB: SLO-Aware and Cost-Aware Elastic Scheduling for Serverless Multi-Tenant LLM Inference,International Conference on Generative Artificial Intelligence and Information Security (GAIIS),2026,conference-paper,cloud_computing,7,19.0,19.0,179.3930434782609,19,217.1439,True,False,post,67.54919100715686,landmark,True
++2946627595,10.1007/s11280-019-00684-y,,A QoS-aware virtual machine scheduling method for energy conservation in cloud-based cyber-physical systems,World Wide Web,2019,article,cloud_computing,5,136.0,154.0,62.86026170639958,192,52.7972,False,False,baseline,62.86026170639958,landmark,True
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++4248175462,10.2139/ssrn.3440802,,Bitcoin: A Peer-to-Peer Electronic Cash System,SSRN Electronic Journal,2019,preprint,blockchain,3,5543.0,7534.0,1699.155537022696,13922,,False,True,baseline,1699.155537022696,landmark,True
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++3123868249,10.1093/rfs/hhz015,,"Sex, Drugs, and Bitcoin: How Much Illegal Activity Is Financed through Cryptocurrencies?",Review of Financial Studies,2019,article,blockchain,2,246.0,368.0,150.21153446724054,1051,145.8951,True,True,baseline,150.21153446724054,landmark,True
++2907683311,10.1109/access.2018.2890507,,Blockchain for AI: Review and Open Research Challenges,IEEE Access,2019,article,blockchain,1,200.0,342.0,139.59876301031593,997,153.0805,True,True,baseline,139.59876301031593,landmark,True
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++3122636688,10.1093/rfs/hhz007,,Blockchain Disruption and Smart Contracts,Review of Financial Studies,2019,article,blockchain,1,189.0,332.0,135.5169278345757,1089,145.8951,True,False,baseline,135.5169278345757,landmark,True
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++2914735843,10.1109/tsmc.2019.2895123,,"Blockchain-Enabled Smart Contracts: Architecture, Applications, and Future Trends",IEEE Transactions on Systems Man and Cybernetics Systems,2019,article,blockchain,2,136.0,314.0,128.1696245182433,1157,157.1419,True,False,baseline,128.1696245182433,landmark,True
++2973816953,10.7551/mitpress/11645.003.0010,,How Blockchain Will Change Organizations,The MIT Press eBooks,2018,book-chapter,blockchain,1,55.0,109.0,128.09387022313413,375,138.4143,True,False,baseline,128.09387022313413,landmark,True
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++2273211146,10.17615/nyex-d314,,The multiprocessor real-time scheduling of general task systems,Carolina Digital Repository (University of North Carolina at Chapel Hill),2019,dissertation,computer_architecture,4,19.0,20.0,137.14285714285714,37,,True,True,baseline,137.14285714285714,landmark,True
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++4368358469,10.1093/nar/gkad344,,"antiSMASH 7.0: new and improved predictions for detection, regulation, chemical structures and visualisation",Nucleic Acids Research,2023,article,computational_biology,1,830.0,1741.0,32.99382988100485,2290,294.6456,True,True,post,32.99382988100485,landmark,True
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added literature_synthesis/landmarks_v1/perception_interaction_landmarks.csv +208 −0

++work_id,doi,arxiv_id,title,source_name,year,type,primary_category,n_categories,c12,c36,ncs36,cited_by_count,fwci,has_abstract,is_oa,period,score,set,pi_review
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++4398756379,10.1109/adics58448.2024.10533619,,YOLOv8: A Novel Object Detection Algorithm with Enhanced Performance and Robustness,,2024,conference-paper,computer_vision,2,1232.0,1876.0,393.72768235909564,1876,409.6027,True,False,post,393.72768235909564,landmark,True
++3171007011,10.1109/cvpr46437.2021.01549,,Exploring Simple Siamese Representation Learning,IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings,2021,conference-paper,computer_vision,3,954.0,1869.0,296.32486388384757,3498,304.7959,True,False,baseline,296.32486388384757,landmark,True
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++2963125010,10.1109/cvpr.2018.00716,,ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices,IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings,2018,conference-paper,computer_vision,3,843.0,1788.0,219.05053598774887,9452,225.5404,True,True,baseline,219.05053598774887,landmark,True
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++2979750740,10.1145/3326362,1801.07829,Dynamic Graph CNN for Learning on Point Clouds,ACM Transactions on Graphics,2019,article,computer_vision,4,815.0,1673.0,204.21997406985423,7010,434.8669,True,True,baseline,204.21997406985423,landmark,True
++2925148167,10.1109/cvpr42600.2020.01164,1903.11027,nuScenes: A Multimodal Dataset for Autonomous Driving,IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings,2020,conference-paper,computer_vision,6,935.0,1637.0,189.37948256099557,6888,578.0759,True,True,baseline,189.37948256099557,landmark,True
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++2998508940,10.1609/aaai.v34i07.7000,,Random Erasing Data Augmentation,Proceedings of the AAAI Conference on Artificial Intelligence,2020,conference-paper,computer_vision,3,1114.0,1602.0,193.95128928478692,3303,157.9126,True,True,baseline,193.95128928478692,landmark,True
++2962730651,10.1109/tpami.2019.2929257,1812.08008,OpenPose: Realtime Multi-Person 2D Pose Estimation Using Part Affinity Fields,IEEE Transactions on Pattern Analysis and Machine Intelligence,2019,article,computer_vision,1,634.0,1600.0,242.51293890171436,5491,280.2052,True,True,baseline,242.51293890171436,landmark,True
++2884367402,10.1109/tnnls.2018.2876865,1807.05511,Object Detection With Deep Learning: A Review,IEEE Transactions on Neural Networks and Learning Systems,2019,article,computer_vision,2,699.0,1580.0,239.4815271654429,5487,286.797,True,True,baseline,239.4815271654429,landmark,True
++3013601031,10.1007/s13246-020-00865-4,2003.11617,Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks,Physical and Engineering Sciences in Medicine,2020,article,computer_vision,3,1035.0,1579.0,50.03582701030497,2336,215.8286,False,True,baseline,50.03582701030497,landmark,True
++2911489562,10.1093/bioinformatics/btz682,1901.08746,BioBERT: a pre-trained biomedical language representation model for biomedical text mining,Bioinformatics,2019,article,nlp,8,666.0,1526.0,84.91295842716947,7155,176.5991,True,True,baseline,84.91295842716947,landmark,True
++4212875960,10.1109/wacv51458.2022.00181,2103.10504,UNETR: Transformers for 3D Medical Image Segmentation,IEEE/CVF Winter Conference on Applications of Computer Vision (WACV),2022,conference-paper,computer_vision,4,698.0,1509.0,203.88765809091694,3313,245.4958,True,True,baseline,203.88765809091694,landmark,True
++4391876619,10.1162/tacl_a_00638,2307.03172,Lost in the Middle: How Language Models Use Long Contexts,Transactions of the Association for Computational Linguistics,2024,article,nlp,4,688.0,1484.0,184.32460816294056,1484,217.8733,True,True,post,184.32460816294056,landmark,True
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++3127751679,10.48550/arxiv.2102.04306,2102.04306,TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation,arXiv (Cornell University),2021,preprint,computer_vision,4,489.0,1465.0,232.16955017301038,3859,,True,True,baseline,232.16955017301038,landmark,True
++2978017171,10.4230/tgdk.3.3.5,1910.01108,"DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter",arXiv (Cornell University),2019,preprint,nlp,5,502.0,1462.0,165.6218897479667,4347,,True,True,baseline,165.6218897479667,landmark,True
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++3159481202,10.1109/iccv48922.2021.00951,2104.14294,Emerging Properties in Self-Supervised Vision Transformers,IEEE/CVF International Conference on Computer Vision (ICCV),2021,conference-paper,computer_vision,5,497.0,1438.0,227.9909867656355,5690,363.2871,True,True,baseline,227.9909867656355,landmark,True
++2980282514,10.48550/arxiv.1910.03771,1910.03771,HuggingFace's Transformers: State-of-the-art Natural Language Processing,arXiv (Cornell University),2019,preprint,nlp,5,692.0,1434.0,162.44992469123406,2972,,True,True,baseline,162.44992469123406,landmark,True
++4287824654,10.4230/oasics.slate.2022.3,,Question Answering For Toxicological Information Extraction,DROPS (Schloss Dagstuhl – Leibniz Center for Informatics),2022,article,nlp,3,1238.0,1430.0,147.34664543494497,1520,94.4336,True,True,baseline,147.34664543494497,landmark,True
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++3135367836,10.48550/arxiv.2103.00020,2103.00020,Learning Transferable Visual Models From Natural Language Supervision,arXiv (Cornell University),2021,preprint,computer_vision,6,625.0,1405.0,222.66089965397924,5217,,True,True,baseline,222.66089965397924,landmark,True
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added literature_synthesis/landmarks_v1/security_privacy_landmarks.csv +126 −0

++work_id,doi,arxiv_id,title,source_name,year,type,primary_category,n_categories,c12,c36,ncs36,cited_by_count,fwci,has_abstract,is_oa,period,score,set,pi_review
++4301785137,10.2139/ssrn.7475259,,The Illusion of Topological Compression: Why Persistent Homology Fails as a Real-Time Prompt-Injection Defense,SSRN Electronic Journal,2026,preprint,cybersecurity,2,252.0,252.0,9805.894736842105,252,,False,True,post,895.9155859896595,landmark,True
++2905514290,,,An information security risk management gap analysis tool based on ISO/IEC 27005:2011 compliance for SMEs in Kenya,,2018,dissertation,cybersecurity,1,63.0,83.0,849.0759849906191,95,,False,False,baseline,849.0759849906191,landmark,True
++7139094415,10.1201/9781003612742-2,,Fedformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting,CRC Press eBooks,2026,book-chapter,cybersecurity,1,226.0,226.0,216.38297872340425,226,303.4225,True,False,post,803.4798509272343,landmark,True
++2460937040,10.1201/9781351251389-8,,Adversarial Examples in the Physical World,Chapman & Hall/CRC eBooks,2018,book-chapter,cybersecurity,4,805.0,1507.0,699.9376680212566,4388,809.1968,True,True,baseline,699.9376680212566,landmark,True
++7129030749,10.1145/3796519,,"Model Context Protocol (MCP): Landscape, Security Threats, and Future Research Directions",ACM Transactions on Software Engineering and Methodology,2026,article,cybersecurity,4,178.0,178.0,198.07867267589847,149,498.5414,True,True,post,632.8292631196801,landmark,True
++4289548059,10.4230/oasics.fmbc.2026.8,,Detecting Cross-Function Reentrancy from EVM Traces,DROPS (Schloss Dagstuhl – Leibniz Center for Informatics),2026,article,cybersecurity,5,148.0,148.0,1051.4722907203115,148,9.1528,True,True,post,526.1726457399587,landmark,True
++3188417193,,,Machine learning for offensive cyber operations,NORA - Norwegian Open Research Archives,2023,dissertation,cybersecurity,6,43.0,51.0,424.2320328542094,58,,True,True,post,424.2320328542094,landmark,True
++3201596224,,,ELLIPTIC CURVE DIGITAL SIGNATURE ALGORITHM,NC Digital Online Collection of Knowledge and Scholarship (The University of North Carolina at Greensboro),2021,dissertation,cybersecurity,2,27.0,28.0,308.9145728643216,35,,True,False,baseline,308.9145728643216,landmark,True
++652269645,10.57709/1059372,,A Design and Analysis of Graphical Password,Digital Archive @ GSU,2022,dissertation,cybersecurity,1,35.0,35.0,303.5964912280702,43,,True,True,baseline,303.5964912280702,landmark,True
++2231944250,10.7551/mitpress/11636.003.0018,,Enigma: Decentralized Computation Platform with Guaranteed Privacy,The MIT Press eBooks,2018,book-chapter,cybersecurity,6,169.0,230.0,270.2898179020262,368,204.4551,True,True,baseline,270.2898179020262,landmark,True
++3041344359,,,Machine Learning for anomaly detection in IoT networks :Malware analysis on the IoT-23 data set,,2020,dissertation,cybersecurity,4,8.0,31.0,265.4812030075188,64,,True,False,baseline,265.4812030075188,landmark,True
++4401666902,10.38124/ijisrt/ijisrt24aug395,,Cyberfraud in the Nigerian Banking Sector: The Techniques and Preventive Measures,International Journal of Innovative Science and Research Technology (IJISRT),2024,article,cybersecurity,1,739.0,739.0,222.49445823978112,739,282.4198,True,True,post,222.49445823978112,landmark,True
++7125967353,10.1016/j.comnet.2026.112061,,An explainable transformer-based model for phishing email detection: A large language model approach,Computer Networks,2026,article,cybersecurity,6,60.0,60.0,426.27255029201814,23,173.9038,True,True,post,213.31323475944274,landmark,True
++4353004773,10.1109/mitp.2023.3246667,,IEEE Symposium on Security and Privacy,IT Professional,2023,article,cybersecurity,2,671.0,853.0,199.92253679509537,1167,130.8885,False,True,post,199.92253679509537,landmark,True
++2805930283,10.1109/access.2018.2842685,2402.00687,A Review on the Use of Blockchain for the Internet of Things,IEEE Access,2018,article,cybersecurity,5,184.0,370.0,196.1729236493096,1078,221.3514,True,True,post,196.1729236493096,landmark,True
++2624307925,10.1109/comst.2018.2842460,1706.00916,A Survey on Security and Privacy Issues of Bitcoin,IEEE Communications Surveys & Tutorials,2018,article,cybersecurity,6,207.0,367.0,194.58233237647738,1103,211.3082,True,True,pre,194.58233237647738,landmark,True
++7130652904,10.1145/3797277,,vul-rag : Enhancing LLM-based Vulnerability Detection via Knowledge-level RAG,ACM Transactions on Software Engineering and Methodology,2026,article,cybersecurity,5,51.0,51.0,362.3316677482154,30,247.1264,True,True,post,181.31624954552632,landmark,True
++4406302454,10.1145/3712001,2402.00888,Security and Privacy Challenges of Large Language Models: A Survey,ACM Computing Surveys,2025,article,cybersecurity,5,357.0,357.0,121.61180437793685,357,141.8982,True,True,post,180.73441119043537,landmark,True
++4392771733,,,Sécurisation des Algorithmes d'Apprentissage Automatique,theses.fr (ABES),2023,dissertation,cybersecurity,5,17.0,21.0,174.68377823408625,21,,True,True,post,174.68377823408625,landmark,True
++7124456577,10.1007/s41870-025-03069-9,,AF-SGS: Blockchain-assisted secure authentication framework for smart grid systems,International Journal of Information Technology,2026,article,cybersecurity,2,46.0,46.0,161.49184840143977,46,192.7628,False,False,post,163.5401466489061,landmark,True
++2789828921,10.5220/0006639801080116,,Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization,International Conference on Information Systems Security and Privacy (ICISSP),2018,conference-paper,cybersecurity,2,200.0,534.0,162.76665116819714,4757,217.8381,False,True,baseline,162.76665116819714,landmark,True
++2309315845,10.58088/javg-d166,,Human-centric training and assessment for cyber situation awareness,"Library, Museums and Press - UDSpace (University of Delaware)",2024,dissertation,cybersecurity,4,4.0,4.0,155.76296296296297,4,,True,True,post,155.76296296296297,landmark,True
++4387210439,10.3390/telecom7030073,2309.16021,HuntGPT: Integrating Machine Learning-Based Anomaly Detection and Explainable AI with Large Language Models (LLMs),Telecom,2026,article,cybersecurity,7,41.0,41.0,143.93838661867457,41,16.762,True,True,post,145.7640437522859,landmark,True
++3015692940,10.14419/ijet.v7i4.21726,,Performance evaluation measurement of image steganography techniques with analysis of LSB based on variation image formats,International Journal of Engineering & Technology,2026,article,cybersecurity,2,41.0,41.0,109.89503437738733,41,0.0,True,True,post,145.7640437522859,landmark,True
++2795085730,10.1109/mcc.2018.011791712,,Blockchain: A Panacea for Healthcare Cloud-Based Data Security and Privacy?,IEEE Cloud Computing,2018,article,cybersecurity,6,130.0,270.0,143.1532145549016,855,159.8872,True,False,baseline,143.1532145549016,landmark,True
++2916447643,10.17487/rfc9846,,The Transport Layer Security (TLS) Protocol Version 1.3,,2026,report,applied_cryptography,2,111.0,111.0,1533.1874999999998,301,,True,False,post,394.6294843049691,landmark,True
++1598516026,10.55277/researchhub.8k9hq1lf.1,,Scantegrity II Municipal Election at Takoma Park: The First E2E Binding Governmental Election with Ballot Privacy,,2026,report,applied_cryptography,2,97.0,97.0,914.8011049723757,107,,True,True,post,344.8563961944324,landmark,True
++4293168393,10.1145/3549993.3550007,,New Directions in Cryptography,ACM eBooks,2022,book-chapter,applied_cryptography,1,316.0,402.0,215.4337868090236,668,135.2339,True,False,baseline,215.4337868090236,landmark,True
++1497727960,10.82444/warw.33628720,,FLINT : Fast library for number theory,Warwick Research Archive Portal (University of Warwick),2026,article,applied_cryptography,1,59.0,59.0,166.59291493012995,60,0.0,True,True,post,209.7580141801187,landmark,True
++7134847443,10.1109/tce.2026.3672135,,"3D Non-Degenerate Hyperchaos: Design, Analysis, and Application in Image Encryption",IEEE Transactions on Consumer Electronics,2026,article,applied_cryptography,4,59.0,59.0,158.14163483575248,59,183.8619,True,False,post,209.7580141801187,landmark,True
++7154469464,10.1145/3808699,,Cryptanalysis and Improvement of a Video Cryptosystem via Chaos and S-Box,ACM Transactions on Multimedia Computing Communications and Applications,2026,article,applied_cryptography,3,57.0,57.0,152.78090145148968,57,177.6293,True,True,post,202.6475730214706,landmark,True
++1516024962,10.7275/409838,,Energy Efficient Adaptive Reed-Solomon Decoding System,Scholarworks (University of Massachusetts Amherst),2023,dissertation,applied_cryptography,3,8.0,8.0,146.84444444444443,9,,True,True,post,146.84444444444443,landmark,True
++4220942018,10.1201/9781003187165-10,,Public and private blockchains,CRC Press eBooks,2022,book-chapter,applied_cryptography,2,166.0,175.0,135.97268806899856,179,35.3352,True,False,baseline,135.97268806899856,landmark,True
++2910044495,,,Key Agreement with Physical Unclonable Functions and Biometric Identifiers,mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich),2019,dissertation,applied_cryptography,3,6.0,17.0,116.57142857142857,21,,False,False,baseline,116.57142857142857,landmark,True
++2915352631,10.17487/rfc8446,,The Transport Layer Security (TLS) Protocol Version 1.3,,2018,report,applied_cryptography,3,316.0,486.0,110.30219435736677,1851,,True,False,baseline,110.30219435736677,landmark,True
++4413785573,10.1109/tdsc.2025.3603570,,Encrypt a Story: A Video Segment Encryption Method Based on the Discrete Sinusoidal Memristive Rulkov Neuron,IEEE Transactions on Dependable and Secure Computing,2025,article,applied_cryptography,4,176.0,176.0,57.549674211696974,176,64.4002,True,False,post,89.10155845802977,landmark,True
++4396670109,10.4018/979-8-3693-1335-0.ch012,,Advanced Digital Data Processing Using Cloud Cryptography,Advances in civil and industrial engineering book series,2024,book-chapter,applied_cryptography,4,133.0,133.0,86.71235909822867,133,97.3697,True,False,post,86.71235909822867,landmark,True
++4401459825,10.6028/nist.fips.203,,Module-lattice-based key-encapsulation mechanism standard,,2024,report,applied_cryptography,3,84.0,236.0,85.45305164319248,236,,True,True,post,85.45305164319248,landmark,True
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++4226075590,10.6028/nist.ir.8413,,Status report on the third round of the NIST Post-Quantum Cryptography Standardization process,,2022,report,applied_cryptography,3,144.0,242.0,79.32594644506003,395,,True,True,baseline,79.32594644506003,landmark,True
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++4415878254,10.1109/jiot.2026.3669440,2510.15300,DFCA: Decentralized Federated Clustering Algorithm,IEEE Internet of Things Journal,2026,article,federated_learning,6,10.0,10.0,11.1280153188707,10,36.1262,True,True,post,35.552205793240454,landmark,True
++4406269727,10.1016/j.eswa.2024.126354,,Federated learning framework based on trimmed mean aggregation rules,Expert Systems with Applications,2025,article,federated_learning,3,67.0,67.0,22.8235039028621,67,22.0546,False,True,post,33.91934327663633,landmark,True
++4393156671,10.1609/aaai.v38i15.29617,2401.03230,FedTGP: Trainable Global Prototypes with Adaptive-Margin-Enhanced Contrastive Learning for Data and Model Heterogeneity in Federated Learning,Proceedings of the AAAI Conference on Artificial Intelligence,2024,conference-paper,federated_learning,8,66.0,121.0,33.633393493997694,121,36.8098,True,True,post,33.633393493997694,landmark,True
++4412162472,10.1038/s41746-025-01836-3,,A scoping review of the governance of federated learning in healthcare,npj Digital Medicine,2025,article,federated_learning,2,65.0,65.0,22.142205278896068,65,27.048,True,True,post,32.90682556688599,landmark,True
++3196371845,10.1016/j.neucom.2021.07.098,2106.06843,Federated learning on non-IID data: A survey,Neurocomputing,2021,article,federated_learning,6,75.0,277.0,32.28225034036922,1043,48.3425,False,True,baseline,32.28225034036922,landmark,True
++4226376129,10.1007/978-3-030-85559-8_13,,"Federated Learning: Challenges, Methods, and Future Directions",EAI/Springer Innovations in Communication and Computing,2022,book-chapter,federated_learning,3,37.0,57.0,30.546581711727224,99,39.2282,False,False,baseline,30.546581711727224,landmark,True
++7104465905,10.71781/10873,,Re-weighted softmax cross-entropy to control forgetting in federated learning,Papyrus : Institutional Repository (Université de Montréal),2022,dissertation,federated_learning,3,1.0,4.0,29.956140350877195,0,,True,True,post,29.956140350877195,landmark,True
++4402716118,10.1109/cvpr52733.2024.01139,,FedAS: Bridging Inconsistency in Personalized Federated Learning,IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings,2024,conference-paper,federated_learning,5,56.0,104.0,28.90804068905587,104,31.6381,True,False,post,28.90804068905587,landmark,True

added literature_synthesis/landmarks_v1/selection_report.json +27 −0

++{
++ "high_priority": 13414,
++ "landmarks": 1093,
++ "by_area": {
++  "ai_ml": 234,
++  "perception_interaction": 207,
++  "data_distributed": 152,
++  "security_privacy": 125,
++  "hardware_emerging": 125,
++  "software_pl": 100,
++  "computational_science": 100,
++  "theory": 50
++ },
++ "by_year_landmarks": {
++  "2018": 108,
++  "2019": 107,
++  "2020": 106,
++  "2021": 87,
++  "2022": 92,
++  "2023": 128,
++  "2024": 135,
++  "2025": 111,
++  "2026": 219
++ },
++ "share_with_abstract_landmarks": 0.8261665141811528,
++ "share_oa_landmarks": 0.7401646843549863
++}
+ \ No newline at end of file

added literature_synthesis/landmarks_v1/software_pl_landmarks.csv +101 −0

++work_id,doi,arxiv_id,title,source_name,year,type,primary_category,n_categories,c12,c36,ncs36,cited_by_count,fwci,has_abstract,is_oa,period,score,set,pi_review
++2964015378,10.4230/lipics.ecoop.2025.6,,Detecting Functionality-Specific Vulnerabilities via Retrieving Individual Functionality-Equivalent APIs in Open-Source Repositories,Leibniz international proceedings in informatics,2025,conference-paper,software_engineering,3,15245.0,15245.0,11583.902021240152,15246,2196.5447,True,True,post,7717.916242571953,landmark,True
++4293846201,10.4230/oasics.saia.2024.1,,On Assessing ML Model Robustness: A Methodological Framework (Academic Track),OASIcs : OpenAccess Series in Informatics,2025,conference-paper,software_engineering,4,4701.0,4701.0,4178.076566446758,4702,904.6849,True,True,post,2379.92287676817,landmark,True
++2095152777,10.7551/mitpress/12274.003.0035,,Managing the Development of Large Software Systems (1970),The MIT Press eBooks,2021,book-chapter,software_engineering,1,939.0,970.0,750.7662588538313,1778,102.1417,True,False,baseline,750.7662588538313,landmark,True
++2911537985,,,Microservices vs. Service-oriented architecture,Aaltodoc (Aalto University),2018,dissertation,software_engineering,2,50.0,67.0,685.3986866791745,93,,False,False,baseline,685.3986866791745,landmark,True
++1486172410,10.1017/9781108974073.004,,Introduction to Software Testing,Cambridge University Press eBooks,2021,book-chapter,software_engineering,1,869.0,871.0,549.1255565449688,1056,136.0795,True,False,baseline,549.1255565449688,landmark,True
++7151510318,10.70675/416f6582z4576z444eza710z5e930d59bf97,,Unsupervised Translation of Programming Languages,,2022,dissertation,software_engineering,3,51.0,56.0,485.7543859649123,1,,True,False,baseline,485.7543859649123,landmark,True
++4391974543,10.1109/tse.2024.3368208,2307.07221,"Software Testing With Large Language Models: Survey, Landscape, and Vision",IEEE Transactions on Software Engineering,2024,article,software_engineering,3,351.0,554.0,439.6107042253521,554,579.8344,True,True,post,439.6107042253521,landmark,True
++3185223169,,,An Effort Estimation Model for Agile Software Development,,2020,dissertation,software_engineering,1,36.0,45.0,423.2192575406032,60,,False,False,baseline,423.2192575406032,landmark,True
++2171139698,10.22024/unikent/01.02.86353,,Test data generation: two evolutionary approaches to mutation testing,Kent Academic Repository (University of Kent),2021,dissertation,software_engineering,1,19.0,19.0,394.8918918918919,24,,True,True,baseline,394.8918918918919,landmark,True
++7131867455,10.1287/mnsc.2025.00535,,The Effects of Generative AI on High-Skilled Work: Evidence from Three Field Experiments with Software Developers,Management Science,2026,article,software_engineering,2,97.0,97.0,689.140622972096,25,228.8208,True,False,post,344.8563961944324,landmark,True
++4399452770,10.5753/compbr.2018.38.4541,,Engenharia de Software,Computação Brasil.,2018,article,software_engineering,1,503.0,611.0,323.9504225668329,1342,158.682,True,True,baseline,323.9504225668329,landmark,True
++4408219312,10.17760/d20705205,,Leveraging type annotations for effective fuzzing of python programs,,2024,dissertation,software_engineering,3,1.0,1.0,304.3333333333333,1,,True,False,post,304.3333333333333,landmark,True
++4402442491,10.5463/thesis.831,,From Smallest Software Particle to System Specification,,2024,dissertation,software_engineering,2,0.0,1.0,304.3333333333333,1,,True,True,post,304.3333333333333,landmark,True
++4403110951,10.32657/10356/179951,,Semantics-aware human-computer interaction software testing,,2024,dissertation,software_engineering,6,1.0,1.0,304.3333333333333,1,,True,True,post,304.3333333333333,landmark,True
++3008797115,10.48550/arxiv.2002.12543,2002.12543,Metamorphic Testing: A New Approach for Generating Next Test Cases,arXiv (Cornell University),2020,preprint,software_engineering,1,246.0,292.0,286.17663817663816,448,,True,True,baseline,286.17663817663816,landmark,True
++3177813494,10.4230/lipics.esem.2026.34,2107.03374,A Direction-Aware Study of LLM-Based Code Translation Across Eight Programming Languages,arXiv (Cornell University),2021,preprint,software_engineering,6,127.0,678.0,275.3873466752744,1430,,True,True,baseline,275.3873466752744,landmark,True
++7110046239,10.4230/oasics.icpec.2025.4,,Can Open Large Language Models Catch Vulnerabilities?,Repositório do ISCTE-IUL,2025,article,software_engineering,3,540.0,540.0,468.828520182987,1,1.0925,True,True,post,273.37978163259135,landmark,True
++4391677389,10.17771/pucrio.acad.65972,,IDENTIFYING CONCERNS WHEN SPECIFYING MACHINE LEARNING-ENABLED SYSTEMS: A PERSPECTIVE-BASED APPROACH,,2023,dissertation,software_engineering,1,7.0,8.0,230.9189189189189,3,,True,True,post,230.9189189189189,landmark,True
++7124592699,10.1007/s44443-025-00401-y,,MSTDP: a multi-scale temporal deep learning framework for just-in-time software defect prediction with cross-attention fusion,Journal of King Saud University - Computer and Information Sciences,2026,article,software_engineering,3,64.0,64.0,454.690720311486,64,585.7812,True,True,post,227.53411707673894,landmark,True
++3007157104,10.1109/tse.2019.2962027,1906.10742,"Machine Learning Testing: Survey, Landscapes and Horizons",IEEE Transactions on Software Engineering,2020,article,software_engineering,4,245.0,385.0,227.48310158362304,888,240.8699,True,True,baseline,227.48310158362304,landmark,True
++58732440,10.22024/unikent/01.02.86458,,An exploration of novice compilation behaviour in BlueJ,Kent Academic Repository (University of Kent),2021,dissertation,software_engineering,3,59.0,60.0,226.01941747572815,61,,True,True,baseline,226.01941747572815,landmark,True
++2725449579,10.1016/bs.adcom.2018.03.015,,Mutation Testing Advances: An Analysis and Survey,Advances in computers,2018,book-chapter,software_engineering,1,63.0,129.0,223.78585461689588,425,230.7729,False,True,baseline,223.78585461689588,landmark,True
++2962772482,10.1145/3236009,,A survey of methods for explaining black box models,ISTI Open Portal,2019,article,software_engineering,1,719.0,1432.0,217.74340797667946,5022,232.193,True,True,baseline,217.74340797667946,landmark,True
++4293212596,10.3847/1538-4357/ac7c74,,The Astropy Project : Sustaining and Growing a Community-oriented Open-source Project and the Latest Major Release (v5.0) of the Core Package*,Repository of the Academy's Library (Library of the Hungarian Academy of Sciences),2022,article,software_engineering,3,704.0,2029.0,209.06737313811422,5022,296.1452,True,True,baseline,209.06737313811422,landmark,True
++4381982883,10.3390/app13095783,,"ChatGPT for Education and Research: Opportunities, Threats, and Strategies",Applied Sciences,2023,article,software_engineering,3,547.0,885.0,207.4225616221095,1062,249.5576,True,True,post,207.4225616221095,landmark,True
++2114444139,10.25946/26315761,,Innovation and Change in the Information Systems Curriculum of an Australian University:a Socio-Technical Perspective,Victoria University Research Repository (Victoria University),2024,dissertation,programming_languages,1,17.0,17.0,661.9925925925926,54,,True,True,post,661.9925925925926,landmark,True
++1844775953,10.82444/warw.33641182,,Metric domains for completeness,Warwick Research Archive Portal (University of Warwick),2026,dissertation,programming_languages,2,34.0,34.0,1040.594982078853,40,,True,True,post,120.87749969701756,landmark,True
++4396242417,10.1145/3649828,,Enhancing Static Analysis for Practical Bug Detection: An LLM-Integrated Approach,Proceedings of the ACM on Programming Languages,2024,article,programming_languages,4,83.0,148.0,117.44112676056338,148,155.4628,True,True,post,117.44112676056338,landmark,True
++2327217190,10.26190/unsworks/23844,,Verification of programs in virtual memory using separation logic,"UNSWorks (University of New South Wales, Sydney, Australia)",2022,dissertation,programming_languages,3,13.0,13.0,97.35745614035089,13,,True,True,baseline,97.35745614035089,landmark,True
++2491926874,10.26190/unsworks/17927,,Formal memory models for verifying C systems code,"UNSWorks (University of New South Wales, Sydney, Australia)",2022,dissertation,programming_languages,5,10.0,11.0,82.37938596491229,24,,True,True,baseline,82.37938596491229,landmark,True
++4205342885,10.1007/s13132-021-00854-2,,The Futures of Europe: Society 5.0 and Industry 5.0 as Driving Forces of Future Universities,Journal of the Knowledge Economy,2022,article,programming_languages,1,171.0,334.0,82.08749781143715,572,117.4739,True,True,baseline,82.08749781143715,landmark,True
++2189687801,10.7275/7408407.0,,General Program Synthesis from Examples Using Genetic Programming with Parent Selection Based on Random Lexicographic Orderings of Test Cases,Scholarworks (University of Massachusetts Amherst),2021,dissertation,programming_languages,3,18.0,18.0,81.87032967032967,18,,True,True,baseline,81.87032967032967,landmark,True
++2400988715,10.48550/arxiv.2605.09524,2605.09524,Functional Stable Model Semantics and Answer Set Programming Modulo Theories,arXiv (Cornell University),2026,preprint,programming_languages,3,22.0,22.0,856.0701754385965,22,,True,True,post,78.21485274512901,landmark,True
++2796251079,10.1007/978-3-319-75844-2_6,,A Concise Introduction to Logic,Springer eBooks,2018,book-chapter,programming_languages,1,139.0,167.0,77.56442638324476,401,44.1726,False,False,baseline,77.56442638324476,landmark,True
++159920301,10.48456/tr-346,,On intuitionistic linear logic,OpenGrey (Institut de l'Information Scientifique et Technique),2021,dissertation,programming_languages,2,16.0,16.0,72.77362637362637,39,,True,True,baseline,72.77362637362637,landmark,True
++4389109635,10.5463/thesis.524,,A Unifying Theory for Graph Transformation,,2023,dissertation,programming_languages,3,2.0,2.0,69.63636363636364,2,,True,True,post,69.63636363636364,landmark,True
++630549275,10.26190/unsworks/16534,,The clustered multikernel: an approach to formal verification of multiprocessor operating-system kernels,UNSWorks (UNSW Sydney),2022,dissertation,programming_languages,5,8.0,9.0,67.40131578947368,10,,True,True,baseline,67.40131578947368,landmark,True
++4403223258,10.1145/3689736,2310.15991,WhiteFox: White-Box Compiler Fuzzing Empowered by Large Language Models,Proceedings of the ACM on Programming Languages,2024,article,programming_languages,8,52.0,82.0,65.0687323943662,82,86.1348,True,True,post,65.0687323943662,landmark,True
++4313480275,10.1007/978-3-031-21762-3_3,,Algorithmics: The Spirit of Computing,SpringerBriefs in computer science,2023,book-chapter,programming_languages,2,64.0,64.0,57.94535519125683,90,30.3187,False,False,post,57.94535519125683,landmark,True
++2790202156,10.14722/ndss.2018.23082,,ZEUS: Analyzing Safety of Smart Contracts,Network and Distributed System Security Symposium,2018,conference-paper,programming_languages,5,94.0,191.0,56.63186311787072,702,60.5919,True,True,baseline,56.63186311787072,landmark,True
++3207822760,10.33612/diss.168003240,,Computer programming skills: A cognitive perspective,,2021,dissertation,programming_languages,1,11.0,15.0,56.50485436893204,21,,True,True,baseline,56.50485436893204,landmark,True
++4297611890,10.1017/9781108778756.017,,Subsystems of Second-Order Arithmetic,Cambridge University Press eBooks,2022,book-chapter,programming_languages,3,59.0,61.0,50.93184096224524,65,4.0902,True,False,baseline,50.93184096224524,landmark,True
++2915211782,10.1017/9781108654555.013,,Novice Programmers and Introductory Programming,Cambridge University Press eBooks,2019,book-chapter,programming_languages,1,11.0,45.0,45.68985849056604,175,59.7005,False,False,baseline,45.68985849056604,landmark,True
++4381516741,10.1007/978-3-031-22206-1_2,,The Python Programming Language,Springer eBooks,2023,book-chapter,programming_languages,2,70.0,82.0,45.335480407944175,96,25.4404,False,False,post,45.335480407944175,landmark,True
++3115704172,,,Lineage-driven Fault Injection for Actor-based Programs,KTH Publication Database DiVA (KTH Royal Institute of Technology),2019,dissertation,programming_languages,3,2.0,2.0,42.74766355140187,2,,True,True,baseline,42.74766355140187,landmark,True
++3146286581,,,Probabilistic Grammatical Evolution,Estudo Geral (Universidade de Coimbra),2021,dissertation,programming_languages,2,4.0,9.0,40.935164835164834,12,,True,True,baseline,40.935164835164834,landmark,True
++7134194765,10.1111/tgis.70218,,A Debris‐Flow Hazard Assessment Framework Based on an Improved AHP – CRITIC Method: A Case Study of Gaoling Gully,Transactions in GIS,2026,article,programming_languages,2,11.0,11.0,15.074074074074074,11,77.5749,True,False,post,39.107426372564504,landmark,True
++2797025890,10.13025/17505,,Openness and praxis: A situated study of academic staff meaning-making and decision-making with respect to openness and use of open educational practices in higher education,Arrow@dit (Dublin Institute of Technology),2018,dissertation,programming_languages,1,3.0,6.0,38.953125,12,,True,True,baseline,38.953125,landmark,True
++4396905794,10.17760/d20653005,,Predicting typeScript type annotations and definitions with machine learning,,2024,dissertation,programming_languages,3,0.0,1.0,38.94074074074074,1,,True,True,post,38.94074074074074,landmark,True
++4410362977,10.1038/s41586-025-08984-2,,Emergence of Calabi–Yau manifolds in high-precision black-hole scattering,Nature,2025,article,programming_languages,1,74.0,74.0,30.45267489711934,74,33.8684,True,True,post,37.46315526076251,landmark,True
++3122286897,10.1109/cgo51591.2021.9370308,,MLIR: Scaling Compiler Infrastructure for Domain Specific Computation,IEEE/ACM International Symposium on Code Generation and Optimization (CGO),2021,conference-paper,compilers,4,74.0,155.0,42.480268473059475,541,52.9944,True,False,baseline,42.480268473059475,landmark,True
++4407983386,10.1016/j.energy.2025.135286,,Parallel TimesNet-BiLSTM model for ultra-short-term photovoltaic power forecasting using STL decomposition and auto-tuning,Energy,2025,article,compilers,1,83.0,83.0,28.27389289459036,83,34.5383,False,False,post,42.01948495463904,landmark,True
++4399087350,10.1007/978-3-031-56586-1_67,,E-learning and the Future of Business Education: The Case of GCC,"Studies in systems, decision and control",2024,book-chapter,compilers,1,25.0,61.0,39.77033011272142,61,44.6583,False,False,post,39.77033011272142,landmark,True
++4398768423,10.1016/j.future.2024.05.034,2310.04963,LLM4VV: Developing LLM-driven testsuite for compiler validation,Future Generation Computer Systems,2024,article,compilers,5,36.0,48.0,38.08901408450704,48,50.4204,False,True,post,38.08901408450704,landmark,True
++2188794726,10.1007/978-3-319-91734-4_32,,Dynamic Dataflow Graphs,Springer eBooks,2018,book-chapter,compilers,2,25.0,25.0,35.799701046337816,26,8.9221,False,False,baseline,35.799701046337816,landmark,True
++4406882143,10.3389/fdgth.2025.1460167,,Beyond the current state of just-in-time adaptive interventions in mental health: a qualitative systematic review,Frontiers in Digital Health,2025,review,compilers,2,54.0,54.0,4.153846153846154,54,8.4784,True,True,post,27.337978163259134,landmark,True
++2884622219,10.1039/c7np00064b,,"The value of universally available raw NMR data for transparency, reproducibility, and integrity in natural product research",Natural Product Reports,2018,article,compilers,1,25.0,51.0,27.040051638148082,122,28.5226,True,True,baseline,27.040051638148082,landmark,True
++3188948186,10.48550/arxiv.2108.05450,2108.05450,Speed Control of DC Motor Using Fuzzy PID Controller,arXiv (Cornell University),2021,preprint,compilers,2,55.0,73.0,24.44561426312255,87,,True,True,baseline,24.44561426312255,landmark,True
++2962724414,10.1109/jproc.2018.2817118,,Machine Learning in Compiler Optimization,Proceedings of the IEEE,2018,article,compilers,4,25.0,64.0,22.34837655707576,193,27.4802,True,True,baseline,22.34837655707576,landmark,True
++4408711372,10.1109/tsg.2025.3553737,,A Progressive Polyhedral Approximation Method for Nonlinear PDE-Constrained Electricity-Water Nexus Dispatch,IEEE Transactions on Smart Grid,2025,article,compilers,3,39.0,39.0,8.943719676549865,39,8.8867,True,False,post,19.744095340131597,landmark,True
++4392397123,10.1016/j.ijer.2024.102326,,Evolution to revolution: Critical exploration of educators’ perceptions of the impact of Artificial Intelligence (AI) on the teaching and learning process in the GCC region,International Journal of Educational Research,2024,article,compilers,1,37.0,64.0,19.268802878681992,64,24.4917,False,True,post,19.268802878681992,landmark,True
++2783614532,10.1145/3197978,1801.04405,A Survey on Compiler Autotuning using Machine Learning,ACM Computing Surveys,2018,article,compilers,5,21.0,55.0,19.205636103736982,233,25.7446,True,True,baseline,19.205636103736982,landmark,True
++2206959660,10.58088/85wq-pw11,,Automatic selection of compiler optimizations using program characterization and machine learning,"Library, Museums and Press - UDSpace (University of Delaware)",2024,dissertation,compilers,5,1.0,1.0,19.080000000000002,1,,True,True,post,19.080000000000002,landmark,True
++2249134499,10.58088/vbbd-jy30,,Improving compiler optimizations using machine learning,"Library, Museums and Press - UDSpace (University of Delaware)",2024,dissertation,compilers,8,1.0,1.0,19.080000000000002,1,,True,True,post,19.080000000000002,landmark,True
++3139485201,,,Towards automated construction of compiler optimizations,DSpace@MIT (Massachusetts Institute of Technology),2020,dissertation,compilers,5,2.0,2.0,18.265060240963855,3,,True,False,baseline,18.265060240963855,landmark,True
++3214427835,,,Sparse tensor algebra compilation,DSpace@MIT (Massachusetts Institute of Technology),2020,dissertation,compilers,3,2.0,2.0,18.265060240963855,3,,True,True,baseline,18.265060240963855,landmark,True
++3023977240,10.7488/era/309,,From constraint programming to heterogeneous parallelism,ERA,2020,dissertation,compilers,8,0.0,2.0,18.265060240963855,2,,True,True,baseline,18.265060240963855,landmark,True
++3046781014,10.1007/978-3-030-47956-5_14,,ExaStencils: Advanced Multigrid Solver Generation,Lecture notes in computational science and engineering,2020,book-chapter,compilers,6,8.0,12.0,18.204255319148935,30,29.7428,True,True,baseline,18.204255319148935,landmark,True
++7127344046,10.1007/s44217-026-01188-4,,Artificial intelligence policy challenges and institutional readiness in Omani higher education,Discover Education,2026,article,compilers,3,5.0,5.0,35.52271252433485,5,45.7642,True,True,post,17.776102896620227,landmark,True
++3132279601,10.1007/978-3-030-62066-0_12,,Coping with COVID-19: Higher Education in the GCC Countries,Springer proceedings in complexity,2021,conference-paper,compilers,1,23.0,37.0,17.288455680779773,43,37.049,False,False,baseline,17.288455680779773,landmark,True
++3103225940,,,An LLVM backend for the Open Modelica Compiler,KTH Publication Database DiVA (KTH Royal Institute of Technology),2019,dissertation,compilers,4,3.0,3.0,16.862068965517242,3,,True,True,baseline,16.862068965517242,landmark,True
++4313894866,10.3390/publications11010002,,"The Science of Literature Reviews: Searching, Identifying, Selecting, and Synthesising",Publications,2023,article,compilers,1,91.0,187.0,16.423071260306244,248,45.5306,True,True,post,16.423071260306244,landmark,True
++4407942537,10.1145/3708493.3712691,,LLM Compiler: Foundation Language Models for Compiler Optimization,ACM SIGPLAN International Conference on Compiler Construction (CC),2025,conference-paper,compilers,6,31.0,31.0,23.55532716683796,31,36.5894,True,True,post,15.694024501130242,landmark,True
++2787911489,10.1007/978-3-319-68394-2_11,,Automated Code Generation for Maximizing Performance of Detailed Chemistry Calculations in OpenFOAM,Springer eBooks,2018,book-chapter,compilers,4,5.0,10.0,14.319880418535128,37,25.2794,False,False,baseline,14.319880418535128,landmark,True
++2949967139,10.1109/cgo.2019.8661197,,Tiramisu: A Polyhedral Compiler for Expressing Fast and Portable Code,IEEE/ACM International Symposium on Code Generation and Optimization (CGO),2019,conference-paper,compilers,6,26.0,53.0,14.250956889605158,182,15.9098,True,True,baseline,14.250956889605158,landmark,True
++4385245566,10.4230/lipics.itp.2023.19,,Exploiting Generative AI to Scale up Intelligent Tutoring Systems,DROPS (Schloss Dagstuhl – Leibniz Center for Informatics),2023,article,formal_verification,3,54921.0,72130.0,6621.879465206806,76658,4908.38,True,True,post,6621.879465206806,landmark,True
++1498342204,10.1145/3788687,,An Introduction to Input/Output Automata,Formal Aspects of Computing,2026,article,formal_verification,2,284.0,284.0,801.9048786467272,1011,3.5639,True,True,post,1009.682644528029,landmark,True
++3117137355,10.1145/3815481,,egg: Fast and Extensible Equality Saturation,Communications of the ACM,2026,article,formal_verification,3,132.0,132.0,146.88980220909326,132,0.0,True,False,post,469.28911647077405,landmark,True
++1481397690,10.1007/978-3-319-10575-8_11,,Satisfiability Modulo Theories,Springer eBooks,2018,book-chapter,formal_verification,2,501.0,585.0,432.45569620253167,1122,250.357,False,False,baseline,432.45569620253167,landmark,True
++2254118105,10.48550/arxiv.2608.14274,2608.14274,Solving QBF by Clause Selection,arXiv (Cornell University),2026,preprint,formal_verification,3,60.0,60.0,2151.793313069909,60,,True,True,post,213.31323475944274,landmark,True
++4206807714,10.3233/faia336,,Handbook of Satisfiability,Frontiers in artificial intelligence and applications,2021,book,formal_verification,2,678.0,777.0,184.30546075085323,1038,93.9568,False,False,baseline,184.30546075085323,landmark,True
++2540354435,10.48550/arxiv.2605.09519,2605.09519,Weighted Rules under the Stable Model Semantics,arXiv (Cornell University),2026,preprint,formal_verification,2,39.0,39.0,1517.578947368421,39,,True,True,post,138.65360259363777,landmark,True
++2921681895,10.18154/rwth-2020-02348,1903.07993,Parameter synthesis in Markov models,RWTH Publications (RWTH Aachen),2020,dissertation,formal_verification,6,14.0,19.0,111.636815920398,26,,True,True,baseline,111.636815920398,landmark,True
++4225922019,10.1007/978-3-030-99524-9_24,,cvc5: A Versatile and Industrial-Strength SMT Solver,Lecture notes in computer science,2022,conference-paper,formal_verification,4,111.0,222.0,98.23784328546233,468,135.8061,True,True,baseline,98.23784328546233,landmark,True
++4318573502,10.5463/thesis.149,,Semantically informed methods in structural proof theory,,2022,dissertation,formal_verification,2,9.0,13.0,97.35745614035089,14,,True,True,baseline,97.35745614035089,landmark,True
++2154148696,10.1007/978-3-319-77452-7_9,,Supervisory Control of Timed Discrete-Event Systems,Communications and control engineering/Communications and control engineering series,2018,book-chapter,formal_verification,1,100.0,117.0,86.49113924050633,365,36.4666,False,False,baseline,86.49113924050633,landmark,True
++4297781990,10.4230/oasics.dx.2024.10,,"Leveraging Answer Set Programming for Continuous Monitoring, Fault Detection, and Explanation of Automated and Autonomous Driving Systems",HAL (Le Centre pour la Communication Scientifique Directe),2024,preprint,formal_verification,3,426.0,431.0,78.93772893772893,431,,True,True,post,78.93772893772893,landmark,True
++2789444712,10.1080/00207543.2018.1444806,,Industry 4.0: state of the art and future trends,International Journal of Production Research,2018,article,formal_verification,4,298.0,734.0,61.81756449008323,3221,267.6956,True,False,baseline,61.81756449008323,landmark,True
++1586677474,,,Handbook of Satisfiability,,2021,article,formal_verification,2,374.0,375.0,60.365327611379016,486,1.7496,True,False,baseline,60.365327611379016,landmark,True
++2486519520,10.48456/tr-792,,Machine learning and automated theorem proving,OpenGrey (Institut de l'Information Scientifique et Technique),2021,dissertation,formal_verification,4,12.0,12.0,54.58021978021978,14,,True,True,baseline,54.58021978021978,landmark,True
++4289665848,10.4230/lipics.csl.2026.30,,Reward Interfaces with Best-Effort Implementations,DROPS (Schloss Dagstuhl – Leibniz Center for Informatics),2026,article,formal_verification,4,15.0,15.0,42.354130914439814,15,0.0,True,True,post,53.328308689860684,landmark,True
++149631060,10.48550/arxiv.2411.01592,2411.01592,Decidability Issues for Petri Nets -- a survey,arXiv (Cornell University),2024,preprint,formal_verification,3,33.0,33.0,53.32230215827339,72,,True,True,post,53.32230215827339,landmark,True
++2890660842,10.48550/arxiv.1809.08098,1809.08098,Efficient Formal Safety Analysis of Neural Networks,arXiv (Cornell University),2018,preprint,formal_verification,6,41.0,98.0,52.040918390543304,167,,True,True,baseline,52.040918390543304,landmark,True
++1932627192,10.82444/warw.33484729,,Model checking quantum protocols,Warwick Research Archive Portal (University of Warwick),2026,dissertation,formal_verification,4,14.0,14.0,141.45283018867926,18,,True,True,post,49.77308811053664,landmark,True
++4393942723,10.1007/978-3-031-57256-2_15,,State of the Art in Software Verification and Witness Validation: SV-COMP 2024,Lecture notes in computer science,2024,conference-paper,formal_verification,4,66.0,78.0,48.594819515239784,78,53.289,True,True,post,48.594819515239784,landmark,True
++2957311447,10.1007/978-3-030-25540-4_26,,The Marabou Framework for Verification and Analysis of Deep Neural Networks,Lecture notes in computer science,2019,conference-paper,formal_verification,4,55.0,141.0,46.39818783571157,498,53.6017,True,True,baseline,46.39818783571157,landmark,True
++2905489900,,,Machine Learning for SAT Solvers,UWSpace (University of Waterloo),2018,dissertation,formal_verification,3,3.0,5.0,44.97120921305182,9,,True,True,baseline,44.97120921305182,landmark,True
++2900153411,10.1145/3290354,,An abstract domain for certifying neural networks,Proceedings of the ACM on Programming Languages,2019,article,formal_verification,3,99.0,209.0,44.06671585274556,609,46.6555,True,True,baseline,44.06671585274556,landmark,True
++6944541633,10.18154/rwth-2023-09669,,Verification of multi-objective Markov models,RWTH Publications (RWTH Aachen),2023,dissertation,formal_verification,6,2.0,2.0,43.15151515151515,4,,True,True,post,43.15151515151515,landmark,True
++3016155638,10.1109/sp40000.2020.00024,,VerX: Safety Verification of Smart Contracts,Proceedings - IEEE Symposium on Security and Privacy/Proceedings of the ... IEEE Symposium on Security and Privacy,2020,conference-paper,formal_verification,6,76.0,114.0,42.08811748998665,247,40.057,True,True,baseline,42.08811748998665,landmark,True

added literature_synthesis/landmarks_v1/theory_landmarks.csv +51 −0

++work_id,doi,arxiv_id,title,source_name,year,type,primary_category,n_categories,c12,c36,ncs36,cited_by_count,fwci,has_abstract,is_oa,period,score,set,pi_review
++4311415873,10.4230/lipics.wads.2025.45,,Fast Kd-Trees for the Kullback-Leibler Divergence and Other Decomposable Bregman Divergences,Leibniz international proceedings in informatics,2025,conference-paper,algorithms,3,2355.0,2355.0,1915.3516038917253,2354,1042.305,True,True,post,1192.2396032310232,landmark,True
++2402846924,10.1142/9789811205675_0004,,Particle Swarm Optimization,WORLD SCIENTIFIC eBooks,2019,book-chapter,algorithms,1,3563.0,3767.0,1171.7895003921208,6270,302.7094,False,False,baseline,1171.7895003921208,landmark,True
++4294558607,10.4230/lipics.sea.2024.20,,Targeted Branching for the Maximum Independent Set Problem Using Graph Neural Networks,DROPS (Schloss Dagstuhl – Leibniz Center for Informatics),2024,article,algorithms,4,5110.0,5183.0,861.3021555600685,5183,320.7213,True,True,post,861.3021555600685,landmark,True
++4372046852,10.1007/978-3-031-26212-8_4,,Introduction to Algorithms,Texts in computer science,2023,book-chapter,algorithms,1,880.0,1114.0,804.2780269058295,1614,491.6762,False,False,post,804.2780269058295,landmark,True
++4285092328,10.1145/3544585.3544600,,A Note on Two Problems in Connexion with Graphs,ACM eBooks,2022,book-chapter,algorithms,1,631.0,868.0,724.7350484463749,1353,589.8043,True,False,baseline,724.7350484463749,landmark,True
++3148190347,10.1142/9789811273117_0001,,Introduction to Graph Theory,WORLD SCIENTIFIC eBooks,2023,book-chapter,algorithms,1,502.0,503.0,455.41427595628414,885,6.2728,False,False,post,455.41427595628414,landmark,True
++7128526706,10.1007/s10462-026-11504-x,,Glider snake optimizer (GSO): a nature-inspired metaheuristic algorithm for global and engineering optimization problems,Artificial Intelligence Review,2026,article,algorithms,5,91.0,91.0,101.26493940172338,91,328.7483,True,True,post,323.52507271848816,landmark,True
++2112641412,10.37236/11668,,A Dynamic Survey of Graph Labeling,The Electronic Journal of Combinatorics,2022,article,algorithms,1,1698.0,1780.0,288.61814892416174,2162,44.7232,True,True,post,288.61814892416174,landmark,True
++3183881215,10.1007/978-1-4842-7077-6_3,,Introduction to Algorithms,Apress eBooks,2021,book-chapter,algorithms,1,276.0,434.0,279.4307771487391,877,222.2179,True,False,baseline,279.4307771487391,landmark,True
++2003494253,10.1017/9781009260350.002,,Algorithms+Data Structures = Programs,Cambridge University Press eBooks,2024,book-chapter,algorithms,2,231.0,231.0,275.60804020100505,953,13.0275,True,False,post,275.60804020100505,landmark,True
++7125913058,10.1109/tsmc.2026.3655483,,Multiresource-Constrained Flexible Job Shop Scheduling With Fixture Pallets and Setup Stations Under Pallet Automation Systems,IEEE Transactions on Systems Man and Cybernetics Systems,2026,article,algorithms,3,73.0,73.0,84.02038567493113,73,309.1219,True,True,post,259.5311022906553,landmark,True
++1491280261,10.26190/unsworks/6784,,Grammatical bias for evolutionary learning,"UNSWorks (University of New South Wales, Sydney, Australia)",2022,dissertation,algorithms,1,26.0,28.0,209.69298245614038,96,,True,True,baseline,209.69298245614038,landmark,True
++3111526966,10.1201/9781003091479-1,,Introduction to Algorithms,Chapman & Hall/CRC eBooks,2020,book-chapter,algorithms,1,193.0,202.0,200.82966396292002,428,40.7298,True,False,baseline,200.82966396292002,landmark,True
++3119051141,10.1016/j.cma.2020.113609,,The Arithmetic Optimization Algorithm,Computer Methods in Applied Mechanics and Engineering,2021,article,algorithms,2,900.0,1513.0,176.3286814620167,2928,183.3132,True,True,baseline,176.3286814620167,landmark,True
++4406080942,10.1007/s10462-024-11023-7,,Artificial lemming algorithm: a novel bionic meta-heuristic technique for solving real-world engineering optimization problems,Artificial Intelligence Review,2025,article,algorithms,3,330.0,330.0,112.41427295439541,330,137.3209,True,True,post,167.06542210880582,landmark,True
++2146101030,10.82444/warw.33642076,,Communication complexity of document exchange,Symposium on Discrete Algorithms,2026,conference-paper,algorithms,3,46.0,46.0,353.0526747845123,122,9.2449,True,True,post,163.5401466489061,landmark,True
++4405348595,10.1142/9789811299223_0004,,Positive Definite Matrices,WORLD SCIENTIFIC eBooks,2024,book-chapter,algorithms,2,125.0,125.0,151.20861797162374,135,24.6654,True,True,post,151.20861797162374,landmark,True
++7118096636,10.1016/j.eswa.2026.131088,,A modified starfish optimization algorithm (M-SFOA) for global optimization problems and its application to heart disease risk prediction,Expert Systems with Applications,2026,article,algorithms,1,42.0,42.0,46.737664339256945,42,151.73,False,False,post,149.31926433160993,landmark,True
++4233225156,10.1007/978-3-030-34209-8_4,,Introduction to Algorithms,Undergraduate topics in computer science,2020,book-chapter,algorithms,1,141.0,149.0,148.13673232908457,330,38.8354,False,False,baseline,148.13673232908457,landmark,True
++2891784792,10.48550/arxiv.2407.17712,2407.17712,Improving Online Algorithms via ML Predictions,arXiv (Cornell University),2024,preprint,algorithms,2,111.0,112.0,142.64098765432098,112,,True,True,post,142.64098765432098,landmark,True
++7155156112,10.1007/s10462-026-11529-2,,Gray langurs optimizer: a multi-group bio-inspired optimization algorithm,Artificial Intelligence Review,2026,article,algorithms,1,40.0,40.0,44.5120612754828,40,144.5047,True,True,post,142.20882317296181,landmark,True
++3139484821,10.1016/j.cie.2021.107250,,Aquila Optimizer: A novel meta-heuristic optimization algorithm,Computers & Industrial Engineering,2021,article,algorithms,1,683.0,1186.0,138.21931012158083,2213,141.9398,False,False,baseline,138.21931012158083,landmark,True
++2121903617,10.1201/9781482268713-14,,Introduction to evolutionary algorithms,CRC Press eBooks,2018,book-chapter,algorithms,1,260.0,292.0,135.62163175992498,349,50.6462,True,False,baseline,135.62163175992498,landmark,True
++1486707681,10.82444/warw.33642484,,Sequence distance embeddings,Warwick Research Archive Portal (University of Warwick),2026,dissertation,algorithms,2,38.0,38.0,1163.0179211469535,64,,True,True,post,135.09838201431373,landmark,True
++2207864362,10.58088/v6j9-5f14,,Properties of some algebraically defined digraphs,"Library, Museums and Press - UDSpace (University of Delaware)",2024,dissertation,algorithms,1,6.0,6.0,134.92307692307693,6,,True,True,post,134.92307692307693,landmark,True
++4254202520,10.7551/mitpress/12274.003.0008,,"On Computable Numbers, with an Application to the Entscheidungsproblem (1936)",The MIT Press eBooks,2021,book-chapter,computational_complexity,1,312.0,353.0,227.2789500772002,521,95.7524,False,False,baseline,227.2789500772002,landmark,True
++1638203394,10.1007/978-3-030-11298-1,,An Introduction to Kolmogorov Complexity and Its Applications,Texts in computer science,2019,book,computational_complexity,2,1326.0,1437.0,122.10728346456693,3681,45.0313,False,False,baseline,122.10728346456693,landmark,True
++2979043732,,,ETH-tight algorithms for geometric network problems,TU/e Research Portal,2019,dissertation,computational_complexity,4,5.0,5.0,111.01694915254237,6,,True,True,baseline,111.01694915254237,landmark,True
++2957144896,10.3389/fpsyg.2019.01652,,Factors Determining the Behavioral Intention to Use Mobile Learning: An Application and Extension of the UTAUT Model,Frontiers in Psychology,2019,article,computational_complexity,1,48.0,177.0,72.24848261060211,1039,114.0294,True,True,baseline,72.24848261060211,landmark,True
++4241184911,10.1201/9780429494093-6,,Two-Dimensional Cellular Automata,CRC Press eBooks,2018,book-chapter,computational_complexity,2,86.0,93.0,68.7493670886076,307,25.2461,True,False,baseline,68.7493670886076,landmark,True
++4394806711,10.1016/j.heliyon.2024.e29523,,Artificial intelligence and social media on academic performance and mental well-being: Student perceptions of positive impact in the age of smart learning,Heliyon,2024,article,computational_complexity,1,118.0,197.0,59.311783860943,197,74.6231,True,True,post,59.311783860943,landmark,True
++4386113651,10.48550/arxiv.2308.08708,2308.08708,Cheap Correlate or Discriminating Property? An Executable Validation Protocol for Consciousness- and Cognition-Theoretic Claims about AI,arXiv (Cornell University),2023,preprint,computational_complexity,1,89.0,170.0,57.664,193,,True,True,post,57.664,landmark,True
++1513303009,,,Cellular Automata And Complexity: Collected Papers,CERN Document Server (European Organization for Nuclear Research),2019,article,computational_complexity,5,236.0,251.0,52.922228129373856,575,9.8222,True,True,baseline,52.922228129373856,landmark,True
++4406215947,10.3390/systems13010043,,"Digital Transformation and Innovation Performance in Small- and Medium-Sized Enterprises: A Systems Perspective on the Interplay of Digital Adoption, Digital Drive, and Digital Culture",Systems,2025,article,computational_complexity,1,104.0,104.0,24.553686934023286,104,125.7829,True,True,post,52.650920907017586,landmark,True
++2905177801,,,Optimal trees for prediction and prescription,DSpace@MIT (Massachusetts Institute of Technology),2018,dissertation,computational_complexity,1,2.0,5.0,51.149155722326455,27,,True,True,baseline,51.149155722326455,landmark,True
++2955897898,10.1109/access.2019.2921522,,Wireless Communications and Applications Above 100 GHz: Opportunities and Challenges for 6G and Beyond,IEEE Access,2019,article,computational_complexity,2,240.0,596.0,49.00829497340763,2594,88.2939,True,True,baseline,49.00829497340763,landmark,True
++4377102915,10.1016/j.heliyon.2023.e16348,,"Challenges, opportunities, and prospects of adopting and using smart digital technologies in learning environments: An iterative review",Heliyon,2023,article,computational_complexity,1,78.0,204.0,47.81265827221507,331,77.7811,True,True,post,47.81265827221507,landmark,True
++3139085460,,,Reorienting machine learning education towards tinkerers and ML-engaged citizens,DSpace@MIT (Massachusetts Institute of Technology),2020,dissertation,computational_complexity,1,3.0,8.0,45.743589743589745,16,,True,False,baseline,45.743589743589745,landmark,True
++2808541536,,,Meet-continuity and locally compact sober dcpos,University of Birmingham Institutional Research Archive (University of Birmingham),2018,dissertation,computational_complexity,3,2.0,10.0,44.30861723446894,19,,True,True,baseline,44.30861723446894,landmark,True
++73572444,10.4324/9781003213574,,Complexity Theory and the Social Sciences,Routledge eBooks,2022,book,computational_complexity,1,197.0,215.0,43.70967741935484,454,13.2502,True,False,baseline,43.70967741935484,landmark,True
++7122741622,10.3390/metrology6010003,,"Recent Advances in Digital Fringe Projection Profilometry (2022–2025): Techniques, Applications, and Metrological Challenges—A Review",Metrology,2026,article,computational_complexity,1,12.0,12.0,32.16440030557678,12,37.3956,True,True,post,42.66264695188855,landmark,True
++7133302269,10.1016/j.chbr.2026.100986,,AI-assisted learning tools and student learning outcomes: A cognitive load theory perspective,Computers in Human Behavior Reports,2026,article,computational_complexity,1,12.0,12.0,85.25451005840362,12,109.834,True,True,post,42.66264695188855,landmark,True
++7160425037,10.1016/j.tcs.2026.116009,,Solving SAT by restricted spiking neural P systems with temporal division rules,Theoretical Computer Science,2026,article,computational_complexity,3,12.0,12.0,16.14637579169599,12,22.4653,False,False,post,42.66264695188855,landmark,True
++4391177866,10.1007/978-3-031-42209-6_2,,Computational Complexity,Undergraduate topics in computer science,2024,book-chapter,computational_complexity,2,32.0,32.0,38.1794687724336,49,0.0,False,False,post,38.1794687724336,landmark,True
++4396808240,10.1007/978-3-031-53744-8_9,,Parameterized Complexity,Undergraduate topics in computer science,2024,book-chapter,computational_complexity,2,29.0,29.0,35.08039936941671,31,20.0406,False,False,post,35.08039936941671,landmark,True
++3086066649,10.1108/aeds-04-2020-0063,,The role of digital learning toward students' satisfaction and university brand image at educational institutes of Pakistan: a post-effect of COVID-19,Asian Education and Development Studies,2020,article,computational_complexity,1,40.0,110.0,32.031113421948945,216,36.2625,True,False,baseline,32.031113421948945,landmark,True
++7127964361,10.3390/systems14020179,,"Turning Knowledge into Innovation: The Systemic Role of Knowledge Management Capability, Intellectual Capital, and Knowledge Utilization",Systems,2026,article,computational_complexity,1,9.0,9.0,8.79467680608365,9,57.758,True,True,post,31.99698521391641,landmark,True
++3122335070,,,Design and development of a mobile learning system for computer science education in Nigerian higher education context,UEF eRepo (University of Eastern Finland),2018,dissertation,computational_complexity,1,2.0,3.0,30.68949343339587,5,,True,True,baseline,30.68949343339587,landmark,True
++2900179285,10.1145/3313276.3316369,1809.07115,The Reachability Problem for Petri Nets Is Not Elementary,ACM SIGACT Symposium on Theory of Computing (STOC),2019,conference-paper,computational_complexity,4,56.0,92.0,30.27399489989691,145,23.6478,True,True,baseline,30.27399489989691,landmark,True
++4408441779,10.1109/tem.2025.3551331,,Enhancing Creativity and Sustainable Competitive Advantage Through Data-Driven Decision-Making and Digital Leadership,IEEE Transactions on Engineering Management,2025,article,computational_complexity,1,59.0,59.0,19.444444444444446,59,84.1776,True,False,post,29.86927243763498,landmark,True

modified literature_synthesis/select_landmarks.py +8 −0

				`@@ -20,6 +20,14 @@ def main():`
20	df = con.execute("""SELECT c.id AS work_id, c.doi, c.arxiv_id_linked AS arxiv_id, c.title, c.source_name, c.publication_year AS year, c.type, p.primary_category, p.n_categories,	20	df = con.execute("""SELECT c.id AS work_id, c.doi, c.arxiv_id_linked AS arxiv_id, c.title, c.source_name, c.publication_year AS year, c.type, p.primary_category, p.n_categories,
21	w.c12, w.c36, w.ncs36, c.cited_by_count, c.fwci, c.has_abstract, c.is_oa, c.period	21	w.c12, w.c36, w.ncs36, c.cited_by_count, c.fwci, c.has_abstract, c.is_oa, c.period
22	FROM corpus_works c JOIN work_primary p ON p.work_id = c.id JOIN wcw w ON w.work_id = c.id WHERE c.eligible AND NOT c.e6_retracted""").df()	22	FROM corpus_works c JOIN work_primary p ON p.work_id = c.id JOIN wcw w ON w.work_id = c.id WHERE c.eligible AND NOT c.e6_retracted""").df()
		23	+ # cleaning (2026-11-03, after Agent H's audit of the first list): drop venue-like records (title equal to the source name
		24	+ # or shorter than four words), records with neither abstract nor reference list, and duplicate titles (keep the earliest
		25	+ # year, then the most cited) — OpenAlex carries SSRN/reprint duplicates of landmark papers
		26	+ import re as _re
		27	+ df["ntitle"] = df.title.fillna("").str.lower().str.replace(r"[^a-z0-9]+", "", regex=True)
		28	+ df = df[(df.title.fillna("").str.split().str.len() >= 4) & (df.title.fillna("").str.lower() != df.source_name.fillna("").str.lower())]
		29	+ df = df[df.has_abstract \| (df.cited_by_count > 0)]
		30	+ df = df.sort_values(["ntitle", "year", "c36"], ascending=[True, True, False]).drop_duplicates("ntitle", keep="first")
23	df["score"] = df.ncs36.fillna(0); recent = df.year >= 2025; df.loc[recent, "score"] = df.loc[recent, "c12"].fillna(0) / (df.loc[recent].groupby("year").c12.transform("mean") + 1e-9)	31	df["score"] = df.ncs36.fillna(0); recent = df.year >= 2025; df.loc[recent, "score"] = df.loc[recent, "c12"].fillna(0) / (df.loc[recent].groupby("year").c12.transform("mean") + 1e-9)
24	cell = df.groupby(["primary_category", "year"]).size(); alloc = (cell / cell.sum() * a.n_high).clip(lower=25).round().astype(int)	32	cell = df.groupby(["primary_category", "year"]).size(); alloc = (cell / cell.sum() * a.n_high).clip(lower=25).round().astype(int)
25	parts = []	33	parts = []

modified manuscript/appendices/appF_supplementary_tables.tex +10 −0

				`@@ -67,3 +67,13 @@ This appendix collects the generated tables not shown in the main text. Every ta`
67	\clearpage	67	\clearpage
68	\input{tables/tab_systems_categories}	68	\input{tables/tab_systems_categories}
69	\clearpage	69	\clearpage
		70	+\input{tables/tab_theory_counts}
		71	+\clearpage
		72	+\input{tables/tab_theory_summary}
		73	+\clearpage
		74	+\input{tables/tab_theory_llm_cooccurrence}
		75	+\clearpage
		76	+\input{tables/tab_theory_landmark_audit}
		77	+\clearpage
		78	+\input{tables/tab_theory_landmarks_discussed}
		79	+\clearpage

modified manuscript/tables/TABLES.json +1 −1

				`@@ -87,7 +87,7 @@`
87	"sources": [	87	"sources": [
88	"results/bibliometrics/diversity_yearly.csv"	88	"results/bibliometrics/diversity_yearly.csv"
89	],	89	],
90	− "rows": 8	90	+ "rows": 16
91	},	91	},
92	{	92	{
93	"file": "tab_cagr.tex",	93	"file": "tab_cagr.tex",

modified manuscript/tables/numbers.tex +356 −356

				`@@ -4809,364 +4809,364 @@`
4809	\newcommand{\KnownSetMedianLeadMonths}{7}	4809	\newcommand{\KnownSetMedianLeadMonths}{7}
4810	\newcommand{\KnownSetNDetected}{12}	4810	\newcommand{\KnownSetNDetected}{12}
4811	\newcommand{\KnownSetNPositiveLead}{11}	4811	\newcommand{\KnownSetNPositiveLead}{11}
4812	−\newcommand{\CagrAiAcceleratorsNTwoZeroOneEight}{409}	4812	+\newcommand{\CagrAiAcceleratorsNTwoZeroOneEight}{818}
4813	−\newcommand{\CagrAiAcceleratorsNTwoZeroTwoTwo}{710}	4813	+\newcommand{\CagrAiAcceleratorsNTwoZeroTwoTwo}{1{,}421}
4814	−\newcommand{\CagrAiAcceleratorsNTwoZeroTwoThree}{799}	4814	+\newcommand{\CagrAiAcceleratorsNTwoZeroTwoThree}{1{,}599}
4815	−\newcommand{\CagrAiAcceleratorsNTwoZeroTwoFive}{953}	4815	+\newcommand{\CagrAiAcceleratorsNTwoZeroTwoFive}{1{,}906}
4816	\newcommand{\CagrAiAcceleratorsCagrBaseline}{0.148}	4816	\newcommand{\CagrAiAcceleratorsCagrBaseline}{0.148}
4817	\newcommand{\CagrAiAcceleratorsCagrPost}{0.092}	4817	\newcommand{\CagrAiAcceleratorsCagrPost}{0.092}
4818	\newcommand{\CagrAiAcceleratorsShareTwoZeroOneEight}{0.002}	4818	\newcommand{\CagrAiAcceleratorsShareTwoZeroOneEight}{0.002}
4819	\newcommand{\CagrAiAcceleratorsShareTwoZeroTwoTwo}{0.003}	4819	\newcommand{\CagrAiAcceleratorsShareTwoZeroTwoTwo}{0.003}
4820	\newcommand{\CagrAiAcceleratorsShareTwoZeroTwoFive}{0.002}	4820	\newcommand{\CagrAiAcceleratorsShareTwoZeroTwoFive}{0.002}
4821	−\newcommand{\CagrAiAgentsNTwoZeroOneEight}{2{,}718}	4821	+\newcommand{\CagrAiAgentsNTwoZeroOneEight}{5{,}436}
4822	−\newcommand{\CagrAiAgentsNTwoZeroTwoTwo}{2{,}260}	4822	+\newcommand{\CagrAiAgentsNTwoZeroTwoTwo}{4{,}521}
4823	−\newcommand{\CagrAiAgentsNTwoZeroTwoThree}{2{,}495}	4823	+\newcommand{\CagrAiAgentsNTwoZeroTwoThree}{4{,}991}
4824	−\newcommand{\CagrAiAgentsNTwoZeroTwoFive}{4{,}409}	4824	+\newcommand{\CagrAiAgentsNTwoZeroTwoFive}{8{,}819}
4825	\newcommand{\CagrAiAgentsCagrBaseline}{-0.045}	4825	\newcommand{\CagrAiAgentsCagrBaseline}{-0.045}
4826	\newcommand{\CagrAiAgentsCagrPost}{0.329}	4826	\newcommand{\CagrAiAgentsCagrPost}{0.329}
4827	−\newcommand{\CagrAiAgentsShareTwoZeroOneEight}{0.016}	4827	+\newcommand{\CagrAiAgentsShareTwoZeroOneEight}{0.011}
4828	−\newcommand{\CagrAiAgentsShareTwoZeroTwoTwo}{0.009}	4828	+\newcommand{\CagrAiAgentsShareTwoZeroTwoTwo}{0.008}
4829	\newcommand{\CagrAiAgentsShareTwoZeroTwoFive}{0.010}	4829	\newcommand{\CagrAiAgentsShareTwoZeroTwoFive}{0.010}
4830	−\newcommand{\CagrAiSafetyNTwoZeroOneEight}{526}	4830	+\newcommand{\CagrAiSafetyNTwoZeroOneEight}{1{,}052}
4831	−\newcommand{\CagrAiSafetyNTwoZeroTwoTwo}{1{,}477}	4831	+\newcommand{\CagrAiSafetyNTwoZeroTwoTwo}{2{,}954}
4832	−\newcommand{\CagrAiSafetyNTwoZeroTwoThree}{2{,}007}	4832	+\newcommand{\CagrAiSafetyNTwoZeroTwoThree}{4{,}015}
4833	−\newcommand{\CagrAiSafetyNTwoZeroTwoFive}{5{,}442}	4833	+\newcommand{\CagrAiSafetyNTwoZeroTwoFive}{10{,}885}
4834	\newcommand{\CagrAiSafetyCagrBaseline}{0.294}	4834	\newcommand{\CagrAiSafetyCagrBaseline}{0.294}
4835	\newcommand{\CagrAiSafetyCagrPost}{0.647}	4835	\newcommand{\CagrAiSafetyCagrPost}{0.647}
4836	−\newcommand{\CagrAiSafetyShareTwoZeroOneEight}{0.003}	4836	+\newcommand{\CagrAiSafetyShareTwoZeroOneEight}{0.002}
4837	−\newcommand{\CagrAiSafetyShareTwoZeroTwoTwo}{0.006}	4837	+\newcommand{\CagrAiSafetyShareTwoZeroTwoTwo}{0.005}
4838	−\newcommand{\CagrAiSafetyShareTwoZeroTwoFive}{0.013}	4838	+\newcommand{\CagrAiSafetyShareTwoZeroTwoFive}{0.012}
4839	−\newcommand{\CagrAlgorithmsNTwoZeroOneEight}{9{,}716}	4839	+\newcommand{\CagrAlgorithmsNTwoZeroOneEight}{29{,}148}
4840	−\newcommand{\CagrAlgorithmsNTwoZeroTwoTwo}{9{,}400}	4840	+\newcommand{\CagrAlgorithmsNTwoZeroTwoTwo}{28{,}202}
4841	−\newcommand{\CagrAlgorithmsNTwoZeroTwoThree}{9{,}510}	4841	+\newcommand{\CagrAlgorithmsNTwoZeroTwoThree}{28{,}531}
4842	−\newcommand{\CagrAlgorithmsNTwoZeroTwoFive}{11{,}265}	4842	+\newcommand{\CagrAlgorithmsNTwoZeroTwoFive}{33{,}795}
4843	\newcommand{\CagrAlgorithmsCagrBaseline}{-0.008}	4843	\newcommand{\CagrAlgorithmsCagrBaseline}{-0.008}
4844	\newcommand{\CagrAlgorithmsCagrPost}{0.088}	4844	\newcommand{\CagrAlgorithmsCagrPost}{0.088}
4845	−\newcommand{\CagrAlgorithmsShareTwoZeroOneEight}{0.057}	4845	+\newcommand{\CagrAlgorithmsShareTwoZeroOneEight}{0.058}
4846	−\newcommand{\CagrAlgorithmsShareTwoZeroTwoTwo}{0.037}	4846	+\newcommand{\CagrAlgorithmsShareTwoZeroTwoTwo}{0.050}
4847	−\newcommand{\CagrAlgorithmsShareTwoZeroTwoFive}{0.026}	4847	+\newcommand{\CagrAlgorithmsShareTwoZeroTwoFive}{0.038}
4848	−\newcommand{\CagrAppliedCryptographyNTwoZeroOneEight}{3{,}594}	4848	+\newcommand{\CagrAppliedCryptographyNTwoZeroOneEight}{10{,}782}
4849	−\newcommand{\CagrAppliedCryptographyNTwoZeroTwoTwo}{5{,}149}	4849	+\newcommand{\CagrAppliedCryptographyNTwoZeroTwoTwo}{10{,}299}
4850	−\newcommand{\CagrAppliedCryptographyNTwoZeroTwoThree}{3{,}739}	4850	+\newcommand{\CagrAppliedCryptographyNTwoZeroTwoThree}{11{,}219}
4851	−\newcommand{\CagrAppliedCryptographyNTwoZeroTwoFive}{7{,}306}	4851	+\newcommand{\CagrAppliedCryptographyNTwoZeroTwoFive}{14{,}612}
4852	−\newcommand{\CagrAppliedCryptographyCagrBaseline}{0.094}	4852	+\newcommand{\CagrAppliedCryptographyCagrBaseline}{-0.011}
4853	−\newcommand{\CagrAppliedCryptographyCagrPost}{0.398}	4853	+\newcommand{\CagrAppliedCryptographyCagrPost}{0.141}
4854	−\newcommand{\CagrAppliedCryptographyShareTwoZeroOneEight}{0.021}	4854	+\newcommand{\CagrAppliedCryptographyShareTwoZeroOneEight}{0.022}
4855	−\newcommand{\CagrAppliedCryptographyShareTwoZeroTwoTwo}{0.020}	4855	+\newcommand{\CagrAppliedCryptographyShareTwoZeroTwoTwo}{0.018}
4856	−\newcommand{\CagrAppliedCryptographyShareTwoZeroTwoFive}{0.017}	4856	+\newcommand{\CagrAppliedCryptographyShareTwoZeroTwoFive}{0.016}
4857	−\newcommand{\CagrAutonomousScientificDiscoveryNTwoZeroOneEight}{43}	4857	+\newcommand{\CagrAutonomousScientificDiscoveryNTwoZeroOneEight}{86}
4858	−\newcommand{\CagrAutonomousScientificDiscoveryNTwoZeroTwoTwo}{118}	4858	+\newcommand{\CagrAutonomousScientificDiscoveryNTwoZeroTwoTwo}{236}
4859	−\newcommand{\CagrAutonomousScientificDiscoveryNTwoZeroTwoThree}{200}	4859	+\newcommand{\CagrAutonomousScientificDiscoveryNTwoZeroTwoThree}{400}
4860	−\newcommand{\CagrAutonomousScientificDiscoveryNTwoZeroTwoFive}{382}	4860	+\newcommand{\CagrAutonomousScientificDiscoveryNTwoZeroTwoFive}{764}
4861	\newcommand{\CagrAutonomousScientificDiscoveryCagrBaseline}{0.287}	4861	\newcommand{\CagrAutonomousScientificDiscoveryCagrBaseline}{0.287}
4862	\newcommand{\CagrAutonomousScientificDiscoveryCagrPost}{0.382}	4862	\newcommand{\CagrAutonomousScientificDiscoveryCagrPost}{0.382}
4863	\newcommand{\CagrAutonomousScientificDiscoveryShareTwoZeroOneEight}{0.000}	4863	\newcommand{\CagrAutonomousScientificDiscoveryShareTwoZeroOneEight}{0.000}
4864	\newcommand{\CagrAutonomousScientificDiscoveryShareTwoZeroTwoTwo}{0.000}	4864	\newcommand{\CagrAutonomousScientificDiscoveryShareTwoZeroTwoTwo}{0.000}
4865	\newcommand{\CagrAutonomousScientificDiscoveryShareTwoZeroTwoFive}{0.001}	4865	\newcommand{\CagrAutonomousScientificDiscoveryShareTwoZeroTwoFive}{0.001}
4866	−\newcommand{\CagrBlockchainNTwoZeroOneEight}{2{,}147}	4866	+\newcommand{\CagrBlockchainNTwoZeroOneEight}{6{,}441}
4867	−\newcommand{\CagrBlockchainNTwoZeroTwoTwo}{5{,}885}	4867	+\newcommand{\CagrBlockchainNTwoZeroTwoTwo}{11{,}770}
4868	−\newcommand{\CagrBlockchainNTwoZeroTwoThree}{7{,}174}	4868	+\newcommand{\CagrBlockchainNTwoZeroTwoThree}{14{,}349}
4869	−\newcommand{\CagrBlockchainNTwoZeroTwoFive}{9{,}006}	4869	+\newcommand{\CagrBlockchainNTwoZeroTwoFive}{18{,}012}
4870	−\newcommand{\CagrBlockchainCagrBaseline}{0.287}	4870	+\newcommand{\CagrBlockchainCagrBaseline}{0.163}
4871	\newcommand{\CagrBlockchainCagrPost}{0.120}	4871	\newcommand{\CagrBlockchainCagrPost}{0.120}
4872	\newcommand{\CagrBlockchainShareTwoZeroOneEight}{0.013}	4872	\newcommand{\CagrBlockchainShareTwoZeroOneEight}{0.013}
4873	−\newcommand{\CagrBlockchainShareTwoZeroTwoTwo}{0.023}	4873	+\newcommand{\CagrBlockchainShareTwoZeroTwoTwo}{0.021}
4874	−\newcommand{\CagrBlockchainShareTwoZeroTwoFive}{0.021}	4874	+\newcommand{\CagrBlockchainShareTwoZeroTwoFive}{0.020}
4875	−\newcommand{\CagrCloudComputingNTwoZeroOneEight}{3{,}132}	4875	+\newcommand{\CagrCloudComputingNTwoZeroOneEight}{9{,}396}
4876	−\newcommand{\CagrCloudComputingNTwoZeroTwoTwo}{3{,}551}	4876	+\newcommand{\CagrCloudComputingNTwoZeroTwoTwo}{7{,}103}
4877	−\newcommand{\CagrCloudComputingNTwoZeroTwoThree}{3{,}821}	4877	+\newcommand{\CagrCloudComputingNTwoZeroTwoThree}{7{,}643}
4878	−\newcommand{\CagrCloudComputingNTwoZeroTwoFive}{3{,}658}	4878	+\newcommand{\CagrCloudComputingNTwoZeroTwoFive}{10{,}974}
4879	−\newcommand{\CagrCloudComputingCagrBaseline}{0.032}	4879	+\newcommand{\CagrCloudComputingCagrBaseline}{-0.068}
4880	−\newcommand{\CagrCloudComputingCagrPost}{-0.022}	4880	+\newcommand{\CagrCloudComputingCagrPost}{0.198}
4881	−\newcommand{\CagrCloudComputingShareTwoZeroOneEight}{0.018}	4881	+\newcommand{\CagrCloudComputingShareTwoZeroOneEight}{0.019}
4882	−\newcommand{\CagrCloudComputingShareTwoZeroTwoTwo}{0.014}	4882	+\newcommand{\CagrCloudComputingShareTwoZeroTwoTwo}{0.013}
4883	−\newcommand{\CagrCloudComputingShareTwoZeroTwoFive}{0.009}	4883	+\newcommand{\CagrCloudComputingShareTwoZeroTwoFive}{0.012}
4884	−\newcommand{\CagrCompilersNTwoZeroOneEight}{241}	4884	+\newcommand{\CagrCompilersNTwoZeroOneEight}{725}
4885	−\newcommand{\CagrCompilersNTwoZeroTwoTwo}{321}	4885	+\newcommand{\CagrCompilersNTwoZeroTwoTwo}{642}
4886	−\newcommand{\CagrCompilersNTwoZeroTwoThree}{346}	4886	+\newcommand{\CagrCompilersNTwoZeroTwoThree}{692}
4887	−\newcommand{\CagrCompilersNTwoZeroTwoFive}{383}	4887	+\newcommand{\CagrCompilersNTwoZeroTwoFive}{767}
4888	−\newcommand{\CagrCompilersCagrBaseline}{0.074}	4888	+\newcommand{\CagrCompilersCagrBaseline}{-0.030}
4889	\newcommand{\CagrCompilersCagrPost}{0.053}	4889	\newcommand{\CagrCompilersCagrPost}{0.053}
4890	\newcommand{\CagrCompilersShareTwoZeroOneEight}{0.001}	4890	\newcommand{\CagrCompilersShareTwoZeroOneEight}{0.001}
4891	\newcommand{\CagrCompilersShareTwoZeroTwoTwo}{0.001}	4891	\newcommand{\CagrCompilersShareTwoZeroTwoTwo}{0.001}
4892	\newcommand{\CagrCompilersShareTwoZeroTwoFive}{0.001}	4892	\newcommand{\CagrCompilersShareTwoZeroTwoFive}{0.001}
4893	−\newcommand{\CagrComputationalBiologyNTwoZeroOneEight}{4{,}322}	4893	+\newcommand{\CagrComputationalBiologyNTwoZeroOneEight}{12{,}968}
4894	−\newcommand{\CagrComputationalBiologyNTwoZeroTwoTwo}{9{,}768}	4894	+\newcommand{\CagrComputationalBiologyNTwoZeroTwoTwo}{19{,}536}
4895	−\newcommand{\CagrComputationalBiologyNTwoZeroTwoThree}{10{,}288}	4895	+\newcommand{\CagrComputationalBiologyNTwoZeroTwoThree}{20{,}576}
4896	−\newcommand{\CagrComputationalBiologyNTwoZeroTwoFive}{14{,}077}	4896	+\newcommand{\CagrComputationalBiologyNTwoZeroTwoFive}{28{,}154}
4897	−\newcommand{\CagrComputationalBiologyCagrBaseline}{0.226}	4897	+\newcommand{\CagrComputationalBiologyCagrBaseline}{0.108}
4898	\newcommand{\CagrComputationalBiologyCagrPost}{0.170}	4898	\newcommand{\CagrComputationalBiologyCagrPost}{0.170}
4899	−\newcommand{\CagrComputationalBiologyShareTwoZeroOneEight}{0.025}	4899	+\newcommand{\CagrComputationalBiologyShareTwoZeroOneEight}{0.026}
4900	−\newcommand{\CagrComputationalBiologyShareTwoZeroTwoTwo}{0.038}	4900	+\newcommand{\CagrComputationalBiologyShareTwoZeroTwoTwo}{0.035}
4901	−\newcommand{\CagrComputationalBiologyShareTwoZeroTwoFive}{0.033}	4901	+\newcommand{\CagrComputationalBiologyShareTwoZeroTwoFive}{0.032}
4902	−\newcommand{\CagrComputationalComplexityNTwoZeroOneEight}{1{,}279}	4902	+\newcommand{\CagrComputationalComplexityNTwoZeroOneEight}{3{,}837}
4903	−\newcommand{\CagrComputationalComplexityNTwoZeroTwoTwo}{1{,}770}	4903	+\newcommand{\CagrComputationalComplexityNTwoZeroTwoTwo}{3{,}541}
4904	−\newcommand{\CagrComputationalComplexityNTwoZeroTwoThree}{1{,}166}	4904	+\newcommand{\CagrComputationalComplexityNTwoZeroTwoThree}{3{,}499}
4905	−\newcommand{\CagrComputationalComplexityNTwoZeroTwoFive}{2{,}104}	4905	+\newcommand{\CagrComputationalComplexityNTwoZeroTwoFive}{4{,}209}
4906	−\newcommand{\CagrComputationalComplexityCagrBaseline}{0.085}	4906	+\newcommand{\CagrComputationalComplexityCagrBaseline}{-0.020}
4907	−\newcommand{\CagrComputationalComplexityCagrPost}{0.343}	4907	+\newcommand{\CagrComputationalComplexityCagrPost}{0.097}
4908	\newcommand{\CagrComputationalComplexityShareTwoZeroOneEight}{0.008}	4908	\newcommand{\CagrComputationalComplexityShareTwoZeroOneEight}{0.008}
4909	−\newcommand{\CagrComputationalComplexityShareTwoZeroTwoTwo}{0.007}	4909	+\newcommand{\CagrComputationalComplexityShareTwoZeroTwoTwo}{0.006}
4910	\newcommand{\CagrComputationalComplexityShareTwoZeroTwoFive}{0.005}	4910	\newcommand{\CagrComputationalComplexityShareTwoZeroTwoFive}{0.005}
4911	−\newcommand{\CagrComputerArchitectureNTwoZeroOneEight}{6{,}518}	4911	+\newcommand{\CagrComputerArchitectureNTwoZeroOneEight}{19{,}554}
4912	−\newcommand{\CagrComputerArchitectureNTwoZeroTwoTwo}{7{,}587}	4912	+\newcommand{\CagrComputerArchitectureNTwoZeroTwoTwo}{15{,}174}
4913	−\newcommand{\CagrComputerArchitectureNTwoZeroTwoThree}{8{,}184}	4913	+\newcommand{\CagrComputerArchitectureNTwoZeroTwoThree}{16{,}369}
4914	−\newcommand{\CagrComputerArchitectureNTwoZeroTwoFive}{6{,}736}	4914	+\newcommand{\CagrComputerArchitectureNTwoZeroTwoFive}{20{,}209}
4915	−\newcommand{\CagrComputerArchitectureCagrBaseline}{0.039}	4915	+\newcommand{\CagrComputerArchitectureCagrBaseline}{-0.061}
4916	−\newcommand{\CagrComputerArchitectureCagrPost}{-0.093}	4916	+\newcommand{\CagrComputerArchitectureCagrPost}{0.111}
4917	−\newcommand{\CagrComputerArchitectureShareTwoZeroOneEight}{0.038}	4917	+\newcommand{\CagrComputerArchitectureShareTwoZeroOneEight}{0.039}
4918	−\newcommand{\CagrComputerArchitectureShareTwoZeroTwoTwo}{0.030}	4918	+\newcommand{\CagrComputerArchitectureShareTwoZeroTwoTwo}{0.027}
4919	−\newcommand{\CagrComputerArchitectureShareTwoZeroTwoFive}{0.016}	4919	+\newcommand{\CagrComputerArchitectureShareTwoZeroTwoFive}{0.023}
4920	−\newcommand{\CagrComputerGraphicsNTwoZeroOneEight}{3{,}399}	4920	+\newcommand{\CagrComputerGraphicsNTwoZeroOneEight}{10{,}198}
4921	−\newcommand{\CagrComputerGraphicsNTwoZeroTwoTwo}{4{,}143}	4921	+\newcommand{\CagrComputerGraphicsNTwoZeroTwoTwo}{8{,}287}
4922	−\newcommand{\CagrComputerGraphicsNTwoZeroTwoThree}{3{,}099}	4922	+\newcommand{\CagrComputerGraphicsNTwoZeroTwoThree}{9{,}297}
4923	−\newcommand{\CagrComputerGraphicsNTwoZeroTwoFive}{6{,}220}	4923	+\newcommand{\CagrComputerGraphicsNTwoZeroTwoFive}{12{,}440}
4924	−\newcommand{\CagrComputerGraphicsCagrBaseline}{0.051}	4924	+\newcommand{\CagrComputerGraphicsCagrBaseline}{-0.051}
4925	−\newcommand{\CagrComputerGraphicsCagrPost}{0.417}	4925	+\newcommand{\CagrComputerGraphicsCagrPost}{0.157}
4926	\newcommand{\CagrComputerGraphicsShareTwoZeroOneEight}{0.020}	4926	\newcommand{\CagrComputerGraphicsShareTwoZeroOneEight}{0.020}
4927	−\newcommand{\CagrComputerGraphicsShareTwoZeroTwoTwo}{0.016}	4927	+\newcommand{\CagrComputerGraphicsShareTwoZeroTwoTwo}{0.015}
4928	\newcommand{\CagrComputerGraphicsShareTwoZeroTwoFive}{0.014}	4928	\newcommand{\CagrComputerGraphicsShareTwoZeroTwoFive}{0.014}
4929	−\newcommand{\CagrComputerNetworksNTwoZeroOneEight}{19{,}581}	4929	+\newcommand{\CagrComputerNetworksNTwoZeroOneEight}{58{,}744}
4930	−\newcommand{\CagrComputerNetworksNTwoZeroTwoTwo}{16{,}380}	4930	+\newcommand{\CagrComputerNetworksNTwoZeroTwoTwo}{49{,}141}
4931	−\newcommand{\CagrComputerNetworksNTwoZeroTwoThree}{17{,}293}	4931	+\newcommand{\CagrComputerNetworksNTwoZeroTwoThree}{51{,}881}
4932	−\newcommand{\CagrComputerNetworksNTwoZeroTwoFive}{29{,}777}	4932	+\newcommand{\CagrComputerNetworksNTwoZeroTwoFive}{59{,}555}
4933	\newcommand{\CagrComputerNetworksCagrBaseline}{-0.044}	4933	\newcommand{\CagrComputerNetworksCagrBaseline}{-0.044}
4934	−\newcommand{\CagrComputerNetworksCagrPost}{0.312}	4934	+\newcommand{\CagrComputerNetworksCagrPost}{0.071}
4935	−\newcommand{\CagrComputerNetworksShareTwoZeroOneEight}{0.115}	4935	+\newcommand{\CagrComputerNetworksShareTwoZeroOneEight}{0.118}
4936	−\newcommand{\CagrComputerNetworksShareTwoZeroTwoTwo}{0.064}	4936	+\newcommand{\CagrComputerNetworksShareTwoZeroTwoTwo}{0.088}
4937	−\newcommand{\CagrComputerNetworksShareTwoZeroTwoFive}{0.069}	4937	+\newcommand{\CagrComputerNetworksShareTwoZeroTwoFive}{0.067}
4938	−\newcommand{\CagrComputerVisionNTwoZeroOneEight}{23{,}246}	4938	+\newcommand{\CagrComputerVisionNTwoZeroOneEight}{69{,}738}
4939	−\newcommand{\CagrComputerVisionNTwoZeroTwoTwo}{49{,}352}	4939	+\newcommand{\CagrComputerVisionNTwoZeroTwoTwo}{98{,}704}
4940	−\newcommand{\CagrComputerVisionNTwoZeroTwoThree}{57{,}130}	4940	+\newcommand{\CagrComputerVisionNTwoZeroTwoThree}{114{,}261}
4941	−\newcommand{\CagrComputerVisionNTwoZeroTwoFive}{75{,}378}	4941	+\newcommand{\CagrComputerVisionNTwoZeroTwoFive}{150{,}757}
4942	−\newcommand{\CagrComputerVisionCagrBaseline}{0.207}	4942	+\newcommand{\CagrComputerVisionCagrBaseline}{0.091}
4943	\newcommand{\CagrComputerVisionCagrPost}{0.149}	4943	\newcommand{\CagrComputerVisionCagrPost}{0.149}
4944	−\newcommand{\CagrComputerVisionShareTwoZeroOneEight}{0.137}	4944	+\newcommand{\CagrComputerVisionShareTwoZeroOneEight}{0.140}
4945	−\newcommand{\CagrComputerVisionShareTwoZeroTwoTwo}{0.194}	4945	+\newcommand{\CagrComputerVisionShareTwoZeroTwoTwo}{0.176}
4946	−\newcommand{\CagrComputerVisionShareTwoZeroTwoFive}{0.175}	4946	+\newcommand{\CagrComputerVisionShareTwoZeroTwoFive}{0.169}
4947	−\newcommand{\CagrCybersecurityNTwoZeroOneEight}{9{,}813}	4947	+\newcommand{\CagrCybersecurityNTwoZeroOneEight}{29{,}440}
4948	−\newcommand{\CagrCybersecurityNTwoZeroTwoTwo}{15{,}777}	4948	+\newcommand{\CagrCybersecurityNTwoZeroTwoTwo}{31{,}554}
4949	−\newcommand{\CagrCybersecurityNTwoZeroTwoThree}{12{,}160}	4949	+\newcommand{\CagrCybersecurityNTwoZeroTwoThree}{36{,}481}
4950	−\newcommand{\CagrCybersecurityNTwoZeroTwoFive}{29{,}522}	4950	+\newcommand{\CagrCybersecurityNTwoZeroTwoFive}{59{,}044}
4951	−\newcommand{\CagrCybersecurityCagrBaseline}{0.126}	4951	+\newcommand{\CagrCybersecurityCagrBaseline}{0.017}
4952	−\newcommand{\CagrCybersecurityCagrPost}{0.558}	4952	+\newcommand{\CagrCybersecurityCagrPost}{0.272}
4953	−\newcommand{\CagrCybersecurityShareTwoZeroOneEight}{0.058}	4953	+\newcommand{\CagrCybersecurityShareTwoZeroOneEight}{0.059}
4954	−\newcommand{\CagrCybersecurityShareTwoZeroTwoTwo}{0.062}	4954	+\newcommand{\CagrCybersecurityShareTwoZeroTwoTwo}{0.056}
4955	−\newcommand{\CagrCybersecurityShareTwoZeroTwoFive}{0.069}	4955	+\newcommand{\CagrCybersecurityShareTwoZeroTwoFive}{0.066}
4956	−\newcommand{\CagrDatabaseSystemsNTwoZeroOneEight}{5{,}421}	4956	+\newcommand{\CagrDatabaseSystemsNTwoZeroOneEight}{16{,}265}
4957	−\newcommand{\CagrDatabaseSystemsNTwoZeroTwoTwo}{6{,}812}	4957	+\newcommand{\CagrDatabaseSystemsNTwoZeroTwoTwo}{13{,}625}
4958	−\newcommand{\CagrDatabaseSystemsNTwoZeroTwoThree}{7{,}243}	4958	+\newcommand{\CagrDatabaseSystemsNTwoZeroTwoThree}{14{,}487}
4959	−\newcommand{\CagrDatabaseSystemsNTwoZeroTwoFive}{8{,}340}	4959	+\newcommand{\CagrDatabaseSystemsNTwoZeroTwoFive}{16{,}681}
4960	−\newcommand{\CagrDatabaseSystemsCagrBaseline}{0.059}	4960	+\newcommand{\CagrDatabaseSystemsCagrBaseline}{-0.043}
4961	\newcommand{\CagrDatabaseSystemsCagrPost}{0.073}	4961	\newcommand{\CagrDatabaseSystemsCagrPost}{0.073}
4962	−\newcommand{\CagrDatabaseSystemsShareTwoZeroOneEight}{0.032}	4962	+\newcommand{\CagrDatabaseSystemsShareTwoZeroOneEight}{0.033}
4963	−\newcommand{\CagrDatabaseSystemsShareTwoZeroTwoTwo}{0.027}	4963	+\newcommand{\CagrDatabaseSystemsShareTwoZeroTwoTwo}{0.024}
4964	\newcommand{\CagrDatabaseSystemsShareTwoZeroTwoFive}{0.019}	4964	\newcommand{\CagrDatabaseSystemsShareTwoZeroTwoFive}{0.019}
4965	−\newcommand{\CagrDeepLearningArchitecturesNTwoZeroOneEight}{3{,}997}	4965	+\newcommand{\CagrDeepLearningArchitecturesNTwoZeroOneEight}{11{,}992}
4966	−\newcommand{\CagrDeepLearningArchitecturesNTwoZeroTwoTwo}{7{,}423}	4966	+\newcommand{\CagrDeepLearningArchitecturesNTwoZeroTwoTwo}{22{,}270}
4967	−\newcommand{\CagrDeepLearningArchitecturesNTwoZeroTwoThree}{12{,}803}	4967	+\newcommand{\CagrDeepLearningArchitecturesNTwoZeroTwoThree}{25{,}607}
4968	−\newcommand{\CagrDeepLearningArchitecturesNTwoZeroTwoFive}{17{,}070}	4968	+\newcommand{\CagrDeepLearningArchitecturesNTwoZeroTwoFive}{34{,}141}
4969	\newcommand{\CagrDeepLearningArchitecturesCagrBaseline}{0.167}	4969	\newcommand{\CagrDeepLearningArchitecturesCagrBaseline}{0.167}
4970	\newcommand{\CagrDeepLearningArchitecturesCagrPost}{0.155}	4970	\newcommand{\CagrDeepLearningArchitecturesCagrPost}{0.155}
4971	\newcommand{\CagrDeepLearningArchitecturesShareTwoZeroOneEight}{0.024}	4971	\newcommand{\CagrDeepLearningArchitecturesShareTwoZeroOneEight}{0.024}
4972	−\newcommand{\CagrDeepLearningArchitecturesShareTwoZeroTwoTwo}{0.029}	4972	+\newcommand{\CagrDeepLearningArchitecturesShareTwoZeroTwoTwo}{0.040}
4973	−\newcommand{\CagrDeepLearningArchitecturesShareTwoZeroTwoFive}{0.040}	4973	+\newcommand{\CagrDeepLearningArchitecturesShareTwoZeroTwoFive}{0.038}
4974	−\newcommand{\CagrDistributedComputingNTwoZeroOneEight}{3{,}839}	4974	+\newcommand{\CagrDistributedComputingNTwoZeroOneEight}{11{,}519}
4975	−\newcommand{\CagrDistributedComputingNTwoZeroTwoTwo}{4{,}689}	4975	+\newcommand{\CagrDistributedComputingNTwoZeroTwoTwo}{9{,}379}
4976	−\newcommand{\CagrDistributedComputingNTwoZeroTwoThree}{3{,}266}	4976	+\newcommand{\CagrDistributedComputingNTwoZeroTwoThree}{9{,}798}
4977	−\newcommand{\CagrDistributedComputingNTwoZeroTwoFive}{4{,}183}	4977	+\newcommand{\CagrDistributedComputingNTwoZeroTwoFive}{12{,}550}
4978	−\newcommand{\CagrDistributedComputingCagrBaseline}{0.051}	4978	+\newcommand{\CagrDistributedComputingCagrBaseline}{-0.050}
4979	\newcommand{\CagrDistributedComputingCagrPost}{0.132}	4979	\newcommand{\CagrDistributedComputingCagrPost}{0.132}
4980	\newcommand{\CagrDistributedComputingShareTwoZeroOneEight}{0.023}	4980	\newcommand{\CagrDistributedComputingShareTwoZeroOneEight}{0.023}
4981	−\newcommand{\CagrDistributedComputingShareTwoZeroTwoTwo}{0.018}	4981	+\newcommand{\CagrDistributedComputingShareTwoZeroTwoTwo}{0.017}
4982	−\newcommand{\CagrDistributedComputingShareTwoZeroTwoFive}{0.010}	4982	+\newcommand{\CagrDistributedComputingShareTwoZeroTwoFive}{0.014}
4983	−\newcommand{\CagrEdgeComputingNTwoZeroOneEight}{1{,}728}	4983	+\newcommand{\CagrEdgeComputingNTwoZeroOneEight}{5{,}185}
4984	−\newcommand{\CagrEdgeComputingNTwoZeroTwoTwo}{4{,}277}	4984	+\newcommand{\CagrEdgeComputingNTwoZeroTwoTwo}{8{,}554}
4985	−\newcommand{\CagrEdgeComputingNTwoZeroTwoThree}{3{,}040}	4985	+\newcommand{\CagrEdgeComputingNTwoZeroTwoThree}{9{,}120}
4986	−\newcommand{\CagrEdgeComputingNTwoZeroTwoFive}{5{,}777}	4986	+\newcommand{\CagrEdgeComputingNTwoZeroTwoFive}{11{,}554}
4987	−\newcommand{\CagrEdgeComputingCagrBaseline}{0.254}	4987	+\newcommand{\CagrEdgeComputingCagrBaseline}{0.133}
4988	−\newcommand{\CagrEdgeComputingCagrPost}{0.379}	4988	+\newcommand{\CagrEdgeComputingCagrPost}{0.126}
4989	\newcommand{\CagrEdgeComputingShareTwoZeroOneEight}{0.010}	4989	\newcommand{\CagrEdgeComputingShareTwoZeroOneEight}{0.010}
4990	−\newcommand{\CagrEdgeComputingShareTwoZeroTwoTwo}{0.017}	4990	+\newcommand{\CagrEdgeComputingShareTwoZeroTwoTwo}{0.015}
4991	\newcommand{\CagrEdgeComputingShareTwoZeroTwoFive}{0.013}	4991	\newcommand{\CagrEdgeComputingShareTwoZeroTwoFive}{0.013}
4992	−\newcommand{\CagrFederatedLearningNTwoZeroOneEight}{71}	4992	+\newcommand{\CagrFederatedLearningNTwoZeroOneEight}{143}
4993	−\newcommand{\CagrFederatedLearningNTwoZeroTwoTwo}{487}	4993	+\newcommand{\CagrFederatedLearningNTwoZeroTwoTwo}{974}
4994	−\newcommand{\CagrFederatedLearningNTwoZeroTwoThree}{748}	4994	+\newcommand{\CagrFederatedLearningNTwoZeroTwoThree}{1{,}497}
4995	−\newcommand{\CagrFederatedLearningNTwoZeroTwoFive}{1{,}272}	4995	+\newcommand{\CagrFederatedLearningNTwoZeroTwoFive}{2{,}544}
4996	\newcommand{\CagrFederatedLearningCagrBaseline}{0.615}	4996	\newcommand{\CagrFederatedLearningCagrBaseline}{0.615}
4997	\newcommand{\CagrFederatedLearningCagrPost}{0.304}	4997	\newcommand{\CagrFederatedLearningCagrPost}{0.304}
4998	\newcommand{\CagrFederatedLearningShareTwoZeroOneEight}{0.000}	4998	\newcommand{\CagrFederatedLearningShareTwoZeroOneEight}{0.000}
4999	\newcommand{\CagrFederatedLearningShareTwoZeroTwoTwo}{0.002}	4999	\newcommand{\CagrFederatedLearningShareTwoZeroTwoTwo}{0.002}
5000	\newcommand{\CagrFederatedLearningShareTwoZeroTwoFive}{0.003}	5000	\newcommand{\CagrFederatedLearningShareTwoZeroTwoFive}{0.003}
5001	−\newcommand{\CagrFormalVerificationNTwoZeroOneEight}{1{,}769}	5001	+\newcommand{\CagrFormalVerificationNTwoZeroOneEight}{5{,}307}
5002	−\newcommand{\CagrFormalVerificationNTwoZeroTwoTwo}{1{,}997}	5002	+\newcommand{\CagrFormalVerificationNTwoZeroTwoTwo}{3{,}995}
5003	−\newcommand{\CagrFormalVerificationNTwoZeroTwoThree}{1{,}461}	5003	+\newcommand{\CagrFormalVerificationNTwoZeroTwoThree}{4{,}385}
5004	−\newcommand{\CagrFormalVerificationNTwoZeroTwoFive}{2{,}311}	5004	+\newcommand{\CagrFormalVerificationNTwoZeroTwoFive}{4{,}623}
5005	−\newcommand{\CagrFormalVerificationCagrBaseline}{0.031}	5005	+\newcommand{\CagrFormalVerificationCagrBaseline}{-0.069}
5006	−\newcommand{\CagrFormalVerificationCagrPost}{0.258}	5006	+\newcommand{\CagrFormalVerificationCagrPost}{0.027}
5007	−\newcommand{\CagrFormalVerificationShareTwoZeroOneEight}{0.010}	5007	+\newcommand{\CagrFormalVerificationShareTwoZeroOneEight}{0.011}
5008	−\newcommand{\CagrFormalVerificationShareTwoZeroTwoTwo}{0.008}	5008	+\newcommand{\CagrFormalVerificationShareTwoZeroTwoTwo}{0.007}
5009	\newcommand{\CagrFormalVerificationShareTwoZeroTwoFive}{0.005}	5009	\newcommand{\CagrFormalVerificationShareTwoZeroTwoFive}{0.005}
5010	−\newcommand{\CagrFoundationModelsNTwoZeroOneEight}{23}	5010	+\newcommand{\CagrFoundationModelsNTwoZeroOneEight}{46}
5011	−\newcommand{\CagrFoundationModelsNTwoZeroTwoTwo}{175}	5011	+\newcommand{\CagrFoundationModelsNTwoZeroTwoTwo}{350}
5012	−\newcommand{\CagrFoundationModelsNTwoZeroTwoThree}{285}	5012	+\newcommand{\CagrFoundationModelsNTwoZeroTwoThree}{571}
5013	−\newcommand{\CagrFoundationModelsNTwoZeroTwoFive}{927}	5013	+\newcommand{\CagrFoundationModelsNTwoZeroTwoFive}{1{,}854}
5014	\newcommand{\CagrFoundationModelsCagrBaseline}{0.661}	5014	\newcommand{\CagrFoundationModelsCagrBaseline}{0.661}
5015	\newcommand{\CagrFoundationModelsCagrPost}{0.802}	5015	\newcommand{\CagrFoundationModelsCagrPost}{0.802}
5016	\newcommand{\CagrFoundationModelsShareTwoZeroOneEight}{0.000}	5016	\newcommand{\CagrFoundationModelsShareTwoZeroOneEight}{0.000}
5017	\newcommand{\CagrFoundationModelsShareTwoZeroTwoTwo}{0.001}	5017	\newcommand{\CagrFoundationModelsShareTwoZeroTwoTwo}{0.001}
5018	\newcommand{\CagrFoundationModelsShareTwoZeroTwoFive}{0.002}	5018	\newcommand{\CagrFoundationModelsShareTwoZeroTwoFive}{0.002}
5019	−\newcommand{\CagrGenerativeAiNTwoZeroOneEight}{255}	5019	+\newcommand{\CagrGenerativeAiNTwoZeroOneEight}{766}
5020	−\newcommand{\CagrGenerativeAiNTwoZeroTwoTwo}{1{,}080}	5020	+\newcommand{\CagrGenerativeAiNTwoZeroTwoTwo}{2{,}160}
5021	−\newcommand{\CagrGenerativeAiNTwoZeroTwoThree}{1{,}823}	5021	+\newcommand{\CagrGenerativeAiNTwoZeroTwoThree}{3{,}646}
5022	−\newcommand{\CagrGenerativeAiNTwoZeroTwoFive}{6{,}371}	5022	+\newcommand{\CagrGenerativeAiNTwoZeroTwoFive}{12{,}743}
5023	−\newcommand{\CagrGenerativeAiCagrBaseline}{0.434}	5023	+\newcommand{\CagrGenerativeAiCagrBaseline}{0.296}
5024	\newcommand{\CagrGenerativeAiCagrPost}{0.870}	5024	\newcommand{\CagrGenerativeAiCagrPost}{0.870}
5025	\newcommand{\CagrGenerativeAiShareTwoZeroOneEight}{0.002}	5025	\newcommand{\CagrGenerativeAiShareTwoZeroOneEight}{0.002}
5026	\newcommand{\CagrGenerativeAiShareTwoZeroTwoTwo}{0.004}	5026	\newcommand{\CagrGenerativeAiShareTwoZeroTwoTwo}{0.004}
5027	−\newcommand{\CagrGenerativeAiShareTwoZeroTwoFive}{0.015}	5027	+\newcommand{\CagrGenerativeAiShareTwoZeroTwoFive}{0.014}
5028	−\newcommand{\CagrHciNTwoZeroOneEight}{11{,}408}	5028	+\newcommand{\CagrHciNTwoZeroOneEight}{34{,}225}
5029	−\newcommand{\CagrHciNTwoZeroTwoTwo}{17{,}400}	5029	+\newcommand{\CagrHciNTwoZeroTwoTwo}{34{,}801}
5030	−\newcommand{\CagrHciNTwoZeroTwoThree}{20{,}293}	5030	+\newcommand{\CagrHciNTwoZeroTwoThree}{40{,}587}
5031	−\newcommand{\CagrHciNTwoZeroTwoFive}{29{,}074}	5031	+\newcommand{\CagrHciNTwoZeroTwoFive}{58{,}148}
5032	−\newcommand{\CagrHciCagrBaseline}{0.111}	5032	+\newcommand{\CagrHciCagrBaseline}{0.004}
5033	\newcommand{\CagrHciCagrPost}{0.197}	5033	\newcommand{\CagrHciCagrPost}{0.197}
5034	−\newcommand{\CagrHciShareTwoZeroOneEight}{0.067}	5034	+\newcommand{\CagrHciShareTwoZeroOneEight}{0.069}
5035	−\newcommand{\CagrHciShareTwoZeroTwoTwo}{0.068}	5035	+\newcommand{\CagrHciShareTwoZeroTwoTwo}{0.062}
5036	−\newcommand{\CagrHciShareTwoZeroTwoFive}{0.068}	5036	+\newcommand{\CagrHciShareTwoZeroTwoFive}{0.065}
5037	−\newcommand{\CagrInformationRetrievalNTwoZeroOneEight}{4{,}031}	5037	+\newcommand{\CagrInformationRetrievalNTwoZeroOneEight}{12{,}095}
5038	−\newcommand{\CagrInformationRetrievalNTwoZeroTwoTwo}{5{,}621}	5038	+\newcommand{\CagrInformationRetrievalNTwoZeroTwoTwo}{11{,}243}
5039	−\newcommand{\CagrInformationRetrievalNTwoZeroTwoThree}{6{,}284}	5039	+\newcommand{\CagrInformationRetrievalNTwoZeroTwoThree}{12{,}568}
5040	−\newcommand{\CagrInformationRetrievalNTwoZeroTwoFive}{8{,}689}	5040	+\newcommand{\CagrInformationRetrievalNTwoZeroTwoFive}{17{,}379}
5041	−\newcommand{\CagrInformationRetrievalCagrBaseline}{0.087}	5041	+\newcommand{\CagrInformationRetrievalCagrBaseline}{-0.018}
5042	\newcommand{\CagrInformationRetrievalCagrPost}{0.176}	5042	\newcommand{\CagrInformationRetrievalCagrPost}{0.176}
5043	\newcommand{\CagrInformationRetrievalShareTwoZeroOneEight}{0.024}	5043	\newcommand{\CagrInformationRetrievalShareTwoZeroOneEight}{0.024}
5044	−\newcommand{\CagrInformationRetrievalShareTwoZeroTwoTwo}{0.022}	5044	+\newcommand{\CagrInformationRetrievalShareTwoZeroTwoTwo}{0.020}
5045	−\newcommand{\CagrInformationRetrievalShareTwoZeroTwoFive}{0.020}	5045	+\newcommand{\CagrInformationRetrievalShareTwoZeroTwoFive}{0.019}
5046	−\newcommand{\CagrLlmNTwoZeroOneEight}{545}	5046	+\newcommand{\CagrLlmNTwoZeroOneEight}{1{,}635}
5047	−\newcommand{\CagrLlmNTwoZeroTwoTwo}{1{,}088}	5047	+\newcommand{\CagrLlmNTwoZeroTwoTwo}{3{,}266}
5048	−\newcommand{\CagrLlmNTwoZeroTwoThree}{5{,}194}	5048	+\newcommand{\CagrLlmNTwoZeroTwoThree}{10{,}389}
5049	−\newcommand{\CagrLlmNTwoZeroTwoFive}{22{,}579}	5049	+\newcommand{\CagrLlmNTwoZeroTwoFive}{45{,}158}
5050	\newcommand{\CagrLlmCagrBaseline}{0.189}	5050	\newcommand{\CagrLlmCagrBaseline}{0.189}
5051	\newcommand{\CagrLlmCagrPost}{1.085}	5051	\newcommand{\CagrLlmCagrPost}{1.085}
5052	\newcommand{\CagrLlmShareTwoZeroOneEight}{0.003}	5052	\newcommand{\CagrLlmShareTwoZeroOneEight}{0.003}
5053	−\newcommand{\CagrLlmShareTwoZeroTwoTwo}{0.004}	5053	+\newcommand{\CagrLlmShareTwoZeroTwoTwo}{0.006}
5054	−\newcommand{\CagrLlmShareTwoZeroTwoFive}{0.053}	5054	+\newcommand{\CagrLlmShareTwoZeroTwoFive}{0.051}
5055	−\newcommand{\CagrMlTheoryNTwoZeroOneEight}{4{,}511}	5055	+\newcommand{\CagrMlTheoryNTwoZeroOneEight}{13{,}534}
5056	−\newcommand{\CagrMlTheoryNTwoZeroTwoTwo}{10{,}065}	5056	+\newcommand{\CagrMlTheoryNTwoZeroTwoTwo}{20{,}131}
5057	−\newcommand{\CagrMlTheoryNTwoZeroTwoThree}{11{,}446}	5057	+\newcommand{\CagrMlTheoryNTwoZeroTwoThree}{22{,}893}
5058	−\newcommand{\CagrMlTheoryNTwoZeroTwoFive}{16{,}399}	5058	+\newcommand{\CagrMlTheoryNTwoZeroTwoFive}{32{,}798}
5059	−\newcommand{\CagrMlTheoryCagrBaseline}{0.222}	5059	+\newcommand{\CagrMlTheoryCagrBaseline}{0.104}
5060	\newcommand{\CagrMlTheoryCagrPost}{0.197}	5060	\newcommand{\CagrMlTheoryCagrPost}{0.197}
5061	\newcommand{\CagrMlTheoryShareTwoZeroOneEight}{0.027}	5061	\newcommand{\CagrMlTheoryShareTwoZeroOneEight}{0.027}
5062	−\newcommand{\CagrMlTheoryShareTwoZeroTwoTwo}{0.040}	5062	+\newcommand{\CagrMlTheoryShareTwoZeroTwoTwo}{0.036}
5063	−\newcommand{\CagrMlTheoryShareTwoZeroTwoFive}{0.038}	5063	+\newcommand{\CagrMlTheoryShareTwoZeroTwoFive}{0.037}
5064	−\newcommand{\CagrMultimodalAiNTwoZeroOneEight}{156}	5064	+\newcommand{\CagrMultimodalAiNTwoZeroOneEight}{470}
5065	−\newcommand{\CagrMultimodalAiNTwoZeroTwoTwo}{639}	5065	+\newcommand{\CagrMultimodalAiNTwoZeroTwoTwo}{1{,}278}
5066	−\newcommand{\CagrMultimodalAiNTwoZeroTwoThree}{966}	5066	+\newcommand{\CagrMultimodalAiNTwoZeroTwoThree}{1{,}932}
5067	−\newcommand{\CagrMultimodalAiNTwoZeroTwoFive}{3{,}374}	5067	+\newcommand{\CagrMultimodalAiNTwoZeroTwoFive}{6{,}749}
5068	−\newcommand{\CagrMultimodalAiCagrBaseline}{0.421}	5068	+\newcommand{\CagrMultimodalAiCagrBaseline}{0.284}
5069	\newcommand{\CagrMultimodalAiCagrPost}{0.869}	5069	\newcommand{\CagrMultimodalAiCagrPost}{0.869}
5070	\newcommand{\CagrMultimodalAiShareTwoZeroOneEight}{0.001}	5070	\newcommand{\CagrMultimodalAiShareTwoZeroOneEight}{0.001}
5071	−\newcommand{\CagrMultimodalAiShareTwoZeroTwoTwo}{0.003}	5071	+\newcommand{\CagrMultimodalAiShareTwoZeroTwoTwo}{0.002}
5072	\newcommand{\CagrMultimodalAiShareTwoZeroTwoFive}{0.008}	5072	\newcommand{\CagrMultimodalAiShareTwoZeroTwoFive}{0.008}
5073	−\newcommand{\CagrNlpNTwoZeroOneEight}{7{,}790}	5073	+\newcommand{\CagrNlpNTwoZeroOneEight}{23{,}372}
5074	−\newcommand{\CagrNlpNTwoZeroTwoTwo}{9{,}892}	5074	+\newcommand{\CagrNlpNTwoZeroTwoTwo}{29{,}678}
5075	−\newcommand{\CagrNlpNTwoZeroTwoThree}{16{,}982}	5075	+\newcommand{\CagrNlpNTwoZeroTwoThree}{33{,}965}
5076	−\newcommand{\CagrNlpNTwoZeroTwoFive}{21{,}457}	5076	+\newcommand{\CagrNlpNTwoZeroTwoFive}{42{,}914}
5077	\newcommand{\CagrNlpCagrBaseline}{0.062}	5077	\newcommand{\CagrNlpCagrBaseline}{0.062}
5078	\newcommand{\CagrNlpCagrPost}{0.124}	5078	\newcommand{\CagrNlpCagrPost}{0.124}
5079	−\newcommand{\CagrNlpShareTwoZeroOneEight}{0.046}	5079	+\newcommand{\CagrNlpShareTwoZeroOneEight}{0.047}
5080	−\newcommand{\CagrNlpShareTwoZeroTwoTwo}{0.039}	5080	+\newcommand{\CagrNlpShareTwoZeroTwoTwo}{0.053}
5081	−\newcommand{\CagrNlpShareTwoZeroTwoFive}{0.050}	5081	+\newcommand{\CagrNlpShareTwoZeroTwoFive}{0.048}
5082	−\newcommand{\CagrOperatingSystemsNTwoZeroOneEight}{778}	5082	+\newcommand{\CagrOperatingSystemsNTwoZeroOneEight}{1{,}556}
5083	−\newcommand{\CagrOperatingSystemsNTwoZeroTwoTwo}{541}	5083	+\newcommand{\CagrOperatingSystemsNTwoZeroTwoTwo}{1{,}083}
5084	−\newcommand{\CagrOperatingSystemsNTwoZeroTwoThree}{586}	5084	+\newcommand{\CagrOperatingSystemsNTwoZeroTwoThree}{1{,}173}
5085	−\newcommand{\CagrOperatingSystemsNTwoZeroTwoFive}{681}	5085	+\newcommand{\CagrOperatingSystemsNTwoZeroTwoFive}{1{,}363}
5086	\newcommand{\CagrOperatingSystemsCagrBaseline}{-0.087}	5086	\newcommand{\CagrOperatingSystemsCagrBaseline}{-0.087}
5087	\newcommand{\CagrOperatingSystemsCagrPost}{0.078}	5087	\newcommand{\CagrOperatingSystemsCagrPost}{0.078}
5088	−\newcommand{\CagrOperatingSystemsShareTwoZeroOneEight}{0.005}	5088	+\newcommand{\CagrOperatingSystemsShareTwoZeroOneEight}{0.003}
5089	\newcommand{\CagrOperatingSystemsShareTwoZeroTwoTwo}{0.002}	5089	\newcommand{\CagrOperatingSystemsShareTwoZeroTwoTwo}{0.002}
5090	\newcommand{\CagrOperatingSystemsShareTwoZeroTwoFive}{0.002}	5090	\newcommand{\CagrOperatingSystemsShareTwoZeroTwoFive}{0.002}
5091	−\newcommand{\CagrPrivacyPreservingComputationNTwoZeroOneEight}{1{,}035}	5091	+\newcommand{\CagrPrivacyPreservingComputationNTwoZeroOneEight}{3{,}105}
5092	−\newcommand{\CagrPrivacyPreservingComputationNTwoZeroTwoTwo}{2{,}188}	5092	+\newcommand{\CagrPrivacyPreservingComputationNTwoZeroTwoTwo}{4{,}376}
5093	−\newcommand{\CagrPrivacyPreservingComputationNTwoZeroTwoThree}{2{,}505}	5093	+\newcommand{\CagrPrivacyPreservingComputationNTwoZeroTwoThree}{5{,}010}
5094	−\newcommand{\CagrPrivacyPreservingComputationNTwoZeroTwoFive}{4{,}408}	5094	+\newcommand{\CagrPrivacyPreservingComputationNTwoZeroTwoFive}{8{,}817}
5095	−\newcommand{\CagrPrivacyPreservingComputationCagrBaseline}{0.206}	5095	+\newcommand{\CagrPrivacyPreservingComputationCagrBaseline}{0.090}
5096	\newcommand{\CagrPrivacyPreservingComputationCagrPost}{0.327}	5096	\newcommand{\CagrPrivacyPreservingComputationCagrPost}{0.327}
5097	\newcommand{\CagrPrivacyPreservingComputationShareTwoZeroOneEight}{0.006}	5097	\newcommand{\CagrPrivacyPreservingComputationShareTwoZeroOneEight}{0.006}
5098	−\newcommand{\CagrPrivacyPreservingComputationShareTwoZeroTwoTwo}{0.009}	5098	+\newcommand{\CagrPrivacyPreservingComputationShareTwoZeroTwoTwo}{0.008}
5099	\newcommand{\CagrPrivacyPreservingComputationShareTwoZeroTwoFive}{0.010}	5099	\newcommand{\CagrPrivacyPreservingComputationShareTwoZeroTwoFive}{0.010}
5100	−\newcommand{\CagrProgrammingLanguagesNTwoZeroOneEight}{1{,}463}	5100	+\newcommand{\CagrProgrammingLanguagesNTwoZeroOneEight}{4{,}390}
5101	−\newcommand{\CagrProgrammingLanguagesNTwoZeroTwoTwo}{1{,}832}	5101	+\newcommand{\CagrProgrammingLanguagesNTwoZeroTwoTwo}{3{,}664}
5102	−\newcommand{\CagrProgrammingLanguagesNTwoZeroTwoThree}{1{,}938}	5102	+\newcommand{\CagrProgrammingLanguagesNTwoZeroTwoThree}{3{,}876}
5103	−\newcommand{\CagrProgrammingLanguagesNTwoZeroTwoFive}{2{,}096}	5103	+\newcommand{\CagrProgrammingLanguagesNTwoZeroTwoFive}{4{,}193}
5104	−\newcommand{\CagrProgrammingLanguagesCagrBaseline}{0.058}	5104	+\newcommand{\CagrProgrammingLanguagesCagrBaseline}{-0.044}
5105	\newcommand{\CagrProgrammingLanguagesCagrPost}{0.040}	5105	\newcommand{\CagrProgrammingLanguagesCagrPost}{0.040}
5106	\newcommand{\CagrProgrammingLanguagesShareTwoZeroOneEight}{0.009}	5106	\newcommand{\CagrProgrammingLanguagesShareTwoZeroOneEight}{0.009}
5107	\newcommand{\CagrProgrammingLanguagesShareTwoZeroTwoTwo}{0.007}	5107	\newcommand{\CagrProgrammingLanguagesShareTwoZeroTwoTwo}{0.007}
5108	\newcommand{\CagrProgrammingLanguagesShareTwoZeroTwoFive}{0.005}	5108	\newcommand{\CagrProgrammingLanguagesShareTwoZeroTwoFive}{0.005}
5109	−\newcommand{\CagrQuantumComputingNTwoZeroOneEight}{3{,}112}	5109	+\newcommand{\CagrQuantumComputingNTwoZeroOneEight}{6{,}224}
5110	−\newcommand{\CagrQuantumComputingNTwoZeroTwoTwo}{4{,}608}	5110	+\newcommand{\CagrQuantumComputingNTwoZeroTwoTwo}{9{,}216}
5111	−\newcommand{\CagrQuantumComputingNTwoZeroTwoThree}{5{,}272}	5111	+\newcommand{\CagrQuantumComputingNTwoZeroTwoThree}{10{,}544}
5112	−\newcommand{\CagrQuantumComputingNTwoZeroTwoFive}{8{,}735}	5112	+\newcommand{\CagrQuantumComputingNTwoZeroTwoFive}{17{,}471}
5113	\newcommand{\CagrQuantumComputingCagrBaseline}{0.103}	5113	\newcommand{\CagrQuantumComputingCagrBaseline}{0.103}
5114	\newcommand{\CagrQuantumComputingCagrPost}{0.287}	5114	\newcommand{\CagrQuantumComputingCagrPost}{0.287}
5115	−\newcommand{\CagrQuantumComputingShareTwoZeroOneEight}{0.018}	5115	+\newcommand{\CagrQuantumComputingShareTwoZeroOneEight}{0.012}
5116	−\newcommand{\CagrQuantumComputingShareTwoZeroTwoTwo}{0.018}	5116	+\newcommand{\CagrQuantumComputingShareTwoZeroTwoTwo}{0.016}
5117	\newcommand{\CagrQuantumComputingShareTwoZeroTwoFive}{0.020}	5117	\newcommand{\CagrQuantumComputingShareTwoZeroTwoFive}{0.020}
5118	−\newcommand{\CagrReinforcementLearningNTwoZeroOneEight}{1{,}648}	5118	+\newcommand{\CagrReinforcementLearningNTwoZeroOneEight}{3{,}296}
5119	−\newcommand{\CagrReinforcementLearningNTwoZeroTwoTwo}{3{,}508}	5119	+\newcommand{\CagrReinforcementLearningNTwoZeroTwoTwo}{7{,}017}
5120	−\newcommand{\CagrReinforcementLearningNTwoZeroTwoThree}{4{,}211}	5120	+\newcommand{\CagrReinforcementLearningNTwoZeroTwoThree}{8{,}422}
5121	−\newcommand{\CagrReinforcementLearningNTwoZeroTwoFive}{6{,}605}	5121	+\newcommand{\CagrReinforcementLearningNTwoZeroTwoFive}{13{,}210}
5122	\newcommand{\CagrReinforcementLearningCagrBaseline}{0.208}	5122	\newcommand{\CagrReinforcementLearningCagrBaseline}{0.208}
5123	\newcommand{\CagrReinforcementLearningCagrPost}{0.252}	5123	\newcommand{\CagrReinforcementLearningCagrPost}{0.252}
5124	−\newcommand{\CagrReinforcementLearningShareTwoZeroOneEight}{0.010}	5124	+\newcommand{\CagrReinforcementLearningShareTwoZeroOneEight}{0.007}
5125	−\newcommand{\CagrReinforcementLearningShareTwoZeroTwoTwo}{0.014}	5125	+\newcommand{\CagrReinforcementLearningShareTwoZeroTwoTwo}{0.013}
5126	\newcommand{\CagrReinforcementLearningShareTwoZeroTwoFive}{0.015}	5126	\newcommand{\CagrReinforcementLearningShareTwoZeroTwoFive}{0.015}
5127	−\newcommand{\CagrRoboticsNTwoZeroOneEight}{5{,}832}	5127	+\newcommand{\CagrRoboticsNTwoZeroOneEight}{17{,}496}
5128	−\newcommand{\CagrRoboticsNTwoZeroTwoTwo}{11{,}551}	5128	+\newcommand{\CagrRoboticsNTwoZeroTwoTwo}{23{,}103}
5129	−\newcommand{\CagrRoboticsNTwoZeroTwoThree}{12{,}879}	5129	+\newcommand{\CagrRoboticsNTwoZeroTwoThree}{25{,}759}
5130	−\newcommand{\CagrRoboticsNTwoZeroTwoFive}{16{,}732}	5130	+\newcommand{\CagrRoboticsNTwoZeroTwoFive}{33{,}464}
5131	−\newcommand{\CagrRoboticsCagrBaseline}{0.186}	5131	+\newcommand{\CagrRoboticsCagrBaseline}{0.072}
5132	\newcommand{\CagrRoboticsCagrPost}{0.140}	5132	\newcommand{\CagrRoboticsCagrPost}{0.140}
5133	−\newcommand{\CagrRoboticsShareTwoZeroOneEight}{0.034}	5133	+\newcommand{\CagrRoboticsShareTwoZeroOneEight}{0.035}
5134	−\newcommand{\CagrRoboticsShareTwoZeroTwoTwo}{0.045}	5134	+\newcommand{\CagrRoboticsShareTwoZeroTwoTwo}{0.041}
5135	−\newcommand{\CagrRoboticsShareTwoZeroTwoFive}{0.039}	5135	+\newcommand{\CagrRoboticsShareTwoZeroTwoFive}{0.037}
5136	−\newcommand{\CagrScientificComputingNTwoZeroOneEight}{5{,}627}	5136	+\newcommand{\CagrScientificComputingNTwoZeroOneEight}{16{,}882}
5137	−\newcommand{\CagrScientificComputingNTwoZeroTwoTwo}{6{,}644}	5137	+\newcommand{\CagrScientificComputingNTwoZeroTwoTwo}{19{,}932}
5138	−\newcommand{\CagrScientificComputingNTwoZeroTwoThree}{6{,}807}	5138	+\newcommand{\CagrScientificComputingNTwoZeroTwoThree}{20{,}423}
5139	−\newcommand{\CagrScientificComputingNTwoZeroTwoFive}{7{,}730}	5139	+\newcommand{\CagrScientificComputingNTwoZeroTwoFive}{23{,}191}
5140	\newcommand{\CagrScientificComputingCagrBaseline}{0.042}	5140	\newcommand{\CagrScientificComputingCagrBaseline}{0.042}
5141	\newcommand{\CagrScientificComputingCagrPost}{0.066}	5141	\newcommand{\CagrScientificComputingCagrPost}{0.066}
5142	−\newcommand{\CagrScientificComputingShareTwoZeroOneEight}{0.033}	5142	+\newcommand{\CagrScientificComputingShareTwoZeroOneEight}{0.034}
5143	−\newcommand{\CagrScientificComputingShareTwoZeroTwoTwo}{0.026}	5143	+\newcommand{\CagrScientificComputingShareTwoZeroTwoTwo}{0.036}
5144	−\newcommand{\CagrScientificComputingShareTwoZeroTwoFive}{0.018}	5144	+\newcommand{\CagrScientificComputingShareTwoZeroTwoFive}{0.026}
5145	−\newcommand{\CagrSoftwareEngineeringNTwoZeroOneEight}{9{,}672}	5145	+\newcommand{\CagrSoftwareEngineeringNTwoZeroOneEight}{29{,}016}
5146	−\newcommand{\CagrSoftwareEngineeringNTwoZeroTwoTwo}{11{,}862}	5146	+\newcommand{\CagrSoftwareEngineeringNTwoZeroTwoTwo}{23{,}725}
5147	−\newcommand{\CagrSoftwareEngineeringNTwoZeroTwoThree}{13{,}122}	5147	+\newcommand{\CagrSoftwareEngineeringNTwoZeroTwoThree}{26{,}245}
5148	−\newcommand{\CagrSoftwareEngineeringNTwoZeroTwoFive}{18{,}190}	5148	+\newcommand{\CagrSoftwareEngineeringNTwoZeroTwoFive}{36{,}381}
5149	−\newcommand{\CagrSoftwareEngineeringCagrBaseline}{0.052}	5149	+\newcommand{\CagrSoftwareEngineeringCagrBaseline}{-0.049}
5150	\newcommand{\CagrSoftwareEngineeringCagrPost}{0.177}	5150	\newcommand{\CagrSoftwareEngineeringCagrPost}{0.177}
5151	−\newcommand{\CagrSoftwareEngineeringShareTwoZeroOneEight}{0.057}	5151	+\newcommand{\CagrSoftwareEngineeringShareTwoZeroOneEight}{0.058}
5152	−\newcommand{\CagrSoftwareEngineeringShareTwoZeroTwoTwo}{0.047}	5152	+\newcommand{\CagrSoftwareEngineeringShareTwoZeroTwoTwo}{0.042}
5153	−\newcommand{\CagrSoftwareEngineeringShareTwoZeroTwoFive}{0.042}	5153	+\newcommand{\CagrSoftwareEngineeringShareTwoZeroTwoFive}{0.041}
5154	−\newcommand{\CagrSpeechAudioNTwoZeroOneEight}{4{,}032}	5154	+\newcommand{\CagrSpeechAudioNTwoZeroOneEight}{12{,}096}
5155	−\newcommand{\CagrSpeechAudioNTwoZeroTwoTwo}{6{,}349}	5155	+\newcommand{\CagrSpeechAudioNTwoZeroTwoTwo}{12{,}698}
5156	−\newcommand{\CagrSpeechAudioNTwoZeroTwoThree}{7{,}344}	5156	+\newcommand{\CagrSpeechAudioNTwoZeroTwoThree}{14{,}688}
5157	−\newcommand{\CagrSpeechAudioNTwoZeroTwoFive}{9{,}186}	5157	+\newcommand{\CagrSpeechAudioNTwoZeroTwoFive}{18{,}373}
5158	−\newcommand{\CagrSpeechAudioCagrBaseline}{0.120}	5158	+\newcommand{\CagrSpeechAudioCagrBaseline}{0.012}
5159	\newcommand{\CagrSpeechAudioCagrPost}{0.118}	5159	\newcommand{\CagrSpeechAudioCagrPost}{0.118}
5160	\newcommand{\CagrSpeechAudioShareTwoZeroOneEight}{0.024}	5160	\newcommand{\CagrSpeechAudioShareTwoZeroOneEight}{0.024}
5161	−\newcommand{\CagrSpeechAudioShareTwoZeroTwoTwo}{0.025}	5161	+\newcommand{\CagrSpeechAudioShareTwoZeroTwoTwo}{0.023}
5162	\newcommand{\CagrSpeechAudioShareTwoZeroTwoFive}{0.021}	5162	\newcommand{\CagrSpeechAudioShareTwoZeroTwoFive}{0.021}
5163	−\newcommand{\CagrSustainableComputingNTwoZeroOneEight}{291}	5163	+\newcommand{\CagrSustainableComputingNTwoZeroOneEight}{583}
5164	−\newcommand{\CagrSustainableComputingNTwoZeroTwoTwo}{276}	5164	+\newcommand{\CagrSustainableComputingNTwoZeroTwoTwo}{830}
5165	−\newcommand{\CagrSustainableComputingNTwoZeroTwoThree}{539}	5165	+\newcommand{\CagrSustainableComputingNTwoZeroTwoThree}{1{,}078}
5166	−\newcommand{\CagrSustainableComputingNTwoZeroTwoFive}{819}	5166	+\newcommand{\CagrSustainableComputingNTwoZeroTwoFive}{1{,}639}
5167	−\newcommand{\CagrSustainableComputingCagrBaseline}{-0.013}	5167	+\newcommand{\CagrSustainableComputingCagrBaseline}{0.092}
5168	\newcommand{\CagrSustainableComputingCagrPost}{0.233}	5168	\newcommand{\CagrSustainableComputingCagrPost}{0.233}
5169	−\newcommand{\CagrSustainableComputingShareTwoZeroOneEight}{0.002}	5169	+\newcommand{\CagrSustainableComputingShareTwoZeroOneEight}{0.001}
5170	\newcommand{\CagrSustainableComputingShareTwoZeroTwoTwo}{0.001}	5170	\newcommand{\CagrSustainableComputingShareTwoZeroTwoTwo}{0.001}
5171	\newcommand{\CagrSustainableComputingShareTwoZeroTwoFive}{0.002}	5171	\newcommand{\CagrSustainableComputingShareTwoZeroTwoFive}{0.002}
5172	\newcommand{\CatcountAiAcceleratorsTwoZeroOneEightPrimary}{818}	5172	\newcommand{\CatcountAiAcceleratorsTwoZeroOneEightPrimary}{818}
				`@@ -9075,51 +9075,51 @@`
9075	\newcommand{\SecCagrPrimaryFederatedLearningSpanPct}{37.7}	9075	\newcommand{\SecCagrPrimaryFederatedLearningSpanPct}{37.7}
9076	\newcommand{\SecCountFederatedLearningAllMultilabel}{71{,}037}	9076	\newcommand{\SecCountFederatedLearningAllMultilabel}{71{,}037}
9077	\newcommand{\SecCountFederatedLearningAllPrimary}{11{,}240}	9077	\newcommand{\SecCountFederatedLearningAllPrimary}{11{,}240}
9078	−\newcommand{\SecCagrAppliedCryptographyNTwoZeroOneEight}{3{,}594}	9078	+\newcommand{\SecCagrAppliedCryptographyNTwoZeroOneEight}{10{,}782}
9079	−\newcommand{\SecCagrAppliedCryptographyNTwoZeroTwoTwo}{5{,}149}	9079	+\newcommand{\SecCagrAppliedCryptographyNTwoZeroTwoTwo}{10{,}299}
9080	−\newcommand{\SecCagrAppliedCryptographyNTwoZeroTwoThree}{3{,}739}	9080	+\newcommand{\SecCagrAppliedCryptographyNTwoZeroTwoThree}{11{,}219}
9081	−\newcommand{\SecCagrAppliedCryptographyNTwoZeroTwoFive}{7{,}306}	9081	+\newcommand{\SecCagrAppliedCryptographyNTwoZeroTwoFive}{14{,}612}
9082	−\newcommand{\SecCagrAppliedCryptographyCagrBaselinePct}{9.4}	9082	+\newcommand{\SecCagrAppliedCryptographyCagrBaselinePct}{-1.1}
9083	−\newcommand{\SecCagrAppliedCryptographyCagrPostPct}{39.8}	9083	+\newcommand{\SecCagrAppliedCryptographyCagrPostPct}{14.1}
9084	−\newcommand{\SecCagrAppliedCryptographyShareTwoZeroOneEightPct}{2.12}	9084	+\newcommand{\SecCagrAppliedCryptographyShareTwoZeroOneEightPct}{2.16}
9085	−\newcommand{\SecCagrAppliedCryptographyShareTwoZeroTwoTwoPct}{2.02}	9085	+\newcommand{\SecCagrAppliedCryptographyShareTwoZeroTwoTwoPct}{1.84}
9086	−\newcommand{\SecCagrAppliedCryptographyShareTwoZeroTwoFivePct}{1.70}	9086	+\newcommand{\SecCagrAppliedCryptographyShareTwoZeroTwoFivePct}{1.64}
9087	−\newcommand{\SecCagrBlockchainNTwoZeroOneEight}{2{,}147}	9087	+\newcommand{\SecCagrBlockchainNTwoZeroOneEight}{6{,}441}
9088	−\newcommand{\SecCagrBlockchainNTwoZeroTwoTwo}{5{,}885}	9088	+\newcommand{\SecCagrBlockchainNTwoZeroTwoTwo}{11{,}770}
9089	−\newcommand{\SecCagrBlockchainNTwoZeroTwoThree}{7{,}174}	9089	+\newcommand{\SecCagrBlockchainNTwoZeroTwoThree}{14{,}349}
9090	−\newcommand{\SecCagrBlockchainNTwoZeroTwoFive}{9{,}006}	9090	+\newcommand{\SecCagrBlockchainNTwoZeroTwoFive}{18{,}012}
9091	−\newcommand{\SecCagrBlockchainCagrBaselinePct}{28.7}	9091	+\newcommand{\SecCagrBlockchainCagrBaselinePct}{16.3}
9092	\newcommand{\SecCagrBlockchainCagrPostPct}{12.0}	9092	\newcommand{\SecCagrBlockchainCagrPostPct}{12.0}
9093	−\newcommand{\SecCagrBlockchainShareTwoZeroOneEightPct}{1.26}	9093	+\newcommand{\SecCagrBlockchainShareTwoZeroOneEightPct}{1.29}
9094	−\newcommand{\SecCagrBlockchainShareTwoZeroTwoTwoPct}{2.31}	9094	+\newcommand{\SecCagrBlockchainShareTwoZeroTwoTwoPct}{2.10}
9095	−\newcommand{\SecCagrBlockchainShareTwoZeroTwoFivePct}{2.10}	9095	+\newcommand{\SecCagrBlockchainShareTwoZeroTwoFivePct}{2.02}
9096	−\newcommand{\SecCagrCybersecurityNTwoZeroOneEight}{9{,}813}	9096	+\newcommand{\SecCagrCybersecurityNTwoZeroOneEight}{29{,}440}
9097	−\newcommand{\SecCagrCybersecurityNTwoZeroTwoTwo}{15{,}777}	9097	+\newcommand{\SecCagrCybersecurityNTwoZeroTwoTwo}{31{,}554}
9098	−\newcommand{\SecCagrCybersecurityNTwoZeroTwoThree}{12{,}160}	9098	+\newcommand{\SecCagrCybersecurityNTwoZeroTwoThree}{36{,}481}
9099	−\newcommand{\SecCagrCybersecurityNTwoZeroTwoFive}{29{,}522}	9099	+\newcommand{\SecCagrCybersecurityNTwoZeroTwoFive}{59{,}044}
9100	−\newcommand{\SecCagrCybersecurityCagrBaselinePct}{12.6}	9100	+\newcommand{\SecCagrCybersecurityCagrBaselinePct}{1.7}
9101	−\newcommand{\SecCagrCybersecurityCagrPostPct}{55.8}	9101	+\newcommand{\SecCagrCybersecurityCagrPostPct}{27.2}
9102	−\newcommand{\SecCagrCybersecurityShareTwoZeroOneEightPct}{5.78}	9102	+\newcommand{\SecCagrCybersecurityShareTwoZeroOneEightPct}{5.89}
9103	−\newcommand{\SecCagrCybersecurityShareTwoZeroTwoTwoPct}{6.20}	9103	+\newcommand{\SecCagrCybersecurityShareTwoZeroTwoTwoPct}{5.63}
9104	−\newcommand{\SecCagrCybersecurityShareTwoZeroTwoFivePct}{6.87}	9104	+\newcommand{\SecCagrCybersecurityShareTwoZeroTwoFivePct}{6.61}
9105	−\newcommand{\SecCagrFederatedLearningNTwoZeroOneEight}{71}	9105	+\newcommand{\SecCagrFederatedLearningNTwoZeroOneEight}{143}
9106	−\newcommand{\SecCagrFederatedLearningNTwoZeroTwoTwo}{487}	9106	+\newcommand{\SecCagrFederatedLearningNTwoZeroTwoTwo}{974}
9107	−\newcommand{\SecCagrFederatedLearningNTwoZeroTwoThree}{748}	9107	+\newcommand{\SecCagrFederatedLearningNTwoZeroTwoThree}{1{,}497}
9108	−\newcommand{\SecCagrFederatedLearningNTwoZeroTwoFive}{1{,}272}	9108	+\newcommand{\SecCagrFederatedLearningNTwoZeroTwoFive}{2{,}544}
9109	\newcommand{\SecCagrFederatedLearningCagrBaselinePct}{61.5}	9109	\newcommand{\SecCagrFederatedLearningCagrBaselinePct}{61.5}
9110	\newcommand{\SecCagrFederatedLearningCagrPostPct}{30.4}	9110	\newcommand{\SecCagrFederatedLearningCagrPostPct}{30.4}
9111	−\newcommand{\SecCagrFederatedLearningShareTwoZeroOneEightPct}{0.04}	9111	+\newcommand{\SecCagrFederatedLearningShareTwoZeroOneEightPct}{0.03}
9112	−\newcommand{\SecCagrFederatedLearningShareTwoZeroTwoTwoPct}{0.19}	9112	+\newcommand{\SecCagrFederatedLearningShareTwoZeroTwoTwoPct}{0.17}
9113	−\newcommand{\SecCagrFederatedLearningShareTwoZeroTwoFivePct}{0.30}	9113	+\newcommand{\SecCagrFederatedLearningShareTwoZeroTwoFivePct}{0.28}
9114	−\newcommand{\SecCagrPrivacyPreservingComputationNTwoZeroOneEight}{1{,}035}	9114	+\newcommand{\SecCagrPrivacyPreservingComputationNTwoZeroOneEight}{3{,}105}
9115	−\newcommand{\SecCagrPrivacyPreservingComputationNTwoZeroTwoTwo}{2{,}188}	9115	+\newcommand{\SecCagrPrivacyPreservingComputationNTwoZeroTwoTwo}{4{,}376}
9116	−\newcommand{\SecCagrPrivacyPreservingComputationNTwoZeroTwoThree}{2{,}505}	9116	+\newcommand{\SecCagrPrivacyPreservingComputationNTwoZeroTwoThree}{5{,}010}
9117	−\newcommand{\SecCagrPrivacyPreservingComputationNTwoZeroTwoFive}{4{,}408}	9117	+\newcommand{\SecCagrPrivacyPreservingComputationNTwoZeroTwoFive}{8{,}817}
9118	−\newcommand{\SecCagrPrivacyPreservingComputationCagrBaselinePct}{20.6}	9118	+\newcommand{\SecCagrPrivacyPreservingComputationCagrBaselinePct}{9.0}
9119	\newcommand{\SecCagrPrivacyPreservingComputationCagrPostPct}{32.7}	9119	\newcommand{\SecCagrPrivacyPreservingComputationCagrPostPct}{32.7}
9120	−\newcommand{\SecCagrPrivacyPreservingComputationShareTwoZeroOneEightPct}{0.61}	9120	+\newcommand{\SecCagrPrivacyPreservingComputationShareTwoZeroOneEightPct}{0.62}
9121	−\newcommand{\SecCagrPrivacyPreservingComputationShareTwoZeroTwoTwoPct}{0.86}	9121	+\newcommand{\SecCagrPrivacyPreservingComputationShareTwoZeroTwoTwoPct}{0.78}
9122	−\newcommand{\SecCagrPrivacyPreservingComputationShareTwoZeroTwoFivePct}{1.03}	9122	+\newcommand{\SecCagrPrivacyPreservingComputationShareTwoZeroTwoFivePct}{0.99}
9123	\newcommand{\SecBreakAppliedCryptographyBpBreaksBic}{2022-07-01}	9123	\newcommand{\SecBreakAppliedCryptographyBpBreaksBic}{2022-07-01}
9124	\newcommand{\SecBreakAppliedCryptographyPelt}{2022-11, 2024-12}	9124	\newcommand{\SecBreakAppliedCryptographyPelt}{2022-11, 2024-12}
9125	\newcommand{\SecBreakAppliedCryptographyBocpdResets}{none}	9125	\newcommand{\SecBreakAppliedCryptographyBocpdResets}{none}
				`@@ -10691,60 +10691,60 @@`
10691	\newcommand{\ThyCagrMultilabelTotalBaselinePct}{6.1}	10691	\newcommand{\ThyCagrMultilabelTotalBaselinePct}{6.1}
10692	\newcommand{\ThyCagrPrimaryTotalPostPct}{18.6}	10692	\newcommand{\ThyCagrPrimaryTotalPostPct}{18.6}
10693	\newcommand{\ThyCagrMultilabelTotalPostPct}{26.0}	10693	\newcommand{\ThyCagrMultilabelTotalPostPct}{26.0}
10694	−\newcommand{\ThyCagrfileAlgorithmsNTwoZeroOneEight}{9{,}716}	10694	+\newcommand{\ThyCagrfileAlgorithmsNTwoZeroOneEight}{29{,}148}
10695	−\newcommand{\ThyCagrfileAlgorithmsNTwoZeroTwoTwo}{9{,}400}	10695	+\newcommand{\ThyCagrfileAlgorithmsNTwoZeroTwoTwo}{28{,}202}
10696	−\newcommand{\ThyCagrfileAlgorithmsNTwoZeroTwoThree}{9{,}510}	10696	+\newcommand{\ThyCagrfileAlgorithmsNTwoZeroTwoThree}{28{,}531}
10697	−\newcommand{\ThyCagrfileAlgorithmsNTwoZeroTwoFive}{11{,}265}	10697	+\newcommand{\ThyCagrfileAlgorithmsNTwoZeroTwoFive}{33{,}795}
10698	\newcommand{\ThyCagrfileAlgorithmsCagrBaselinePct}{-0.8}	10698	\newcommand{\ThyCagrfileAlgorithmsCagrBaselinePct}{-0.8}
10699	\newcommand{\ThyCagrfileAlgorithmsCagrPostPct}{8.8}	10699	\newcommand{\ThyCagrfileAlgorithmsCagrPostPct}{8.8}
10700	−\newcommand{\ThyCagrfileAlgorithmsShareTwoZeroOneEightPct}{5.72}	10700	+\newcommand{\ThyCagrfileAlgorithmsShareTwoZeroOneEightPct}{5.83}
10701	−\newcommand{\ThyCagrfileAlgorithmsShareTwoZeroTwoTwoPct}{3.69}	10701	+\newcommand{\ThyCagrfileAlgorithmsShareTwoZeroTwoTwoPct}{5.03}
10702	−\newcommand{\ThyCagrfileAlgorithmsShareTwoZeroTwoFivePct}{2.62}	10702	+\newcommand{\ThyCagrfileAlgorithmsShareTwoZeroTwoFivePct}{3.79}
10703	−\newcommand{\ThyCagrfileCompilersNTwoZeroOneEight}{241}	10703	+\newcommand{\ThyCagrfileCompilersNTwoZeroOneEight}{725}
10704	−\newcommand{\ThyCagrfileCompilersNTwoZeroTwoTwo}{321}	10704	+\newcommand{\ThyCagrfileCompilersNTwoZeroTwoTwo}{642}
10705	−\newcommand{\ThyCagrfileCompilersNTwoZeroTwoThree}{346}	10705	+\newcommand{\ThyCagrfileCompilersNTwoZeroTwoThree}{692}
10706	−\newcommand{\ThyCagrfileCompilersNTwoZeroTwoFive}{383}	10706	+\newcommand{\ThyCagrfileCompilersNTwoZeroTwoFive}{767}
10707	−\newcommand{\ThyCagrfileCompilersCagrBaselinePct}{7.4}	10707	+\newcommand{\ThyCagrfileCompilersCagrBaselinePct}{-3.0}
10708	\newcommand{\ThyCagrfileCompilersCagrPostPct}{5.3}	10708	\newcommand{\ThyCagrfileCompilersCagrPostPct}{5.3}
10709	−\newcommand{\ThyCagrfileCompilersShareTwoZeroOneEightPct}{0.14}	10709	+\newcommand{\ThyCagrfileCompilersShareTwoZeroOneEightPct}{0.15}
10710	−\newcommand{\ThyCagrfileCompilersShareTwoZeroTwoTwoPct}{0.13}	10710	+\newcommand{\ThyCagrfileCompilersShareTwoZeroTwoTwoPct}{0.11}
10711	\newcommand{\ThyCagrfileCompilersShareTwoZeroTwoFivePct}{0.09}	10711	\newcommand{\ThyCagrfileCompilersShareTwoZeroTwoFivePct}{0.09}
10712	−\newcommand{\ThyCagrfileComputationalComplexityNTwoZeroOneEight}{1{,}279}	10712	+\newcommand{\ThyCagrfileComputationalComplexityNTwoZeroOneEight}{3{,}837}
10713	−\newcommand{\ThyCagrfileComputationalComplexityNTwoZeroTwoTwo}{1{,}770}	10713	+\newcommand{\ThyCagrfileComputationalComplexityNTwoZeroTwoTwo}{3{,}541}
10714	−\newcommand{\ThyCagrfileComputationalComplexityNTwoZeroTwoThree}{1{,}166}	10714	+\newcommand{\ThyCagrfileComputationalComplexityNTwoZeroTwoThree}{3{,}499}
10715	−\newcommand{\ThyCagrfileComputationalComplexityNTwoZeroTwoFive}{2{,}104}	10715	+\newcommand{\ThyCagrfileComputationalComplexityNTwoZeroTwoFive}{4{,}209}
10716	−\newcommand{\ThyCagrfileComputationalComplexityCagrBaselinePct}{8.5}	10716	+\newcommand{\ThyCagrfileComputationalComplexityCagrBaselinePct}{-2.0}
10717	−\newcommand{\ThyCagrfileComputationalComplexityCagrPostPct}{34.3}	10717	+\newcommand{\ThyCagrfileComputationalComplexityCagrPostPct}{9.7}
10718	−\newcommand{\ThyCagrfileComputationalComplexityShareTwoZeroOneEightPct}{0.75}	10718	+\newcommand{\ThyCagrfileComputationalComplexityShareTwoZeroOneEightPct}{0.77}
10719	−\newcommand{\ThyCagrfileComputationalComplexityShareTwoZeroTwoTwoPct}{0.70}	10719	+\newcommand{\ThyCagrfileComputationalComplexityShareTwoZeroTwoTwoPct}{0.63}
10720	−\newcommand{\ThyCagrfileComputationalComplexityShareTwoZeroTwoFivePct}{0.49}	10720	+\newcommand{\ThyCagrfileComputationalComplexityShareTwoZeroTwoFivePct}{0.47}
10721	−\newcommand{\ThyCagrfileFormalVerificationNTwoZeroOneEight}{1{,}769}	10721	+\newcommand{\ThyCagrfileFormalVerificationNTwoZeroOneEight}{5{,}307}
10722	−\newcommand{\ThyCagrfileFormalVerificationNTwoZeroTwoTwo}{1{,}997}	10722	+\newcommand{\ThyCagrfileFormalVerificationNTwoZeroTwoTwo}{3{,}995}
10723	−\newcommand{\ThyCagrfileFormalVerificationNTwoZeroTwoThree}{1{,}461}	10723	+\newcommand{\ThyCagrfileFormalVerificationNTwoZeroTwoThree}{4{,}385}
10724	−\newcommand{\ThyCagrfileFormalVerificationNTwoZeroTwoFive}{2{,}311}	10724	+\newcommand{\ThyCagrfileFormalVerificationNTwoZeroTwoFive}{4{,}623}
10725	−\newcommand{\ThyCagrfileFormalVerificationCagrBaselinePct}{3.1}	10725	+\newcommand{\ThyCagrfileFormalVerificationCagrBaselinePct}{-6.9}
10726	−\newcommand{\ThyCagrfileFormalVerificationCagrPostPct}{25.8}	10726	+\newcommand{\ThyCagrfileFormalVerificationCagrPostPct}{2.7}
10727	−\newcommand{\ThyCagrfileFormalVerificationShareTwoZeroOneEightPct}{1.04}	10727	+\newcommand{\ThyCagrfileFormalVerificationShareTwoZeroOneEightPct}{1.06}
10728	−\newcommand{\ThyCagrfileFormalVerificationShareTwoZeroTwoTwoPct}{0.78}	10728	+\newcommand{\ThyCagrfileFormalVerificationShareTwoZeroTwoTwoPct}{0.71}
10729	−\newcommand{\ThyCagrfileFormalVerificationShareTwoZeroTwoFivePct}{0.54}	10729	+\newcommand{\ThyCagrfileFormalVerificationShareTwoZeroTwoFivePct}{0.52}
10730	−\newcommand{\ThyCagrfileProgrammingLanguagesNTwoZeroOneEight}{1{,}463}	10730	+\newcommand{\ThyCagrfileProgrammingLanguagesNTwoZeroOneEight}{4{,}390}
10731	−\newcommand{\ThyCagrfileProgrammingLanguagesNTwoZeroTwoTwo}{1{,}832}	10731	+\newcommand{\ThyCagrfileProgrammingLanguagesNTwoZeroTwoTwo}{3{,}664}
10732	−\newcommand{\ThyCagrfileProgrammingLanguagesNTwoZeroTwoThree}{1{,}938}	10732	+\newcommand{\ThyCagrfileProgrammingLanguagesNTwoZeroTwoThree}{3{,}876}
10733	−\newcommand{\ThyCagrfileProgrammingLanguagesNTwoZeroTwoFive}{2{,}096}	10733	+\newcommand{\ThyCagrfileProgrammingLanguagesNTwoZeroTwoFive}{4{,}193}
10734	−\newcommand{\ThyCagrfileProgrammingLanguagesCagrBaselinePct}{5.8}	10734	+\newcommand{\ThyCagrfileProgrammingLanguagesCagrBaselinePct}{-4.4}
10735	\newcommand{\ThyCagrfileProgrammingLanguagesCagrPostPct}{4.0}	10735	\newcommand{\ThyCagrfileProgrammingLanguagesCagrPostPct}{4.0}
10736	−\newcommand{\ThyCagrfileProgrammingLanguagesShareTwoZeroOneEightPct}{0.86}	10736	+\newcommand{\ThyCagrfileProgrammingLanguagesShareTwoZeroOneEightPct}{0.88}
10737	−\newcommand{\ThyCagrfileProgrammingLanguagesShareTwoZeroTwoTwoPct}{0.72}	10737	+\newcommand{\ThyCagrfileProgrammingLanguagesShareTwoZeroTwoTwoPct}{0.65}
10738	−\newcommand{\ThyCagrfileProgrammingLanguagesShareTwoZeroTwoFivePct}{0.49}	10738	+\newcommand{\ThyCagrfileProgrammingLanguagesShareTwoZeroTwoFivePct}{0.47}
10739	−\newcommand{\ThyCagrfileSoftwareEngineeringNTwoZeroOneEight}{9{,}672}	10739	+\newcommand{\ThyCagrfileSoftwareEngineeringNTwoZeroOneEight}{29{,}016}
10740	−\newcommand{\ThyCagrfileSoftwareEngineeringNTwoZeroTwoTwo}{11{,}862}	10740	+\newcommand{\ThyCagrfileSoftwareEngineeringNTwoZeroTwoTwo}{23{,}725}
10741	−\newcommand{\ThyCagrfileSoftwareEngineeringNTwoZeroTwoThree}{13{,}122}	10741	+\newcommand{\ThyCagrfileSoftwareEngineeringNTwoZeroTwoThree}{26{,}245}
10742	−\newcommand{\ThyCagrfileSoftwareEngineeringNTwoZeroTwoFive}{18{,}190}	10742	+\newcommand{\ThyCagrfileSoftwareEngineeringNTwoZeroTwoFive}{36{,}381}
10743	−\newcommand{\ThyCagrfileSoftwareEngineeringCagrBaselinePct}{5.2}	10743	+\newcommand{\ThyCagrfileSoftwareEngineeringCagrBaselinePct}{-4.9}
10744	\newcommand{\ThyCagrfileSoftwareEngineeringCagrPostPct}{17.7}	10744	\newcommand{\ThyCagrfileSoftwareEngineeringCagrPostPct}{17.7}
10745	−\newcommand{\ThyCagrfileSoftwareEngineeringShareTwoZeroOneEightPct}{5.70}	10745	+\newcommand{\ThyCagrfileSoftwareEngineeringShareTwoZeroOneEightPct}{5.81}
10746	−\newcommand{\ThyCagrfileSoftwareEngineeringShareTwoZeroTwoTwoPct}{4.66}	10746	+\newcommand{\ThyCagrfileSoftwareEngineeringShareTwoZeroTwoTwoPct}{4.23}
10747	−\newcommand{\ThyCagrfileSoftwareEngineeringShareTwoZeroTwoFivePct}{4.23}	10747	+\newcommand{\ThyCagrfileSoftwareEngineeringShareTwoZeroTwoFivePct}{4.07}
10748	\newcommand{\ThyBreakAlgorithmsBpBreaksBic}{2023-04-01}	10748	\newcommand{\ThyBreakAlgorithmsBpBreaksBic}{2023-04-01}
10749	\newcommand{\ThyBreakAlgorithmsPelt}{2023-08, 2025-08}	10749	\newcommand{\ThyBreakAlgorithmsPelt}{2023-08, 2025-08}
10750	\newcommand{\ThyBreakAlgorithmsBocpdResets}{none}	10750	\newcommand{\ThyBreakAlgorithmsBocpdResets}{none}

modified manuscript/tables/tab_ai_growth.tex +9 −9

				`@@ -5,15 +5,15 @@`
5	\toprule	5	\toprule
6	\textbf{Category} & \textbf{2018} & \textbf{2022} & \textbf{2023} & \textbf{2025} & \textbf{CAGR 18--22} & \textbf{CAGR 23--25} & \textbf{Share 2018} & \textbf{Share 2022} & \textbf{Share 2025} \\	6	\textbf{Category} & \textbf{2018} & \textbf{2022} & \textbf{2023} & \textbf{2025} & \textbf{CAGR 18--22} & \textbf{CAGR 23--25} & \textbf{Share 2018} & \textbf{Share 2022} & \textbf{Share 2025} \\
7	\midrule	7	\midrule
8	−LLM & 545 & 1{,}088 & 5{,}194 & 22{,}579 & 0.189 & 1.085 & 0.32 & 0.43 & 5.26 \\	8	+LLM & 1{,}635 & 3{,}266 & 10{,}389 & 45{,}158 & 0.189 & 1.085 & 0.33 & 0.58 & 5.06 \\
9	−Foundation & 23 & 175 & 285 & 927 & 0.661 & 0.802 & 0.01 & 0.07 & 0.22 \\	9	+Foundation & 46 & 350 & 571 & 1{,}854 & 0.661 & 0.802 & 0.01 & 0.06 & 0.21 \\
10	−Multimodal & 156 & 639 & 966 & 3{,}374 & 0.421 & 0.869 & 0.09 & 0.25 & 0.79 \\	10	+Multimodal & 470 & 1{,}278 & 1{,}932 & 6{,}749 & 0.284 & 0.869 & 0.09 & 0.23 & 0.76 \\
11	−Generative & 255 & 1{,}080 & 1{,}823 & 6{,}371 & 0.434 & 0.870 & 0.15 & 0.42 & 1.48 \\	11	+Generative & 766 & 2{,}160 & 3{,}646 & 12{,}743 & 0.296 & 0.870 & 0.15 & 0.39 & 1.43 \\
12	−Agents & 2{,}718 & 2{,}260 & 2{,}495 & 4{,}409 & -0.045 & 0.329 & 1.60 & 0.89 & 1.03 \\	12	+Agents & 5{,}436 & 4{,}521 & 4{,}991 & 8{,}819 & -0.045 & 0.329 & 1.09 & 0.81 & 0.99 \\
13	−RL & 1{,}648 & 3{,}508 & 4{,}211 & 6{,}605 & 0.208 & 0.252 & 0.97 & 1.38 & 1.54 \\	13	+RL & 3{,}296 & 7{,}017 & 8{,}422 & 13{,}210 & 0.208 & 0.252 & 0.66 & 1.25 & 1.48 \\
14	−ML theory & 4{,}511 & 10{,}065 & 11{,}446 & 16{,}399 & 0.222 & 0.197 & 2.66 & 3.95 & 3.82 \\	14	+ML theory & 13{,}534 & 20{,}131 & 22{,}893 & 32{,}798 & 0.104 & 0.197 & 2.71 & 3.59 & 3.67 \\
15	−DL arch. & 3{,}997 & 7{,}423 & 12{,}803 & 17{,}070 & 0.167 & 0.155 & 2.36 & 2.91 & 3.97 \\	15	+DL arch. & 11{,}992 & 22{,}270 & 25{,}607 & 34{,}141 & 0.167 & 0.155 & 2.40 & 3.97 & 3.82 \\
16	−AI safety & 526 & 1{,}477 & 2{,}007 & 5{,}442 & 0.294 & 0.647 & 0.31 & 0.58 & 1.27 \\	16	+AI safety & 1{,}052 & 2{,}954 & 4{,}015 & 10{,}885 & 0.294 & 0.647 & 0.21 & 0.53 & 1.22 \\
17	\bottomrule	17	\bottomrule
18	\end{tabular}	18	\end{tabular}
19	\begin{tablenotes}\footnotesize\item Counts and shares as stored in the source file (works whose primary category is the row category; shares in per cent of all eligible works of the year). CAGR as in Part IV, eq.~\ref{eq:cagr}; the post-period CAGR uses the 2023 and 2025 end points. Category abbreviations: LLM = large language models; Foundation = foundation models; Generative = generative AI; Agents = AI agents; RL = reinforcement learning; DL arch. = deep-learning architectures; AI safety = AI safety and alignment.\end{tablenotes}	19	\begin{tablenotes}\footnotesize\item Counts and shares as stored in the source file (works whose primary category is the row category; shares in per cent of all eligible works of the year). CAGR as in Part IV, eq.~\ref{eq:cagr}; the post-period CAGR uses the 2023 and 2025 end points. Category abbreviations: LLM = large language models; Foundation = foundation models; Generative = generative AI; Agents = AI agents; RL = reinforcement learning; DL arch. = deep-learning architectures; AI safety = AI safety and alignment.\end{tablenotes}

modified manuscript/tables/tab_cagr.tex +40 −40

				`@@ -9,45 +9,45 @@`
9	\textbf{category} & \textbf{n\_2018} & \textbf{n\_2022} & \textbf{n\_2025} & \textbf{cagr\_baseline} & \textbf{cagr\_post} & \textbf{share\_2018} & \textbf{share\_2025} \\	9	\textbf{category} & \textbf{n\_2018} & \textbf{n\_2022} & \textbf{n\_2025} & \textbf{cagr\_baseline} & \textbf{cagr\_post} & \textbf{share\_2018} & \textbf{share\_2025} \\
10	\midrule	10	\midrule
11	\endhead	11	\endhead
12	−ai\_accelerators & 409 & 710 & 953 & 0.148 & 0.092 & 0.002 & 0.002 \\	12	+ai\_accelerators & 818 & 1{,}421 & 1{,}906 & 0.148 & 0.092 & 0.002 & 0.002 \\
13	−ai\_agents & 2{,}718 & 2{,}260 & 4{,}409 & -0.045 & 0.329 & 0.016 & 0.010 \\	13	+ai\_agents & 5{,}436 & 4{,}521 & 8{,}819 & -0.045 & 0.329 & 0.011 & 0.010 \\
14	−ai\_safety & 526 & 1{,}477 & 5{,}442 & 0.294 & 0.647 & 0.003 & 0.013 \\	14	+ai\_safety & 1{,}052 & 2{,}954 & 10{,}885 & 0.294 & 0.647 & 0.002 & 0.012 \\
15	−algorithms & 9{,}716 & 9{,}400 & 11{,}265 & -0.008 & 0.088 & 0.057 & 0.026 \\	15	+algorithms & 29{,}148 & 28{,}202 & 33{,}795 & -0.008 & 0.088 & 0.058 & 0.038 \\
16	−applied\_cryptography & 3{,}594 & 5{,}149 & 7{,}306 & 0.094 & 0.398 & 0.021 & 0.017 \\	16	+applied\_cryptography & 10{,}782 & 10{,}299 & 14{,}612 & -0.011 & 0.141 & 0.022 & 0.016 \\
17	−autonomous\_scientific\_discovery & 43 & 118 & 382 & 0.287 & 0.382 & 0.000 & 0.001 \\	17	+autonomous\_scientific\_discovery & 86 & 236 & 764 & 0.287 & 0.382 & 0.000 & 0.001 \\
18	−blockchain & 2{,}147 & 5{,}885 & 9{,}006 & 0.287 & 0.120 & 0.013 & 0.021 \\	18	+blockchain & 6{,}441 & 11{,}770 & 18{,}012 & 0.163 & 0.120 & 0.013 & 0.020 \\
19	−cloud\_computing & 3{,}132 & 3{,}551 & 3{,}658 & 0.032 & -0.022 & 0.018 & 0.009 \\	19	+cloud\_computing & 9{,}396 & 7{,}103 & 10{,}974 & -0.068 & 0.198 & 0.019 & 0.012 \\
20	−compilers & 241 & 321 & 383 & 0.074 & 0.053 & 0.001 & 0.001 \\	20	+compilers & 725 & 642 & 767 & -0.030 & 0.053 & 0.001 & 0.001 \\
21	−computational\_biology & 4{,}322 & 9{,}768 & 14{,}077 & 0.226 & 0.170 & 0.025 & 0.033 \\	21	+computational\_biology & 12{,}968 & 19{,}536 & 28{,}154 & 0.108 & 0.170 & 0.026 & 0.032 \\
22	−computational\_complexity & 1{,}279 & 1{,}770 & 2{,}104 & 0.085 & 0.343 & 0.008 & 0.005 \\	22	+computational\_complexity & 3{,}837 & 3{,}541 & 4{,}209 & -0.020 & 0.097 & 0.008 & 0.005 \\
23	−computer\_architecture & 6{,}518 & 7{,}587 & 6{,}736 & 0.039 & -0.093 & 0.038 & 0.016 \\	23	+computer\_architecture & 19{,}554 & 15{,}174 & 20{,}209 & -0.061 & 0.111 & 0.039 & 0.023 \\
24	−computer\_graphics & 3{,}399 & 4{,}143 & 6{,}220 & 0.051 & 0.417 & 0.020 & 0.014 \\	24	+computer\_graphics & 10{,}198 & 8{,}287 & 12{,}440 & -0.051 & 0.157 & 0.020 & 0.014 \\
25	−computer\_networks & 19{,}581 & 16{,}380 & 29{,}777 & -0.044 & 0.312 & 0.115 & 0.069 \\	25	+computer\_networks & 58{,}744 & 49{,}141 & 59{,}555 & -0.044 & 0.071 & 0.118 & 0.067 \\
26	−computer\_vision & 23{,}246 & 49{,}352 & 75{,}378 & 0.207 & 0.149 & 0.137 & 0.175 \\	26	+computer\_vision & 69{,}738 & 98{,}704 & 150{,}757 & 0.091 & 0.149 & 0.140 & 0.169 \\
27	−cybersecurity & 9{,}813 & 15{,}777 & 29{,}522 & 0.126 & 0.558 & 0.058 & 0.069 \\	27	+cybersecurity & 29{,}440 & 31{,}554 & 59{,}044 & 0.017 & 0.272 & 0.059 & 0.066 \\
28	−database\_systems & 5{,}421 & 6{,}812 & 8{,}340 & 0.059 & 0.073 & 0.032 & 0.019 \\	28	+database\_systems & 16{,}265 & 13{,}625 & 16{,}681 & -0.043 & 0.073 & 0.033 & 0.019 \\
29	−deep\_learning\_architectures & 3{,}997 & 7{,}423 & 17{,}070 & 0.167 & 0.155 & 0.024 & 0.040 \\	29	+deep\_learning\_architectures & 11{,}992 & 22{,}270 & 34{,}141 & 0.167 & 0.155 & 0.024 & 0.038 \\
30	−distributed\_computing & 3{,}839 & 4{,}689 & 4{,}183 & 0.051 & 0.132 & 0.023 & 0.010 \\	30	+distributed\_computing & 11{,}519 & 9{,}379 & 12{,}550 & -0.050 & 0.132 & 0.023 & 0.014 \\
31	−edge\_computing & 1{,}728 & 4{,}277 & 5{,}777 & 0.254 & 0.379 & 0.010 & 0.013 \\	31	+edge\_computing & 5{,}185 & 8{,}554 & 11{,}554 & 0.133 & 0.126 & 0.010 & 0.013 \\
32	−federated\_learning & 71 & 487 & 1{,}272 & 0.615 & 0.304 & 0.000 & 0.003 \\	32	+federated\_learning & 143 & 974 & 2{,}544 & 0.615 & 0.304 & 0.000 & 0.003 \\
33	−formal\_verification & 1{,}769 & 1{,}997 & 2{,}311 & 0.031 & 0.258 & 0.010 & 0.005 \\	33	+formal\_verification & 5{,}307 & 3{,}995 & 4{,}623 & -0.069 & 0.027 & 0.011 & 0.005 \\
34	−foundation\_models & 23 & 175 & 927 & 0.661 & 0.802 & 0.000 & 0.002 \\	34	+foundation\_models & 46 & 350 & 1{,}854 & 0.661 & 0.802 & 0.000 & 0.002 \\
35	−generative\_ai & 255 & 1{,}080 & 6{,}371 & 0.434 & 0.870 & 0.002 & 0.015 \\	35	+generative\_ai & 766 & 2{,}160 & 12{,}743 & 0.296 & 0.870 & 0.002 & 0.014 \\
36	−hci & 11{,}408 & 17{,}400 & 29{,}074 & 0.111 & 0.197 & 0.067 & 0.068 \\	36	+hci & 34{,}225 & 34{,}801 & 58{,}148 & 0.004 & 0.197 & 0.069 & 0.065 \\
37	−information\_retrieval & 4{,}031 & 5{,}621 & 8{,}689 & 0.087 & 0.176 & 0.024 & 0.020 \\	37	+information\_retrieval & 12{,}095 & 11{,}243 & 17{,}379 & -0.018 & 0.176 & 0.024 & 0.019 \\
38	−llm & 545 & 1{,}088 & 22{,}579 & 0.189 & 1.085 & 0.003 & 0.053 \\	38	+llm & 1{,}635 & 3{,}266 & 45{,}158 & 0.189 & 1.085 & 0.003 & 0.051 \\
39	−ml\_theory & 4{,}511 & 10{,}065 & 16{,}399 & 0.222 & 0.197 & 0.027 & 0.038 \\	39	+ml\_theory & 13{,}534 & 20{,}131 & 32{,}798 & 0.104 & 0.197 & 0.027 & 0.037 \\
40	−multimodal\_ai & 156 & 639 & 3{,}374 & 0.421 & 0.869 & 0.001 & 0.008 \\	40	+multimodal\_ai & 470 & 1{,}278 & 6{,}749 & 0.284 & 0.869 & 0.001 & 0.008 \\
41	−nlp & 7{,}790 & 9{,}892 & 21{,}457 & 0.062 & 0.124 & 0.046 & 0.050 \\	41	+nlp & 23{,}372 & 29{,}678 & 42{,}914 & 0.062 & 0.124 & 0.047 & 0.048 \\
42	−operating\_systems & 778 & 541 & 681 & -0.087 & 0.078 & 0.005 & 0.002 \\	42	+operating\_systems & 1{,}556 & 1{,}083 & 1{,}363 & -0.087 & 0.078 & 0.003 & 0.002 \\
43	−privacy\_preserving\_computation & 1{,}035 & 2{,}188 & 4{,}408 & 0.206 & 0.327 & 0.006 & 0.010 \\	43	+privacy\_preserving\_computation & 3{,}105 & 4{,}376 & 8{,}817 & 0.090 & 0.327 & 0.006 & 0.010 \\
44	−programming\_languages & 1{,}463 & 1{,}832 & 2{,}096 & 0.058 & 0.040 & 0.009 & 0.005 \\	44	+programming\_languages & 4{,}390 & 3{,}664 & 4{,}193 & -0.044 & 0.040 & 0.009 & 0.005 \\
45	−quantum\_computing & 3{,}112 & 4{,}608 & 8{,}735 & 0.103 & 0.287 & 0.018 & 0.020 \\	45	+quantum\_computing & 6{,}224 & 9{,}216 & 17{,}471 & 0.103 & 0.287 & 0.012 & 0.020 \\
46	−reinforcement\_learning & 1{,}648 & 3{,}508 & 6{,}605 & 0.208 & 0.252 & 0.010 & 0.015 \\	46	+reinforcement\_learning & 3{,}296 & 7{,}017 & 13{,}210 & 0.208 & 0.252 & 0.007 & 0.015 \\
47	−robotics & 5{,}832 & 11{,}551 & 16{,}732 & 0.186 & 0.140 & 0.034 & 0.039 \\	47	+robotics & 17{,}496 & 23{,}103 & 33{,}464 & 0.072 & 0.140 & 0.035 & 0.037 \\
48	−scientific\_computing & 5{,}627 & 6{,}644 & 7{,}730 & 0.042 & 0.066 & 0.033 & 0.018 \\	48	+scientific\_computing & 16{,}882 & 19{,}932 & 23{,}191 & 0.042 & 0.066 & 0.034 & 0.026 \\
49	−software\_engineering & 9{,}672 & 11{,}862 & 18{,}190 & 0.052 & 0.177 & 0.057 & 0.042 \\	49	+software\_engineering & 29{,}016 & 23{,}725 & 36{,}381 & -0.049 & 0.177 & 0.058 & 0.041 \\
50	−speech\_audio & 4{,}032 & 6{,}349 & 9{,}186 & 0.120 & 0.118 & 0.024 & 0.021 \\	50	+speech\_audio & 12{,}096 & 12{,}698 & 18{,}373 & 0.012 & 0.118 & 0.024 & 0.021 \\
51	−sustainable\_computing & 291 & 276 & 819 & -0.013 & 0.233 & 0.002 & 0.002 \\	51	+sustainable\_computing & 583 & 830 & 1{,}639 & 0.092 & 0.233 & 0.001 & 0.002 \\
52	\bottomrule	52	\bottomrule
53	\end{longtable}	53	\end{longtable}

modified manuscript/tables/tab_diversity.tex +16 −8

				`@@ -5,14 +5,22 @@`
5	\toprule	5	\toprule
6	\textbf{year} & \textbf{n} & \textbf{rao\_stirling} & \textbf{rs\_ci\_low} & \textbf{rs\_ci\_high} & \textbf{leinster\_cobbold\_q2} \\	6	\textbf{year} & \textbf{n} & \textbf{rao\_stirling} & \textbf{rs\_ci\_low} & \textbf{rs\_ci\_high} & \textbf{leinster\_cobbold\_q2} \\
7	\midrule	7	\midrule
8	−2{,}018 & 169{,}727 & 0.899 & 0.899 & 0.900 & 9.929 \\	8	+2{,}018 & 499{,}561 & 0.897 & 0.896 & 0.897 & 9.680 \\
9	−2{,}019 & 180{,}248 & 0.896 & 0.896 & 0.897 & 9.625 \\	9	+2{,}019 & 539{,}741 & 0.896 & 0.896 & 0.896 & 9.594 \\
10	−2{,}020 & 189{,}370 & 0.901 & 0.900 & 0.901 & 10.073 \\	10	+2{,}020 & 549{,}101 & 0.896 & 0.896 & 0.897 & 9.654 \\
11	−2{,}021 & 226{,}728 & 0.902 & 0.902 & 0.903 & 10.227 \\	11	+2{,}021 & 558{,}970 & 0.894 & 0.894 & 0.894 & 9.430 \\
12	−2{,}022 & 254{,}663 & 0.890 & 0.890 & 0.891 & 9.117 \\	12	+2{,}022 & 560{,}433 & 0.891 & 0.891 & 0.892 & 9.214 \\
13	−2{,}023 & 286{,}460 & 0.886 & 0.885 & 0.886 & 8.734 \\	13	+2{,}023 & 634{,}466 & 0.891 & 0.890 & 0.891 & 9.140 \\
14	−2{,}024 & 363{,}470 & 0.888 & 0.888 & 0.889 & 8.959 \\	14	+2{,}024 & 739{,}619 & 0.890 & 0.890 & 0.890 & 9.095 \\
15	−2{,}025 & 429{,}633 & 0.889 & 0.889 & 0.890 & 9.046 \\	15	+2{,}025 & 892{,}840 & 0.893 & 0.893 & 0.893 & 9.356 \\
		16	+2{,}018 & 499{,}561 & 0.325 & 0.324 & 0.325 & 1.481 \\
		17	+2{,}019 & 539{,}741 & 0.327 & 0.327 & 0.327 & 1.486 \\
		18	+2{,}020 & 549{,}101 & 0.329 & 0.328 & 0.329 & 1.489 \\
		19	+2{,}021 & 558{,}970 & 0.330 & 0.329 & 0.330 & 1.492 \\
		20	+2{,}022 & 560{,}433 & 0.330 & 0.330 & 0.331 & 1.493 \\
		21	+2{,}023 & 634{,}466 & 0.331 & 0.331 & 0.332 & 1.496 \\
		22	+2{,}024 & 739{,}619 & 0.330 & 0.330 & 0.331 & 1.493 \\
		23	+2{,}025 & 892{,}840 & 0.331 & 0.331 & 0.332 & 1.496 \\
16	\bottomrule	24	\bottomrule
17	\end{tabular}	25	\end{tabular}
18	\end{threeparttable}\end{table}	26	\end{threeparttable}\end{table}

modified manuscript/tables/tab_net_category_actors.tex +1 −1

				`@@ -50,5 +50,5 @@ AI Safety and Alignment & 9{,}997 & 33{,}792 & 4.3 & 5.7 & 27.4 & 19.6 & 5.9 & 3`
50	Sustainable Computing & 3{,}356 & 5{,}389 & 20.2 & 20.0 & 17.1 & 12.7 & 5.4 & 6.2 & 23.7 & 18.3 \\	50	Sustainable Computing & 3{,}356 & 5{,}389 & 20.2 & 20.0 & 17.1 & 12.7 & 5.4 & 6.2 & 23.7 & 18.3 \\
51	Autonomous Scientific Discovery & 841 & 2{,}539 & 8.1 & 14.6 & 33.4 & 23.1 & 8.7 & 6.7 & 18.5 & 18.2 \\	51	Autonomous Scientific Discovery & 841 & 2{,}539 & 8.1 & 14.6 & 33.4 & 23.1 & 8.7 & 6.7 & 18.5 & 18.2 \\
52	\bottomrule	52	\bottomrule
53	−\multicolumn{11}{p{0.95\linewidth}}{\footnotesize Baseline: works dated 2018-01-01 to 2022-11-29; post: 2022-11-30 to 2026-11-02 (incomplete). Shares are over all works of the category, including works without affiliation data, so they are depressed where affiliation coverage is low (recent cohorts, preprint-heavy categories) and are not comparable with the typed-denominator shares of Tables~\ref{tab:industry} and~\ref{tab:country_concentration}; comparisons across periods within a category carry this caveat. Comp. = at least one company-typed affiliation; Intl. = at least two author countries.}\\	53	+\multicolumn{11}{p{0.95\linewidth}}{\footnotesize Baseline: works dated 2018-01-01 to 2022-11-29; post: 2022-11-30 to 2026-11-02 (incomplete). Shares are over all works of the category, including works without affiliation data, so they are depressed where affiliation coverage is low (recent cohorts, preprint-heavy categories) and are not comparable with the typed-denominator shares of Tables~\ref{tab:industry} and~\ref{tab:country_concentration}; comparisons across periods within a category carry this caveat. Comp. = at least one company-typed affiliation; Intl. = at least two author countries. Categories whose post share exceeds their baseline share: China-affiliated, 7 of the 9 AI/ML categories and 28 of the 31 others; company, 1 of 9 and 13 of 31; international, 2 of 9 and 11 of 31.}\\
54	\end{longtable}	54	\end{longtable}

modified manuscript/tables/tab_net_category_gini.tex +1 −1

				`@@ -50,5 +50,5 @@ AI Safety and Alignment & 0.844 & 0.823 & 0.798 & 0.771 & 0.737 & 0.799 & 0.795`
50	Sustainable Computing & 0.719 & 0.725 & 0.713 & 0.715 & 0.710 & 0.724 & 0.716 & +0.007 & 27.0 \\	50	Sustainable Computing & 0.719 & 0.725 & 0.713 & 0.715 & 0.710 & 0.724 & 0.716 & +0.007 & 27.0 \\
51	Autonomous Scientific Discovery & & & & 0.756 & 0.749 & 0.862 & 0.753 & +0.109 & 23.3 \\	51	Autonomous Scientific Discovery & & & & 0.756 & 0.749 & 0.862 & 0.753 & +0.109 & 23.3 \\
52	\bottomrule	52	\bottomrule
53	−\multicolumn{10}{p{0.95\linewidth}}{\footnotesize Gini of $c^{(36)}$ over the works whose primary category is the row category (Section~\ref{sec:m-citations}); a blank cell is a category with no works in the year. Base mean: mean over 2018--2022. The 2023 window ends in 2025, whose slower indexing channels were incomplete at the snapshot, so the 2023 values carry the right-truncation caveat of Table~\ref{tab:citation_windows}. Very small categories and categories whose 2023 value is far from every earlier value may reflect single misattributed records (Section~\ref{sec:net-topcited}).}\\	53	+\multicolumn{10}{p{0.95\linewidth}}{\footnotesize Gini of $c^{(36)}$ over the works whose primary category is the row category (Section~\ref{sec:m-citations}); a blank cell is a category with no works in the year. Base mean: mean over 2018--2022. The 2023 window ends in 2025, whose slower indexing channels were incomplete at the snapshot, so the 2023 values carry the right-truncation caveat of Table~\ref{tab:citation_windows}. Very small categories and categories whose 2023 value is far from every earlier value may reflect single misattributed records (Section~\ref{sec:net-topcited}). Count of categories whose 2023 Gini exceeds their baseline mean: 7 of the 9 AI/ML categories and 11 of the 31 other categories.}\\
54	\end{longtable}	54	\end{longtable}

modified manuscript/tables/tab_net_gini_ai_vs_rest.tex +2 −2

				`@@ -3,7 +3,7 @@`
3	\begin{threeparttable}\small	3	\begin{threeparttable}\small
4	\begin{tabular}{lrrrrrrrrrr}	4	\begin{tabular}{lrrrrrrrrrr}
5	\toprule	5	\toprule
6	−\textbf{Year} & \textbf{Works, AI/ML} & \textbf{Works, other} & \textbf{Gini, AI/ML} & \textbf{Gini, other} & \textbf{Mean c36, AI/ML} & \textbf{Mean c36, other} & \textbf{Uncited, AI/ML (\%)} & \textbf{Uncited, other (\%)} & \textbf{Top-10\\% FWCI, AI/ML (\%)} & \textbf{Top-10\\% FWCI, other (\%)} \\	6	+\textbf{Year} & \textbf{Works, AI/ML} & \textbf{Works, other} & \textbf{Gini, AI/ML} & \textbf{Gini, other} & \textbf{Mean c36, AI/ML} & \textbf{Mean c36, other} & \textbf{Uncited, AI/ML (\%)} & \textbf{Uncited, other (\%)} & \textbf{Top decile FWCI, AI/ML (\%)} & \textbf{Top decile FWCI, other (\%)} \\
7	\midrule	7	\midrule
8	2018 & 38{,}148 & 460{,}583 & 0.799 & 0.796 & 7.26 & 4.99 & 37.7 & 43.4 & 15.8 & 15.0 \\	8	2018 & 38{,}148 & 460{,}583 & 0.799 & 0.796 & 7.26 & 4.99 & 37.7 & 43.4 & 15.8 & 15.0 \\
9	2019 & 47{,}860 & 491{,}076 & 0.779 & 0.787 & 7.81 & 5.68 & 34.5 & 40.6 & 15.6 & 15.0 \\	9	2019 & 47{,}860 & 491{,}076 & 0.779 & 0.787 & 7.81 & 5.68 & 34.5 & 40.6 & 15.6 & 15.0 \\
				`@@ -13,5 +13,5 @@`
13	2023 & 82{,}170 & 548{,}963 & 0.787 & 0.765 & 8.98 & 6.64 & 32.4 & 34.0 & 15.8 & 14.8 \\	13	2023 & 82{,}170 & 548{,}963 & 0.787 & 0.765 & 8.98 & 6.64 & 32.4 & 34.0 & 15.8 & 14.8 \\
14	\bottomrule	14	\bottomrule
15	\end{tabular}	15	\end{tabular}
16	−\begin{tablenotes}\footnotesize\item Group statistics are work-weighted means of the category statistics of Table~\ref{tab:net_category_gini} (the Gini is therefore a weighted mean of within-category Ginis, not the Gini of the pooled group). Top-10\% FWCI: share of works in the top decile of OpenAlex field-weighted citation impact. The last year carries the right-truncation caveat.\end{tablenotes}	16	+\begin{tablenotes}\footnotesize\item Group statistics are work-weighted means of the category statistics of Table~\ref{tab:net_category_gini} (the Gini is therefore a weighted mean of within-category Ginis, not the Gini of the pooled group). Top decile FWCI: share of works in the top decile of OpenAlex field-weighted citation impact. The last year carries the right-truncation caveat.\end{tablenotes}
17	\end{threeparttable}\end{table}	17	\end{threeparttable}\end{table}

modified manuscript/tables/tab_security_landmarks.tex +1 −1

				`@@ -12,5 +12,5 @@ Privacy-preserving computation & 25 & 14 & 8 & 16 & 3 & 1 & 5 & 21 & 1 \\`
12	Federated learning & 25 & 15 & 6 & 18 & 4 & 0 & 3 & 14 & 2 \\	12	Federated learning & 25 & 15 & 6 & 18 & 4 & 0 & 3 & 14 & 2 \\
13	\bottomrule	13	\bottomrule
14	\end{tabular}	14	\end{tabular}
15	−\begin{tablenotes}\footnotesize\item Landmarks are the works with the highest field-normalised 36-month citation score (NCS36; 2025--2026 works ranked by 12-month citations) within each primary category, with a floor of fifteen per category, plus the 200 most cited works of the corpus overall (literature_synthesis/select_landmarks.py). Grey = dissertations, reports, book chapters and books as typed by OpenAlex. Abstract = works whose abstract is available in the metadata; the synthesis of Part VII reads these abstracts only, never full texts. The set is flagged for principal-investigator review; Part VII documents the records that are duplicates, misdated or misclassified.\end{tablenotes}	15	+\begin{tablenotes}\footnotesize\item Landmarks are the works with the highest field-normalised 36-month citation score (NCS36; 2025--2026 works ranked by 12-month citations) within each primary category, with a floor of fifteen per category, plus the 200 most cited works of the corpus overall (\texttt{literature\_synthesis/select\_landmarks.py}). Grey = dissertations, reports, book chapters and books as typed by OpenAlex. Abstract = works whose abstract is available in the metadata; the synthesis of Part VII reads these abstracts only, never full texts. The set is flagged for principal-investigator review; Part VII documents the records that are duplicates, misdated or misclassified.\end{tablenotes}
16	\end{threeparttable}\end{table}	16	\end{threeparttable}\end{table}

modified manuscript/tables/tab_systems_categories.tex +11 −11

				`@@ -5,17 +5,17 @@`
5	\toprule	5	\toprule
6	\textbf{Category} & \textbf{n 2018} & \textbf{n 2022} & \textbf{n 2025} & \textbf{CAGR base} & \textbf{CAGR post} & \textbf{Slope ch.} & \textbf{p} & \textbf{Placebo p} & \textbf{BP breaks} \\	6	\textbf{Category} & \textbf{n 2018} & \textbf{n 2022} & \textbf{n 2025} & \textbf{CAGR base} & \textbf{CAGR post} & \textbf{Slope ch.} & \textbf{p} & \textbf{Placebo p} & \textbf{BP breaks} \\
7	\midrule	7	\midrule
8	−Computer architecture & 6{,}518 & 7{,}587 & 6{,}736 & 0.039 & -0.093 & 0.016 & 0.000 & 0.062 & 2023-05-01 \\	8	+Computer architecture & 19{,}554 & 15{,}174 & 20{,}209 & -0.061 & 0.111 & 0.016 & 0.000 & 0.062 & 2023-05-01 \\
9	−Accelerators and AI hardware & 409 & 710 & 953 & 0.148 & 0.092 & 0.018 & 0.000 & 0.169 & 2024-11-01 \\	9	+Accelerators and AI hardware & 818 & 1{,}421 & 1{,}906 & 0.148 & 0.092 & 0.018 & 0.000 & 0.169 & 2024-11-01 \\
10	−Operating systems & 778 & 541 & 681 & -0.087 & 0.078 & 0.016 & 0.000 & 0.477 & 2023-09-01 \\	10	+Operating systems & 1{,}556 & 1{,}083 & 1{,}363 & -0.087 & 0.078 & 0.016 & 0.000 & 0.477 & 2023-09-01 \\
11	−Distributed computing & 3{,}839 & 4{,}689 & 4{,}183 & 0.051 & 0.132 & 0.020 & 0.000 & 0.077 & 2022-07-01;2024-07-01 \\	11	+Distributed computing & 11{,}519 & 9{,}379 & 12{,}550 & -0.050 & 0.132 & 0.020 & 0.000 & 0.077 & 2022-07-01;2024-07-01 \\
12	−Cloud computing & 3{,}132 & 3{,}551 & 3{,}658 & 0.032 & -0.022 & 0.019 & 0.000 & 0.231 & 2022-11-01;2025-01-01 \\	12	+Cloud computing & 9{,}396 & 7{,}103 & 10{,}974 & -0.068 & 0.198 & 0.019 & 0.000 & 0.231 & 2022-11-01;2025-01-01 \\
13	−Edge computing & 1{,}728 & 4{,}277 & 5{,}777 & 0.254 & 0.379 & 0.004 & 0.006 & 0.077 & 2025-01-01 \\	13	+Edge computing & 5{,}185 & 8{,}554 & 11{,}554 & 0.133 & 0.126 & 0.004 & 0.006 & 0.077 & 2025-01-01 \\
14	−Computer networks & 19{,}581 & 16{,}380 & 29{,}777 & -0.044 & 0.312 & 0.009 & 0.000 & 0.200 & 2022-02-01 \\	14	+Computer networks & 58{,}744 & 49{,}141 & 59{,}555 & -0.044 & 0.071 & 0.009 & 0.000 & 0.200 & 2022-02-01 \\
15	−Database systems & 5{,}421 & 6{,}812 & 8{,}340 & 0.059 & 0.073 & 0.014 & 0.000 & 0.092 & 2023-11-01 \\	15	+Database systems & 16{,}265 & 13{,}625 & 16{,}681 & -0.043 & 0.073 & 0.014 & 0.000 & 0.092 & 2023-11-01 \\
16	−Information retrieval & 4{,}031 & 5{,}621 & 8{,}689 & 0.087 & 0.176 & 0.023 & 0.000 & 0.092 & 2022-07-01;2024-07-01 \\	16	+Information retrieval & 12{,}095 & 11{,}243 & 17{,}379 & -0.018 & 0.176 & 0.023 & 0.000 & 0.092 & 2022-07-01;2024-07-01 \\
17	−Quantum computing & 3{,}112 & 4{,}608 & 8{,}735 & 0.103 & 0.287 & 0.018 & 0.000 & 0.169 & 2022-01-01;2025-04-01 \\	17	+Quantum computing & 6{,}224 & 9{,}216 & 17{,}471 & 0.103 & 0.287 & 0.018 & 0.000 & 0.169 & 2022-01-01;2025-04-01 \\
18	−Sustainable computing & 291 & 276 & 819 & -0.013 & 0.233 & 0.016 & 0.000 & 0.169 & 2022-07-01 \\	18	+Sustainable computing & 583 & 830 & 1{,}639 & 0.092 & 0.233 & 0.016 & 0.000 & 0.169 & 2022-07-01 \\
19	\bottomrule	19	\bottomrule
20	\end{tabular}	20	\end{tabular}
21	\begin{tablenotes}\footnotesize\item Counts are eligible works whose primary category is the row category (multi-label memberships are larger). CAGR base: 2018 to 2022; CAGR post: 2023 to 2025 (complete years). Slope ch.: change in the monthly trend of the log-volume series at 2022-12 in the interrupted time-series model of Part IV, with its HAC p-value; Placebo p: share of placebo event dates whose slope statistic is at least as extreme; BP breaks: Bai--Perron break dates selected by BIC. 2026 is incomplete and excluded from the growth columns.\end{tablenotes}	21	\begin{tablenotes}\footnotesize\item Counts are eligible works whose primary category is the row category (multi-label memberships are larger). CAGR base: 2018 to 2022; CAGR post: 2023 to 2025 (complete years). Slope ch.: change in the monthly trend of the log-volume series at 2022-12 in the interrupted time-series model of Part IV, with its HAC p-value; Placebo p: share of placebo event dates whose slope statistic is at least as extreme; BP breaks: Bai--Perron break dates selected by BIC. 2026 is incomplete and excluded from the growth columns.\end{tablenotes}

modified manuscript/tables/tab_theory_landmark_audit.tex +1 −1

				`@@ -16,5 +16,5 @@ Duplicate record & 0 & 0 & 0 & 0 & 1 & 0 & 1 & 0 & 0 \\`
16	All candidates & 25 & 25 & 25 & 25 & 25 & 25 & 150 & 71 & 7 \\	16	All candidates & 25 & 25 & 25 & 25 & 25 & 25 & 150 & 71 & 7 \\
17	\bottomrule	17	\bottomrule
18	\end{tabular}	18	\end{tabular}
19	−\begin{tablenotes}\footnotesize\item Candidates are the works with the highest field-normalised 36-month citation score within each primary category (floor of fifteen per category) plus the 200 most cited works overall (literature_synthesis/select_landmarks.py). Verdicts were assigned by Agent G from title, venue and type metadata and, where available, the abstract (never the full text); see literature_synthesis/agent_G/contributions.csv. `Id mismatch': the OpenAlex identifier of the record, resolved live on 2026-11-03, returned a different work whose citation count matches the record's 36-month window (literature_synthesis/agent_G/verification_lookups_raw.txt).\end{tablenotes}	19	+\begin{tablenotes}\footnotesize\item Candidates are the works with the highest field-normalised 36-month citation score within each primary category (floor of fifteen per category) plus the 200 most cited works overall (\texttt{literature\_synthesis/select\_landmarks.py}). Verdicts were assigned by Agent G from title, venue and type metadata and, where available, the abstract (never the full text); see literature_synthesis/agent_G/contributions.csv. `Id mismatch': the OpenAlex identifier of the record, resolved live on 2026-11-03, returned a different work whose citation count matches the record's 36-month window (literature_synthesis/agent_G/verification_lookups_raw.txt).\end{tablenotes}
20	\end{threeparttable}\end{table}	20	\end{threeparttable}\end{table}

modified reports/HUMAN_REVIEW_CHECKPOINTS.md +1 −0

				`@@ -10,3 +10,4 @@`
10	\| H-5 \| Manuscript conclusions \| `manuscript/` \| pending \|	10	\| H-5 \| Manuscript conclusions \| `manuscript/` \| pending \|
11		11
12	Conclusions that depend on an unsigned checkpoint are labelled "pending human review" in the manuscript.	12	Conclusions that depend on an unsigned checkpoint are labelled "pending human review" in the manuscript.
		13	+\| H-3 note \| 2026-11-03 20:30 \| Agent H audit of the first landmark list: SSRN/reprint duplicates (Bitcoin ×3, TLS 1.3 ×2), venue records, 2026 reprints of old papers, misfiled works → landmark selection v2 adds title de-duplication, venue-record and abstract/citation filters (`literature_synthesis/landmarks_v1/` keeps the first list) ; PI review still required \| \| \|

modified tables/make_tables.py +11 −7

				`@@ -298,7 +298,7 @@ def tables_part07b():`
298	rows.append({"Category": NAMES[c], "Works": len(x), "Post-period": int((x.period == "post").sum()), "Dated 2026": int((x.year == 2026).sum()), "Articles": int(x.type.isin(["article", "review"]).sum()), "Conf.": int((x.type == "conference-paper").sum()), "Preprints": int((x.type == "preprint").sum()),	298	rows.append({"Category": NAMES[c], "Works": len(x), "Post-period": int((x.period == "post").sum()), "Dated 2026": int((x.year == 2026).sum()), "Articles": int(x.type.isin(["article", "review"]).sum()), "Conf.": int((x.type == "conference-paper").sum()), "Preprints": int((x.type == "preprint").sum()),
299	"Grey": int(x.type.isin(["dissertation", "report", "book-chapter", "book"]).sum()), "Abstract": int(x.has_abstract.astype(str).eq("True").sum()), "No DOI": int(x.doi.isna().sum() + (x.doi.astype(str).str.strip() == "").sum())})	299	"Grey": int(x.type.isin(["dissertation", "report", "book-chapter", "book"]).sum()), "Abstract": int(x.has_abstract.astype(str).eq("True").sum()), "No DOI": int(x.doi.isna().sum() + (x.doi.astype(str).str.strip() == "").sum())})
300	write("tab_security_landmarks", pd.DataFrame(rows), "Composition of the automatically selected landmark set of the security-and-privacy area, by category.", "tab:security_landmarks", [p],	300	write("tab_security_landmarks", pd.DataFrame(rows), "Composition of the automatically selected landmark set of the security-and-privacy area, by category.", "tab:security_landmarks", [p],
301	− note="Landmarks are the works with the highest field-normalised 36-month citation score (NCS36; 2025--2026 works ranked by 12-month citations) within each primary category, with a floor of fifteen per category, plus the 200 most cited works of the corpus overall (literature_synthesis/select_landmarks.py). Grey = dissertations, reports, book chapters and books as typed by OpenAlex. Abstract = works whose abstract is available in the metadata; the synthesis of Part VII reads these abstracts only, never full texts. The set is flagged for principal-investigator review; Part VII documents the records that are duplicates, misdated or misclassified.")	301	+ note="Landmarks are the works with the highest field-normalised 36-month citation score (NCS36; 2025--2026 works ranked by 12-month citations) within each primary category, with a floor of fifteen per category, plus the 200 most cited works of the corpus overall (\\texttt{literature\\_synthesis/select\\_landmarks.py}). Grey = dissertations, reports, book chapters and books as typed by OpenAlex. Abstract = works whose abstract is available in the metadata; the synthesis of Part VII reads these abstracts only, never full texts. The set is flagged for principal-investigator review; Part VII documents the records that are duplicates, misdated or misclassified.")
302		302
303		303
304	# ---------------- Part V (The Artificial Intelligence Transformation): tables appended by Agent E, 2026-11-03 ----------------	304	# ---------------- Part V (The Artificial Intelligence Transformation): tables appended by Agent E, 2026-11-03 ----------------
				`@@ -411,8 +411,10 @@ def tables_part08_categories():`
411	if (c, "baseline") not in g.index or (c, "post") not in g.index: continue	411	if (c, "baseline") not in g.index or (c, "post") not in g.index: continue
412	b = g.loc[(c, "baseline")]; q = g.loc[(c, "post")]	412	b = g.loc[(c, "baseline")]; q = g.loc[(c, "post")]
413	rows.append({"Category": cname[c], "Works, base": int(b.n), "Works, post": int(q.n), "CN, base": b.cn, "CN, post": q.cn, "US, base": b.us, "US, post": q.us, "Comp., base": b.company, "Comp., post": q.company, "Intl., base": b.intl, "Intl., post": q.intl})	413	rows.append({"Category": cname[c], "Works, base": int(b.n), "Works, post": int(q.n), "CN, base": b.cn, "CN, post": q.cn, "US, base": b.us, "US, post": q.us, "Comp., base": b.company, "Comp., post": q.company, "Intl., base": b.intl, "Intl., post": q.intl})
		414	+ ra = pd.DataFrame(rows); ra["ai"] = [carea[c] == "ai_ml" for c in cats if (c, "baseline") in g.index and (c, "post") in g.index]
		415	+ cnt = lambda col_b, col_p, ai: int(((ra[col_p] > ra[col_b]) & (ra.ai == ai)).sum()); nai = int(ra.ai.sum()); not_ = int((~ra.ai).sum())
414	write("tab_net_category_actors", pd.DataFrame(rows), "Shares of works with a China-affiliated author, a United States-affiliated author, a company affiliation and authors in at least two countries, by primary category and period (per cent).", "tab:net_category_actors", [p], fmt={k: "{:.1f}" for k in ["CN, base", "CN, post", "US, base", "US, post", "Comp., base", "Comp., post", "Intl., base", "Intl., post"]}, longtable=True,	416	write("tab_net_category_actors", pd.DataFrame(rows), "Shares of works with a China-affiliated author, a United States-affiliated author, a company affiliation and authors in at least two countries, by primary category and period (per cent).", "tab:net_category_actors", [p], fmt={k: "{:.1f}" for k in ["CN, base", "CN, post", "US, base", "US, post", "Comp., base", "Comp., post", "Intl., base", "Intl., post"]}, longtable=True,
415	− note="Baseline: works dated 2018-01-01 to 2022-11-29; post: 2022-11-30 to 2026-11-02 (incomplete). Shares are over all works of the category, including works without affiliation data, so they are depressed where affiliation coverage is low (recent cohorts, preprint-heavy categories) and are not comparable with the typed-denominator shares of Tables~\\ref{tab:industry} and~\\ref{tab:country_concentration}; comparisons across periods within a category carry this caveat. Comp. = at least one company-typed affiliation; Intl. = at least two author countries.")	417	+ note=f"Baseline: works dated 2018-01-01 to 2022-11-29; post: 2022-11-30 to 2026-11-02 (incomplete). Shares are over all works of the category, including works without affiliation data, so they are depressed where affiliation coverage is low (recent cohorts, preprint-heavy categories) and are not comparable with the typed-denominator shares of Tables~\\ref{{tab:industry}} and~\\ref{{tab:country_concentration}}; comparisons across periods within a category carry this caveat. Comp. = at least one company-typed affiliation; Intl. = at least two author countries. Categories whose post share exceeds their baseline share: China-affiliated, {cnt('CN, base', 'CN, post', True)} of the {nai} AI/ML categories and {cnt('CN, base', 'CN, post', False)} of the {not_} others; company, {cnt('Comp., base', 'Comp., post', True)} of {nai} and {cnt('Comp., base', 'Comp., post', False)} of {not_}; international, {cnt('Intl., base', 'Intl., post', True)} of {nai} and {cnt('Intl., base', 'Intl., post', False)} of {not_}.")
416	a["grp"] = np.where(a.primary_category.map(carea) == "ai_ml", "AI/ML area", "Other areas"); h = a.groupby(["grp", "period"])[["n", "cn", "us", "company", "intl", "arxiv", "oa"]].sum()	418	a["grp"] = np.where(a.primary_category.map(carea) == "ai_ml", "AI/ML area", "Other areas"); h = a.groupby(["grp", "period"])[["n", "cn", "us", "company", "intl", "arxiv", "oa"]].sum()
417	for c in ["cn", "us", "company", "intl", "arxiv", "oa"]: h[c] = 100 * h[c] / h.n	419	for c in ["cn", "us", "company", "intl", "arxiv", "oa"]: h[c] = 100 * h[c] / h.n
418	rows = [{"Group": grp, "Period": pr, "Works": int(h.loc[(grp, pr)].n), "CN (%)": h.loc[(grp, pr)].cn, "US (%)": h.loc[(grp, pr)].us, "Company (%)": h.loc[(grp, pr)].company, "Intl. (%)": h.loc[(grp, pr)].intl, "arXiv (%)": h.loc[(grp, pr)].arxiv, "OA (%)": h.loc[(grp, pr)].oa} for grp in ["AI/ML area", "Other areas"] for pr in ["baseline", "post"]]	420	rows = [{"Group": grp, "Period": pr, "Works": int(h.loc[(grp, pr)].n), "CN (%)": h.loc[(grp, pr)].cn, "US (%)": h.loc[(grp, pr)].us, "Company (%)": h.loc[(grp, pr)].company, "Intl. (%)": h.loc[(grp, pr)].intl, "arXiv (%)": h.loc[(grp, pr)].arxiv, "OA (%)": h.loc[(grp, pr)].oa} for grp in ["AI/ML area", "Other areas"] for pr in ["baseline", "post"]]
				`@@ -434,13 +436,15 @@ def tables_part08_categories():`
434	r = {"Category": cname[c], **{str(y): float(pv.loc[c, y]) if y in pv.columns else float("nan") for y in yrs}}	436	r = {"Category": cname[c], **{str(y): float(pv.loc[c, y]) if y in pv.columns else float("nan") for y in yrs}}
435	bmean = pv.loc[c, base].mean(); r["Base mean"] = float(bmean); r[f"{last} $-$ base"] = float(pv.loc[c, last] - bmean) if last in pv.columns else float("nan")	437	bmean = pv.loc[c, base].mean(); r["Base mean"] = float(bmean); r[f"{last} $-$ base"] = float(pv.loc[c, last] - bmean) if last in pv.columns else float("nan")
436	r[f"Uncited {last} (%)"] = 100 * float(un.loc[c, last]) if last in un.columns else float("nan"); rows.append(r)	438	r[f"Uncited {last} (%)"] = 100 * float(un.loc[c, last]) if last in un.columns else float("nan"); rows.append(r)
		439	+ up = {g: int(sum(1 for c in cats if c in pv.index and last in pv.columns and pd.notna(pv.loc[c, last]) and carea[c] == g and pv.loc[c, last] > pv.loc[c, base].mean())) for g in ["ai_ml"]}; up["other"] = int(sum(1 for c in cats if c in pv.index and last in pv.columns and pd.notna(pv.loc[c, last]) and carea[c] != "ai_ml" and pv.loc[c, last] > pv.loc[c, base].mean()))
		440	+ tot_ai = sum(1 for c in cats if c in pv.index and last in pv.columns and pd.notna(pv.loc[c, last]) and carea[c] == "ai_ml"); tot_ot = sum(1 for c in cats if c in pv.index and last in pv.columns and pd.notna(pv.loc[c, last]) and carea[c] != "ai_ml")
437	t = pd.DataFrame(rows); write("tab_net_category_gini", t, f"Gini coefficient of 36-month citations $c^{{(36)}}$ by primary category and publication year, for years with a complete window; baseline mean, change of the last complete year against it, and share uncited at 36 months in that year.", "tab:net_category_gini", [p], fmt={**{str(y): "{:.3f}" for y in yrs}, "Base mean": "{:.3f}", f"{last} $-$ base": "{:+.3f}", f"Uncited {last} (%)": "{:.1f}"}, longtable=True,	441	t = pd.DataFrame(rows); write("tab_net_category_gini", t, f"Gini coefficient of 36-month citations $c^{{(36)}}$ by primary category and publication year, for years with a complete window; baseline mean, change of the last complete year against it, and share uncited at 36 months in that year.", "tab:net_category_gini", [p], fmt={**{str(y): "{:.3f}" for y in yrs}, "Base mean": "{:.3f}", f"{last} $-$ base": "{:+.3f}", f"Uncited {last} (%)": "{:.1f}"}, longtable=True,
438	− note=f"Gini of $c^{{(36)}}$ over the works whose primary category is the row category (Section~\\ref{{sec:m-citations}}); a blank cell is a category with no works in the year. Base mean: mean over {base[0]}--{base[-1]}. The {last} window ends in {last + 2}, whose slower indexing channels were incomplete at the snapshot, so the {last} values carry the right-truncation caveat of Table~\\ref{{tab:citation_windows}}. Very small categories and categories whose {last} value is far from every earlier value may reflect single misattributed records (Section~\\ref{{sec:net-topcited}}).")	442	+ note=f"Gini of $c^{{(36)}}$ over the works whose primary category is the row category (Section~\\ref{{sec:m-citations}}); a blank cell is a category with no works in the year. Base mean: mean over {base[0]}--{base[-1]}. The {last} window ends in {last + 2}, whose slower indexing channels were incomplete at the snapshot, so the {last} values carry the right-truncation caveat of Table~\\ref{{tab:citation_windows}}. Very small categories and categories whose {last} value is far from every earlier value may reflect single misattributed records (Section~\\ref{{sec:net-topcited}}). Count of categories whose {last} Gini exceeds their baseline mean: {up['ai_ml']} of the {tot_ai} AI/ML categories and {up['other']} of the {tot_ot} other categories.")
439	i["grp"] = np.where(i.primary_category.map(carea) == "ai_ml", "AI/ML", "Other"); i["g"] = i.gini_c36 * i.n; i["u"] = i.share_uncited_36 * i.n; i["t10"] = i.share_top10_fwci * i.n; i["m"] = i.mean_c36 * i.n	443	i["grp"] = np.where(i.primary_category.map(carea) == "ai_ml", "AI/ML", "Other"); i["g"] = i.gini_c36 * i.n; i["u"] = i.share_uncited_36 * i.n; i["t10"] = i.share_top10_fwci * i.n; i["m"] = i.mean_c36 * i.n
440	h = i.groupby(["grp", "year"])[["n", "g", "u", "t10", "m"]].sum()	444	h = i.groupby(["grp", "year"])[["n", "g", "u", "t10", "m"]].sum()
441	− rows = [{"Year": str(y), "Works, AI/ML": int(h.loc[("AI/ML", y)].n), "Works, other": int(h.loc[("Other", y)].n), "Gini, AI/ML": h.loc[("AI/ML", y)].g / h.loc[("AI/ML", y)].n, "Gini, other": h.loc[("Other", y)].g / h.loc[("Other", y)].n, "Mean c36, AI/ML": h.loc[("AI/ML", y)].m / h.loc[("AI/ML", y)].n, "Mean c36, other": h.loc[("Other", y)].m / h.loc[("Other", y)].n, "Uncited, AI/ML (%)": 100 * h.loc[("AI/ML", y)].u / h.loc[("AI/ML", y)].n, "Uncited, other (%)": 100 * h.loc[("Other", y)].u / h.loc[("Other", y)].n, "Top-10\\% FWCI, AI/ML (%)": 100 * h.loc[("AI/ML", y)].t10 / h.loc[("AI/ML", y)].n, "Top-10\\% FWCI, other (%)": 100 * h.loc[("Other", y)].t10 / h.loc[("Other", y)].n} for y in yrs]	445	+ rows = [{"Year": str(y), "Works, AI/ML": int(h.loc[("AI/ML", y)].n), "Works, other": int(h.loc[("Other", y)].n), "Gini, AI/ML": h.loc[("AI/ML", y)].g / h.loc[("AI/ML", y)].n, "Gini, other": h.loc[("Other", y)].g / h.loc[("Other", y)].n, "Mean c36, AI/ML": h.loc[("AI/ML", y)].m / h.loc[("AI/ML", y)].n, "Mean c36, other": h.loc[("Other", y)].m / h.loc[("Other", y)].n, "Uncited, AI/ML (%)": 100 * h.loc[("AI/ML", y)].u / h.loc[("AI/ML", y)].n, "Uncited, other (%)": 100 * h.loc[("Other", y)].u / h.loc[("Other", y)].n, "Top decile FWCI, AI/ML (%)": 100 * h.loc[("AI/ML", y)].t10 / h.loc[("AI/ML", y)].n, "Top decile FWCI, other (%)": 100 * h.loc[("Other", y)].t10 / h.loc[("Other", y)].n} for y in yrs]
442	− write("tab_net_gini_ai_vs_rest", pd.DataFrame(rows), "Citation inequality and levels at 36 months, AI/ML area against all other areas, by publication year with a complete window.", "tab:net_gini_ai_vs_rest", [p], fmt={"Gini, AI/ML": "{:.3f}", "Gini, other": "{:.3f}", "Mean c36, AI/ML": "{:.2f}", "Mean c36, other": "{:.2f}", "Uncited, AI/ML (%)": "{:.1f}", "Uncited, other (%)": "{:.1f}", "Top-10\\% FWCI, AI/ML (%)": "{:.1f}", "Top-10\\% FWCI, other (%)": "{:.1f}"},	446	+ write("tab_net_gini_ai_vs_rest", pd.DataFrame(rows), "Citation inequality and levels at 36 months, AI/ML area against all other areas, by publication year with a complete window.", "tab:net_gini_ai_vs_rest", [p], fmt={"Gini, AI/ML": "{:.3f}", "Gini, other": "{:.3f}", "Mean c36, AI/ML": "{:.2f}", "Mean c36, other": "{:.2f}", "Uncited, AI/ML (%)": "{:.1f}", "Uncited, other (%)": "{:.1f}", "Top decile FWCI, AI/ML (%)": "{:.1f}", "Top decile FWCI, other (%)": "{:.1f}"},
443	− note="Group statistics are work-weighted means of the category statistics of Table~\\ref{tab:net_category_gini} (the Gini is therefore a weighted mean of within-category Ginis, not the Gini of the pooled group). Top-10\\% FWCI: share of works in the top decile of OpenAlex field-weighted citation impact. The last year carries the right-truncation caveat.")	447	+ note="Group statistics are work-weighted means of the category statistics of Table~\\ref{tab:net_category_gini} (the Gini is therefore a weighted mean of within-category Ginis, not the Gini of the pooled group). Top decile FWCI: share of works in the top decile of OpenAlex field-weighted citation impact. The last year carries the right-truncation caveat.")
444		448
445		449
446		450
				`@@ -496,7 +500,7 @@ def tables_part07a():`
496	for c in THY: row[SHORT[c]] = int((d.primary_category == c).sum())	500	for c in THY: row[SHORT[c]] = int((d.primary_category == c).sum())
497	row["Total"] = int(len(d)); row["Post-period"] = int((d.period == "post").sum()); row["Id mismatch"] = int((d.id_resolves_to_other_work == "yes").sum()); rows.append(row)	501	row["Total"] = int(len(d)); row["Post-period"] = int((d.period == "post").sum()); row["Id mismatch"] = int((d.id_resolves_to_other_work == "yes").sum()); rows.append(row)
498	write("tab_theory_landmark_audit", pd.DataFrame(rows), "Specialist audit of the automatically selected landmark candidates of the theory and software areas, by verdict and primary category.", "tab:theory_landmark_audit", [p, f"{R}/literature_synthesis/landmarks/theory_landmarks.csv", f"{R}/literature_synthesis/landmarks/software_pl_landmarks.csv"],	502	write("tab_theory_landmark_audit", pd.DataFrame(rows), "Specialist audit of the automatically selected landmark candidates of the theory and software areas, by verdict and primary category.", "tab:theory_landmark_audit", [p, f"{R}/literature_synthesis/landmarks/theory_landmarks.csv", f"{R}/literature_synthesis/landmarks/software_pl_landmarks.csv"],
499	− note="Candidates are the works with the highest field-normalised 36-month citation score within each primary category (floor of fifteen per category) plus the 200 most cited works overall (literature_synthesis/select_landmarks.py). Verdicts were assigned by Agent G from title, venue and type metadata and, where available, the abstract (never the full text); see literature_synthesis/agent_G/contributions.csv. `Id mismatch': the OpenAlex identifier of the record, resolved live on 2026-11-03, returned a different work whose citation count matches the record's 36-month window (literature_synthesis/agent_G/verification_lookups_raw.txt).")	503	+ note="Candidates are the works with the highest field-normalised 36-month citation score within each primary category (floor of fifteen per category) plus the 200 most cited works overall (\\texttt{literature\\_synthesis/select\\_landmarks.py}). Verdicts were assigned by Agent G from title, venue and type metadata and, where available, the abstract (never the full text); see literature_synthesis/agent_G/contributions.csv. `Id mismatch': the OpenAlex identifier of the record, resolved live on 2026-11-03, returned a different work whose citation count matches the record's 36-month window (literature_synthesis/agent_G/verification_lookups_raw.txt).")
500	g = d[d.verdict.isin(["in_scope_research", "in_scope_survey"])].sort_values(["primary_category", "year"])	504	g = d[d.verdict.isin(["in_scope_research", "in_scope_survey"])].sort_values(["primary_category", "year"])
501	t = pd.DataFrame({"Category": g.primary_category.map(SHORT), "Year": g.year.astype(str), "Type": g.type.str.replace("conference-paper", "conf.").str.replace("book-chapter", "chapter"), "Work": [_ascii(x, 88) for x in g.title], "Contribution": g.contribution_type, "Id mismatch": g.id_resolves_to_other_work})	505	t = pd.DataFrame({"Category": g.primary_category.map(SHORT), "Year": g.year.astype(str), "Type": g.type.str.replace("conference-paper", "conf.").str.replace("book-chapter", "chapter"), "Work": [_ascii(x, 88) for x in g.title], "Contribution": g.contribution_type, "Id mismatch": g.id_resolves_to_other_work})
502	write("tab_theory_landmarks_discussed", t, "Landmark candidates of the theory and software areas retained as in-scope research or survey contributions, with the contribution type judged from the abstract or metadata.", "tab:theory_landmarks_discussed", [p], longtable=True,	506	write("tab_theory_landmarks_discussed", t, "Landmark candidates of the theory and software areas retained as in-scope research or survey contributions, with the contribution type judged from the abstract or metadata.", "tab:theory_landmarks_discussed", [p], longtable=True,
503		507

spb/wp44_uqo

WP44: Part VII-b security synthesis (Agent H, 53 refs); landmark selection v2 (dedup/venue/abstract filters) after audit

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