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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,chatgptforgoodonopportunitiesandchallengesoflargelanguagemodelsforeducation,990.7064044933976,landmark,True84226278401,10.48550/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,discernmentandsociallearningasacompaniontraininglayer,894.2065022118338,landmark,True94396531244,10.22215/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,thefusionofmultilingualsemanticsearchandlargelanguagemodelsanewparadigmforenhancedtopicexplorationandcontextualsearch,778.8148148148148,landmark,True104221143046,10.48550/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,bnainotokenandmindunitypillarsofasystemicrevolutioninartificialintelligence,716.353275252574,landmark,True114367369313,10.18260/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,promptengineeringforchatgptaquickguidetotechniquestipsandbestpractices,661.7577775818233,landmark,True124389437528,10.61969/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,educationintheeraofgenerativeartificialintelligenceaiunderstandingthepotentialbenefitsofchatgptinpromotingteachingandlearning,553.3612067681362,landmark,True134292779060,10.4230/lipics.giscience.2023.43,,"Aion Framework: Dimensional Emergence of AI Consciousness, Observer-Induced Collapse, and Cosmological Portal Dynamics",DROPS (Schloss Dagstuhl – Leibniz Center for Informatics),2023,article,llm,4,11174.0,13333.0,534.676590443066,13690,2570.1233,True,True,post,aionframeworkdimensionalemergenceofaiconsciousnessobserverinducedcollapseandcosmologicalportaldynamics,534.676590443066,landmark,True144414281281,10.1038/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,deepseekr1incentivizesreasoninginllmsthroughreinforcementlearning,480.23175285698454,landmark,True154360836968,10.48550/arxiv.2303.12712,2303.12712,Sparks of Artificial General Intelligence: Early experiments with GPT-4,arXiv (Cornell University),2023,preprint,llm,2,1098.0,1420.0,472.11108641004154,1523,,True,True,post,sparksofartificialgeneralintelligenceearlyexperimentswithgpt4,472.11108641004154,landmark,True167135096569,10.1287/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,navigatingthejaggedtechnologicalfrontierfieldexperimentalevidenceoftheeffectsofartificialintelligenceonknowledgeworkerproductivityandquality,463.9910854309336,landmark,True174385223296,10.1007/978-3-031-37776-1_17,,"Open AI in Education, the Responsible and Ethical Use of ChatGPT Towards Lifelong Learning",Sustainable development goals series,2023,book-chapter,llm,2,432.0,612.0,421.27957250425067,696,475.0832,False,True,post,openaiineducationtheresponsibleandethicaluseofchatgpttowardslifelonglearning,421.27957250425067,landmark,True184406800520,10.1007/s11432-024-4222-0,2309.07864,The rise and potential of large language model based agents: a survey,Science China Information Sciences,2025,article,llm,3,914.0,914.0,311.35347115247697,914,317.0864,False,True,post,theriseandpotentialoflargelanguagemodelbasedagentsasurvey,399.3920128401127,landmark,True194366420437,10.3390/educsci13040410,,What Is the Impact of ChatGPT on Education? 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Environments,2023,article,llm,3,941.0,1389.0,325.547952647582,1931,453.5275,True,True,post,whatifthedevilismyguardianangelchatgptasacasestudyofusingchatbotsineducation,325.547952647582,landmark,True254287689466,10.4230/oasics.slate.2025.1,,From Prediction to Precision: Leveraging LLMs for Equitable and Data-Driven Writing Placement in Developmental Education,OASIcs : OpenAccess Series in Informatics,2025,conference-paper,llm,3,722.0,722.0,548.6111682082906,722,151.0788,True,True,post,frompredictiontoprecisionleveragingllmsforequitableanddatadrivenwritingplacementindevelopmentaleducation,315.49347184962954,landmark,True264320495408,10.1108/lhtn-01-2023-0009,,Chatting about ChatGPT: how may AI and GPT impact academia and libraries?,Library Hi Tech News,2023,article,llm,3,895.0,1210.0,283.59468877147174,1346,315.8243,True,True,post,chattingaboutchatgpthowmayaiandgptimpactacademiaandlibraries,283.59468877147174,landmark,True274387355843,10.1201/9781003616719-7,2310.01728,TimeLLM: Time Series Forecasting by Reprogramming Large Language Models,CRC Press eBooks,2026,book-chapter,foundation_models,4,197.0,197.0,188.61702127659572,197,110.0789,True,True,post,timellmtimeseriesforecastingbyreprogramminglargelanguagemodels,499.48767120160613,landmark,True284392947521,10.1038/s41591-024-02857-3,,Towards a general-purpose foundation model for computational pathology,Nature Medicine,2024,article,foundation_models,2,770.0,1505.0,186.93297525958593,1505,223.3796,False,True,post,towardsageneralpurposefoundationmodelforcomputationalpathology,186.93297525958593,landmark,True297133233472,10.52202/075280-0441,2305.14314,QLoRA: Efficient Finetuning of Quantized LLMs,neural information processing systems,2023,conference-paper,foundation_models,7,287.0,532.0,122.68895117800083,1428,269.751,True,True,post,qloraefficientfinetuningofquantizedllms,122.68895117800083,landmark,True304410551352,10.1038/s41586-025-09005-y,2405.13063,A foundation model for the Earth 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framework for chemical foundation models,Digital Discovery,2026,article,foundation_models,2,25.0,25.0,70.59021819073303,25,85.533,True,True,post,chemberta3anopensourcetrainingframeworkforchemicalfoundationmodels,63.38676030477235,landmark,True377119541039,10.1038/s43247-025-03127-x,,On the foundations of Earth foundation models,Communications Earth & Environment,2026,article,foundation_models,3,24.0,24.0,26.707236765289682,24,83.0902,True,True,post,onthefoundationsofearthfoundationmodels,60.851289892581455,landmark,True384390690043,10.1145/3800677,2401.02458,Data-Centric Foundation Models in Computational Healthcare: A Survey,ACM Computing Surveys,2026,article,foundation_models,4,22.0,22.0,10.015503875968992,22,13.1625,True,True,post,datacentricfoundationmodelsincomputationalhealthcareasurvey,55.78034906819967,landmark,True394361229539,10.48550/arxiv.2303.16199,2303.16199,LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention,arXiv (Cornell University),2023,preprint,foundation_models,9,135.0,161.0,53.528087966208936,166,,True,True,post,llamaadapterefficientfinetuningoflanguagemodelswithzeroinitattention,53.528087966208936,landmark,True407128029160,10.1038/s41593-026-02202-6,,A generalizable foundation model for analysis of human brain MRI,Nature Neuroscience,2026,article,foundation_models,2,20.0,20.0,22.2560306377414,20,72.2524,True,True,post,ageneralizablefoundationmodelforanalysisofhumanbrainmri,50.70940824381788,landmark,True414385948838,10.1038/s41591-023-02504-3,,A visual–language foundation model for pathology image analysis using medical Twitter,Nature Medicine,2023,article,foundation_models,3,179.0,471.0,49.064992972535784,680,61.8263,False,False,post,avisuallanguagefoundationmodelforpathologyimageanalysisusingmedicaltwitter,49.064992972535784,landmark,True424411932262,10.1038/s41586-025-09215-4,,A foundation model to predict and capture human 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Sciences,2026,article,multimodal_ai,4,22.0,22.0,24.48163370151554,22,79.4776,True,True,post,semcapsentimentawaresemanticcaptioningformultimodalaspectbasedsentimentanalysis,55.78034906819967,landmark,True694399567248,10.1038/s41586-024-07618-3,,A multimodal generative AI copilot for human pathology,Nature,2024,article,multimodal_ai,8,268.0,452.0,53.54798769997698,452,63.8469,True,True,post,amultimodalgenerativeaicopilotforhumanpathology,53.54798769997698,landmark,True707143512276,10.1007/s11831-026-10560-4,,Multimodal Machine Learning Approaches in Predictive Healthcare Analytics: A Comprehensive Survey,Archives of Computational Methods in Engineering,2026,article,multimodal_ai,5,21.0,21.0,23.368832169628472,21,75.865,True,True,post,multimodalmachinelearningapproachesinpredictivehealthcareanalyticsacomprehensivesurvey,53.24487865600877,landmark,True714410882873,10.1016/j.media.2025.103621,2411.03782,Navigating the landscape of multimodal AI in medicine: A scoping review on technical 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Hallucination in Large Vision-Language Models,Conference on Empirical Methods in Natural Language Processing (EMNLP),2023,conference-paper,multimodal_ai,6,124.0,326.0,45.49377601933825,559,63.561,True,True,post,evaluatingobjecthallucinationinlargevisionlanguagemodels,45.49377601933825,landmark,True774400228804,10.46569/df65vg26z,,The Conglomeration of Everything: Shifting Animation Production Practices due to Artificial Intelligence,,2024,dissertation,generative_ai,4,2.0,2.0,540.0,2,,True,True,post,theconglomerationofeverythingshiftinganimationproductionpracticesduetoartificialintelligence,540.0,landmark,True784383312437,10.1186/s41239-023-00408-3,2305.00280,A comprehensive AI policy education framework for university teaching and learning,International Journal of Educational Technology in Higher 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Multimedia,2026,article,generative_ai,3,66.0,66.0,176.9042016806723,66,205.676,True,False,post,structurepreservingfrequencyregularizedtextguidedoptimaltransportforunpairedrainstreaksandraindropsremoval,167.341047204599,landmark,True864398243313,10.1186/s41239-024-00468-z,,A scoping review on how generative artificial intelligence transforms assessment in higher education,International Journal of Educational Technology in Higher Education,2024,article,generative_ai,1,181.0,463.0,139.39774582546502,463,177.182,True,True,post,ascopingreviewonhowgenerativeartificialintelligencetransformsassessmentinhighereducation,139.39774582546502,landmark,True877127274868,10.1002/jcal.70198,,Parental AI Investment Behaviours and AI ‐Mediated Informal Digital Learning of English: The Chain Mediating Role of Children's Perceived AI Value and Effort Expectancy for AI,Journal of Computer Assisted 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Intelligence,2024,article,generative_ai,1,207.0,394.0,118.623567721886,394,150.7769,True,True,post,theimpactofgenerativeaionhighereducationlearningandteachingastudyofeducatorsperspectives,118.623567721886,landmark,True904409269614,10.3390/info16040296,,Generative AI and Academic Integrity in Higher Education: A Systematic Review and Research Agenda,Information,2025,review,generative_ai,1,271.0,271.0,14.199554069119287,271,53.4431,True,True,post,generativeaiandacademicintegrityinhighereducationasystematicreviewandresearchagenda,118.41929483552576,landmark,True914395957952,10.54675/ewzm9535,,Guidance for generative AI in education and research,UNESCO eBooks,2023,book,generative_ai,2,164.0,400.0,116.73200564528776,726,252.3485,False,False,post,guidanceforgenerativeaiineducationandresearch,116.73200564528776,landmark,True924394863715,10.2139/ssrn.4759930,,How Generative AI Can Augment Human Creativity,SSRN Electronic Journal,2024,preprint,generative_ai,1,111.0,131.0,114.27954959430369,131,,False,True,post,howgenerativeaicanaugmenthumancreativity,114.27954959430369,landmark,True934401077562,10.1080/10447318.2024.2383033,,Factors Influencing University Students’ Behavioral Intention to Use Generative Artificial Intelligence: Integrating the Theory of Planned Behavior and AI Literacy,International Journal of Human-Computer Interaction,2024,article,generative_ai,3,172.0,353.0,106.27949087773035,353,135.087,True,False,post,factorsinfluencinguniversitystudentsbehavioralintentiontousegenerativeartificialintelligenceintegratingthetheoryofplannedbehaviorandailiteracy,106.27949087773035,landmark,True947118187240,10.3390/en19010284,,Patch Time Series Transformer−Based Short−Term Photovoltaic Power Prediction Enhanced by Artificial Fish,Energies,2026,article,generative_ai,1,40.0,40.0,44.5120612754828,40,144.5047,True,True,post,patchtimeseriestransformerbasedshorttermphotovoltaicpowerpredictionenhancedbyartificialfish,101.41881648763577,landmark,True954407778399,10.1186/s41239-025-00507-3,,"Investigating the higher education institutions’ guidelines and policies regarding the use of generative AI in teaching, learning, research, and administration",International Journal of Educational Technology in Higher Education,2025,article,generative_ai,3,213.0,213.0,184.92680518328933,213,232.7014,True,True,post,investigatingthehighereducationinstitutionsguidelinesandpoliciesregardingtheuseofgenerativeaiinteachinglearningresearchandadministration,93.07494391131729,landmark,True964401917994,10.38124/ijisrt/ijisrt24aug265,,Building Face Ageing Model Using Face Synthesis,International Journal of Innovative Science and Research Technology (IJISRT),2024,article,generative_ai,3,737.0,737.0,87.31165251080317,737,103.9631,True,True,post,buildingfaceageingmodelusingfacesynthesis,87.31165251080317,landmark,True974411505840,10.1038/s41598-025-97652-6,,AI tutoring outperforms in-class active learning: an RCT introducing a novel research-based design in an authentic educational setting,Scientific Reports,2025,article,generative_ai,1,196.0,196.0,66.76726514867121,196,81.1441,True,True,post,aitutoringoutperformsinclassactivelearninganrctintroducinganovelresearchbaseddesigninanauthenticeducationalsetting,85.64642726111826,landmark,True982804078698,10.48550/arxiv.1805.08318,1805.08318,Self-Attention Generative Adversarial Networks,arXiv (Cornell University),2018,preprint,generative_ai,4,365.0,936.0,85.0107976109918,2327,,True,True,baseline,selfattentiongenerativeadversarialnetworks,85.0107976109918,landmark,True994321648656,10.1163/22134913-bja10078,2302.10913,From Paintbrush to Pixel: A Review of Deep Neural Networks in 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Sciences,2023,article,generative_ai,2,158.0,352.0,82.50027309715541,461,108.0946,True,True,post,exploringthepotentialimpactofartificialintelligenceaioninternationalstudentsinhighereducationgenerativeaichatbotsanalyticsandinternationalstudentsuccess,82.50027309715541,landmark,True1024406728221,10.1109/access.2025.3532853,,Agentic AI: Autonomous Intelligence for Complex Goals—A Comprehensive Survey,IEEE Access,2025,article,ai_agents,1,694.0,694.0,236.41062251621338,694,288.7899,True,True,post,agenticaiautonomousintelligenceforcomplexgoalsacomprehensivesurvey,303.25826795518407,landmark,True1037161734976,10.1038/s41586-026-10652-y,,A multi-agent system for automating scientific discovery,Nature,2026,article,ai_agents,3,62.0,62.0,175.0637411130179,62,220.9602,True,True,post,amultiagentsystemforautomatingscientificdiscovery,157.19916555583544,landmark,True1044225355126,10.1007/978-981-19-0493-6_1,,Introduction to Multi-agent Systems,Springer tracts in human-centered 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University Press eBooks,2025,book-chapter,reinforcement_learning,3,395.0,395.0,664.9588792863116,444,16.8612,True,False,post,bayesianinversereinforcementlearning,172.60376922521283,landmark,True1322973229164,10.32657/10356/90191,,Continuous control for robot based on deep reinforcement learning,,2019,dissertation,reinforcement_learning,2,5.0,47.0,171.15412186379928,951,,True,True,baseline,continuouscontrolforrobotbasedondeepreinforcementlearning,171.15412186379928,landmark,True1332982316857,10.1038/s41586-019-1724-z,,Grandmaster level in StarCraft II using multi-agent reinforcement learning,Nature,2019,article,reinforcement_learning,2,403.0,1021.0,155.2486170001325,3422,181.8864,False,False,baseline,grandmasterlevelinstarcraftiiusingmultiagentreinforcementlearning,155.2486170001325,landmark,True1347123365826,10.1109/tce.2026.3652706,,Event-Triggered Zero-Sum Game for Input Saturated Nonlinear Systems With State Constraints,IEEE Transactions on Consumer Electronics,2026,article,reinforcement_learning,3,55.0,55.0,155.29848001961267,55,196.0131,True,False,post,eventtriggeredzerosumgameforinputsaturatednonlinearsystemswithstateconstraints,139.45087267049917,landmark,True1352902907165,10.1126/science.aar6404,,"A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play",Science,2018,article,reinforcement_learning,3,271.0,733.0,138.34973973017864,3551,180.9159,True,True,baseline,ageneralreinforcementlearningalgorithmthatmasterschessshogiandgothroughselfplay,138.34973973017864,landmark,True1367133226216,10.52202/068431-1787,2103.01955,The Surprising Effectiveness of PPO in Cooperative Multi-Agent Games,neural information processing systems,2022,conference-paper,reinforcement_learning,5,259.0,482.0,95.76250774959783,1027,87.4682,True,True,baseline,thesurprisingeffectivenessofppoincooperativemultiagentgames,95.76250774959783,landmark,True1373041807988,10.1007/s42438-020-00164-x,,Post-Covid-19 Education and Education Technology ‘Solutionism’: a Seller’s Market,Postdigital Science and Education,2020,article,reinforcement_learning,1,148.0,322.0,93.76380474425054,594,104.1384,True,True,baseline,postcovid19educationandeducationtechnologysolutionismasellersmarket,93.76380474425054,landmark,True1382781726626,10.48550/arxiv.1801.01290,1801.01290,Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor,arXiv (Cornell University),2018,preprint,reinforcement_learning,4,254.0,775.0,90.80433444998975,3598,,True,True,baseline,softactorcriticoffpolicymaximumentropydeepreinforcementlearningwithastochasticactor,90.80433444998975,landmark,True1392963923407,,1802.09477,Addressing Function Approximation Error in Actor-Critic Methods,UvA-DARE (University of Amsterdam),2018,article,reinforcement_learning,4,131.0,472.0,89.08741767072895,362,45.5472,True,True,baseline,addressingfunctionapproximationerrorinactorcriticmethods,89.08741767072895,landmark,True1407133481537,10.1016/j.neucom.2026.133224,,Filter-based predefined-time optimal fault-tolerant consensus control for nonlinear multi-agent systems via reinforcement learning,Neurocomputing,2026,article,reinforcement_learning,3,35.0,35.0,122.87423247935634,35,146.6674,False,False,post,filterbasedpredefinedtimeoptimalfaulttolerantconsensuscontrolfornonlinearmultiagentsystemsviareinforcementlearning,88.74146442668129,landmark,True1412899138465,,,Learning to Learn with Gradients,ProQuest Demo Repository,2018,dissertation,reinforcement_learning,3,8.0,20.0,88.61723446893788,51,,True,False,baseline,learningtolearnwithgradients,88.61723446893788,landmark,True1424391631327,10.48550/arxiv.2402.03300,2402.03300,Axiom-World: A Pre-Registered Study of Two-Stage Post-Training for Rule-Grounded Planning in a Verifiable Toy World,arXiv (Cornell University),2024,preprint,reinforcement_learning,9,52.0,99.0,86.3639344262295,99,,True,True,post,axiomworldapreregisteredstudyoftwostageposttrainingforrulegroundedplanninginaverifiabletoyworld,86.3639344262295,landmark,True1432895974005,,,Exploration and Exploitation in Reinforcement Learning,KTH Publication Database DiVA (KTH Royal Institute of Technology),2018,dissertation,reinforcement_learning,3,14.0,18.0,79.75551102204409,45,,True,True,baseline,explorationandexploitationinreinforcementlearning,79.75551102204409,landmark,True1444409147637,10.1038/s41586-025-08744-2,,Mastering diverse control tasks through world models,Nature,2025,article,reinforcement_learning,2,170.0,170.0,57.91038303711279,170,70.7411,True,True,post,masteringdiversecontroltasksthroughworldmodels,74.28516650199033,landmark,True1452754517384,10.1609/aaai.v32i1.11694,1709.06560,Deep Reinforcement Learning That Matters,Proceedings of the AAAI Conference on Artificial Intelligence,2018,conference-paper,reinforcement_learning,3,261.0,452.0,69.98997654610542,1579,68.6606,True,True,baseline,deepreinforcementlearningthatmatters,69.98997654610542,landmark,True1462761873684,10.1609/aaai.v32i1.11796,,Rainbow: Combining Improvements in Deep Reinforcement Learning,Proceedings of the AAAI Conference on Artificial Intelligence,2018,conference-paper,reinforcement_learning,1,240.0,442.0,68.44152573756327,1772,72.3017,True,True,baseline,rainbowcombiningimprovementsindeepreinforcementlearning,68.44152573756327,landmark,True1477133208539,10.52202/075280-2338,,Direct Preference Optimization: Your Language Model is Secretly a Reward Model,neural information processing systems,2023,conference-paper,reinforcement_learning,3,111.0,293.0,67.57117047961324,474,89.6019,False,False,post,directpreferenceoptimizationyourlanguagemodelissecretlyarewardmodel,67.57117047961324,landmark,True1484327734444,10.5204/thesis.eprints.238632,,Leveraging control priors in deep reinforcement learning for robotics,Queensland University of Technology,2023,dissertation,reinforcement_learning,2,5.0,8.0,66.54620123203286,9,,True,True,post,leveragingcontrolpriorsindeepreinforcementlearningforrobotics,66.54620123203286,landmark,True1494383112908,10.1109/tpami.2023.3292075,2009.07888,Transfer Learning in Deep Reinforcement Learning: A Survey,IEEE Transactions on Pattern Analysis and Machine Intelligence,2023,article,reinforcement_learning,6,398.0,716.0,65.732229267823,846,67.6448,True,True,post,transferlearningindeepreinforcementlearningasurvey,65.732229267823,landmark,True1507122543576,10.1002/rnc.70380,,SMS‐Based Event‐Triggered Fault Tolerant Control of Nonlinear Systems Using Adaptive Dynamic Programming,International Journal of Robust and Nonlinear Control,2026,article,reinforcement_learning,6,25.0,25.0,70.59021819073303,25,89.0969,True,False,post,smsbasedeventtriggeredfaulttolerantcontrolofnonlinearsystemsusingadaptivedynamicprogramming,63.38676030477235,landmark,True1512742132707,10.26153/tsw/13849,,Curriculum learning in reinforcement learning,Texas Digital Library (University of Texas),2021,dissertation,reinforcement_learning,4,13.0,13.0,59.128571428571426,13,,True,True,baseline,curriculumlearninginreinforcementlearning,59.128571428571426,landmark,True1522962862931,10.4230/oasics.dx.2024.27,,On a Method to Measure Supervised Multiclass Model’s Interpretability: Application to Degradation Diagnosis (Short Paper),HAL (Le Centre pour la Communication Scientifique Directe),2024,conference-paper,ml_theory,1,11888.0,12586.0,3498.428847235165,12586,1941.7911,True,True,post,onamethodtomeasuresupervisedmulticlassmodelsinterpretabilityapplicationtodegradationdiagnosisshortpaper,3498.428847235165,landmark,True15346659105,10.4230/lipics.esem.2026.21,2010.16061,"Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation",arXiv (Cornell University),2020,preprint,ml_theory,4,3748.0,3906.0,608.1723816912336,4311,,True,True,baseline,evaluationfromprecisionrecallandfmeasuretorocinformednessmarkednessandcorrelation,608.1723816912336,landmark,True1544288283362,10.5281/zenodo.20632938,,Spiral Time: A Geometric Reframing of Temporal Structure and Its Applications in Machine Learning,,2026,article,ml_theory,3,174.0,174.0,576.9349593495934,174,17.3333,True,True,post,spiraltimeageometricreframingoftemporalstructureanditsapplicationsinmachinelearning,441.17185172121555,landmark,True1551568555062,10.1145/3501714.3501732,,Equivalence and Synthesis of Causal Models,ACM eBooks,2022,book-chapter,ml_theory,1,557.0,576.0,308.68124677113826,964,48.0029,True,False,baseline,equivalenceandsynthesisofcausalmodels,308.68124677113826,landmark,True1562100415632,10.48550/arxiv.2604.24555,2604.24555,Efficient learning by implicit exploration in bandit problems with side observations,arXiv (Cornell University),2026,preprint,ml_theory,3,116.0,116.0,667.5471698113207,116,,True,True,post,efficientlearningbyimplicitexplorationinbanditproblemswithsideobservations,294.1145678141437,landmark,True1573208688400,10.1007/978-981-16-2102-4_36,,Anomaly Detection: A Survey,Lecture notes in networks and systems,2021,book-chapter,ml_theory,1,734.0,833.0,280.4690126251966,958,168.1579,False,False,baseline,anomalydetectionasurvey,280.4690126251966,landmark,True1583105488314,,,Risk bounds for statistical learning,FreiDok plus (Universitätsbibliothek Freiburg),2020,dissertation,ml_theory,1,50.0,59.0,272.44754218635364,96,,False,False,baseline,riskboundsforstatisticallearning,272.44754218635364,landmark,True1592945976633,10.1038/s42256-019-0048-x,1811.10154,Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead,Nature Machine Intelligence,2019,article,ml_theory,2,620.0,1556.0,236.59828408639194,10272,292.6299,False,True,baseline,stopexplainingblackboxmachinelearningmodelsforhighstakesdecisionsanduseinterpretablemodelsinstead,236.59828408639194,landmark,True1604400823820,10.58496/bjml/2024/007,,Random Forest Algorithm Overview,Babylonian Journal of Machine Learning,2024,article,ml_theory,1,353.0,782.0,235.4406851738956,782,299.2578,True,True,post,randomforestalgorithmoverview,235.4406851738956,landmark,True1612999309192,10.1186/s12864-019-6413-7,,The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation,BMC Genomics,2020,article,ml_theory,2,897.0,1781.0,222.7600450619602,6051,266.8933,True,True,baseline,theadvantagesofthematthewscorrelationcoefficientmccoverf1scoreandaccuracyinbinaryclassificationevaluation,222.7600450619602,landmark,True1622999615587,10.1038/s42256-019-0138-9,,From local explanations to global understanding with explainable AI for trees,Nature Machine Intelligence,2020,article,ml_theory,1,808.0,1767.0,221.00898350616714,10196,280.6263,False,True,baseline,fromlocalexplanationstoglobalunderstandingwithexplainableaifortrees,221.00898350616714,landmark,True1632439568532,10.1145/3233231,,The Mythos of Model Interpretability,Communications of the ACM,2018,article,ml_theory,2,592.0,1085.0,204.78781392529854,5034,234.6672,True,True,baseline,themythosofmodelinterpretability,204.78781392529854,landmark,True1643194464139,,,Penerapan Algoritma K-Nearest Neighbor untuk Penentuan Resiko Kredit Kepemilikan Kendaraan Bermotor,,2020,dissertation,ml_theory,1,15.0,21.0,197.50232018561485,25,,True,False,baseline,penerapanalgoritmaknearestneighboruntukpenentuanresikokreditkepemilikankendaraanbermotor,197.50232018561485,landmark,True1653041133507,10.1109/jproc.2020.3004555,1911.02685,A Comprehensive Survey on Transfer Learning,Proceedings of the IEEE,2020,article,ml_theory,4,593.0,1538.0,192.3666194864092,6556,273.5231,True,True,baseline,acomprehensivesurveyontransferlearning,192.3666194864092,landmark,True16657706852,10.48550/arxiv.2604.24537,2604.24537,Stochastic simultaneous optimistic optimization,arXiv (Cornell University),2026,preprint,ml_theory,1,72.0,72.0,414.33962264150944,72,,True,True,post,stochasticsimultaneousoptimisticoptimization,182.55386967774436,landmark,True1672436219157,10.1137/16m1080173,1606.04838,Optimization Methods for Large-Scale Machine Learning,SIAM Review,2018,article,ml_theory,4,492.0,956.0,180.43976968901882,3128,199.3045,True,True,baseline,optimizationmethodsforlargescalemachinelearning,180.43976968901882,landmark,True1683135028703,10.1007/s42979-021-00592-x,,"Machine Learning: Algorithms, Real-World Applications and Research Directions",SN Computer Science,2021,article,ml_theory,1,512.0,1518.0,176.91139356202336,5534,266.7657,False,True,baseline,machinelearningalgorithmsrealworldapplicationsandresearchdirections,176.91139356202336,landmark,True1692946444988,10.48550/arxiv.2606.04665,2606.04665,Towards Accurate Model Selection in Deep Unsupervised Domain Adaptation,arXiv (Cornell University),2026,preprint,ml_theory,4,68.0,68.0,2646.035087719298,68,,True,True,post,towardsaccuratemodelselectionindeepunsuperviseddomainadaptation,172.4119880289808,landmark,True1703141797743,10.1109/tkde.2021.3070203,1707.08114,A Survey on Multi-Task Learning,IEEE Transactions on Knowledge and Data Engineering,2021,article,ml_theory,6,882.0,1299.0,151.38860358173145,2878,131.001,True,True,baseline,asurveyonmultitasklearning,151.38860358173145,landmark,True1713123554940,10.1007/978-3-030-57077-4_11,,Pattern Recognition and Machine Learning,EAI/Springer Innovations in Communication and Computing,2021,book-chapter,ml_theory,1,147.0,182.0,140.86542176432712,255,92.5445,False,False,baseline,patternrecognitionandmachinelearning,140.86542176432712,landmark,True1722889326414,10.21105/joss.00861,,UMAP: Uniform Manifold Approximation and Projection,The Journal of Open Source Software,2018,article,ml_theory,2,211.0,797.0,139.93100250694124,9681,230.917,True,True,baseline,umapuniformmanifoldapproximationandprojection,139.93100250694124,landmark,True1733021654819,10.1109/msp.2020.2975749,1908.07873,"Federated Learning: Challenges, Methods, and Future Directions",IEEE Signal Processing Magazine,2020,article,ml_theory,7,429.0,1091.0,136.45772552644502,5140,195.766,True,True,baseline,federatedlearningchallengesmethodsandfuturedirections,136.45772552644502,landmark,True1742995022099,10.1561/2200000083,1912.04977,Advances and Open Problems in Federated Learning,Foundations and Trends® in Machine Learning,2020,article,ml_theory,6,570.0,1087.0,135.95742222478987,6330,186.8632,True,True,baseline,advancesandopenproblemsinfederatedlearning,135.95742222478987,landmark,True1752608595939,10.1007/978-3-030-05318-5_4,,Auto-WEKA: Automatic Model Selection and Hyperparameter Optimization in WEKA,The Springer series on challenges in machine learning,2019,book-chapter,ml_theory,2,300.0,423.0,131.5813535083268,650,138.539,True,True,baseline,autowekaautomaticmodelselectionandhyperparameteroptimizationinweka,131.5813535083268,landmark,True1763157286395,10.4230/oasics.dx.2026.1,2104.13478,"A Realization-Theoretic Foundation for Fault Diagnosis, with Diagnosability Equivalence Predictions",arXiv (Cornell University),2021,preprint,ml_theory,2,169.0,307.0,128.25943970767358,519,,True,True,baseline,arealizationtheoreticfoundationforfaultdiagnosiswithdiagnosabilityequivalencepredictions,128.25943970767358,landmark,True1772964121744,10.4230/oasics.dx.2026.15,2108.04578,AutoResearch Lite: a reproducible paper-inspired experiment runner (Adam mechanism reproduction and convergence-speed comparison),DROPS (Schloss Dagstuhl – Leibniz Center for Informatics),2026,article,deep_learning_architectures,3,45775.0,45775.0,50938.490122130635,45780,2749.203,True,True,baseline,autoresearchliteareproduciblepaperinspiredexperimentrunneradammechanismreproductionandconvergencespeedcomparison,116061.15811803818,landmark,True1782963399829,10.57702/dcy1c3gw,1710.09412,Mixup: Beyond empirical risk minimization,arXiv (Cornell University),2024,preprint,deep_learning_architectures,6,772.0,773.0,674.336578903792,773,,True,True,baseline,mixupbeyondempiricalriskminimization,674.336578903792,landmark,True1794322614756,10.4230/lipics.giscience.2025.9,,Identifying Resilient Communities in Road Networks: A Path-Based Embedding Approach,Leibniz international proceedings in informatics,2025,conference-paper,deep_learning_architectures,2,1542.0,1542.0,1370.4731047566265,1543,164.3965,True,True,post,identifyingresilientcommunitiesinroadnetworksapathbasedembeddingapproach,673.8101573298181,landmark,True1804382203079,10.1609/aaai.v37i9.26317,2205.13504,Are 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Semantic Information,DROPS (Schloss Dagstuhl – Leibniz Center for Informatics),2025,article,deep_learning_architectures,2,496.0,496.0,168.96205874357614,0,0.0,True,True,post,enrichinglocationrepresentationwithdetailedsemanticinformation,216.73789755874824,landmark,True1947118302872,10.1007/s13278-025-01574-0,,A frame work to predict the influencing nodes in social networking platform using graph neural network model,Social Network Analysis and Mining,2026,article,deep_learning_architectures,3,85.0,85.0,87.58695652173913,85,292.0234,True,True,post,aframeworktopredicttheinfluencingnodesinsocialnetworkingplatformusinggraphneuralnetworkmodel,215.514985036226,landmark,True1952892035503,10.1007/s10618-019-00619-1,1809.04356,Deep learning for time series classification: a review,Data Mining and Knowledge 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Medicine,2025,article,ai_safety,1,303.0,303.0,42.26726953026878,303,319.4957,True,True,post,biasrecognitionandmitigationstrategiesinartificialintelligencehealthcareapplications,132.4023850006063,landmark,True2124407243994,10.1080/08839514.2025.2463722,,"AI Ethics: Integrating Transparency, Fairness, and Privacy in AI Development",Applied Artificial Intelligence,2025,article,ai_safety,1,300.0,300.0,41.8487817131374,300,317.3728,True,True,post,aiethicsintegratingtransparencyfairnessandprivacyinaidevelopment,131.09147029763,landmark,True2134360615722,10.3390/su15075614,,Chatbots in Education and Research: A Critical Examination of Ethical Implications and Solutions,Sustainability,2023,article,ai_safety,2,322.0,557.0,130.5473071452147,658,154.3873,True,True,post,chatbotsineducationandresearchacriticalexaminationofethicalimplicationsandsolutions,130.5473071452147,landmark,True2147133236347,10.1016/j.aiopen.2026.02.006,2506.04133,"TRiSM for Agentic AI: A review of Trust, Risk, and Security 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Medicine,2026,review,ai_safety,1,41.0,41.0,12.755555555555555,41,37.52,True,True,post,advancinghealthcareaigovernancethroughacomprehensivematuritymodelbasedonsystematicreview,103.95428689982666,landmark,True2202969896603,10.1145/3457607,1908.09635,A Survey on Bias and Fairness in Machine Learning,ACM Computing Surveys,2021,article,ai_safety,3,952.0,1794.0,102.2847403248446,5442,822.4428,True,True,baseline,asurveyonbiasandfairnessinmachinelearning,102.2847403248446,landmark,True2214409249263,10.1371/journal.pdig.0000810,2412.03576,Ethical challenges and evolving strategies in the integration of artificial intelligence into clinical practice,PLOS Digital Health,2025,article,ai_safety,3,218.0,218.0,14.499719652368936,218,44.6351,True,True,post,ethicalchallengesandevolvingstrategiesintheintegrationofartificialintelligenceintoclinicalpractice,95.25980174961113,landmark,True2227118631510,10.1016/j.cie.2026.111805,,Towards trustworthy AI in Industry 5.0: Ante-hoc interpretability with deep 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research,Nature,2023,article,llm,2,1183.0,1590.0,145.96961527351755,1831,160.4149,False,True,post,chatgptfiveprioritiesforresearch,145.96961527351755,landmark,True2334399790131,10.1186/s40561-024-00316-7,,The effects of over-reliance on AI dialogue systems on students' cognitive abilities: a systematic review,Smart Learning Environments,2024,review,ai_safety,3,613.0,1484.0,81.80055076520247,1484,96.2475,True,True,post,theeffectsofoverrelianceonaidialoguesystemsonstudentscognitiveabilitiesasystematicreview,81.80055076520247,landmark,True2343133702157,10.1145/3442188.3445922,,On the Dangers of Stochastic Parrots,"ACM Conference on Fairness, Accountability, and Transparency (FAccT)",2021,conference-paper,llm,3,623.0,1464.0,232.11321601174575,6788,321.4548,True,True,baseline,onthedangersofstochasticparrots,232.11321601174575,landmark,True235