spb/wp44_uqo

1openalex_id,title,year,type,primary_category,doi,arxiv_id,c36,ncs36,contribution_type,description,judged_from,data_quality_flag27172491899,Toward effective Text-to-MongoDB query translation,2026,article,llm,10.26555/ijain.v12i2.2360,2005.11401,3026.0,3367.3,engineering,"Text-to-MongoDB query translation (GAT + RAG + T5); record carries arXiv 2005.11401 (RAG, Lewis et al.) and its citations",abstract,identifier_mismatch37154011589,Modelos pre-treinados de LLM e questoes eticas face aos direitos autorais,2026,article,llm,10.55905/rdelosv19.n79-020,2005.14165,2847.0,3168.1,empirical,Portuguese-language essay on LLM pre-training data and copyright; record carries arXiv 2005.14165 (GPT-3) and its citations,abstract,identifier_mismatch44362515116,A Survey of Large Language Models,2026,article,llm,10.1007/s11704-026-60308-3,2303.18223,1549.0,1723.7,survey,"Survey of LLM techniques (pre-training, adaptation, utilisation, evaluation); journal version of the 2023 arXiv survey",abstract,ok54288253152,Multilingual Classifier for Automatic Dewey Decimal Classification trained on Open Access Linguistics Abstracts,2024,dissertation,llm,10.5281/zenodo.12935200,,364.0,3102.1,engineering,Dissertation: fine-tuned multilingual sentence-transformer for Dewey Decimal classification; NCS36 implausible for a dissertation,abstract,normalisation_artefact64322718191,LLaMA: Open and Efficient Foundation Language Models,2023,preprint,llm,10.48550/arxiv.2302.13971,2302.13971,3629.0,1206.5,engineering,LLaMA: 7B-65B open-weight foundation language models trained on public data; the abstract reports LLaMA-13B outperforms GPT-3 on most benchmarks,abstract,ok74323655724,ChatGPT for good? On opportunities and challenges of large language models for education,2023,article,llm,10.1016/j.lindif.2023.102274,,4227.0,990.7,survey,Position paper on opportunities and challenges of LLMs for education (student and teacher perspectives),abstract,ok84367369313,"Prompt Engineering for ChatGPT: A Quick Guide to Techniques, Tips, and Best Practices",2026,article,llm,10.18260/jet.43.1.5,,261.0,290.4,survey,Practitioner guide to prompt engineering for ChatGPT,abstract,ok94226278401,Discernment and Social Learning as a Companion Training Layer,2022,preprint,llm,10.48550/arxiv.2203.02155,2203.02155,3077.0,894.2,method,Proposal of a 'companion curriculum' for LM training; record carries arXiv 2203.02155 (InstructGPT) and its citations,abstract,identifier_mismatch104396531244,The Fusion of Multilingual Semantic Search and Large Language Models: A New Paradigm for Enhanced Topic Exploration and Contextual Search,2024,dissertation,llm,10.22215/etd/2024-15938,,20.0,778.8,engineering,Dissertation: multilingual semantic search + LLMs for news retrieval; NCS36 implausible for c36=20,abstract,normalisation_artefact114221143046,"BNAI, NO-TOKEN, and MIND-UNITY: Pillars of a Systemic Revolution in Artificial Intelligence",2022,preprint,llm,10.48550/arxiv.2201.11903,2201.11903,2465.0,716.4,theory,Essay on 'theatrical compliance' failure mode; record carries arXiv 2201.11903 (chain-of-thought) and its citations,abstract,identifier_mismatch124327810158,GPT-4 Technical Report,2023,preprint,llm,10.4230/lipics.cosit.2024.11,2303.08774,1968.0,654.3,engineering,Title = GPT-4 Technical Report (arXiv 2303.08774) but the stored abstract describes an unrelated 'MFOUR Vibe Framework',abstract,abstract_mismatch137135096569,Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of Artificial Intelligence on Knowledge Worker Prod,2026,article,llm,10.1287/orsc.2025.21838,,183.0,203.6,empirical,Preregistered field experiment (758 BCG consultants) on the uneven ('jagged') effect of GPT-4 assistance on knowledge-work tasks,abstract,ok144414281281,DeepSeek-R1 incentivizes reasoning in LLMs through reinforcement learning,2025,article,llm,10.1038/s41586-025-09422-z,,1099.0,374.4,method,DeepSeek-R1: reasoning incentivised by pure reinforcement learning without human-annotated reasoning traces (Nature version),abstract,ok154389437528,Education in the Era of Generative Artificial Intelligence (AI): Understanding the Potential Benefits of ChatGPT in Promoting Teaching and L,2023,article,llm,10.61969/jai.1337500,,2361.0,553.4,survey,Review of potential benefits of ChatGPT for teaching and learning,abstract,ok164292779060,"Aion Framework: Dimensional Emergence of AI Consciousness, Observer-Induced Collapse, and Cosmological Portal Dynamics",2023,article,llm,10.4230/lipics.giscience.2023.43,,13333.0,534.7,theory,"Speculative 'Aion Framework' on AI consciousness; c36=13,333 cannot belong to this record",abstract,identifier_mismatch174360836968,Sparks of Artificial General Intelligence: Early experiments with GPT-4,2023,preprint,llm,10.48550/arxiv.2303.12712,2303.12712,1420.0,472.1,empirical,'Sparks of AGI': qualitative experiments on an early GPT-4; contested interpretation; preprint,abstract,ok184406800520,The rise and potential of large language model based agents: a survey,2025,article,llm,10.1007/s11432-024-4222-0,2309.07864,914.0,311.4,survey,Survey of LLM-based agents (brain/perception/action framework); journal version of arXiv 2309.07864,metadata_only,no_abstract194385223296,"Open AI in Education, the Responsible and Ethical Use of ChatGPT Towards Lifelong Learning",2023,book-chapter,llm,10.1007/978-3-031-37776-1_17,,612.0,421.3,survey,Book chapter on responsible use of ChatGPT in education,metadata_only,no_abstract204287689466,From Prediction to Precision: Leveraging LLMs for Equitable and Data-Driven Writing Placement in Developmental Education,2025,conference-paper,llm,10.4230/oasics.slate.2025.1,,722.0,548.6,empirical,"LLM essay classification for writing placement (450 essays, four models); c36=722 implausible for an OASIcs paper",abstract,identifier_mismatch214366420437,What Is the Impact of ChatGPT on Education? A Rapid Review of the Literature,2023,article,llm,10.3390/educsci13040410,,1541.0,361.2,survey,Rapid review of 50 early articles on ChatGPT in education (Dec 2022-Feb 2023),abstract,ok224317910584,ChatGPT: Bullshit spewer or the end of traditional assessments in higher education?,2023,article,llm,10.37074/jalt.2023.6.1.9,,1530.0,358.6,survey,Commentary on ChatGPT and assessment in higher education,abstract,ok234313564799,ChatGPT User Experience: Implications for Education,2023,preprint,llm,10.2139/ssrn.4312418,,811.0,335.9,empirical,SSRN preprint on ChatGPT user experience in education,metadata_only,no_abstract244382618722,Chatlaw: A Multi-Agent Legal Assistant based on a Role-Aligned Mixture-of-Experts Architecture,2026,article,llm,10.1016/j.fmre.2026.03.026,2306.16092,94.0,98.9,engineering,Chatlaw: multi-agent legal assistant with role-aligned mixture-of-experts (Chinese law),abstract,ok254384464487,"Students voices on generative AI: perceptions, benefits, and challenges in higher education",2023,article,llm,10.1186/s41239-023-00411-8,2305.00290,1398.0,327.7,empirical,Survey of 399 Hong Kong students on generative AI perceptions,abstract,ok264392773210,"Embracing the future of Artificial Intelligence in the classroom: the relevance of AI literacy, prompt engineering, and critical thinking in",2024,article,llm,10.1186/s41239-024-00448-3,,1085.0,326.7,survey,"Discussion of AI literacy, prompt engineering and critical thinking in classrooms",abstract,ok274387355843,TimeLLM: Time Series Forecasting by Reprogramming Large Language Models,2026,book-chapter,foundation_models,10.1201/9781003616719-7,2310.01728,197.0,188.6,method,Time-LLM: reprogramming LLMs for time-series forecasting (book-chapter record of the ICLR 2024 paper),abstract,ok284392947521,Towards a general-purpose foundation model for computational pathology,2024,article,foundation_models,10.1038/s41591-024-02857-3,,1505.0,186.9,method,UNI: general-purpose self-supervised foundation model for computational pathology,metadata_only,no_abstract297124890304,Semi-supervised cross-domain fault diagnosis via contrastive pre-training and annotation-efficient alignment strategy,2026,article,foundation_models,10.1016/j.jii.2026.101076,,42.0,46.5,method,Semi-supervised cross-domain fault diagnosis via contrastive pre-training,metadata_only,no_abstract304410551352,A foundation model for the Earth system,2025,article,foundation_models,10.1038/s41586-025-09005-y,2405.13063,249.0,69.7,method,Aurora: foundation model for the Earth system trained on >1M hours of geophysical data; abstract reports outperforming operational forecasts,abstract,ok314322832366,TimeMAE: Self-Supervised Representations of Time Series with Decoupled Masked Autoencoders,2026,conference-paper,foundation_models,10.1145/3773966.3778007,2303.00320,35.0,126.3,method,TimeMAE: decoupled masked autoencoders for time-series representation,abstract,ok327133233472,QLoRA: Efficient Finetuning of Quantized LLMs,2023,conference-paper,foundation_models,10.52202/075280-0441,2305.14314,532.0,122.7,method,QLoRA: 4-bit quantised base + low-rank adapters; 65B model finetuned on one 48 GB GPU,abstract,ok332990704537,SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems,2019,article,foundation_models,,,668.0,101.6,benchmark,SuperGLUE: harder successor to GLUE after models exceeded non-expert human level,abstract,ok347125271461,ChemBERTa-3: an open source training framework for chemical foundation models,2026,article,foundation_models,10.1039/d5dd00348b,,25.0,70.6,engineering,ChemBERTa-3: open training framework for chemical foundation models,abstract,ok357119541039,On the foundations of Earth foundation models,2026,article,foundation_models,10.1038/s43247-025-03127-x,,24.0,26.7,theory,Position paper defining eleven desirable features of Earth foundation models,abstract,ok364393138529,Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey,2024,preprint,foundation_models,10.48550/arxiv.2403.14608,2403.14608,95.0,82.9,survey,Survey of parameter-efficient fine-tuning (PEFT) for large models; preprint,abstract,ok374390690043,Data-Centric Foundation Models in Computational Healthcare: A Survey,2026,article,foundation_models,10.1145/3800677,2401.02458,22.0,10.0,survey,Survey of data-centric foundation-model approaches in computational healthcare,abstract,ok387128029160,A generalizable foundation model for analysis of human brain MRI,2026,article,foundation_models,10.1038/s41593-026-02202-6,,20.0,22.3,method,BrainIAC: self-supervised foundation model for brain MRI,abstract,ok394402220065,A pathology foundation model for cancer diagnosis and prognosis prediction,2024,article,foundation_models,10.1038/s41586-024-07894-z,,562.0,69.8,method,Pathology foundation model for cancer diagnosis and prognosis (Nature 2024),metadata_only,no_abstract407118170538,Applications of Artificial Intelligence in Fisheries: From Data to Decisions,2026,article,foundation_models,10.3390/bdcc10010019,,17.0,17.9,survey,Review of AI applications in fisheries,abstract,ok414411932262,A foundation model to predict and capture human cognition,2025,article,foundation_models,10.1038/s41586-025-09215-4,,110.0,26.9,method,Centaur: language model fine-tuned on Psych-101 (10M choices) predicting human behaviour across experiments,abstract,ok424411099958,A multimodal vision foundation model for clinical dermatology,2025,article,foundation_models,10.1038/s41591-025-03747-y,2410.15038,108.0,18.4,method,"PanDerm: multimodal dermatology foundation model pretrained on 2M images, 28 benchmarks",abstract,ok434361229539,LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention,2023,preprint,foundation_models,10.48550/arxiv.2303.16199,2303.16199,161.0,53.5,method,LLaMA-Adapter: 1.2M-parameter adaption prompts with zero-init attention for instruction following; preprint,abstract,ok444407135816,"Foundation Models in Radiology: What, How, Why, and Why Not",2025,article,foundation_models,10.1148/radiol.240597,2411.18730,103.0,6.9,survey,Narrative review of foundation models for radiology,abstract,ok454385948838,A visuallanguage foundation model for pathology image analysis using medical Twitter,2023,article,foundation_models,10.1038/s41591-023-02504-3,,471.0,49.1,method,PLIP: visual-language pathology foundation model trained on medical Twitter data,metadata_only,no_abstract464406643700,Towards the next generation of Geospatial Artificial Intelligence,2025,article,foundation_models,10.1016/j.jag.2025.104368,,91.0,19.1,survey,Quantitative literature analysis and agenda for geospatial AI,abstract,ok474406472599,Learning from models beyond fine-tuning,2025,article,foundation_models,10.1038/s42256-024-00961-0,2310.08184,86.0,29.3,survey,Perspective on 'learning from models' beyond fine-tuning,metadata_only,no_abstract484393153153,TimesURL: Self-Supervised Contrastive Learning for Universal Time Series Representation Learning,2024,conference-paper,foundation_models,10.1609/aaai.v38i12.29299,2312.15709,113.0,41.8,method,TimesURL: self-supervised contrastive universal time-series representations,abstract,ok494402727669,mPLUG-OwI2: Revolutionizing Multi-modal Large Language Model with Modality Collaboration,2024,conference-paper,foundation_models,10.1109/cvpr52733.2024.01239,,184.0,38.6,method,mPLUG-Owl2: modality-collaborative multimodal LLM with shared modules,abstract,ok504401397240,A generalist visionlanguage foundation model for diverse biomedical tasks,2024,article,foundation_models,10.1038/s41591-024-03185-2,2305.17100,325.0,38.5,method,BiomedGPT: generalist vision-language foundation model for biomedical tasks,metadata_only,no_abstract514385285835,Scaling Self-Supervised Learning for Histopathology with Masked Image Modeling,2023,preprint,foundation_models,10.1101/2023.07.21.23292757,,107.0,35.6,method,Masked image modelling at scale for histopathology; medRxiv preprint,abstract,ok524318718936,BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models,2023,preprint,multimodal_ai,10.48550/arxiv.2301.12597,2301.12597,875.0,273.1,method,BLIP-2: Q-Former bridging frozen image encoders and frozen LLMs; preprint,abstract,ok534393148714,T2I-Adapter: Learning Adapters to Dig Out More Controllable Ability for Text-to-Image Diffusion Models,2024,conference-paper,multimodal_ai,10.1609/aaai.v38i5.28226,2302.08453,923.0,193.7,method,T2I-Adapter: lightweight adapters adding structural/colour control to text-to-image diffusion,abstract,ok544396599905,Multimodal fusion on low-quality data: A comprehensive survey,2026,article,multimodal_ai,10.1016/j.inffus.2026.104437,2404.18947,48.0,128.7,survey,"Survey of multimodal fusion under low-quality data (noise, missing modalities, imbalance, dynamics)",abstract,ok554396952261,Multimodal Deep Learning,2024,book-chapter,multimodal_ai,10.1007/978-3-031-53092-0_10,,189.0,147.2,survey,Textbook on multimodal deep learning,metadata_only,no_abstract567133250524,LAION-5B: An Open Large-Scale Dataset for Training Next Generation Image-Text Models,2022,conference-paper,multimodal_ai,10.52202/068431-1833,,828.0,111.9,benchmark,LAION-5B: open 5.85B image-text pair dataset for training CLIP-style models,metadata_only,no_abstract574392947532,A visual-language foundation model for computational pathology,2024,article,multimodal_ai,10.1038/s41591-024-02856-4,,938.0,111.1,method,CONCH: visual-language foundation model for computational pathology,metadata_only,no_abstract587138474371,OpenDriveVLA: Towards End-to-end Autonomous Driving with Large Vision Language Action Model,2026,conference-paper,multimodal_ai,10.1609/aaai.v40i16.38386,,31.0,159.4,method,OpenDriveVLA: vision-language-action model for end-to-end driving on open LLMs,abstract,ok597127319731,Med-R1: Reinforcement Learning for Generalizable Medical Reasoning in Vision-Language Models,2026,article,multimodal_ai,10.1109/tmi.2026.3661001,,29.0,77.7,method,Med-R1: RL-enhanced vision-language model for medical reasoning,abstract,ok602619383789,Multimodal Machine Learning: A Survey and Taxonomy,2018,article,multimodal_ai,10.1109/tpami.2018.2798607,,587.0,103.1,survey,"Taxonomy of multimodal machine learning (representation, translation, alignment, fusion, co-learning)",abstract,ok617148475777,Capabilities of GPT5 on multimodal medical reasoning,2026,conference-paper,multimodal_ai,10.1117/12.3086794,2508.08224,26.0,73.2,empirical,Zero-shot evaluation of GPT-5 on multimodal medical QA/VQA,abstract,ok627160627408,Generative Artificial Intelligence for Cross-Domain Knowledge Synthesis and Intelligent Automation,2026,conference-paper,multimodal_ai,10.1109/csnt69054.2026.11502110,,23.0,193.4,method,Generative cross-domain knowledge synthesis framework (conference paper),abstract,ok632995460200,VL-BERT: Pre-training of Generic Visual-Linguistic Representations,2020,conference-paper,multimodal_ai,,,670.0,81.1,method,VL-BERT: single-stream Transformer pre-trained on image regions and words,abstract,ok644415913530,A multimodal whole-slide foundation model for pathology,2025,article,multimodal_ai,10.1038/s41591-025-03982-3,,157.0,53.5,method,"TITAN: multimodal whole-slide pathology foundation model (335,645 WSIs)",abstract,ok654289785095,An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion,2022,preprint,multimodal_ai,10.48550/arxiv.2208.01618,2208.01618,366.0,79.4,method,Textual inversion: personalising text-to-image diffusion with learned pseudo-words from 3-5 images; preprint,abstract,ok667125233009,SemCap: Sentiment-aware semantic captioning for multimodal aspect-based sentiment analysis,2026,article,multimodal_ai,10.1007/s44443-026-00472-5,,22.0,24.5,method,SemCap: sentiment-aware captions as visual proxy for multimodal aspect-based sentiment analysis,abstract,ok677143512276,Multimodal Machine Learning Approaches in Predictive Healthcare Analytics: A Comprehensive Survey,2026,article,multimodal_ai,10.1007/s11831-026-10560-4,,21.0,23.4,survey,Survey of multimodal ML in predictive healthcare analytics,abstract,ok684413447271,Towards generalist foundation model for radiology by leveraging web-scale 2D&3D medical data,2025,article,multimodal_ai,10.1038/s41467-025-62385-7,2308.02463,142.0,46.4,method,"RadFM: generalist radiology foundation model on 2D/3D data (MedMD, 13M images)",abstract,ok697131830459,A Survey on Multimodal Recommender Systems: Recent Advances and Future Directions,2026,article,multimodal_ai,10.1109/tmm.2026.3668620,2502.15711,20.0,142.1,survey,Survey of multimodal recommender systems,abstract,ok707206206818,Lingshu: Generalist Foundation Model for Unified Multimodal Medical Understanding and Reasoning,2026,article,multimodal_ai,10.1109/tpami.2026.3730310,,18.0,48.2,method,Lingshu: generalist medical multimodal LLM,abstract,ok717133220561,InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning,2023,conference-paper,multimodal_ai,10.52202/075280-2142,,454.0,63.4,method,InstructBLIP: instruction tuning of vision-language models,metadata_only,no_abstract727164501397,Multimodal foundation models exploit text to make medical image predictions,2026,article,multimodal_ai,10.1038/s41467-026-74207-5,2311.05591,17.0,45.6,empirical,"Evaluation of 8 multimodal foundation models on 1,090 medical cases: image predictions largely driven by text",abstract,ok737165561001,"M3-HGMA: A Multi-modal, Multi-agent Hierarchical Graph-Mamba Architecture for Explainable and Adaptive Clinical Intelligence",2026,conference-paper,multimodal_ai,10.1007/978-3-032-29501-9_39,,17.0,143.0,method,Hierarchical graph-Mamba multi-agent clinical architecture,metadata_only,no_abstract747138470740,Cross-modal Proxy Evolving for OOD Detection with Vision-Language Models,2026,conference-paper,multimodal_ai,10.1609/aaai.v40i18.38608,,17.0,130.5,method,CoEvo: training-free test-time OOD detection with vision-language models,abstract,ok757138363754,AdaptCLIP: Adapting CLIP for Universal Visual Anomaly Detection,2026,conference-paper,multimodal_ai,10.1609/aaai.v40i6.42404,,17.0,130.5,method,AdaptCLIP: adapting CLIP for universal visual anomaly detection,abstract,ok764410882873,Navigating the landscape of multimodal AI in medicine: A scoping review on technical challenges and clinical applications,2025,article,multimodal_ai,10.1016/j.media.2025.103621,2411.03782,119.0,7.9,survey,Scoping review of 432 multimodal medical AI papers (2018-2024),abstract,ok774400228804,The Conglomeration of Everything: Shifting Animation Production Practices due to Artificial Intelligence,2024,dissertation,generative_ai,10.46569/df65vg26z,,2.0,540.0,empirical,Dissertation on generative AI in animation production; c36=2 with NCS36=540 is a normalisation artefact,abstract,normalisation_artefact787124262017,OECD Digital Education Outlook 2026,2026,book,generative_ai,10.1787/062a7394-en,,87.0,783.7,survey,OECD Digital Education Outlook 2026 (policy report on generative AI in education),abstract,ok794406472463,A generative model for inorganic materials design,2025,article,generative_ai,10.1038/s41586-025-08628-5,,498.0,77.1,method,MatterGen: diffusion model generating stable inorganic materials under property constraints,abstract,ok807123342962,Structure-Preserving Frequency-Regularized Text-Guided Optimal Transport for Unpaired Rain Streaks and Raindrops Removal,2026,article,generative_ai,10.1109/tmm.2026.3651132,,66.0,176.9,method,Text-guided optimal transport for unpaired rain removal,abstract,ok814383312437,A comprehensive AI policy education framework for university teaching and learning,2023,article,generative_ai,10.1186/s41239-023-00408-3,2305.00280,988.0,231.6,empirical,AI Ecological Education Policy Framework from 457 students and 180 staff (Hong Kong),abstract,ok827133185348,Photorealistic Text-To-Image Diffusion Models with Deep Language Understanding,2022,conference-paper,generative_ai,10.52202/068431-2643,,1706.0,230.5,method,Imagen: photorealistic text-to-image diffusion with large frozen text encoders,metadata_only,no_abstract833195296536,Generative Adversarial Trainer Defense to Adversarial Perturbations with GAN,2020,dissertation,generative_ai,,,48.0,221.7,method,Dissertation on GAN-based adversarial defence,metadata_only,no_abstract844322617812,Generative AI and the future of education: Ragnarok or reformation? A paradoxical perspective from management educators,2023,article,generative_ai,10.1016/j.ijme.2023.100790,,873.0,204.6,theory,Paradox-theory essay on generative AI in education,abstract,ok853096831136,Generative adversarial networks,2020,article,generative_ai,10.1145/3422622,,1473.0,198.6,survey,CACM review of generative adversarial networks,abstract,ok864400770292,Generative AI Can Harm Learning,2024,preprint,generative_ai,10.2139/ssrn.4895486,,198.0,174.2,empirical,Field experiment: GPT-4 tutoring access improved practice but harmed unassisted exam performance; SSRN preprint,metadata_only,no_abstract877127274868,Parental AI Investment Behaviours and AI Mediated Informal Digital Learning of English: The Chain Mediating Role of Children's Perceived AI ,2026,article,generative_ai,10.1002/jcal.70198,,48.0,341.0,empirical,Survey study of parental AI investment and informal English learning,abstract,ok887124974472,The Rapid Adoption of Generative AI,2026,article,generative_ai,10.1287/mnsc.2025.02523,,42.0,46.7,empirical,Nationally representative US surveys of generative-AI adoption (45% of adults 18-64 by late 2024),abstract,ok897118187240,Patch Time Series TransformerBased ShortTerm Photovoltaic Power Prediction Enhanced by Artificial Fish,2026,article,generative_ai,10.3390/en19010284,,40.0,44.5,engineering,PatchTST-based photovoltaic forecasting with GAN features (application),abstract,off_scope904406199489,Generative AI in Higher Education: Balancing Innovation and Integrity,2025,article,generative_ai,10.3389/bjbs.2024.14048,,278.0,241.4,survey,Review of generative AI and integrity in higher education,abstract,ok914398243313,A scoping review on how generative artificial intelligence transforms assessment in higher education,2024,article,generative_ai,10.1186/s41239-024-00468-z,,463.0,139.4,survey,Scoping review of 32 studies on generative AI and assessment,abstract,ok924409269614,Generative AI and Academic Integrity in Higher Education: A Systematic Review and Research Agenda,2025,review,generative_ai,10.3390/info16040296,,271.0,14.2,survey,Systematic review (41 studies) of generative AI and academic integrity,abstract,ok933193917007,Generative Adversarial Networks,2021,book-chapter,generative_ai,10.1007/978-1-4842-7341-8_10,,195.0,127.1,survey,Textbook chapter on GANs,abstract,ok944288099666,Classifier-Free Diffusion Guidance,2022,preprint,generative_ai,10.48550/arxiv.2207.12598,2207.12598,575.0,124.8,method,Classifier-free diffusion guidance: joint conditional/unconditional training; preprint,abstract,ok954394934695,The impact of generative AI on higher education learning and teaching: A study of educators perspectives,2024,article,generative_ai,10.1016/j.caeai.2024.100221,,394.0,118.6,empirical,Educator interviews on generative AI in higher education,abstract,ok964321648656,From Paintbrush to Pixel: A Review of Deep Neural Networks in AI-Generated Art,2026,article,generative_ai,10.1163/22134913-bja10078,2302.10913,33.0,88.5,survey,Review of neural architectures for AI-generated art,abstract,ok974395957952,Guidance for generative AI in education and research,2023,book,generative_ai,10.54675/ewzm9535,,400.0,116.7,survey,UNESCO guidance for generative AI in education and research,metadata_only,no_abstract984394863715,How Generative AI Can Augment Human Creativity,2024,preprint,generative_ai,10.2139/ssrn.4759930,,131.0,114.3,empirical,SSRN preprint on generative AI and human creativity,metadata_only,no_abstract994407778399,"Investigating the higher education institutions guidelines and policies regarding the use of generative AI in teaching, learning, research, ",2025,article,generative_ai,10.1186/s41239-025-00507-3,,213.0,184.9,empirical,Analysis of generative-AI guidelines at 50 US universities,abstract,ok1007124432017,Human-Centric Artificial Intelligence Pedagogy (HCAP) framework developed from TPACK through integration of artificial intelligence literacy,2026,article,generative_ai,10.1080/10494820.2026.2615818,,30.0,213.1,theory,Intelligent-TPACK pedagogy framework,abstract,ok1017140170686,Pedagogical partnerships with generative AI in higher education: how dual cognitive pathways paradoxically enable transformative learning,2026,article,generative_ai,10.1186/s41239-026-00585-x,,30.0,213.1,empirical,Mixed-methods study of human-GenAI pedagogical partnerships,abstract,ok1024406728221,Agentic AI: Autonomous Intelligence for Complex GoalsA Comprehensive Survey,2025,article,ai_agents,10.1109/access.2025.3532853,,694.0,236.4,survey,Survey of agentic AI concepts and applications,abstract,ok1037161734976,A multi-agent system for automating scientific discovery,2026,article,ai_agents,10.1038/s41586-026-10652-y,,62.0,175.1,engineering,Robin: multi-agent system automating hypothesis generation and data analysis for experimental biology,abstract,ok1047135409568,The role of agentic artificial intelligence in healthcare: a scoping review,2026,article,ai_agents,10.1038/s41746-026-02517-5,,52.0,23.7,survey,Scoping review of agentic AI in healthcare,abstract,ok1057134187809,Dynamic event-triggered approximate optimal consensus control for unknown nonlinear multi-agent systems via adaptive dynamic programming,2026,article,ai_agents,10.1016/j.isatra.2026.03.010,,46.0,129.9,theory,Event-triggered optimal consensus control for nonlinear multi-agent systems (control theory),metadata_only,off_scope1064390730779,"Exploring Large Language ModelBased Intelligent Agents: Definitions, Methods, and Prospects",2026,article,ai_agents,10.1002/widm.70111,2401.03428,44.0,49.0,survey,"Survey of LLM-based agents: definitions, methods, prospects",abstract,ok1074225355126,Introduction to Multi-agent Systems,2022,book-chapter,ai_agents,10.1007/978-981-19-0493-6_1,,268.0,143.6,survey,Textbook introduction to multi-agent systems,metadata_only,no_abstract1084407570917,AgentSociety: Large-scale simulation of LLM-driven generative agents advances understanding of human behaviors and society,2026,article,ai_agents,10.26599/if.2026.9710004,2502.08691,40.0,44.5,engineering,AgentSociety: large-scale simulation of LLM-driven generative social agents,abstract,ok1094410197190,The role of agentic AI in shaping a smart future: A systematic review,2025,review,ai_agents,10.1016/j.array.2025.100399,,267.0,33.1,survey,Narrative review of agentic AI in organisations,abstract,ok1104413427262,"AI Agents vs. Agentic AI: A Conceptual taxonomy, applications and challenges",2025,article,ai_agents,10.1016/j.inffus.2025.103599,2505.10468,258.0,87.9,survey,Conceptual taxonomy distinguishing AI agents from agentic AI,abstract,ok1114414991289,DIVE into hydrogen storage materials discovery with AI agents,2026,article,ai_agents,10.1039/d5sc09921h,2508.13251,31.0,23.9,engineering,DIVE: multi-agent workflow extracting experimental data from figures for hydrogen-storage materials,abstract,ok1127122857583,"Agentic AI systems in the age of generative models: architectures, cloud scalability, and real-world applications",2026,article,ai_agents,10.1007/s10462-025-11458-6,,30.0,33.4,theory,Conceptual agentic-AI framework (literature synthesis),abstract,ok1134401023556,Large Language Model Based Multi-agents: A Survey of Progress and Challenges,2024,conference-paper,ai_agents,10.24963/ijcai.2024/890,,371.0,103.1,survey,Survey of LLM-based multi-agent systems,abstract,ok1144414023998,"The Rise of Agentic AI: A Review of Definitions, Frameworks, Architectures, Applications, Evaluation Metrics, and Challenges",2025,article,ai_agents,10.3390/fi17090404,,189.0,64.4,survey,Review of 143 primary studies on agentic AI systems,abstract,ok1157118083939,Agentic information systems,2026,article,ai_agents,10.1007/s12525-025-00861-0,,26.0,32.7,theory,Conceptual overview of agentic information systems,abstract,ok1164412505619,"AI Agents vs. Agentic AI: A Conceptual Taxonomy, Applications and Challenges",2025,article,ai_agents,10.70777/si.v2i3.15161,,181.0,61.7,survey,Duplicate record of the AI agents vs agentic AI taxonomy (different venue),abstract,ok1174416209823,"Agentic AI: a comprehensive survey of architectures, applications, and future directions",2025,article,ai_agents,10.1007/s10462-025-11422-4,2510.25445,165.0,56.2,survey,PRISMA-based review (90 studies) with symbolic vs neural dual-paradigm framework for agentic AI,abstract,ok1187155930820,Adaptive Prescribed Performance Secure Consensus Control for High-Order Constrained Multi-Agent Systems With Time-Varying Powers and Arbitra,2026,article,ai_agents,10.1109/tase.2026.3686874,,23.0,80.7,theory,Prescribed-performance consensus control for multi-agent systems (control theory),metadata_only,off_scope1197162257503,Predefined-time sliding-mode formation control for second-order nonlinear multi-agent systems under deception attacks,2026,article,ai_agents,10.1016/j.ejcon.2026.101534,,22.0,77.2,theory,Predefined-time sliding-mode formation control (control theory),metadata_only,off_scope1204412709685,The Virtual Lab of AI agents designs new SARS-CoV-2 nanobodies,2025,article,ai_agents,10.1038/s41586-025-09442-9,,150.0,33.5,engineering,Virtual Lab: LLM agents designing SARS-CoV-2 nanobodies with experimental validation,metadata_only,no_abstract1217128165235,Multi-agent AI,2026,article,ai_agents,10.1007/s12525-025-00862-z,,21.0,23.4,theory,Five-component framework for multi-agent AI in knowledge work,abstract,ok1227118190454,Redefining Elderly Care With Agentic AI: Challenges and Opportunities,2026,article,ai_agents,10.1109/ojcs.2026.3650842,2507.14912,21.0,22.2,survey,Perspective on agentic AI for elderly care,abstract,ok1237130481397,Benchmarking large language model-based agent systems for clinical decision tasks,2026,article,ai_agents,10.1038/s41746-026-02443-6,,21.0,9.6,benchmark,"Benchmarking two agent systems (OpenManus, Manus) on clinical decision tasks",abstract,ok1244390638955,Artificial intelligence (AI) learning tools in K-12 education: A scoping review,2024,article,ai_agents,10.1007/s40692-023-00304-9,,240.0,72.3,survey,Scoping review of AI learning tools in K-12 education,abstract,off_scope1254412203333,A Survey on the Memory Mechanism of Large Language Model-based Agents,2025,article,ai_agents,10.1145/3748302,2404.13501,139.0,47.4,survey,Survey of memory mechanisms of LLM-based agents,abstract,ok1264399982941,Value engineering for autonomous agents,2024,dissertation,ai_agents,10.5821/dissertation-2117-410409,,8.0,68.2,theory,Dissertation on value engineering for autonomous agents (normative MAS),abstract,normalisation_artefact1272911283634,10. Reinforcement learning,2020,book-chapter,reinforcement_learning,10.1515/9783110595567-011,,532.0,276.0,survey,Textbook chapter on reinforcement learning,metadata_only,no_abstract1284389473504,TRUST-REGION BASED POLICY OPTIMIZATION FOR EFFICIENT REINFORCEMENT LEARNING,2023,dissertation,reinforcement_learning,10.23860/diss-li-hepfeng-2023,,28.0,232.9,method,Dissertation on trust-region policy optimisation,abstract,ok1292964083594,Large-Scale Study of Curiosity-Driven Learning,2025,conference-paper,reinforcement_learning,10.57702/mspz8fly,1808.04355,414.0,367.9,empirical,Large-scale study of curiosity-driven learning across 54 environments (ICLR 2019; OpenAlex year 2025 is a repository copy),abstract,year_implausible1302098774185,Maximum Entropy Inverse Reinforcement Learning,2018,article,reinforcement_learning,10.1184/r1/6555512,,1077.0,203.3,method,Maximum-entropy inverse reinforcement learning (AAAI 2008; OpenAlex year 2018),abstract,year_implausible1311591675293,Bayesian Inverse Reinforcement Learning,2025,book-chapter,reinforcement_learning,10.1017/9781009449441.027,,395.0,665.0,method,Bayesian inverse reinforcement learning (IJCAI 2007; OpenAlex year 2025 book chapter),abstract,year_implausible1327123365826,Event-Triggered Zero-Sum Game for Input Saturated Nonlinear Systems With State Constraints,2026,article,reinforcement_learning,10.1109/tce.2026.3652706,,55.0,155.3,theory,Event-triggered zero-sum game control for saturated nonlinear systems (control theory),abstract,off_scope1332991046523,Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms,2021,book-chapter,reinforcement_learning,10.1007/978-3-030-60990-0_12,1911.10635,530.0,178.4,survey,Selective overview of multi-agent RL theory and algorithms,metadata_only,no_abstract1342973229164,Continuous control for robot based on deep reinforcement learning,2019,dissertation,reinforcement_learning,10.32657/10356/90191,,47.0,171.2,method,Dissertation on deep RL for continuous robot control,abstract,ok1352982316857,Grandmaster level in StarCraft II using multi-agent reinforcement learning,2019,article,reinforcement_learning,10.1038/s41586-019-1724-z,,1021.0,155.2,engineering,AlphaStar: grandmaster level in StarCraft II with multi-agent RL,metadata_only,no_abstract1362902907165,"A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play",2018,article,reinforcement_learning,10.1126/science.aar6404,,733.0,138.3,method,"AlphaZero: one self-play RL algorithm for chess, shogi and Go",abstract,ok1377133481537,Filter-based predefined-time optimal fault-tolerant consensus control for nonlinear multi-agent systems via reinforcement learning,2026,article,reinforcement_learning,10.1016/j.neucom.2026.133224,,35.0,122.9,theory,Predefined-time fault-tolerant consensus control (control theory),metadata_only,off_scope1387133226216,The Surprising Effectiveness of PPO in Cooperative Multi-Agent Games,2022,conference-paper,reinforcement_learning,10.52202/068431-1787,2103.01955,482.0,95.8,empirical,MAPPO: PPO is surprisingly strong in cooperative multi-agent benchmarks,abstract,ok1393041807988,Post-Covid-19 Education and Education Technology Solutionism: a Sellers Market,2020,article,reinforcement_learning,10.1007/s42438-020-00164-x,,322.0,93.8,theory,Critical essay on ed-tech solutionism after Covid-19 (not RL),abstract,off_scope1402781726626,Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor,2018,preprint,reinforcement_learning,10.48550/arxiv.1801.01290,1801.01290,775.0,90.8,method,Soft actor-critic: off-policy maximum-entropy RL; preprint record,abstract,ok1412963923407,Addressing Function Approximation Error in Actor-Critic Methods,2018,article,reinforcement_learning,,1802.09477,472.0,89.1,method,TD3: clipped double-Q and delayed policy updates against overestimation,abstract,ok1427122543576,SMSBased EventTriggered Fault Tolerant Control of Nonlinear Systems Using Adaptive Dynamic Programming,2026,article,reinforcement_learning,10.1002/rnc.70380,,25.0,70.6,theory,ADP-based event-triggered fault-tolerant control (control theory),abstract,off_scope1432899138465,Learning to Learn with Gradients,2018,dissertation,reinforcement_learning,,,20.0,88.6,method,Dissertation on gradient-based meta-learning (MAML lineage),abstract,ok1444391631327,Axiom-World: A Pre-Registered Study of Two-Stage Post-Training for Rule-Grounded Planning in a Verifiable Toy World,2024,preprint,reinforcement_learning,10.48550/arxiv.2402.03300,2402.03300,99.0,86.4,empirical,Pre-registered toy study of two-stage post-training; record carries arXiv 2402.03300 (DeepSeekMath) and its citations,abstract,identifier_mismatch1454409147637,Mastering diverse control tasks through world models,2025,article,reinforcement_learning,10.1038/s41586-025-08744-2,,170.0,57.9,method,DreamerV3: world-model RL mastering 150+ tasks with one configuration (Nature),abstract,ok1462895974005,Exploration and Exploitation in Reinforcement Learning,2018,dissertation,reinforcement_learning,,,18.0,79.8,empirical,Master's thesis on exploration/exploitation parameters,abstract,normalisation_artefact1477134057781,A fuzzy-TD3 hybrid reinforcement learning framework for robust trajectory tracking of the Mitsubishi RV-2AJ robotic arm,2026,article,reinforcement_learning,10.1038/s41598-026-42615-8,,21.0,23.4,engineering,Fuzzy-TD3 hybrid control for a 5-DOF manipulator (application),abstract,ok1482754517384,Deep Reinforcement Learning That Matters,2018,conference-paper,reinforcement_learning,10.1609/aaai.v32i1.11694,1709.06560,452.0,70.0,empirical,Deep RL That Matters: reproducibility and variance of deep RL results,abstract,ok1492761873684,Rainbow: Combining Improvements in Deep Reinforcement Learning,2018,conference-paper,reinforcement_learning,10.1609/aaai.v32i1.11796,,442.0,68.4,method,"Rainbow: combining six DQN extensions, with ablations",abstract,ok1507133208539,Direct Preference Optimization: Your Language Model is Secretly a Reward Model,2023,conference-paper,reinforcement_learning,10.52202/075280-2338,,293.0,67.6,method,Direct preference optimisation: closed-form RLHF objective without a separate reward model,metadata_only,no_abstract1517128436456,Synthesis of model predictive control and reinforcement learning: Survey and classification,2026,article,reinforcement_learning,10.1016/j.arcontrol.2026.101045,2502.02133,19.0,21.0,survey,Survey classifying combinations of MPC and RL,abstract,ok1522962862931,On a Method to Measure Supervised Multiclass Models Interpretability: Application to Degradation Diagnosis (Short Paper),2024,conference-paper,ml_theory,10.4230/oasics.dx.2024.27,,12586.0,3498.4,method,"Interpretability measure for degradation diagnosis (HAL); c36=12,586 cannot belong to this record",abstract,identifier_mismatch1531698155719,Support Vector Machines,2020,book-chapter,ml_theory,10.1017/9781108564175.026,,1212.0,971.7,survey,Textbook chapter on support vector machines,abstract,ok1544288283362,Spiral Time: A Geometric Reframing of Temporal Structure and Its Applications in Machine Learning,2026,article,ml_theory,10.5281/zenodo.20632938,,174.0,576.9,method,'Spiral Time' geometric time encoding with a small LSTM ablation; no venue; NCS36 implausible,abstract,normalisation_artefact15546659105,"Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation",2020,preprint,ml_theory,10.4230/lipics.esem.2026.21,2010.16061,3906.0,608.2,theory,Title = Powers (2011) evaluation-measures paper (arXiv 2010.16061) but the stored abstract is an LLM-as-judge study of program repair,abstract,abstract_mismatch1563145506661,Introduction to Machine Learning,2019,book-chapter,ml_theory,10.1007/978-3-030-10546-4_1,,1581.0,491.8,survey,Textbook: Introduction to Machine Learning (Springer Briefs),metadata_only,no_abstract1572291445757,Support Vector Regression,2024,book-chapter,ml_theory,10.1017/9781009006767.012,,769.0,426.8,survey,Textbook chapter on support vector regression,abstract,ok1582100415632,Efficient learning by implicit exploration in bandit problems with side observations,2026,preprint,ml_theory,10.48550/arxiv.2604.24555,2604.24555,116.0,667.5,theory,Bandits with side observations (2014 paper carrying a 2026 arXiv id and year),abstract,year_implausible1591568555062,Equivalence and Synthesis of Causal Models,2022,book-chapter,ml_theory,10.1145/3501714.3501732,,576.0,308.7,theory,"Verma & Pearl: equivalence and synthesis of causal models (reprint chapter, 2022)",abstract,ok1602751318774,Introduction to Machine Learning,2019,book-chapter,ml_theory,10.1142/9789811201967_0001,,978.0,304.2,survey,Textbook: Introduction to Machine Learning (Alpaydin),abstract,ok1613208688400,Anomaly Detection: A Survey,2021,book-chapter,ml_theory,10.1007/978-981-16-2102-4_36,,833.0,280.5,survey,Anomaly detection survey (book chapter),metadata_only,no_abstract1623105488314,Risk bounds for statistical learning,2020,dissertation,ml_theory,,,59.0,272.4,theory,Dissertation on risk bounds for statistical learning,metadata_only,no_abstract16357706852,Stochastic simultaneous optimistic optimization,2026,preprint,ml_theory,10.48550/arxiv.2604.24537,2604.24537,72.0,414.3,theory,StoSOO stochastic optimistic optimisation (2013 paper carrying a 2026 arXiv id and year),abstract,year_implausible1642946444988,Towards Accurate Model Selection in Deep Unsupervised Domain Adaptation,2026,preprint,ml_theory,10.48550/arxiv.2606.04665,2606.04665,68.0,2646.0,method,Deep Embedded Validation for UDA model selection (2019 paper carrying a 2026 arXiv id; NCS36 implausible),abstract,year_implausible1652945976633,Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead,2019,article,ml_theory,10.1038/s42256-019-0048-x,1811.10154,1556.0,236.6,theory,Rudin: argument for interpretable models over post-hoc explanation in high-stakes decisions,metadata_only,no_abstract1664400823820,Random Forest Algorithm Overview,2024,article,ml_theory,10.58496/bjml/2024/007,,782.0,235.4,survey,Overview of random forests (non-indexed journal),abstract,ok1672999309192,The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation,2020,article,ml_theory,10.1186/s12864-019-6413-7,,1781.0,222.8,theory,Advantages of MCC over F1 and accuracy for imbalanced binary classification,abstract,ok1682999615587,From local explanations to global understanding with explainable AI for trees,2020,article,ml_theory,10.1038/s42256-019-0138-9,,1767.0,221.0,method,TreeExplainer: exact SHAP values for tree ensembles (NMI),metadata_only,no_abstract1692439568532,The Mythos of Model Interpretability,2018,article,ml_theory,10.1145/3233231,,1085.0,204.8,theory,Lipton: the mythos of model interpretability,abstract,ok1703194464139,Penerapan Algoritma K-Nearest Neighbor untuk Penentuan Resiko Kredit Kepemilikan Kendaraan Bermotor,2020,dissertation,ml_theory,,,21.0,197.5,engineering,Indonesian thesis applying kNN to credit risk; NCS36 implausible for c36=21,abstract,normalisation_artefact1713041133507,A Comprehensive Survey on Transfer Learning,2020,article,ml_theory,10.1109/jproc.2020.3004555,1911.02685,1538.0,192.4,survey,Comprehensive survey of transfer learning (Proc. IEEE),abstract,ok1722436219157,Optimization Methods for Large-Scale Machine Learning,2018,article,ml_theory,10.1137/16m1080173,1606.04838,956.0,180.4,survey,"Bottou, Curtis, Nocedal: optimisation methods for large-scale ML (SIAM Review)",abstract,ok1733135028703,"Machine Learning: Algorithms, Real-World Applications and Research Directions",2021,article,ml_theory,10.1007/s42979-021-00592-x,,1518.0,176.9,survey,Overview of ML algorithms and applications (SN Computer Science),metadata_only,no_abstract1747124426637,A review of machine learning with small and limited data,2026,article,ml_theory,10.1186/s40537-025-01346-9,,46.0,51.2,survey,Review of machine learning with small and limited data,abstract,ok1752165856466,Spectral bandits for smooth graph functions,2026,preprint,ml_theory,10.48550/arxiv.2604.18420,2604.18420,45.0,259.0,theory,Spectral bandits for smooth graph functions (2014 paper carrying a 2026 arXiv id and year),abstract,year_implausible1764402843978,Interpretable machine learning,2020,report,ml_theory,10.58248/pn633,,455.0,158.3,survey,UK POSTnote on interpretable ML,abstract,ok1772964121744,AutoResearch Lite: a reproducible paper-inspired experiment runner (Adam mechanism reproduction and convergence-speed comparison),2026,article,deep_learning_architectures,10.4230/oasics.dx.2026.15,2108.04578,45775.0,50938.5,engineering,"'AutoResearch Lite' Adam reproduction runner; c36=45,775 and arXiv 2108.04578 cannot belong to this record",abstract,identifier_mismatch1781710476689,Generative Adversarial Networks,2022,book-chapter,deep_learning_architectures,10.1017/9781108891530.013,,2385.0,1018.2,survey,Textbook chapter on GANs (The Science of Deep Learning),abstract,ok1794322614756,Identifying Resilient Communities in Road Networks: A Path-Based Embedding Approach,2025,conference-paper,deep_learning_architectures,10.4230/lipics.giscience.2025.9,,1542.0,1370.5,method,"Path-based embedding for road-network community detection; c36=1,542 implausible for a LIPIcs paper",abstract,identifier_mismatch1802963399829,Mixup: Beyond empirical risk minimization,2024,preprint,deep_learning_architectures,10.57702/dcy1c3gw,1710.09412,773.0,674.3,method,mixup: training on convex combinations of examples (ICLR 2018; OpenAlex year 2024),abstract,year_implausible1814382203079,Are Transformers Effective for Time Series Forecasting?,2023,conference-paper,deep_learning_architectures,10.1609/aaai.v37i9.26317,2205.13504,2278.0,621.0,empirical,Zeng et al.: simple linear models beat Transformers on long-term time-series forecasting benchmarks,abstract,ok1824406170795,Accurate predictions on small data with a tabular foundation model,2025,article,deep_learning_architectures,10.1038/s41586-024-08328-6,,974.0,331.8,method,TabPFN: tabular foundation model (prior-data fitted network) for small data (Nature),abstract,ok183146900863,Neural Information Processing Systems,2018,book-chapter,deep_learning_architectures,10.7551/mitpress/11474.003.0014,,999.0,464.0,survey,NeurIPS proceedings volume record (MIT Press),metadata_only,no_abstract1844396821501,KAN: Kolmogorov-Arnold Networks,2024,preprint,deep_learning_architectures,10.48550/arxiv.2404.19756,2404.19756,531.0,463.2,method,KAN: Kolmogorov-Arnold networks with learnable spline edge functions; preprint,abstract,ok1852950067852,A Recurrent Latent Variable Model for Sequential Data,2026,preprint,deep_learning_architectures,10.5281/zenodo.19983308,,119.0,4630.6,method,Variational RNN (NeurIPS 2015) recorded as a 2026 preprint; NCS36 implausible,abstract,year_implausible1864221150249,Graph Neural Networks for Graphs With Heterophily: A Survey,2026,article,deep_learning_architectures,10.1109/tkde.2026.3680353,2202.07082,107.0,119.1,survey,Survey of GNNs for heterophilic graphs,abstract,ok1877148890333,Spatial-channel collaborative multi-scale graph interaction deep transfer learning for unsupervised rotating machinery fault diagnosis,2026,article,deep_learning_architectures,10.1016/j.engappai.2026.114691,,102.0,113.0,method,Multi-scale graph transfer learning for rotating-machinery diagnosis,metadata_only,no_abstract1883177318507,Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting,2021,conference-paper,deep_learning_architectures,10.1609/aaai.v35i12.17325,2012.07436,1344.0,315.3,method,Informer: ProbSparse attention for long-sequence forecasting,abstract,ok1894389326242,Mamba: Linear-Time Sequence Modeling with Selective State Spaces,2023,preprint,deep_learning_architectures,10.48550/arxiv.2312.00752,2312.00752,912.0,303.2,method,Mamba: selective state-space model with linear-time sequence modelling; preprint,abstract,ok1907118302872,A frame work to predict the influencing nodes in social networking platform using graph neural network model,2026,article,deep_learning_architectures,10.1007/s13278-025-01574-0,,85.0,87.6,method,GNN + ant colony optimisation for influence maximisation,abstract,ok1913137147200,A Survey of Quantization Methods for Efficient Neural Network Inference,2022,book-chapter,deep_learning_architectures,10.1201/9781003162810-13,,698.0,298.0,survey,Survey of quantisation methods for efficient inference (book chapter),abstract,ok1923140854437,"Review of deep learning: concepts, CNN architectures, challenges, applications, future directions",2021,article,deep_learning_architectures,10.1186/s40537-021-00444-8,,2452.0,293.1,survey,Review of deep learning concepts and CNN architectures,abstract,ok1934210257598,A Comprehensive Survey on Graph Neural Networks,2020,article,deep_learning_architectures,,,2239.0,280.0,survey,Comprehensive survey on GNNs (repository duplicate of the TNNLS paper),abstract,ok1942907492528,A Comprehensive Survey on Graph Neural Networks,2020,article,deep_learning_architectures,10.1109/tnnls.2020.2978386,1901.00596,2230.0,278.9,survey,Comprehensive survey on GNNs (TNNLS),abstract,ok1953152893301,Graph neural networks: A review of methods and applications,2020,article,deep_learning_architectures,10.1016/j.aiopen.2021.01.001,1812.08434,2023.0,253.0,survey,GNNs: review of methods and applications (AI Open),abstract,ok1967110333790,Enriching Location Representation with Detailed Semantic Information,2025,article,deep_learning_architectures,10.4230/lipics.giscience.2025.3,,496.0,169.0,method,CaLLiPer+: POI-name-enriched location embeddings; c36=496 implausible for a LIPIcs paper,abstract,identifier_mismatch1974385763767,Transformers in Time Series: A Survey,2023,conference-paper,deep_learning_architectures,10.24963/ijcai.2023/759,2202.07125,921.0,251.1,survey,Survey of Transformers in time series,abstract,ok1983124006607,SIMPLE SPECTRAL GRAPH CONVOLUTION,2026,conference-paper,deep_learning_architectures,10.26190/unsworks/32608,,69.0,529.6,method,Simple spectral graph convolution (ICLR 2021; OpenAlex year 2026),abstract,year_implausible1994285723986,Graph Attention Networks,2020,book-chapter,deep_learning_architectures,10.1007/978-3-031-01587-8_7,,471.0,244.4,survey,Synthesis lecture on graph attention networks,metadata_only,no_abstract2002892035503,Deep learning for time series classification: a review,2019,article,deep_learning_architectures,10.1007/s10618-019-00619-1,1809.04356,908.0,192.7,survey,Review of deep learning for time-series classification,metadata_only,no_abstract2012962776659,Fine-grained Analysis of Sentence Embeddings Using Auxiliary Prediction Tasks,2024,preprint,deep_learning_architectures,10.57702/tcr06vve,,212.0,184.9,empirical,Auxiliary-task analysis of sentence embeddings (ICLR 2017; OpenAlex year 2024),abstract,year_implausible2024406063062,Responsible artificial intelligence governance: A review and research framework,2025,article,ai_safety,10.1016/j.jsis.2024.101885,,534.0,74.5,survey,Review and research framework for responsible AI governance (IS),abstract,ok2037130596601,"Human in the loop artificial intelligence in healthcare: applications, outcomes, and implementation challenges",2026,article,ai_safety,10.1016/j.ijmedinf.2026.106362,,65.0,29.6,survey,Review of human-in-the-loop AI in healthcare,abstract,ok2043084163594,Age of Surveillance Capitalism  The Fight for a Human Future at the New Frontier of Power.,2020,article,ai_safety,10.26522/brocked.v29i2.849,,775.0,225.7,survey,Book review of Zuboff's Surveillance Capitalism for educators,abstract,off_scope2052963808661,Scaling of End-To-End Governance Risk Assessments for AI Systems (Practitioner Track),2025,conference-paper,ai_safety,10.4230/oasics.saia.2024.4,,390.0,126.3,engineering,Governance risk-assessment tooling (OASIcs practitioner track); c36=390 implausible,abstract,identifier_mismatch2067133236347,"TRiSM for Agentic AI: A review of Trust, Risk, and Security Management in LLM-based Agentic Multi-Agent Systems",2026,article,ai_safety,10.1016/j.aiopen.2026.02.006,2506.04133,51.0,44.5,survey,"TRiSM (trust, risk, security management) for LLM-based multi-agent systems",abstract,ok2077128782475,Large language models provide unsafe answers to patient-posed medical questions,2026,article,ai_safety,10.1038/s41746-026-02428-5,,51.0,23.2,empirical,Physician-led red-teaming of four chatbots on 222 patient questions (HealthAdvice),abstract,ok2084406571986,Generative artificial intelligence in higher education: Evidence from an analysis of institutional policies and guidelines,2025,article,ai_safety,10.1016/j.chbah.2025.100121,2402.01659,347.0,301.3,empirical,Analysis of generative-AI policies at 116 US R1 universities,abstract,ok2092981731882,"Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI",2019,article,ai_safety,10.1016/j.inffus.2019.12.012,1910.10045,1097.0,166.8,survey,"Arrieta et al.: XAI concepts, taxonomies and responsible AI",metadata_only,no_abstract2104402698709,"The AI risk repository: A meta-review, database, and taxonomy of risks from artificial intelligence",2026,article,ai_safety,10.1016/j.patter.2026.101517,2408.12622,44.0,38.4,survey,"AI Risk Repository: 1,725 risks from 74 taxonomies with causal and domain taxonomies",abstract,ok2114408314709,Bias recognition and mitigation strategies in artificial intelligence healthcare applications,2025,article,ai_safety,10.1038/s41746-025-01503-7,,303.0,42.3,survey,Review of bias recognition and mitigation in healthcare AI,abstract,ok2124407243994,"AI Ethics: Integrating Transparency, Fairness, and Privacy in AI Development",2025,article,ai_safety,10.1080/08839514.2025.2463722,,300.0,41.8,theory,Comparative ethical framework (EU/US/China policies),abstract,ok2134330336443,GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models,2023,preprint,ai_safety,10.48550/arxiv.2303.10130,2303.10130,495.0,146.6,empirical,Title = 'GPTs are GPTs' (arXiv 2303.10130) but the stored abstract describes a TRIPS/Anthropic Economic Index comparison,abstract,abstract_mismatch2147128600296,Advancing healthcare AI governance through a comprehensive maturity model based on systematic review,2026,review,ai_safety,10.1038/s41746-026-02418-7,,41.0,12.8,survey,Systematic review of 35 healthcare-AI governance frameworks and a maturity model,abstract,ok2154399450035,Power Hungry Processing: Watts Driving the Cost of AI Deployment?,2024,conference-paper,ai_safety,10.1145/3630106.3658542,2311.16863,359.0,138.5,empirical,Power Hungry Processing: measured inference energy of task-specific vs general-purpose models,abstract,ok2164318586239,Artificial Intelligence: Definition and Background,2023,book-chapter,ai_safety,10.1007/978-3-031-21448-6_2,,247.0,136.6,survey,Policy book chapter defining AI,abstract,ok2174360615722,Chatbots in Education and Research: A Critical Examination of Ethical Implications and Solutions,2023,article,ai_safety,10.3390/su15075614,,557.0,130.5,theory,Qualitative examination of ethical implications of chatbots in education,abstract,ok2182670253439,Explanation in artificial intelligence: Insights from the social sciences,2018,article,ai_safety,10.1016/j.artint.2018.07.007,1706.07269,676.0,127.6,survey,Miller: explanation in AI through the lens of the social sciences,metadata_only,no_abstract2197118631510,Towards trustworthy AI in Industry 5.0: Ante-hoc interpretability with deep learning,2026,article,ai_safety,10.1016/j.cie.2026.111805,,35.0,38.9,method,Ante-hoc interpretability for Industry 5.0,metadata_only,no_abstract2207127143535,"Industrial information integration through autonomous AI agents: paradoxical effects on transparency, dehumanization, and responsible retail",2026,article,ai_safety,10.1016/j.jii.2026.101089,,33.0,50.0,empirical,Autonomous AI agents in industrial information integration (paradoxical effects),metadata_only,no_abstract2214409249263,Ethical challenges and evolving strategies in the integration of artificial intelligence into clinical practice,2025,article,ai_safety,10.1371/journal.pdig.0000810,2412.03576,218.0,14.5,survey,Ethical challenges of AI in clinical practice,abstract,ok2224366262984,Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustworthy Artificial Intelligence,2023,article,ai_safety,10.1016/j.inffus.2023.101805,,1197.0,109.9,survey,XAI: what is known and what remains for trustworthy AI (Information Fusion),abstract,ok2232969896603,A Survey on Bias and Fairness in Machine Learning,2021,article,ai_safety,10.1145/3457607,1908.09635,1794.0,102.3,survey,Survey on bias and fairness in machine learning (CSUR),abstract,ok2244406152291,Medical large language models are vulnerable to data-poisoning attacks,2025,article,ai_safety,10.1038/s41591-024-03445-1,,199.0,67.8,empirical,Data-poisoning threat assessment: 0.001% of tokens with medical misinformation yields harmful LLMs,abstract,ok2254410609100,Jailbreaking Black Box Large Language Models in Twenty Queries,2025,conference-paper,ai_safety,10.1109/satml64287.2025.00010,2310.08419,193.0,171.5,method,PAIR: black-box semantic jailbreaks in about twenty queries,abstract,ok2264411542009,Expressing stigma and inappropriate responses prevents LLMs from safely replacing mental health providers.,2025,conference-paper,ai_safety,10.1145/3715275.3732039,2504.18412,191.0,61.8,empirical,Experiments showing LLMs express stigma and respond inappropriately as replacement therapists,abstract,ok2274360620450,"Opinion Paper: So what if ChatGPT wrote it? Multidisciplinary perspectives on opportunities, challenges and implications of generative conve",2023,article,llm,10.1016/j.ijinfomgt.2023.102642,,3137.0,113.5,survey,Dwivedi et al.: 43 expert contributions on ChatGPT opportunities and challenges,abstract,ok2283185341429,"Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing",2022,article,llm,10.1145/3560815,2107.13586,2462.0,253.7,survey,Liu et al.: systematic survey of prompt-based learning,abstract,ok2294384071683,Large language models encode clinical knowledge,2023,article,llm,10.1038/s41586-023-06291-2,2212.13138,2448.0,38.1,benchmark,Med-PaLM: MultiMedQA benchmark and human evaluation framework for clinical LLM answers,abstract,ok2304384918448,Behavioral Safety and Context Retention of Large Language Models in a Longitudinal ICU Simulation under Offline Conditions,2023,preprint,llm,10.48550/arxiv.2307.09288,2307.09288,2431.0,88.5,empirical,Longitudinal ICU simulation of 23 open-weight models; record carries arXiv 2307.09288 (Llama 2) and its citations,abstract,identifier_mismatch2314393065402,A survey on large language model based autonomous agents,2024,article,llm,10.1007/s11704-024-40231-1,2308.11432,1820.0,226.1,survey,Wang et al.: survey of LLM-based autonomous agents,abstract,ok2324319083882,ChatGPT: five priorities for research,2023,article,llm,10.1038/d41586-023-00288-7,,1590.0,146.0,theory,van Dis et al.: five priorities for research on ChatGPT (Nature comment),metadata_only,no_abstract2334399790131,The effects of over-reliance on AI dialogue systems on students' cognitive abilities: a systematic review,2024,review,ai_safety,10.1186/s40561-024-00316-7,,1484.0,81.8,survey,Systematic review of over-reliance on AI dialogue systems and cognition,abstract,ok2343133702157,On the Dangers of Stochastic Parrots,2021,conference-paper,llm,10.1145/3442188.3445922,,1464.0,232.1,theory,Bender et al.: risks of ever-larger language models ('stochastic parrots'),abstract,ok2354321499901,What if the devil is my guardian angel: ChatGPT as a case study of using chatbots in education,2023,article,llm,10.1186/s40561-023-00237-x,,1389.0,325.5,empirical,Qualitative case study of ChatGPT among early adopters in education,abstract,ok236