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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,990.7064044933976,landmark,True84367369313,10.18260/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,True94226278401,10.48550/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,True104396531244,10.22215/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,True114221143046,10.48550/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,True124327810158,10.4230/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,True137135096569,10.1287/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,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,556.3784815078109,landmark,True154389437528,10.61969/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,True164292779060,10.4230/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,534.676590443066,landmark,True174360836968,10.48550/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,472.11108641004154,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,462.72059335590455,landmark,True194385223296,10.1007/978-3-031-37776-1_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,421.27957250425067,landmark,True204287689466,10.4230/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,365.5188932198721,landmark,True214366420437,10.3390/educsci13040410,,What Is the Impact of ChatGPT on Education? A Rapid Review of the Literature,Education Sciences,2023,article,llm,1,955.0,1541.0,361.17307057589915,1967,462.692,True,True,post,361.17307057589915,landmark,True224317910584,10.37074/jalt.2023.6.1.9,,ChatGPT: Bullshit spewer or the end of traditional assessments in higher education?,Journal of Applied Learning & Teaching,2023,article,llm,3,1057.0,1530.0,358.59493704161304,1771,416.3993,True,True,post,358.59493704161304,landmark,True234313564799,10.2139/ssrn.4312418,,ChatGPT User Experience: Implications for Education,SSRN Electronic Journal,2023,preprint,llm,2,595.0,811.0,335.9058974941328,912,,False,True,post,335.9058974941328,landmark,True244382618722,10.1016/j.fmre.2026.03.026,2306.16092,Chatlaw: A Multi-Agent Legal Assistant based on a Role-Aligned Mixture-of-Experts Architecture,Fundamental Research,2026,article,llm,6,94.0,94.0,98.93209876543209,94,193.5725,True,True,post,334.1907344564603,landmark,True254384464487,10.1186/s41239-023-00411-8,2305.00290,"Students’ voices on 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pathology,Nature Medicine,2024,article,foundation_models,2,770.0,1505.0,186.93297525958593,1505,223.3796,False,True,post,186.93297525958593,landmark,True297124890304,10.1016/j.jii.2026.101076,,Semi-supervised cross-domain fault diagnosis via contrastive pre-training and annotation-efficient alignment strategy,Journal of Industrial Information Integration,2026,article,foundation_models,1,42.0,42.0,46.52414937759336,42,116.7549,False,False,post,149.31926433160993,landmark,True304410551352,10.1038/s41586-025-09005-y,2405.13063,A foundation model for the Earth system,Nature,2025,article,foundation_models,3,249.0,249.0,69.74305555555556,249,109.0217,True,True,post,126.0584548639171,landmark,True314322832366,10.1145/3773966.3778007,2303.00320,TimeMAE: Self-Supervised Representations of Time Series with Decoupled Masked Autoencoders,,2026,conference-paper,foundation_models,6,35.0,35.0,126.32971014492755,35,165.8809,True,True,post,124.4327202763416,landmark,True327133233472,10.52202/075280-0441,2305.14314,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,122.68895117800083,landmark,True332990704537,,,SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems,neural information processing systems,2019,article,foundation_models,5,262.0,668.0,101.57304226845103,462,52.8392,True,False,baseline,101.57304226845103,landmark,True347125271461,10.1039/d5dd00348b,,ChemBERTa-3: an open source training framework for chemical foundation models,Digital Discovery,2026,article,foundation_models,2,25.0,25.0,70.59021819073303,25,85.533,True,True,post,88.88051448310114,landmark,True357119541039,10.1038/s43247-025-03127-x,,On the foundations of Earth foundation 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diagnosis and prognosis prediction,Nature,2024,article,foundation_models,2,275.0,562.0,69.80487182450983,562,83.6371,False,True,post,69.80487182450983,landmark,True407118170538,10.3390/bdcc10010019,,Applications of Artificial Intelligence in Fisheries: From Data to Decisions,Big Data and Cognitive Computing,2026,article,foundation_models,8,17.0,17.0,17.85,17,28.0244,True,True,post,60.43874984850878,landmark,True414411932262,10.1038/s41586-025-09215-4,,A foundation model to predict and capture human cognition,Nature,2025,article,foundation_models,1,110.0,110.0,26.933816382433506,110,60.8153,True,True,post,55.6884740362686,landmark,True424411099958,10.1038/s41591-025-03747-y,2410.15038,A multimodal vision foundation model for clinical dermatology,Nature Medicine,2025,article,foundation_models,4,108.0,108.0,18.44502277163305,108,39.3828,True,True,post,54.67595632651827,landmark,True434361229539,10.48550/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,53.528087966208936,landmark,True444407135816,10.1148/radiol.240597,2411.18730,"Foundation Models in Radiology: What, How, Why, and Why Not",Radiology,2025,article,foundation_models,4,103.0,103.0,6.850784973366975,103,21.0891,True,True,post,52.14466205214242,landmark,True454385948838,10.1038/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,49.064992972535784,landmark,True464406643700,10.1016/j.jag.2025.104368,,Towards the next generation of Geospatial Artificial Intelligence,International Journal of Applied Earth Observation and Geoinformation,2025,article,foundation_models,2,91.0,91.0,19.115723270440252,91,404.3482,True,True,post,46.06955579364039,landmark,True474406472599,10.1038/s42256-024-00961-0,2310.08184,Learning from models beyond fine-tuning,Nature Machine Intelligence,2025,article,foundation_models,4,86.0,86.0,29.29584083053941,86,31.2093,False,True,post,43.538261519264545,landmark,True484393153153,10.1609/aaai.v38i12.29299,2312.15709,TimesURL: Self-Supervised Contrastive Learning for Universal Time Series Representation Learning,Proceedings of the AAAI Conference on Artificial Intelligence,2024,conference-paper,foundation_models,5,68.0,113.0,41.75133936818769,113,44.645,True,True,post,41.75133936818769,landmark,True494402727669,10.1109/cvpr52733.2024.01239,,mPLUG-OwI2: Revolutionizing Multi-modal Large Language Model with Modality Collaboration,IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern 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University),2023,preprint,multimodal_ai,7,654.0,875.0,273.1441896618733,908,,True,True,post,273.1441896618733,landmark,True534393148714,10.1609/aaai.v38i5.28226,2302.08453,T2I-Adapter: Learning Adapters to Dig Out More Controllable Ability for Text-to-Image Diffusion Models,Proceedings of the AAAI Conference on Artificial Intelligence,2024,conference-paper,multimodal_ai,7,683.0,923.0,193.71569872998148,923,194.9761,True,True,post,193.71569872998148,landmark,True544396599905,10.1016/j.inffus.2026.104437,2404.18947,Multimodal fusion on low-quality data: A comprehensive survey,Information Fusion,2026,article,multimodal_ai,3,48.0,48.0,128.6576012223071,48,99.7217,True,True,post,170.6505878075542,landmark,True554396952261,10.1007/978-3-031-53092-0_10,,Multimodal Deep Learning,Springer optimization and its applications,2024,book-chapter,multimodal_ai,3,188.0,189.0,147.22445489525438,189,23.3148,False,False,post,147.22445489525438,landmark,True567133250524,10.52202/068431-1833,,LAION-5B: An Open Large-Scale Dataset for Training Next Generation Image-Text Models,neural information processing systems,2022,conference-paper,multimodal_ai,4,217.0,828.0,111.87473883318704,309,6.4706,False,False,baseline,111.87473883318704,landmark,True574392947532,10.1038/s41591-024-02856-4,,A visual-language foundation model for computational pathology,Nature Medicine,2024,article,multimodal_ai,4,475.0,938.0,111.12392137738586,938,132.4965,False,True,post,111.12392137738586,landmark,True587138474371,10.1609/aaai.v40i16.38386,,OpenDriveVLA: Towards End-to-end Autonomous Driving with Large Vision Language Action Model,Proceedings of the AAAI Conference on Artificial Intelligence,2026,conference-paper,multimodal_ai,4,31.0,31.0,159.42857142857142,30,608.3281,True,True,post,110.21183795904541,landmark,True597127319731,10.1109/tmi.2026.3661001,,Med-R1: Reinforcement Learning for Generalizable Medical Reasoning in Vision-Language Models,IEEE Transactions on Medical Imaging,2026,article,multimodal_ai,6,29.0,29.0,77.73063407181054,28,87.2565,True,False,post,103.10139680039732,landmark,True602619383789,10.1109/tpami.2018.2798607,,Multimodal Machine Learning: A Survey and Taxonomy,IEEE Transactions on Pattern Analysis and Machine Intelligence,2018,article,multimodal_ai,3,263.0,587.0,103.0608512817748,4909,120.007,True,True,baseline,103.0608512817748,landmark,True617148475777,10.1117/12.3086794,2508.08224,Capabilities of GPT‑5 on multimodal medical reasoning,,2026,conference-paper,multimodal_ai,7,26.0,26.0,73.1907514450867,26,238.4333,True,True,post,92.4357350624252,landmark,True627160627408,10.1109/csnt69054.2026.11502110,,Generative Artificial Intelligence for Cross-Domain Knowledge Synthesis and Intelligent Automation,IEEE International Conference on Communication Systems and Network Technologies 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multimodal aspect-based sentiment analysis,Journal of King Saud University - Computer and Information Sciences,2026,article,multimodal_ai,4,22.0,22.0,24.48163370151554,22,79.4776,True,True,post,78.21485274512901,landmark,True677143512276,10.1007/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,74.65963216580496,landmark,True684413447271,10.1038/s41467-025-62385-7,2308.02463,Towards generalist foundation model for radiology by leveraging web-scale 2D&3D medical data,Nature Communications,2025,article,multimodal_ai,4,142.0,142.0,46.43212351171006,142,51.2274,True,True,post,71.88875739227402,landmark,True697131830459,10.1109/tmm.2026.3668620,2502.15711,A Survey on Multimodal Recommender Systems: Recent Advances and Future Directions,IEEE Transactions on Multimedia,2026,article,multimodal_ai,3,20.0,20.0,142.0908500973394,20,173.9038,True,True,post,71.10441158648091,landmark,True707206206818,10.1109/tpami.2026.3730310,,Lingshu: Generalist Foundation Model for Unified Multimodal Medical Understanding and Reasoning,IEEE Transactions on Pattern Analysis and Machine Intelligence,2026,article,multimodal_ai,6,18.0,18.0,48.246600458365165,18,56.0935,True,False,post,63.99397042783282,landmark,True717133220561,10.52202/075280-2142,,InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning,neural information processing systems,2023,conference-paper,multimodal_ai,5,290.0,454.0,63.3563629226367,398,45.2545,False,False,post,63.3563629226367,landmark,True727164501397,10.1038/s41467-026-74207-5,2311.05591,Multimodal foundation models exploit text to make medical image predictions,Nature Communications,2026,article,multimodal_ai,6,17.0,17.0,45.56623376623377,17,6.2326,True,True,post,60.43874984850878,landmark,True737165561001,10.1007/978-3-032-29501-9_39,,"M3-HGMA: A Multi-modal, Multi-agent Hierarchical Graph-Mamba Architecture for Explainable and Adaptive Clinical Intelligence",Learning and analytics in intelligent systems,2026,conference-paper,multimodal_ai,2,17.0,17.0,142.98214285714286,17,160.2226,False,False,post,60.43874984850878,landmark,True747138470740,10.1609/aaai.v40i18.38608,,Cross-modal Proxy Evolving for OOD Detection with Vision-Language Models,Proceedings of the AAAI Conference on Artificial Intelligence,2026,conference-paper,multimodal_ai,7,17.0,17.0,130.47598850731976,17,157.1634,True,True,post,60.43874984850878,landmark,True757138363754,10.1609/aaai.v40i6.42404,,AdaptCLIP: Adapting CLIP for Universal Visual Anomaly Detection,Proceedings of the AAAI Conference on Artificial Intelligence,2026,conference-paper,multimodal_ai,6,17.0,17.0,130.47598850731976,17,157.1634,True,True,post,60.43874984850878,landmark,True764410882873,10.1016/j.media.2025.103621,2411.03782,Navigating the landscape of multimodal AI in medicine: A scoping review on technical challenges and clinical applications,Medical Image Analysis,2025,article,multimodal_ai,6,119.0,119.0,7.914984580880291,119,24.365,True,True,post,60.24480373014512,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,540.0,landmark,True787124262017,10.1787/062a7394-en,,OECD Digital Education Outlook 2026,Digital education outlook,2026,book,generative_ai,1,87.0,87.0,783.6796875,87,992.8809,True,False,post,309.304190401192,landmark,True794406472463,10.1038/s41586-025-08628-5,,A generative model for inorganic materials design,Nature,2025,article,generative_ai,2,498.0,498.0,77.09849967384214,498,102.9572,True,True,post,252.1169097278342,landmark,True807123342962,10.1109/tmm.2026.3651132,,Structure-Preserving Frequency-Regularized Text-Guided Optimal Transport for Unpaired Rain Streaks and Raindrops Removal,IEEE Transactions on Multimedia,2026,article,generative_ai,3,66.0,66.0,176.9042016806723,66,205.676,True,False,post,234.64455823538702,landmark,True814383312437,10.1186/s41239-023-00408-3,2305.00280,A comprehensive AI policy education framework for university teaching and learning,International Journal of Educational Technology in Higher Education,2023,article,generative_ai,1,451.0,988.0,231.56326653406123,1524,357.6522,True,True,post,231.56326653406123,landmark,True827133185348,10.52202/068431-2643,,Photorealistic Text-To-Image Diffusion Models with Deep Language Understanding,neural information processing systems,2022,conference-paper,generative_ai,5,651.0,1706.0,230.5051986104071,530,10.1405,False,False,baseline,230.5051986104071,landmark,True833195296536,,,Generative Adversarial Trainer Defense to Adversarial Perturbations with GAN,Seoul National University Open Repository (Seoul National University),2020,dissertation,generative_ai,4,37.0,48.0,221.6522377109318,65,,False,True,baseline,221.6522377109318,landmark,True844322617812,10.1016/j.ijme.2023.100790,,Generative AI and the future of education: Ragnarök or reformation? A paradoxical perspective from management educators,The International Journal of Management Education,2023,article,generative_ai,1,491.0,873.0,204.6100523119792,1151,270.2366,True,True,post,204.6100523119792,landmark,True853096831136,10.1145/3422622,,Generative adversarial networks,Communications of the ACM,2020,article,generative_ai,3,565.0,1473.0,198.6115392180884,14434,457.4443,True,False,baseline,198.6115392180884,landmark,True864400770292,10.2139/ssrn.4895486,,Generative AI Can Harm Learning,SSRN Electronic Journal,2024,preprint,generative_ai,1,123.0,198.0,174.24635549016986,198,,False,True,post,174.24635549016986,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 Learning,2026,article,generative_ai,1,48.0,48.0,341.0180402336145,48,439.3359,True,False,post,170.6505878075542,landmark,True887124974472,10.1287/mnsc.2025.02523,,The Rapid Adoption of Generative AI,Management Science,2026,article,generative_ai,1,42.0,42.0,46.737664339256945,42,151.73,True,False,post,149.31926433160993,landmark,True897118187240,10.3390/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,142.20882317296181,landmark,True904406199489,10.3389/bjbs.2024.14048,,Generative AI in Higher Education: Balancing Innovation and Integrity,British Journal of Biomedical Science,2025,article,generative_ai,2,278.0,278.0,241.35986779790812,278,301.5286,True,True,post,140.739961655297,landmark,True914398243313,10.1186/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,139.39774582546502,landmark,True924409269614,10.3390/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,137.19614967117084,landmark,True933193917007,10.1007/978-1-4842-7341-8_10,,Generative Adversarial Networks,Apress eBooks,2021,book-chapter,generative_ai,3,61.0,195.0,127.1017316017316,255,116.749,True,False,baseline,127.1017316017316,landmark,True944288099666,10.48550/arxiv.2207.12598,2207.12598,Classifier-Free Diffusion Guidance,arXiv (Cornell University),2022,preprint,generative_ai,3,211.0,575.0,124.81517402920616,731,,True,True,baseline,124.81517402920616,landmark,True954394934695,10.1016/j.caeai.2024.100221,,The impact of generative AI on higher education learning and teaching: A study of educators’ perspectives,Computers and Education Artificial Intelligence,2024,article,generative_ai,1,207.0,394.0,118.623567721886,394,150.7769,True,True,post,118.623567721886,landmark,True964321648656,10.1163/22134913-bja10078,2302.10913,From Paintbrush to Pixel: A Review of Deep Neural Networks in AI-Generated Art,Art & Perception,2026,article,generative_ai,4,33.0,33.0,88.45210084033614,33,9.3489,True,True,post,117.32227911769351,landmark,True974395957952,10.54675/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,116.73200564528776,landmark,True984394863715,10.2139/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,114.27954959430369,landmark,True994407778399,10.1186/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,107.83313608841102,landmark,True1007124432017,10.1080/10494820.2026.2615818,,Human-Centric Artificial Intelligence Pedagogy (HCAP) framework developed from TPACK through integration of artificial intelligence literacy and competency,Interactive Learning Environments,2026,article,generative_ai,2,30.0,30.0,213.13627514600907,30,265.4321,True,True,post,106.65661737972137,landmark,True1017140170686,10.1186/s41239-026-00585-x,,Pedagogical partnerships with generative AI in higher education: how dual cognitive pathways paradoxically enable transformative learning,International Journal of Educational Technology in Higher Education,2026,article,generative_ai,2,30.0,30.0,213.13627514600907,30,274.5849,True,True,post,106.65661737972137,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,351.3436452833674,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,220.42367591809082,landmark,True1047135409568,10.1038/s41746-026-02517-5,,The role of agentic artificial intelligence in healthcare: a scoping review,npj Digital Medicine,2026,article,ai_agents,1,52.0,52.0,23.673009161381255,52,97.7788,True,True,post,184.8714701248504,landmark,True1057134187809,10.1016/j.isatra.2026.03.010,,Dynamic event-triggered approximate optimal consensus control for unknown nonlinear multi-agent systems via adaptive dynamic programming,ISA Transactions,2026,article,ai_agents,3,46.0,46.0,129.88600147094877,46,163.9382,False,False,post,163.5401466489061,landmark,True1064390730779,10.1002/widm.70111,2401.03428,"Exploring Large Language Model‐Based Intelligent Agents: Definitions, Methods, and Prospects",Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery,2026,article,ai_agents,3,44.0,44.0,48.96326740303108,44,32.5136,True,True,post,156.42970549025802,landmark,True1074225355126,10.1007/978-981-19-0493-6_1,,Introduction to Multi-agent Systems,Springer tracts in human-centered computing,2022,book-chapter,ai_agents,1,266.0,268.0,143.62252453934906,287,6.1939,False,False,baseline,143.62252453934906,landmark,True1084407570917,10.26599/if.2026.9710004,2502.08691,AgentSociety: Large-scale simulation of LLM-driven generative agents advances understanding of human behaviors and society,iFuture,2026,article,ai_agents,2,40.0,40.0,44.5120612754828,40,126.4417,True,True,post,142.20882317296181,landmark,True1094410197190,10.1016/j.array.2025.100399,,The role of agentic AI in shaping a smart future: A systematic review,Array,2025,review,ai_agents,4,267.0,267.0,33.148798521256936,267,36.646,True,True,post,135.17111425167016,landmark,True1104413427262,10.1016/j.inffus.2025.103599,2505.10468,"AI Agents vs. Agentic AI: A Conceptual taxonomy, applications and challenges",Information Fusion,2025,article,ai_agents,8,258.0,258.0,87.88752249161823,258,107.36,True,True,post,130.61478455779363,landmark,True1114414991289,10.1039/d5sc09921h,2508.13251,“DIVE” into hydrogen storage materials discovery with AI agents,Chemical Science,2026,article,ai_agents,3,31.0,31.0,23.90492957746479,31,43.4978,True,True,post,110.21183795904541,landmark,True1127122857583,10.1007/s10462-025-11458-6,,"Agentic AI systems in the age of generative models: architectures, cloud scalability, and real-world applications",Artificial Intelligence Review,2026,article,ai_agents,7,30.0,30.0,33.384045956612105,30,108.3786,True,True,post,106.65661737972137,landmark,True1134401023556,10.24963/ijcai.2024/890,,Large Language Model Based Multi-agents: A Survey of Progress and Challenges,,2024,conference-paper,ai_agents,4,231.0,371.0,103.12387591961277,302,91.8723,True,True,post,103.12387591961277,landmark,True1144414023998,10.3390/fi17090404,,"The Rise of Agentic AI: A Review of Definitions, Frameworks, Architectures, Applications, Evaluation Metrics, and Challenges",Future Internet,2025,article,ai_agents,3,189.0,189.0,64.3827199647901,189,78.6474,True,True,post,95.68292357140696,landmark,True1157118083939,10.1007/s12525-025-00861-0,,Agentic information systems,Electronic Markets,2026,article,ai_agents,1,26.0,26.0,32.66095238095238,26,314.5617,True,True,post,92.4357350624252,landmark,True1164412505619,10.70777/si.v2i3.15161,,"AI Agents vs. Agentic AI: A Conceptual Taxonomy, Applications and Challenges",SuperIntelligence - Robotics - Safety & Alignment,2025,article,ai_agents,6,181.0,181.0,61.65752546892597,181,75.3184,True,True,post,91.63285273240561,landmark,True1174416209823,10.1007/s10462-025-11422-4,2510.25445,"Agentic AI: a comprehensive survey of architectures, applications, and future directions",Artificial Intelligence Review,2025,article,ai_agents,4,165.0,165.0,56.20713647719771,165,68.6604,True,True,post,83.53271105440291,landmark,True1187155930820,10.1109/tase.2026.3686874,,Adaptive Prescribed Performance Secure Consensus Control for High-Order Constrained Multi-Agent Systems With Time-Varying Powers and Arbitrary Initial States,IEEE Transactions on Automation Science and Engineering,2026,article,ai_agents,2,23.0,23.0,80.74592420071988,23,96.3814,False,False,post,81.77007332445305,landmark,True1197162257503,10.1016/j.ejcon.2026.101534,,Predefined-time sliding-mode formation control for second-order nonlinear multi-agent systems under deception attacks,European Journal of Control,2026,article,ai_agents,1,22.0,22.0,77.23523184416683,22,92.1909,False,False,post,78.21485274512901,landmark,True1204412709685,10.1038/s41586-025-09442-9,,The Virtual Lab of AI agents designs new SARS-CoV-2 nanobodies,Nature,2025,article,ai_agents,2,150.0,150.0,33.53365384615385,150,107.0846,False,False,post,75.93882823127537,landmark,True1217128165235,10.1007/s12525-025-00862-z,,Multi-agent AI,Electronic Markets,2026,article,ai_agents,2,21.0,21.0,23.368832169628472,21,75.865,True,True,post,74.65963216580496,landmark,True1227118190454,10.1109/ojcs.2026.3650842,2507.14912,Redefining Elderly Care With Agentic AI: Challenges and Opportunities,IEEE Open Journal of the Computer Society,2026,article,ai_agents,4,21.0,21.0,22.217391304347824,21,315.9642,True,True,post,74.65963216580496,landmark,True1237130481397,10.1038/s41746-026-02443-6,,Benchmarking large language model-based agent systems for clinical decision tasks,npj Digital Medicine,2026,article,ai_agents,3,21.0,21.0,9.560253699788584,21,39.4876,True,True,post,74.65963216580496,landmark,True1244390638955,10.1007/s40692-023-00304-9,,Artificial intelligence (AI) learning tools in K-12 education: A scoping review,Journal of Computers in Education,2024,article,ai_agents,1,146.0,240.0,72.25801079505747,240,91.8438,True,True,post,72.25801079505747,landmark,True1254412203333,10.1145/3748302,2404.13501,A Survey on the Memory Mechanism of Large Language Model-based Agents,ACM Transactions on Information Systems,2025,article,ai_agents,4,139.0,139.0,47.35025436563928,139,56.1767,True,True,post,70.3699808276485,landmark,True1264399982941,10.5821/dissertation-2117-410409,,Value engineering for autonomous agents,,2024,dissertation,ai_agents,2,7.0,8.0,68.17851239669422,1,,True,True,post,68.17851239669422,landmark,True1272911283634,10.1515/9783110595567-011,,10. 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Learning,Cambridge University Press eBooks,2025,book-chapter,reinforcement_learning,3,395.0,395.0,664.9588792863116,444,16.8612,True,False,post,199.9722476756918,landmark,True1327123365826,10.1109/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,195.53713186282252,landmark,True1332991046523,10.1007/978-3-030-60990-0_12,1911.10635,Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms,"Studies in systems, decision and control",2021,book-chapter,reinforcement_learning,3,348.0,530.0,178.44967189838442,1264,321.3096,False,True,baseline,178.44967189838442,landmark,True1342973229164,10.32657/10356/90191,,Continuous control for robot based on deep reinforcement learning,,2019,dissertation,reinforcement_learning,2,5.0,47.0,171.15412186379928,951,,True,True,baseline,171.15412186379928,landmark,True1352982316857,10.1038/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,155.2486170001325,landmark,True1362902907165,10.1126/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,138.34973973017864,landmark,True1377133481537,10.1016/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,124.4327202763416,landmark,True1387133226216,10.52202/068431-1787,2103.01955,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,95.76250774959783,landmark,True1393041807988,10.1007/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,93.76380474425054,landmark,True1402781726626,10.48550/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,90.80433444998975,landmark,True1412963923407,,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,89.08741767072895,landmark,True1427122543576,10.1002/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,88.88051448310114,landmark,True1432899138465,,,Learning to Learn with Gradients,ProQuest Demo Repository,2018,dissertation,reinforcement_learning,3,8.0,20.0,88.61723446893788,51,,True,False,baseline,88.61723446893788,landmark,True1444391631327,10.48550/arxiv.2402.03300,2402.03300,Axiom-World: A Pre-Registered Study of 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Reports,2026,article,reinforcement_learning,3,21.0,21.0,23.368832169628472,21,75.865,True,True,post,74.65963216580496,landmark,True1482754517384,10.1609/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,69.98997654610542,landmark,True1492761873684,10.1609/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,68.44152573756327,landmark,True1507133208539,10.52202/075280-2338,,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,67.57117047961324,landmark,True1517128436456,10.1016/j.arcontrol.2026.101045,2502.02133,Synthesis of model predictive control and reinforcement learning: Survey and classification,Annual Reviews in Control,2026,article,reinforcement_learning,3,19.0,19.0,21.046639004149377,19,47.2579,True,True,post,67.54919100715686,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,3498.428847235165,landmark,True1531698155719,10.1017/9781108564175.026,,Support Vector Machines,Cambridge University Press eBooks,2020,book-chapter,ml_theory,2,1184.0,1212.0,971.6935447844538,1401,239.6315,True,False,baseline,971.6935447844538,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,618.608380802384,landmark,True15546659105,10.4230/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,608.1723816912336,landmark,True1563145506661,10.1007/978-3-030-10546-4_1,,Introduction to Machine Learning,Springer briefs in electrical and computer engineering,2019,book-chapter,ml_theory,2,1164.0,1581.0,491.7969737509803,1594,403.288,False,False,baseline,491.7969737509803,landmark,True1572291445757,10.1017/9781009006767.012,,Support Vector Regression,Cambridge University Press eBooks,2024,book-chapter,ml_theory,2,769.0,769.0,426.8477591694058,809,0.6804,True,False,post,426.8477591694058,landmark,True1582100415632,10.48550/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,412.4055872015893,landmark,True1591568555062,10.1145/3501714.3501732,,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,308.68124677113826,landmark,True1602751318774,10.1142/9789811201967_0001,,Introduction to Machine Learning,Series in machine perception and artificial intelligence,2019,book-chapter,ml_theory,6,932.0,978.0,304.22355491996126,1639,51.9116,True,False,baseline,304.22355491996126,landmark,True1613208688400,10.1007/978-981-16-2102-4_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,280.4690126251966,landmark,True1623105488314,,,Risk bounds for statistical learning,FreiDok plus (Universitätsbibliothek Freiburg),2020,dissertation,ml_theory,1,50.0,59.0,272.44754218635364,96,,False,False,baseline,272.44754218635364,landmark,True16357706852,10.48550/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,255.97588171133128,landmark,True1642946444988,10.48550/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,241.7549993940351,landmark,True1652945976633,10.1038/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,236.59828408639194,landmark,True1664400823820,10.58496/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,235.4406851738956,landmark,True1672999309192,10.1186/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,222.7600450619602,landmark,True1682999615587,10.1038/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,221.00898350616714,landmark,True1692439568532,10.1145/3233231,,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,204.78781392529854,landmark,True1703194464139,,,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,197.50232018561485,landmark,True1713041133507,10.1109/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,192.3666194864092,landmark,True1722436219157,10.1137/16m1080173,1606.04838,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,180.43976968901882,landmark,True1733135028703,10.1007/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,176.91139356202336,landmark,True1747124426637,10.1186/s40537-025-01346-9,,A review of machine learning with small and limited data,Journal Of Big Data,2026,article,ml_theory,3,46.0,46.0,51.18887046680522,46,166.1805,True,True,post,163.5401466489061,landmark,True1752165856466,10.48550/arxiv.2604.18420,2604.18420,Spectral bandits for smooth graph functions,arXiv (Cornell University),2026,preprint,ml_theory,2,45.0,45.0,258.9622641509434,45,,True,True,post,159.98492606958206,landmark,True1764402843978,10.58248/pn633,,Interpretable machine learning,,2020,report,ml_theory,2,236.0,455.0,158.34677419354838,952,,True,True,baseline,158.34677419354838,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,162740.22201855818,landmark,True1781710476689,10.1017/9781108891530.013,,Generative Adversarial Networks,Cambridge University Press eBooks,2022,book-chapter,deep_learning_architectures,4,2376.0,2385.0,1018.1648538070052,2402,89.9299,True,False,baseline,1018.1648538070052,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,780.6511542175108,landmark,True1802963399829,10.57702/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,674.336578903792,landmark,True1814382203079,10.1609/aaai.v37i9.26317,2205.13504,Are Transformers Effective for Time Series Forecasting?,Proceedings of the AAAI Conference on Artificial Intelligence,2023,conference-paper,deep_learning_architectures,3,917.0,2278.0,621.0117979458593,3320,733.0685,True,True,baseline,621.0117979458593,landmark,True1824406170795,10.1038/s41586-024-08328-6,,Accurate predictions on small data with a tabular foundation model,Nature,2025,article,deep_learning_architectures,4,974.0,974.0,331.79242987145795,974,405.3046,True,True,post,493.09612464841473,landmark,True183146900863,10.7551/mitpress/11474.003.0014,,Neural Information Processing Systems,The MIT Press eBooks,2018,book-chapter,deep_learning_architectures,1,835.0,999.0,463.99318537042825,3730,234.5719,False,False,baseline,463.99318537042825,landmark,True1844396821501,10.48550/arxiv.2404.19756,2404.19756,KAN: Kolmogorov-Arnold Networks,arXiv (Cornell University),2024,preprint,deep_learning_architectures,4,448.0,531.0,463.22473919523094,531,,True,True,post,463.22473919523094,landmark,True1852950067852,10.5281/zenodo.19983308,,A Recurrent Latent Variable Model for Sequential Data,arXiv (Cornell University),2026,preprint,deep_learning_architectures,3,119.0,119.0,4630.561403508772,119,,True,True,post,423.0712489395614,landmark,True1864221150249,10.1109/tkde.2026.3680353,2202.07082,Graph Neural Networks for Graphs With Heterophily: A Survey,IEEE Transactions on Knowledge and Data Engineering,2026,article,deep_learning_architectures,2,107.0,107.0,119.0697639119165,107,83.0902,True,True,post,380.4086019876729,landmark,True1877148890333,10.1016/j.engappai.2026.114691,,Spatial-channel collaborative multi-scale graph interaction deep transfer learning for unsupervised rotating machinery fault diagnosis,Engineering Applications of Artificial Intelligence,2026,article,deep_learning_architectures,1,102.0,102.0,112.98721991701245,102,283.5476,False,False,post,362.63249909105264,landmark,True1883177318507,10.1609/aaai.v35i12.17325,2012.07436,Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting,Proceedings of the AAAI Conference on Artificial Intelligence,2021,conference-paper,deep_learning_architectures,5,412.0,1344.0,315.30176520033626,7301,468.762,True,True,baseline,315.30176520033626,landmark,True1894389326242,10.48550/arxiv.2312.00752,2312.00752,Mamba: Linear-Time Sequence Modeling with Selective State Spaces,arXiv (Cornell University),2023,preprint,deep_learning_architectures,8,228.0,912.0,303.2150076098295,1059,,True,True,post,303.2150076098295,landmark,True1907118302872,10.1007/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,302.1937492425439,landmark,True1913137147200,10.1201/9781003162810-13,,A Survey of Quantization Methods for Efficient Neural Network Inference,Chapman & Hall/CRC eBooks,2022,book-chapter,deep_learning_architectures,3,421.0,698.0,297.97864484582374,1148,452.6307,True,True,baseline,297.97864484582374,landmark,True1923140854437,10.1186/s40537-021-00444-8,,"Review of deep learning: concepts, CNN architectures, challenges, applications, future directions",Journal Of Big Data,2021,article,deep_learning_architectures,7,871.0,2452.0,293.13246585010404,7842,405.9988,True,True,baseline,293.13246585010404,landmark,True1934210257598,,,A Comprehensive Survey on Graph Neural Networks,UTS ePRESS (University of Technology Sydney),2020,article,deep_learning_architectures,6,1768.0,2239.0,280.04477310147604,2955,236.1125,True,True,baseline,280.04477310147604,landmark,True1942907492528,10.1109/tnnls.2020.2978386,1901.00596,A Comprehensive Survey on Graph Neural Networks,IEEE Transactions on Neural Networks and Learning Systems,2020,article,deep_learning_architectures,7,934.0,2230.0,278.91909067275196,10106,369.8433,True,True,baseline,278.91909067275196,landmark,True1953152893301,10.1016/j.aiopen.2021.01.001,1812.08434,Graph neural networks: A review of methods and applications,AI Open,2020,article,deep_learning_architectures,5,1157.0,2023.0,253.0283948120974,6773,287.256,True,True,baseline,253.0283948120974,landmark,True1967110333790,10.4230/lipics.giscience.2025.3,,Enriching Location Representation with Detailed 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,251.10439201808387,landmark,True1974385763767,10.24963/ijcai.2023/759,2202.07125,Transformers in Time Series: A Survey,International Joint Conference on Artificial Intelligence,2023,conference-paper,deep_learning_architectures,4,511.0,921.0,251.0763239280669,1196,259.4717,True,True,post,251.0763239280669,landmark,True1983124006607,10.26190/unsworks/32608,,SIMPLE SPECTRAL GRAPH CONVOLUTION,"UNSWorks (University of New South Wales, Sydney, Australia)",2026,conference-paper,deep_learning_architectures,2,69.0,69.0,529.5790121767684,69,0.0,True,True,post,245.31021997335915,landmark,True1994285723986,10.1007/978-3-031-01587-8_7,,Graph Attention Networks,Synthesis lectures on artificial intelligence and machine learning,2020,book-chapter,deep_learning_architectures,3,247.0,471.0,244.37756010685663,806,284.0555,False,False,baseline,244.37756010685663,landmark,True2002892035503,10.1007/s10618-019-00619-1,1809.04356,Deep learning for time series classification: a review,Data Mining and Knowledge Discovery,2019,article,deep_learning_architectures,3,416.0,908.0,192.68934023629362,3299,229.9061,False,True,baseline,192.68934023629362,landmark,True2012962776659,10.57702/tcr06vve,,Fine-grained Analysis of Sentence Embeddings Using Auxiliary Prediction Tasks,arXiv (Cornell University),2024,preprint,deep_learning_architectures,4,212.0,212.0,184.94095048849147,212,,True,True,post,184.94095048849147,landmark,True2024406063062,10.1016/j.jsis.2024.101885,,Responsible artificial intelligence governance: A review and research framework,The Journal of Strategic Information Systems,2025,article,ai_safety,1,534.0,534.0,74.49083144938457,534,564.6901,True,True,post,270.3422285033403,landmark,True2037130596601,10.1016/j.ijmedinf.2026.106362,,"Human in the loop artificial intelligence in healthcare: applications, outcomes, and implementation challenges",International Journal of Medical Informatics,2026,article,ai_safety,6,65.0,65.0,29.591261451726567,65,120.3431,True,True,post,231.08933765606298,landmark,True2043084163594,10.26522/brocked.v29i2.849,,Age of Surveillance Capitalism – The Fight for a Human Future at the New Frontier of Power.,Brock Education Journal,2020,article,ai_safety,2,484.0,775.0,225.67375365464028,2135,237.3333,True,True,baseline,225.67375365464028,landmark,True2052963808661,10.4230/oasics.saia.2024.4,,Scaling of End-To-End Governance Risk Assessments for AI Systems (Practitioner Track),OASIcs : OpenAccess Series in Informatics,2025,conference-paper,ai_safety,1,390.0,390.0,126.28760836308005,390,127.9549,True,True,post,197.44095340131597,landmark,True2067133236347,10.1016/j.aiopen.2026.02.006,2506.04133,"TRiSM for Agentic AI: A review of Trust, Risk, and Security Management in LLM-based Agentic Multi-Agent Systems",AI Open,2026,article,ai_safety,5,51.0,51.0,44.51662971175166,51,274.4005,True,True,post,181.31624954552632,landmark,True2077128782475,10.1038/s41746-026-02428-5,,Large language models provide unsafe answers to patient-posed medical questions,npj Digital Medicine,2026,article,ai_safety,3,51.0,51.0,23.217758985200845,50,94.0181,True,True,post,181.31624954552632,landmark,True2084406571986,10.1016/j.chbah.2025.100121,2402.01659,Generative artificial intelligence in higher education: Evidence from an analysis of institutional policies and guidelines,Computers in Human Behavior Artificial Humans,2025,article,ai_safety,3,347.0,347.0,301.26573426573424,347,363.8008,True,True,post,175.6718226416837,landmark,True2092981731882,10.1016/j.inffus.2019.12.012,1910.10045,"Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI",Information Fusion,2019,article,ai_safety,4,225.0,1097.0,166.80483138995626,10111,264.5411,False,True,baseline,166.80483138995626,landmark,True2104402698709,10.1016/j.patter.2026.101517,2408.12622,"The AI risk repository: A meta-review, database, and taxonomy of risks from artificial intelligence",Patterns,2026,article,ai_safety,4,44.0,44.0,38.40650406504065,44,146.3469,True,True,post,156.42970549025802,landmark,True2114408314709,10.1038/s41746-025-01503-7,,Bias recognition and mitigation strategies in artificial intelligence healthcare applications,npj Digital Medicine,2025,article,ai_safety,1,303.0,303.0,42.26726953026878,303,319.4957,True,True,post,153.39643302717624,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,151.87765646255073,landmark,True2134330336443,10.48550/arxiv.2303.10130,2303.10130,GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models,RePEc: Research Papers in Economics,2023,preprint,ai_safety,5,397.0,495.0,146.63793103448276,527,,True,True,post,146.63793103448276,landmark,True2147128600296,10.1038/s41746-026-02418-7,,Advancing healthcare AI governance through a comprehensive maturity model based on systematic review,npj Digital Medicine,2026,review,ai_safety,1,41.0,41.0,12.755555555555555,41,37.52,True,True,post,145.7640437522859,landmark,True2154399450035,10.1145/3630106.3658542,2311.16863,Power Hungry Processing: Watts Driving the Cost of AI Deployment?,,2024,conference-paper,ai_safety,3,210.0,359.0,138.53682019255788,359,285.6538,True,True,post,138.53682019255788,landmark,True2164318586239,10.1007/978-3-031-21448-6_2,,Artificial Intelligence: Definition and Background,Research for policy,2023,book-chapter,ai_safety,1,95.0,247.0,136.55931293612454,373,215.6652,True,True,post,136.55931293612454,landmark,True2174360615722,10.3390/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,130.5473071452147,landmark,True2182670253439,10.1016/j.artint.2018.07.007,1706.07269,Explanation in artificial intelligence: Insights from the social sciences,Artificial Intelligence,2018,article,ai_safety,1,267.0,676.0,127.59130157926435,5207,192.3734,False,True,baseline,127.59130157926435,landmark,True2197118631510,10.1016/j.cie.2026.111805,,Towards trustworthy AI in Industry 5.0: Ante-hoc interpretability with deep learning,Computers & Industrial Engineering,2026,article,ai_safety,2,35.0,35.0,38.94805361604745,35,126.4417,False,True,post,124.4327202763416,landmark,True2207127143535,10.1016/j.jii.2026.101089,,"Industrial information integration through autonomous AI agents: paradoxical effects on transparency, dehumanization, and responsible retail operations",Journal of Industrial Information Integration,2026,article,ai_safety,2,33.0,33.0,50.00583941605839,33,209.0741,False,False,post,117.32227911769351,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 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