Papers — How Did We Get Here

Every paper in the Toptronic® library, grouped by year. The timeline runs newest first: today at the top, the foundations of the discipline at the bottom.

2026

• A Generalization Theory for JEPA-Based World Models - Cui, Jingyi; Zhang, Qi; Wen, Hongwei; Wang, Yisen (2026)
• A Harness Reducing over-Defensive Writing in Agents by Isolating Objections - Xiaochen Chang (2026)
• ACT-JEPA: Novel Joint-Embedding Predictive Architecture for Efficient Policy Representation Learning - Vujinovic, Aleksandar; Kovacevic, Aleksandar (2026)
• Accelerating CPU Speculative Decoding via Unified Graph Execution and Transactional KV Isolation - Zhangdie Yuan (2026)
• Accordion: Elastic-Depth Inference via Fixed-Prefix Layer Looping - Yudong Zhang; Wei Ding; Kaili Zheng; Yuchen Ding; Zhenyu Zhang (2026)
• ActSWM: Action-Sensitive World Models for Long-Horizon Planning in Open-World Games - Zhenfeng Gan; ZiTong Zeng; Jiajun Cheng; et al. (2026)
• AdaJEPA: An Adaptive Latent World Model - Wang, Ying; Bounou, Oumayma; LeCun, Yann; Ren, Mengye (2026)
• Adaptive DHiSparse: Speculative Decoding over a Load-Adaptive KV Hierarchy - Naji Alazhar; Wassel Alazhar (2026)
• An immune world model for multiscale forecasting and therapeutic hypothesis generation - Taoyong Cui, Xi Wang, Zonghang Li, Jinchao Ding, Lingsen You, Yuzhi Xu, Wanghan Xu, Fang Wu, Kejun Ying, Wanli Ouyang, Pheng Ann Heng, Ling Yang, Zhenfei Yin, Yingcheng Wu (2026)
• Answering in the User's Variety: Calibrated Accommodation Pairs for Post-Training - Zhizhuo Kou; Heming Li; Jian Yang; Junyu Luo; Jingshu Peng; Sirui Han; Yike Guo (2026)
• AquaJEPA: An Action-Conditioned Multimodal JEPA Family for Underwater Robot Dynamics - Alan-Barsag Gazzaev; Alexey Gavrilov; Sergey Muravyov (2026)
• Arti-JEPA: Adapting Video World Model to Real-Time MRI of the Vocal Tract for Speech-Production Analysis - Hong Nguyen, Sean Foley, Christina Hagedorn, Yijing Lu, Sudarsana Reddy Kadiri, Dani Byrd, Shrikanth Narayanan (2026)
• Attention Residuals - Kimi Team: Guangyu Chen; Yu Zhang; Jianlin Su; Weixin Xu; Siyuan Pan; Yaoyu Wang; et al. (2026)
• Auto-JEPA: A Latent World Model of Continuous Intent for End-to-End Autonomous Driving - Jiwei Yang; Zhengxian Chen; Chaosheng Huang; et al. (2026)
• BellowsKV: One Latent for Many Layers, Expanded on Demand - Jiamu Zhang; Hanjie Chen (2026)
• BenchMeUp: Building Software Engineering Benchmarks from Natural-Language Specifications - Islem Bouzenia; Michael Pradel (2026)
• Beyond Binary Labels: Diagnostic Evaluation of LLM Safety with Prompt-Level Rubrics - Hongyu Li; Xinlong Huang; Zhan Yu; Jing Zhao; Zhe Zhao; Juntao Zhong; Xiong Kuang; Rui Yuan; ZhenXiang Yan; Huilin Xu; Haitao Yu; Lilin Wang; Rui Chen; Blaze Chen; Fengzong Lian; Renfen Hu (2026)
• Beyond QASecClaw: Agent Scaling, Verifier Feedback, and Evidence Reliability in Software Assurance - S M Abdulla Al Mamun; Akif Islam; Mohd. Ruhul Ameen (2026)
• Beyond Representation Learning: A Systematic Study of Joint-Embedding Predictive Generation for 3D Brain MRI - Meng Zhou; Wenhao You; Yuxing Chen; et al. (2026)
• BioM-JEPA: joint-embedding prediction of graph-connected gene blocks in single cells - Yuhao Wang; Zelin Zang; Yuxuan Liu; et al. (2026)
• Business World Model - Pang, Cecil; Sayama, Hiroki (2026)
• CALM: Consistency-Aware Logit Masking in Decoding Space for Mitigating Overthinking of Large Reasoning Models - Shengjing Liu; Feiyang Li; Yuxuan Yang; Yile Wang; Hui Huang (2026)
• CASEL: Consequence-Aware Selective Execution for Reliable Long-Horizon Tool-Using Agents - Yanyan Zhang; Fei Shen; Ting Yan; Hao Zhang (2026)
• CONCUR: Concordant Auxiliary Rewards for Multi-Task, Multi-Reward Reinforcement Learning - Jixuan Leng; Si Si; Fei Wang; Cho-Jui Hsieh; Jiong Zhang; Hsiang-Fu Yu; Inderjit S. Dhillon (2026)
• Can Agentic Code Review Catch Vulnerabilities Before They Are Merged? - Yihan Yang; Chenyuan Yang; Zijie Zhao; Anh Nguyen Hoang; Ziqi Zhang; Weidong Wang; Zhiheng Lyu; Lingming Zhang (2026)
• CardioState-JEPA: Delay-Aware Cross-Modal Learning of a Shared Cardiac Representation - Hamza Shafiq; Hung Manh Pham; Bin Zhu; et al. (2026)
• Causal-JEPA: Learning World Models through Object-Level Latent Masking - Nam, Heejeong; Lidec, Quentin Le; Maes, Lucas; LeCun, Yann; Balestriero, Randall (2026)
• Challenges and Research Directions for Large Language Model Inference Hardware - Xiaoyu Ma; David Patterson (2026)
• Characterizing and Defending Utility-Hijacking in Instruction-Tuning Data Selection - Yahao Ren; Jian Lou; Huadi Zheng; Jiawei Duan (2026)
• Clinical Graph-JEPA: Predictive Patient-State Knowledge Graphs for Cognitive Decision Support - Kushagra Yadav; Nalin Prabhath; Amit Lamba; et al. (2026)
• CoJEPA: Combining Contrastive Learning and JEPA for Global-Local Music Representations - Gabriel Meseguer-Brocal; Yuexuan Kong; Romain Hennequin (2026)
• Code Comprehensor: Hierarchical Documentation and Cross-Modal Retrieval for Large Repositories - Sujoy Roychowdhury; Joseph H. R. Isaac; Jiangning Gao; Martin Klitte; Diarmuid Corcoran (2026)
• Code World Model Preparedness Report - Song, Daniel; Ney, Peter; Menghini, Cristina; Ahmad, Faizan; et al. (2026)
• Computer-Using World Model - Guan, Yiming; Yu, Rui; Zhang, John; Wang, Lu; et al. (2026)
• Context Is Not Free: Carbon-Aware LLM Routing for Multi-Turn LLM Web Services across Geo-Distributed Data Centers - Xiaoyang Zhang; Yuejun Yan; Qingang Zhang; Yonggang Wen (2026)
• Context Is a Pipeline, Not a Window: Information Attrition in LLM Agent Harnesses - Yuvraj Sehgal; Mahsa Panahandeh; Naser Ezzati-Jivan (2026)
• Context or Knowledge: How LLMs Attribute Their Own Answers - Kirill Afendulev; Alexey Dontsov; Alla Chepurova; Elena Tutubalina; Anton Korznikov (2026)
• Covo-Audio Technical Report - Wang, Wenfu; Li, Chenxing; Zhang, Liqiang; Zhao, Yiyang; et al. (2026)
• DCKV: Learned Transform Coding of the Prompt KV Cache in Hybrid Language Models - Egor Maximov; Aleksei Merkelov; Vasiliy Alekseev; Ivan Krivorotov; Valentin Beliaev; Nikita Arsenin; Dmitry Bocharov; Aleksei Goncharov (2026)
• DIAL: Steerable Language Models with Distilled Instruction-Adherence Levels - Thanh Q. Tran; John Russell Himawan; Arun Verma; Kiwan Wong; Wei Xiao; Daniela Rus; Bryan Kian Hsiang Low (2026)
• Decision Models as Judges for Hallucination Detection - Haozheng Liu; Zhaoqian Xue; Zhongyin Wang; Junting Mu; Zihui Ma; Shuya Feng; Lingyao Li (2026)
• DeepSeek-OCR 2: Visual Causal Flow - Wei, Haoran; Sun, Yaofeng; Li, Yukun (2026)
• DeepSeek-V4.1-Flash: Pushing the Limits of KV Cache Compression - DeepSeek-AI; :; Xu, Anyi; Li, B.; et al. (2026)
• DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligence - DeepSeek-AI; Xu, Anyi; Lin, Bangcai; Xue, Bing; et al. (2026)
• Deriving Neural Scaling Laws from the Statistics of Natural Language - Francesco Cagnetta, Allan Raventos, Surya Ganguli, Matthieu Wyart (2026)
• Diagnosing JEPA World Models with Action-Conditioned Predictive Consistency - Guo An; Zijing Wu; Honghua Dong; et al. (2026)
• Discriminative World Models for Web Agents - Kelvin Li; Dhruv Pendharkar; Anish Pahilajani; et al. (2026)
• Distributed JEPA: A Self-Supervised Framework for Energy Forecasting - Liana Toderean, Tudor Cioara, Vasilis Michalakopoulos, Efstathios Sarantinopoulos, Ionut Anghel, Elissaios Sarmas (2026)
• Distributed LLM Inference via Communication-Efficient Speculative Decoding - Joohyun Shin; Byeonghyeon Park; Keumseo Ryum; Joonhyuk Kang (2026)
• Do Sparse Autoencoders Identify Reasoning Features in Language Models? - arXiv:2601.05679 author list (2026)
• DocSkill: Skill-Integrated Agentic Reasoning for Multilingual Multi-Hop Document Question Answering - Jing Huang; Zhiya Tan; Changtao Miao; Shutao Gong; Yang Wang; Joey Tianyi Zhou; Jianshu Li; Ying Yan (2026)
• Does Latent Planning Survive Point Clouds? Action-Conditioned JEPA World Models for Geometric Observations - Fabio F. Oberweger; Michael Schwingshackl (2026)
• Douyin Multimodal Embedding Model Technical Report - Chen, Haonan; Li, Chu; Wang, Zhicheng; Liu, Yuanwei; et al. (2026)
• DualPace: Decoupled Scheduling and Cooperative GPU--CPU Decoding for LLM Serving - YuXuan Lin; KeQiang Wu; Kingsum Chow (2026)
• ETC-Bench: A Multilingual Benchmark for Evaluation-Side Transcript Canonicalization - Zhiheng Qian; Junhao Du; Jing Peng; Hanqi Li; Kunyang Peng; Feng Xue; Kai Yu (2026)
• EXAONE 4.5 Technical Report - Choi, Eunbi; Choi, Kibong; Chun, Sehyun; Hong, Seokhee; et al. (2026)
• EnvSched: Environment-Aware Scheduling for Rollout-Efficient Agent Reinforcement Learning - Sihan Yang; Weichen Bai; Ruichuan An; Yiming Cheng; Zhiyuan Feng; Tianyi Jiang; Renye Yan; Ning Ding; Yifan Zhang; Renrui Zhang; Yehui Tang; Wentao Zhang (2026)
• Evaluating the Applicability of LLM Inference Optimizations on Apple Silicon - Kyrylo Yemets; Andrii Yaroshevych; Victor Muryn; Ostap Khomenko; Pavlo Kryven; Maksym Kmet; Maksym Shamrai; Mariya Hirna (2026)
• FOLD: Position-Free Far Memory with Local Order for Long-Context Speculative Decoding - Shuxuan Li; Jialiang Kang; Yanghao Zhou; Ziye Zhu; Xiao Yang (2026)
• FeatureArc: Benchmarking Coding Agents on Evolving Features - Yunlong Wang; Alyssa Wu; Cuiyun Gao; Jia Feng; Songyuanyi Lu; Zhichao Hu (2026)
• Flow-JEPA: Flow Matching for Robust Latent Dynamics in JEPA World Models - Yanchen Huo; Ziying Song; Yadan Luo (2026)
• Folding Attention Biases into Keys Makes Compacted KV Caches Run on Standard Kernels - Utkarsh Kanwat (2026)
• From Clinical Judgment to Checkable Rules: Clinician-Led Guardrails for Mental-Health Chatbots - Chenhan Lyu; Kit Wislocki; Sarah Elizabeth Stoeckl; Maitreyi Sinha; Amir Rahmani (2026)
• From RLHF to Direct Alignment: A Theoretical Unification of Preference Learning for Large Language Models - Tarun Raheja, Nilay Pochhi (2026)
• From Self-Confidence to Statistical Accuracy: Scaling Human-in-the-Loop Evaluation with Direct and LLM-Assisted Auditing - Harine Choi; Chenchen Kuai; Zihao Li; Jiwan Jiang; Yunlong Zhang; Zhiwen Fan; Zhengzhong Tu; Yang Zhou (2026)
• GEPA-Curator: Curated Learning for Self Improving Agents - Zack Yu; Nicholas Lee; Minjae Lee; Lakshya Agarwal; Luca Manolache; Wonjun Kang; Matei Zaharia; Michael W. Mahoney; Kurt Keutzer; Sehoon Kim; Amir Gholami (2026)
• GLAM: Training a latent world model over global spatiotemporal memory for active exploration and navigation - I-Tak Ieong; Ruizhi Feng; Zhaoyang Lu; et al. (2026)
• GUI-JEPA: Candidate-Conditioned JEPA Latents as a Fast Action Policy for Mobile GUI Agents - Bingchao Wang; Yongcheng Jing; Shunyu Liu; Chengyu Wang; Jun Huang; Jie Yang; Wei Liu; Dacheng Tao (2026)
• Gemini Embedding 2: A Native Multimodal Embedding Model from Gemini - Shanbhogue, Madhuri; Li, Zhe; Zhang, Shanfeng; Ábrego, Gustavo Hernández; et al. (2026)
• Gemma 4 Technical Report - Gemma Team; Abd, Sherif El; Aggarwal, Vaibhav; Algayres, Robin; et al. (2026)
• Grounding LLM Agents in Business Processes: Process-Aware Knowledge Graphs for Reliable Multi-Step Service Invocation - Saiful Khan; Afrah Farea (2026)
• HP-JEPA: Hierarchical Partitioning for Multi-Resolution Graph Joint-Embedding Predictive Learning - Ruichen Xu; Jingxiang Qu; Wenhan Gao; et al. (2026)
• HQ-JEPA: Hybrid Quantum Joint-Embedding Predictive Architecture for Cross-Modal Remote Sensing Representation Learning - Hossain, Md Aminur; Patel, Ayush V.; Singh, Sanjay K.; Banerjee, Biplab (2026)
• Heads of Security: Learned Attention Gates Improve Prompt Injection Robustness - Igor Shilov; Xiaoxue Yang; Alexander Panfilov; Evgenii Kortukov; Matthieu Meeus; Peter Romov; Wojciech Samek; Jonas Geiping; Yves-Alexandre de Montjoye (2026)
• Hidden in Plain Sight: an Empirical Study about The Instruction Stacks of Coding Agents - Felipe de Sant'Anna Paixão; Dhyego Tavares; Eduardo Almeida; Iftekhar Ahmed (2026)
• Hierarchical Planning with Latent World Models - Zhang, Wancong; Terver, Basile; Zholus, Artem; Chitnis, Soham; Sutaria, Harsh; Assran, Mido; Balestriero, Randall; Bar, Amir; Bardes, Adrien; LeCun, Yann; Ballas, Nicolas (2026)
• Human-JEPA: A Human-Centric Vision Model that Perceives and Anticipates - Hui Wei; Licai Sun; Guoying Zhao (2026)
• HyScale: Scaling Long-Context Training for Learned Sparse Attention - Yangshen Deng; Xinjing Huang; Yulong Wang; Yunzhuo Liu; Jun Hu; Shuyong Tan; Chun-Chih Tseng; Mingji Han; Ray Ying; Key Zhang; Luo Mai (2026)
• Hypergraph-Enhanced Evidence Selection for Long-Context Multimodal Document QA - Wenxin Zhao; Jiachuan Wang; Xinyi Zhu; Jiake Ge; Haoyang Li; Yongqi Zhang; Shuangyin Li; Lei Chen (2026)
• Inherited, Not Invented: Tracing the Provenance of Wait in Reasoning Models - Zhizhuo Kou; Sizai Hou; Long Chen; Jian Yang; Junyu Luo; Jingshu Peng; Wenkai Zhang; Sirui Han; Yike Guo (2026)
• Instella-MoE Technical Report - Liu, Jiang; Ranjan, Sudhanshu; Mishra, Prakamya; Dukler, Yonatan; et al. (2026)
• InterQ-RL: Interaction-Aware Correction of Execution Mismatch in Quantized Agentic RL - Yingbo Hao; Song Liu; Guanghua Yu; Zhaojing Zhou; Jianchen Zhu; Yi Zou (2026)
• JEDI: JEPA-to-Edge Distillation for Efficient Cropland Segmentation from Satellite Imagery - Kishor Kumar Bhaumik, Nicolas Roque dos Santos, Jia Chen, Evangelos E. Papalexakis (2026)
• JEPA Policy: Diffusion-Free Imitation Learning via Paired Action and Future Representation Prediction - Jie Xu, Kangjin Yu, Ziyi Jin, Junjie Gao, Liqing Chen, Yixian Li, Shuai Tian, Zhongpu Xia (2026)
• JEPA-x: Cross-Predictive Physics Grounding for Forecastable Latent Dynamics - Kehan Wen; Ziming Li; Siyuan Luo; et al. (2026)
• JEPADepth: Masked Predictive Representation Learning for Self-Supervised Monocular Depth Estimation - Ionuţ Grigore; Călin-Adrian Popa (2026)
• JEPLO: Joint-Embedding Predictive Learning for LiDAR-Based Legged Locomotion - Qihao Yuan; Yixuan Qiu; Ziyu Cao; et al. (2026)
• Joint-Embedding Prediction of Masked Point Tubes for Self-Supervised Learning on 4D Point Cloud Videos - Jheng-Ling Lee; Shang-Tse Chen (2026)
• K-EXAONE 2.0 Technical Report - Choi, Eunbi; Choi, Kibong; Chun, Sehyun; Hong, Seokhee; et al. (2026)
• K-EXAONE 2.0 Technical Report: Journey to Global Frontier-Scale Foundation Models - LG AI Research (2026)
• KVStream: Adaptive Parallel Storage Management for KV Caching in Large-Scale LLM Serving - Avinash Maurya; Jaiaid Mobin; Jie Ye; M. Mustafa Rafique; Franck Cappello; Bogdan Nicolae (2026)
• Kimi K2.5: Visual Agentic Intelligence - Kimi Team; Bai, Tongtong; Bai, Yifan; Bao, Yiping; et al. (2026)
• Kimi K3: Open Frontier Intelligence - Kimi Team; Bai, Tongtong; Bai, Yifan; Bao, Yiping; et al. (2026)
• LIFT: Scoped Constraints for Evaluating and Training Instruction Following - Eran Goldstein; Yuval Belfer; Yonatan Belinkov; Daniel Gissin (2026)
• LLMs in Cloud: A Discussion on Architecture, Orchestration, and Inference - Jannatun Noor; Athar Noor Mohammad Rafee; Md. Sadman Haque; Sreezon Das Gupta; Atiar Osman; Md. Abu Obaida Zishan (2026)
• LazyFlash: Forward-Dense, Backward-Sparse Attention for Accelerated Long-Context Fine-Tuning - Keqi Deng; Ruchao Fan; Jinyu Li (2026)
• Le MuMo JEPA: Multi-Modal Self-Supervised Representation Learning with Learnable Fusion Tokens - Cornelissen, Ciem; Leroux, Sam; Simoens, Pieter (2026)
• LeVJEPA: Efficient & Scalable Video Pretraining without the Heuristics - Lukas Kuhn; Lucas Maes; Giuseppe Serra; et al. (2026)
• LeWorldModel: Stable End-to-End Joint-Embedding Predictive Architecture from Pixels - Maes, Lucas; Lidec, Quentin Le; Scieur, Damien; LeCun, Yann; Balestriero, Randall (2026)
• Long-Context Drafting Does Not Require Long-Context Drafters - Aref Jafari; Vansh Bhatia; Matt Qin; Subhajit Dutta Chowdhury; Ben Lee; Vikram Appia; Ali Ghodsi; Mehdi Rezagholizadeh (2026)
• Looped World Models - Lu, Hongyuan Adam; Wei, Z. L. Victor; Zhang, Qun; Zeng, Jinrui; et al. (2026)
• Lossless Inference Acceleration for ASR via EAGLE-Style Speculative Decoding - Sen Zhang; Xianghu Yue; Yiming Chen; Wenhuan Lu; Jianguo Wei; Haizhou Li (2026)
• LpWM: A Case for Sparse Representations in World Models - Yilun Kuang; Yash Dagade; Quentin Le Lidec; et al. (2026)
• MCF: A Memory- and Communication-Aware Co-Designed Framework for Long-Context LLM Inference - Huaizhi Zhang; Jiacheng Zhu; Xuqi Zhu; Junkyu Lee; Klaus McDonald-Maier; Xiaojun Zhai (2026)
• MOSS-TTS Technical Report - Gong, Yitian; Jiang, Botian; Zhao, Yiwei; Yuan, Yucheng; et al. (2026)
• MR-JEPA: A General Purpose Video Foundation Model for Cardiac MRI - Athira J. Jacob; Puneet Sharma; Dorin Comaniciu; et al. (2026)
• Metacognitive Decoding: Efficient Inference for Diffusion Language Agents - Mingyu Zhao; Shuhang Lin; Ligong Han; Xiaojing Yu; Zhenting Wang; Yifei Pang; Yang Zhou; Dimitris N. Metaxas (2026)
• MiMo-V2-Flash Technical Report - Core Team; Xiao, Bangjun; Xia, Bingquan; Yang, Bo; et al. (2026)
• Mind the Intent Gap: A Methodology for Collaborative Agent-Driven Software Engineering - Nicolas Tacheny; Tom Mens; Saïd Mahmoudi (2026)
• MoE-Prefill: Zero Redundancy Overheads in MoE Prefill Serving - Su, Zhaoyuan; Ruwase, Olatunji; Ganesan, Karthik; Qiao, Aurick; et al. (2026)
• Mol-JEPA: A multimodal Joint Embedding Predictive Architecture for Molecules - Florian Rottach; Sebastian Schieferdecker; William Rudman; et al. (2026)
• Motus2: A Self-Evolving General World Model for Dexterous Manipulation - Hongzhe Bi; Zihao Zhou; Yihang Tang; et al. (2026)
• Natural Hard-Negative Mining from Positives for Instruction-Following Retrieval - Chaolang Li; Jingyu Li; Zhaocheng Du; Xuyang Xie; Gang Wang; Pengwen Dai (2026)
• No Gaussian Required: Contrastive Inverse Dynamics for JEPA World Models - Jack Boylan; Chris Hokamp (2026)
• ODEWorld: A Continuous Predictive Architecture via Physical-Time Flow - Dongxiu Liu; Haoyi Niu; Peng Cheng; et al. (2026)
• OpenAI GPT-5 System Card - OpenAI (2026)
• Orthogonal JEPA: Factorized Predictive States for Latent World Models - Taoyong Cui; Pheng Ann Heng; Wanli Ouyang (2026)
• P-JEPA: Procedural Video Representation Learning via Joint Embedding Predictive Architecture - Tristram, Felix; Gasperini, Stefano; Killeen, Benjamin; Walch, Marcel; Benz, Christian; Navab, Nassir; Ghazaei, Ghazal (2026)
• PaCoRe: Learning to Scale Test-Time Compute with Parallel Coordinated Reasoning - Jingcheng Hu, Yinmin Zhang, Shijie Shang, Xiaobo Yang, Yue Peng, Zhewei Huang, Hebin Zhou, Xin Wu, Jie Cheng, Fanqi Wan, Xiangwen Kong, Chengyuan Yao, Kaiwen Yan, Ailin Huang, Hongyu Zhou, Qi Han, Zheng Ge, Daxin Jiang, Xiangyu Zhang, Heung-Yeung Shum (2026)
• PhyLatent: Learning Dynamics-Relevant Representations for JEPA World Models - Zeng, Xi; Ren, Haojie; Song, Ziying (2026)
• Predicting Consequences and Reinforcing Navigation Policies with Latent World Models - Zengmao Wang; Wei Gao; Shuhan Shen (2026)
• Predicting Functions, Not Features: KANs with Function-Space Joint-Embedding Predictive Learning for Medical Image Segmentation - Yungeng Liu; Xuanzi Fang; Yuge Zhang; et al. (2026)
• Prefetch-Steered Routing: Deterministic Cache-Aware Expert Selection for Offloaded Mixture-of-Experts Decoding - Siming Huang (2026)
• Prefix Keys and Values Matter Late: RIPE Caching for Self-Speculative Decoding in Looped Language Models - Dezhen Lu; Runguo Li; Zixiang Ni (2026)
• Programmable World Model - Huang, Zheng-Hui; Lin, Guixu; Lin, Jiacheng; Huang, Yi-Chuan; et al. (2026)
• Prompting Strategy Effects Depend on the Judge: A Multilingual Study of LLM-as-a-Judge Robustness - Asal Mehrabi; Mohammad Aliannejadi; Clemencia Siro (2026)
• Qwen-Audio-3.0-ASR Technical Report - Bian, Chuanmeng; Chen, Daren; Chen, Peixin; Chen, Zhigao; et al. (2026)
• Qwen-Audio-VAE Technical Report - Jiang, Ziyue; Guo, Dake; Zhang, Zekai; Hu, Hangrui; et al. (2026)
• Qwen-Image-2.0 Technical Report - Zhao, Bing; Wu, Chenfei; Li, Deqing; Meng, Hao; et al. (2026)
• Qwen3-Coder-Next Technical Report - Cao, R. et al. (Qwen Team) (2026)
• RANGER: Retrieval Agent for Navigating Graphs with Semantic Evaluation Rewards - Artem Mikhaylov; Pavel Konoplev; Nikolay Efanov; Artyom Sorokin; Evgeny Burnaev (2026)
• RIFT: KV Cache Compression for Draft Models with Complementary Compressibility Training - Dor Tsur; Maor Ashkenazi; Maximilian Golub; Peter Hicks; Yaniv Galron; Talor Abramovich; Yoav Miron; Tiyasa Mitra; Bita Darvish Rouhani; Ran Zilberstein (2026)
• Rad-JEPA 3D: Radiology Joint-Embedding Predictive Model for 3D Computed Tomography - Quoc-Huy Trinh; Minh-Van Nguyen; Ulas Bagci (2026)
• ReplaySSM: Cache SSM Inputs, Not State - Ze-Wei Liou; Harris Nover; Amey Naik; Andrii Skliar; Igor Shovkun; Ido Shahaf; Tri Dao (2026)
• Rethinking Agentic Reinforcement Learning In Large Language Models - Fangming Cui; Ruixiao Zhu; Cheng Fang; Sunan Li; Jiahong Li (2026)
• Rethinking Retrieval in the Long-Context Era: Training-Free Semantic Trees for Adaptive Context Construction - Zhe Xu; Tianhao Liao; Zhenqing Ling; Ying Shen; Yaliang Li; Daoyuan Chen (2026)
• Risk-Aware World Modeling with Flow-Guided Occupancy Evolution for Selective Trajectory Planning in Automated Driving - Rongxiang Zeng, Linsen Cai, Jiafu Zhang, Yijie Zhong, Yide Tao, Shuai Wang, Nan Zheng, Hai L. Vu, Alvaro Garcia Hernandez, Yongqi Dong (2026)
• RubriX: A Fast, Fine-Tunable Non-Autoregressive Rubric Judge - Balamurugan Paramasivam; Sudalai Rajkumar (2026)
• RubricAtlas: Mining Type-Level Rubrics from Expert Reports to Evaluate Deep Research Agents - Delin Wang; Sinuo Wang; Rui Sun; Beidi Luan; Qiming Shi; Jing Li; Daxin Jiang; Zuo Bai (2026)
• SARIA: Persistence-Aware KV Caching with Coordinated DRAM--SSD Reload for LLM Agents - Hui Li; Qihui Zhou; Xiao Yan; Shumeng Zhang; Zijie Dai; Zequn Gong; Mingcong Song; Guoping Long; Hongjie Si; James Cheng (2026)
• SCALE: State-Calibrated Latent Embeddings for JEPA Planning in the Right Geometry - Jiaming Hu; Yan Zheng; Tian Wang (2026)
• SEA-LION-v4.8: A Technical Report - Aulia, Adila; Dabeer, Ahmed; Jeongmi, Ahn; Sajeban, Antonyrex; et al. (2026)
• SJEPA: Learning Elegant Latent Dynamics with Hybrid Symbolic-Neural Predictors - Yongchao Huang (2026)
• SPIN: Shadow Predictive Indexer for Sparse Attention - Yao Fu; Jiahan Chang; Ritchie Zhao; Bryce Long; Yueying Li; Mahdi Kamani; Samkit Jain; Rahul Raman; Tara Safavi; Shreya Gupta; Parsa Ashrafi Fashi; Minseok Lee; et al. (2026)
• SR-JEPA: Learning Predictive Latent State in 3D Scenes - Zihan Zhou; Qifu Wen; Xi Zeng (2026)
• Sabi\'a-4 Technical Report - Laitz, Thiago; Almeida, Thales Sales; Abonizio, Hugo; Junior, Roseval Malaquias; et al. (2026)
• Scaling Inherently Interpretable Language Models - Guide Labs Team; Madsen, Andreas; Ismail, Aya Abdelsalam; Nguyen, Giang; et al. (2026)
• Schema-Adaptive Action-Conditioned JEPA for Cross-Machine CNC Transfer under Partial Sensor Overlap - Ayoub Louaye Bouaziz, Matthieu Ostertag, Anton Demasles (2026)
• Select-TTT: Query-Conditioned Selective Fast Weight Updates for In-Place Test-Time Training - Yue Wu; Yang Zhang; Weinan Gan; Chongming Gao; Xinsen Guo; Xiaoyuan Li; Fuli Feng (2026)
• Self-Compacting Language Model Agents - Tianjian Li; Jingyu Zhang; William Jurayj; Xi Wang; Chuanyang Jin; Mehrdad Farajtabar; Eric Nalisnick; Daniel Khashabi (2026)
• Semigroup-JEPA: Latent Dynamics Consistency for Zero-Shot Physics Generalization - Andy Zeyi Liu, Haoran Sun, Lucas Baker, Randall Balestriero, John Sous (2026)
• SiameseNorm: Breaking the Barrier to Reconciling Pre/Post-Norm - Tianyu Li; Dongchen Han; Zixuan Cao; Haofeng Huang; Mengyu Zhou; Ming Chen; Erchao Zhao; Xiaoxi Jiang; Guanjun Jiang; Gao Huang (2026)
• Sim-to-Real Traffic Scene Understanding by Decoupling Semantics from Caption Generation with V-JEPA - Nguyen Hoai Thuong Bui, Thanh Nguyen Vo, Trinh Tra Giang Nguyen, Ha Duc Bui (2026)
• SpecFit: Not All Draft Tokens Cost the Same in Offloaded MoE Inference - Hongliang Cao; Xiuyu Li; Siqi Wang; Ziming Wang; Jingmin Zhu; Hailong Yang (2026)
• Spectral-Target Physical Latent Structuring for JEPA-Style World Models - Penghao Zhu; Salvatore Penachio; Kaustav Mukherjee; et al. (2026)
• Speculative Decoding for Mixture-of-Experts Models: Expert Bytes, Kernel Bandwidth and the Best Draft Length - Yuzheng Lan (2026)
• Spine: Compiling Subtask Trees into Structured Context for Long-Horizon Coding Agents - Jiahong Xiang; Kunqiu Chen; Lingming Zhang; Yuqun Zhang (2026)
• StageWAM: Joint-Embedding Stage Prediction for World-Action Models in Robot Manipulation - Xiao Liu; Yuguang Yang; Xi Wang; et al. (2026)
• Stratum: Repository Memory for Coding Agents Beyond the Context Window - Yuvraj Sehgal; Mahsa Panahandeh; Naser Ezzati-Jivan (2026)
• T-SAR-JEPA: Self-Supervised Temporal Anomaly Detection in SAR Amplitude Stacks via Latent Prediction - Kerod Woldesenbet, Abem Woldesenbet (2026)
• Tandem: Serving Hybrid Linear-Attention GUI Agents on Mobile NPUs - Zhuomin Chen; Jingchao Ni; Xu Zheng; Yong Chen; Mo Sha; Wei Cheng; Dongsheng Luo (2026)
• Temporal-Distance JEPA: Plan-Aware Representation Learning for Latent World Model Predictive Control - Jiaxin Bai; Jiaxuan Xiong (2026)
• The MiniMax-M2 Series: Mini Activations Unleashing Max Real-World Intelligence - MiniMax Team (2026)
• Toward Physically Grounded JEPA World Models for Goal-Conditioned Robotic Planning - Muyuan Liu; Yue Huang; Zheng Liang; et al. (2026)
• Tracing the Unlabeled Storm: Cross-Variable Transfer in a Lagrangian Atmospheric JEPA Framework - K M Anirudh; S Sandeep; Hariprasad Kodamana (2026)
• Trajectory-Triggered Sparse Collaborative Decoding - Amit Ranjan Trivedi; Sruthikesh Surineni; Kyong Ho Lee; Mohit Mittal; Steven Li; Rex Hill (2026)
• Transformers learn factored representations - Adam Shai; Loren Amdahl-Culleton; Casper L. Christensen; Henry R. Bigelow; Fernando E. Rosas; Alexander B. Boyd; Eric A. Alt; Kyle J. Ray; Paul M. Riechers (2026)
• TrawMem: Reframing Agent Memory as Task-Oriented Role-Adaptive Workspaces - Mingyu Zhang; Zixu Li; Zhiheng Fu; Zhiwei Chen; Jinhe Bi; Yupeng Hu (2026)
• TrojanWorld: Backdooring World-Model Agents via Imagination Steering - Wenkai Huang; Siyuan Liang; Gaolei Li; et al. (2026)
• UlaCache: Unified Logical Addressing for Sparse LLM Serving - Isaac Zhu; Alan Cao; Hugh Yin; Rocky Wang; Orion Wang; Hscos Zhang; Jack Li; Jason Zhao (2026)
• UniJEPA: A Unified Joint-Embedding Predictive Architecture for Task-Agnostic Visual World Modeling - An Lanji, Dawei Liu, Jin Li, Haoran Xu, Mei Chen, Yu Tian (2026)
• Unifying Semantic Priors and High-Frequency Traces: Enhancing V-JEPA with Mixture-of-Experts for Robust Synthetic Image Forensics - Simone Teglia, Irene Amerini (2026)
• VL-JEPA: Joint Embedding Predictive Architecture for Vision-language - Chen, Delong; Shukor, Mustafa; Moutakanni, Theo; Chung, Willy; Yu, Jade; Kasarla, Tejaswi; Bang, Yejin; Bolourchi, Allen; LeCun, Yann; Fung, Pascale (2026)
• Var-JEPA: A Variational Formulation of the Joint-Embedding Predictive Architecture - Bridging Predictive and Generative Self-Supervised Learning - Gögl, Moritz; Yau, Christopher (2026)
• WA-JEPA: Rethinking the Video JEPA Paradigm for World-Action Modeling in Autonomous Driving - Xinlin Wang; Yujiao Xiang; Yuheng Zhou; et al. (2026)
• WAKMan: Workload Aware KVCache Manager - Suhas Kumar Morisetty; Vaibhavi Lokegaonkar; Sachin Mathew Varghese; Wesley Harris (2026)
• When Agents Fail: Model or Harness--Who's to Blame? - Shao Zhang; Hangfan Zhang; Yuanyuan Shi; Hao Li; Min-Le Su; Kangcong Li; Jingping Wang; Chunjiang Mu; Chen Zhang; Yang Chen; Bo Zhang; Shuyue Hu (2026)
• When Graph-JEPA Learns the Wrong Thing: Diagnosing and Repairing Category-Conditional Collapse - Gollam Rabby; Sören Auer (2026)
• When Should a World Model Move? Loss-Conditioned State Execution - Jintao Xu, Zhengyu Chen, Ben Zhang, Yongzhi Qi, Jianshen Zhang (2026)
• When the World Lies: Backdoor Attacks on Latent World Models for Downstream Control - Roberto Riaño, Gorka Abad, Stjepan Picek, Aitor Urbieta (2026)
• Where Should the Evaluation Budget Go in LLM-Driven Agent Scaffold Evolution? - Sichun Luo; Yi Huang; Mukai Li; Haochen Luo; Guanzhi Deng; Haibo Wang; Jian Xu; Zefa Hu; Junlan Feng; Qi Liu (2026)
• Why Reasoning Fails to Plan: A Planning-Centric Analysis of Long-Horizon Decision Making in LLM Agents - Zehong Wang; Fang Wu; Hongru Wang; Xiangru Tang; Bolian Li; Zhenfei Yin; Yijun Ma; Yiyang Li; Weixiang Sun; Xiusi Chen; Yanfang Ye (2026)
• Words or Deeds? How LLMs Predict Users' Decisions in Human-Agent Dialogue - Baiqiao Zhang; Shuai Ma; Zhifeng Liao; Chao Zhou; Jianshu Wang; Yuhan Zhao; Chaoyue Zhang; Tongjun Si; Yuying Tang; Xiangxian Li; Yong-Jin Liu; Yulong Bian (2026)
• World Models: A Comprehensive Survey of Architectures, Methodologies, Reasoning Paradigms, and Applications - Arif Hassan Zidan, Yi Pan, Hanqi Jiang, Ruiyu Yan, Wei Ruan, Zihao Wu, Lifeng Chen, Weihang You, Xinliang Li, Bowen Chen, Huawen Hu, Peilong Wang, Sizhuang Liu, Jing Zhang, Siyuan Li, Zhengliang Liu, Yu Bao, Lin Zhao, Lichao Sun, Dajiang Zhu, Xiang Li, Jinglei Lv, Quanzheng Li, Wei Liu, Tianming Liu, Wei Zhang (2026)
• Your Policy Is Secretly a Harness: The Decision Geometry of Agent Interfaces - Jian Mu; Ruibo Shen; Yao Shu (2026)
• Your Wish Is My Commit: Over-Compliant Refactoring in AI Coding Agents - Haocong Jiang; Sadra Sabouri; Sepand Haghighi; Souti Chattopadhyay (2026)
• ZipFusion: Fusing Lossless KV Decompression with Attention for Efficient LLM Decoding - Yuetao Chen; Peng Wang; Hong Xu (2026)
• kw-bench: Breaking Frontier Harnesses on Real-World Tasks - George Juraj Salapa (2026)

2025

• 2 OLMo 2 Furious - Team OLMo; Pete Walsh; Luca Soldaini (2025)
• 2 OLMo 2 Furious (OLMo 2) - OLMo Team; Petrus Kaundiya Muyinda; et al. (AI2) (2025)
• Absolute Zero: Reinforced Self-play Reasoning with Zero Data - Mark Shao, et al. (2025)
• Agentic Retrieval-Augmented Generation: A Survey on Agentic RAG - Aditi Singh, Abul Ehtesham, Saket Kumar, et al. (2025)
• AlphaProof: Enhanced mathematical reasoning via reinforcement learning (technical report) - Google DeepMind (Istvan Szita, John Agapiou, et al.) (2025)
• Audio-JEPA: Joint-Embedding Predictive Architecture for Audio Representation Learning (2025)
• BrowseComp: A Simple Yet Challenging Benchmark for Browsing Agents - Wei, Jason, Sun, Zhiqing, Papay, Spencer et al. (2025)
• Circuit Tracing: Revealing Computational Graphs in Language Models - Adil? (actual: 'Joshua Batson? — authors: Batson, Li? Anthropic Interpretability Team') (2025)
• Constitutional Classifiers: Defending against Universal Jailbreaks across Thousands of Hours of Red Teaming - Mrinank Sharma; Meg Tong; Jesse Mu (2025)
• Cosmos World Foundation Model Platform for Physical AI - NVIDIA (Agarwal et al.) (2025)
• DAPO: An Open-Source LLM Reinforcement Learning System at Scale - Qiying Yu, Zheng Zhang, Ruofei Zhu, Yufeng Yuan, Xiaochen Zuo, Yu Yue, Weinan Dai, Tiantian Fan, Gaohong Liu, Lingjun Liu, Xin Liu, Haibin Lin, Zhiqi Lin, Bole Ma, Guangming Sheng, Yuxuan Tong, Chi Zhang, Mofan Zhang, Wang Zhang, Hang Zhu, Jinhua Zhu, Jiaze Chen, Jiangjie Chen, Chengyi Wang, Hongli Yu, Yuxuan Song, Xiangpeng Wei, Hao Zhou, Jingjing Liu, Wei-Ying Ma, Ya-Qin Zhang, Lin Yan, Mu Qiao, Yonghui Wu, Mingxuan Wang (2025)
• DINO-WM: World Models on Pre-trained Visual Features enable Zero-shot Planning - Zhou, Gaoyue; Pan, Hengkai; LeCun, Yann; Pinto, Lerrel (2025)
• DINOv3 - O. Simeoni et al. (Meta AI) (2025)
• DeepSeek-OCR: Contexts Optical Compression - Haoran Wei, Chenglong Li, et al. (DeepSeek-AI) (2025)
• DeepSeek-R1: incentivizing reasoning capability in LLMs via reinforcement learning - DeepSeek-AI (2025)
• DeepSeek-V3.2: Pushing the Frontier of Open Large Language Models - DeepSeek-AI (2025)
• Dynamic Cheatsheet: Test-Time Learning with Adaptive Memory - Mirac Suzgun, Mert Yuksekgonul, Federico Bianchi, et al. (2025)
• EAGLE-3: Scaling up Inference Acceleration of Large Language Models via Training-Time Test - Li, Yuhui, Wei, Fangyun, Zhang, Chao et al. (2025)
• Emergent Misalignment: Narrow finetuning can produce broadly misaligned LLMs - Betley, Jan, Tan, Daniel, Warncke, Niels et al. (2025)
• From System 1 to System 2: A Survey of Reasoning Large Language Models - Li et al. (2025)
• GLM-4.5: Agentic, Reasoning, and Coding (ARC) Foundation Models - GLM-4. 5 Team, :, Aohan Zeng, Xin Lv, Qinkai Zheng, Zhenyu Hou (2025)
• GPT-5 System Card - OpenAI (2025)
• Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context - Google DeepMind (2025)
• Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities - Gheorghe Comanici, Eric Bieber, Mike Schaekermann, et al. (2025)
• Gemma 3 Technical Report - Team Gemma, Google (2025)
• Genie 3: Building richer worlds for agents (Genie 3 announcement) - Bruce Blinn — no: DeepMind Genie 3 team (2025)
• Hierarchical Reasoning Model - Guan Wang, Jin Li, Yuhao Sun, Xing Chen et al. (2025)
• Humanity's Last Exam - Long Phan, Alice Gatti, Ziwen Han, Nathaniel Li, Josephina Hu, Hugh Zhang, Chen Bo Calvin Zhang, Mohamed Shaaban, John Ling, Sean Shi, Michael Choi, Anish Agrawal, Arnav Chopra, Adam Khoja, Ryan Kim, Richard Ren, Jason Hausenloy, Oliver Zhang, Mantas Mazeika, Dmitry Dodonov, Tung Nguyen, Jaeho Lee, Daron Anderson, Mikhail Doroshenko, Alun Cennyth Stokes, Mobeen Mahmood, Oleksandr Pokutnyi, Oleg Iskra, Jessica P. Wang, John-Clark Levin, Mstyslav Kazakov, Fiona Feng, Steven Y. Feng, Haoran Zhao, Michael Yu, Varun Gangal, Chelsea Zou, Zihan Wang, Serguei Popov, Robert Gerbicz, Geoff Galgon, Johannes Schmitt, Will Yeadon, Yongki Lee, Scott Sauers, Alvaro Sanchez, Fabian Giska, Marc Roth, Søren Riis, Saiteja Utpala, Noah Burns, Gashaw M. Goshu, Mohinder Maheshbhai Naiya, Chidozie Agu, Zachary Giboney, Antrell Cheatom, Francesco Fournier-Facio, Sarah-Jane Crowson, Lennart Finke, Zerui Cheng, Jennifer Zampese, Ryan G. Hoerr, Mark Nandor, Hyunwoo Park, Tim Gehrunger, Jiaqi Cai, Ben McCarty, Alexis C Garretson, Edwin Taylor, Damien Sileo, Qiuyu Ren, Usman Qazi, Lianghui Li, Jungbae Nam, John B. Wydallis, Pavel Arkhipov, Jack Wei Lun Shi, Aras Bacho, Chris G. Willcocks, Hangrui Cao, Sumeet Motwani, Emily de Oliveira Santos, Johannes Veith, Edward Vendrow, Doru Cojoc, Kengo Zenitani, Joshua Robinson, Longke Tang, Yuqi Li, Joshua Vendrow, Natanael Wildner Fraga, Vladyslav Kuchkin, Andrey Pupasov Maksimov, Pierre Marion, Denis Efremov, Jayson Lynch, Kaiqu Liang, Aleksandar Mikov, Andrew Gritsevskiy, Julien Guillod, Gözdenur Demir, Dakotah Martinez, Ben Pageler, Kevin Zhou, Saeed Soori, Ori Press, Henry Tang, Paolo Rissone, Sean R. Green, Lina Brüssel, Moon Twayana, Aymeric Dieuleveut, Joseph Marvin Imperial, Ameya Prabhu, Jinzhou Yang, Nick Crispino, Arun Rao, Dimitri Zvonkine, Gabriel Loiseau, Mikhail Kalinin, Marco Lukas, Ciprian Manolescu, Nate Stambaugh, Subrata Mishra, Tad Hogg, Carlo Bosio, Brian P Coppola, Julian Salazar, Jaehyeok Jin, Rafael Sayous, Stefan Ivanov, Philippe Schwaller, Shaipranesh Senthilkuma, Andres M Bran, Andres Algaba, Kelsey Van den Houte, Lynn Van Der Sypt, Brecht Verbeken, David Noever, Alexei Kopylov, Benjamin Myklebust, Bikun Li, Lisa Schut, Evgenii Zheltonozhskii, Qiaochu Yuan, Derek Lim, Richard Stanley, Tong Yang, John Maar, Julian Wykowski, Martí Oller, Anmol Sahu, Cesare Giulio Ardito, Yuzheng Hu, Ariel Ghislain Kemogne Kamdoum, Alvin Jin, Tobias Garcia Vilchis, Yuexuan Zu, Martin Lackner, James Koppel, Gongbo Sun, Daniil S. Antonenko, Steffi Chern, Bingchen Zhao, Pierrot Arsene, Joseph M Cavanagh, Daofeng Li, Jiawei Shen, Donato Crisostomi, Wenjin Zhang, Ali Dehghan, Sergey Ivanov, David Perrella, Nurdin Kaparov, Allen Zang, Ilia Sucholutsky, Arina Kharlamova, Daniil Orel, Vladislav Poritski, Shalev Ben-David, Zachary Berger, Parker Whitfill, Michael Foster, Daniel Munro, Linh Ho, Shankar Sivarajan, Dan Bar Hava, Aleksey Kuchkin, David Holmes, Alexandra Rodriguez-Romero, Frank Sommerhage, Anji Zhang, Richard Moat, Keith Schneider, Zakayo Kazibwe, Don Clarke, Dae Hyun Kim, Felipe Meneguitti Dias, Sara Fish, Veit Elser, Tobias Kreiman, Victor Efren Guadarrama Vilchis, Immo Klose, Ujjwala Anantheswaran, Adam Zweiger, Kaivalya Rawal, Jeffery Li, Jeremy Nguyen, Nicolas Daans, Haline Heidinger, Maksim Radionov, Václav Rozhoň, Vincent Ginis, Christian Stump, Niv Cohen, Rafał Poświata, Josef Tkadlec, Alan Goldfarb, Chenguang Wang, Piotr Padlewski, Stanislaw Barzowski, Kyle Montgomery, Ryan Stendall, Jamie Tucker-Foltz, Jack Stade, T. Ryan Rogers, Tom Goertzen, Declan Grabb, Abhishek Shukla, Alan Givré, John Arnold Ambay, Archan Sen, Muhammad Fayez Aziz, Mark H Inlow, Hao He, Ling Zhang, Younesse Kaddar, Ivar Ängquist, Yanxu Chen, Harrison K Wang, Kalyan Ramakrishnan, Elliott Thornley, Antonio Terpin, Hailey Schoelkopf, Eric Zheng, Avishy Carmi, Ethan D. L. Brown, Kelin Zhu, Max Bartolo, Richard Wheeler, Martin Stehberger, Peter Bradshaw, JP Heimonen, Kaustubh Sridhar, Ido Akov, Jennifer Sandlin, Yury Makarychev, Joanna Tam, Hieu Hoang, David M. Cunningham, Vladimir Goryachev, Demosthenes Patramanis, Michael Krause, Andrew Redenti, David Aldous, Jesyin Lai, Shannon Coleman, Jiangnan Xu, Sangwon Lee, Ilias Magoulas, Sandy Zhao, Ning Tang, Michael K. Cohen, Orr Paradise, Jan Hendrik Kirchner, Maksym Ovchynnikov, Jason O. Matos, Adithya Shenoy, Michael Wang, Yuzhou Nie, Anna Sztyber-Betley, Paolo Faraboschi, Robin Riblet, Jonathan Crozier, Shiv Halasyamani, Shreyas Verma, Prashant Joshi, Eli Meril, Ziqiao Ma, Jérémy Andréoletti, Raghav Singhal, Jacob Platnick, Volodymyr Nevirkovets, Luke Basler, Alexander Ivanov, Seri Khoury, Nils Gustafsson, Marco Piccardo, Hamid Mostaghimi, Qijia Chen, Virendra Singh, Tran Quoc Khánh, Paul Rosu, Hannah Szlyk, Zachary Brown, Himanshu Narayan, Aline Menezes, Jonathan Roberts, William Alley, Kunyang Sun, Arkil Patel, Max Lamparth, Anka Reuel, Linwei Xin, Hanmeng Xu, Jacob Loader, Freddie Martin, Zixuan Wang, Andrea Achilleos, Thomas Preu, Tomek Korbak, Ida Bosio, Fereshteh Kazemi, Ziye Chen, Biró Bálint, Eve J. Y. Lo, Jiaqi Wang, Maria Inês S. Nunes, Jeremiah Milbauer, M Saiful Bari, Zihao Wang, Behzad Ansarinejad, Yewen Sun, Stephane Durand, Hossam Elgnainy, Guillaume Douville, Daniel Tordera, George Balabanian, Hew Wolff, Lynna Kvistad, Hsiaoyun Milliron, Ahmad Sakor, Murat Eron, Andrew Favre D. O., Shailesh Shah, Xiaoxiang Zhou, Firuz Kamalov, Sherwin Abdoli, Tim Santens, Shaul Barkan, Allison Tee, Robin Zhang, Alessandro Tomasiello, G. Bruno De Luca, Shi-Zhuo Looi, Vinh-Kha Le, Noam Kolt, Jiayi Pan, Emma Rodman, Jacob Drori, Carl J Fossum, Niklas Muennighoff, Milind Jagota, Ronak Pradeep, Honglu Fan, Jonathan Eicher, Michael Chen, Kushal Thaman, William Merrill, Moritz Firsching, Carter Harris, Stefan Ciobâcă, Jason Gross, Rohan Pandey, Ilya Gusev, Adam Jones, Shashank Agnihotri, Pavel Zhelnov, Mohammadreza Mofayezi, Alexander Piperski, David K. Zhang, Kostiantyn Dobarskyi, Roman Leventov, Ignat Soroko, Joshua Duersch, Vage Taamazyan, Andrew Ho, Wenjie Ma, William Held, Ruicheng Xian, Armel Randy Zebaze, Mohanad Mohamed, Julian Noah Leser, Michelle X Yuan, Laila Yacar, Johannes Lengler, Katarzyna Olszewska, Claudio Di Fratta, Edson Oliveira, Joseph W. Jackson, Andy Zou, Muthu Chidambaram, Timothy Manik, Hector Haffenden, Dashiell Stander, Ali Dasouqi, Alexander Shen, Bita Golshani, David Stap, Egor Kretov, Mikalai Uzhou, Alina Borisovna Zhidkovskaya, Nick Winter, Miguel Orbegozo Rodriguez, Robert Lauff, Dustin Wehr, Colin Tang, Zaki Hossain, Shaun Phillips, Fortuna Samuele, Fredrik Ekström, Angela Hammon, Oam Patel, Faraz Farhidi, George Medley, Forough Mohammadzadeh, Madellene Peñaflor, Haile Kassahun, Alena Friedrich, Rayner Hernandez Perez, Daniel Pyda, Taom Sakal, Omkar Dhamane, Ali Khajegili Mirabadi, Eric Hallman, Kenchi Okutsu, Mike Battaglia, Mohammad Maghsoudimehrabani, Alon Amit, Dave Hulbert, Roberto Pereira, Simon Weber, Handoko, Anton Peristyy, Stephen Malina, Mustafa Mehkary, Rami Aly, Frank Reidegeld, Anna-Katharina Dick, Cary Friday, Mukhwinder Singh, Hassan Shapourian, Wanyoung Kim, Mariana Costa, Hubeyb Gurdogan, Harsh Kumar, Chiara Ceconello, Chao Zhuang, Haon Park, Micah Carroll, Andrew R. Tawfeek, Stefan Steinerberger, Daattavya Aggarwal, Michael Kirchhof, Linjie Dai, Evan Kim, Johan Ferret, Jainam Shah, Yuzhou Wang, Minghao Yan, Krzysztof Burdzy, Lixin Zhang, Antonio Franca, Diana T. Pham, Kang Yong Loh, Joshua Robinson, Abram Jackson, Paolo Giordano, Philipp Petersen, Adrian Cosma, Jesus Colino, Colin White, Jacob Votava, Vladimir Vinnikov, Ethan Delaney, Petr Spelda, Vit Stritecky, Syed M. Shahid, Jean-Christophe Mourrat, Lavr Vetoshkin, Koen Sponselee, Renas Bacho, Zheng-Xin Yong, Florencia de la Rosa, Nathan Cho, Xiuyu Li, Guillaume Malod, Orion Weller, Guglielmo Albani, Leon Lang, Julien Laurendeau, Dmitry Kazakov, Fatimah Adesanya, Julien Portier, Lawrence Hollom, Victor Souza, Yuchen Anna Zhou, Julien Degorre, Yiğit Yalın, Gbenga Daniel Obikoya, Rai, Filippo Bigi, M. C. Boscá, Oleg Shumar, Kaniuar Bacho, Gabriel Recchia, Mara Popescu, Nikita Shulga, Ngefor Mildred Tanwie, Thomas C. H. Lux, Ben Rank, Colin Ni, Matthew Brooks, Alesia Yakimchyk, Huanxu, Liu, Stefano Cavalleri, Olle Häggström, Emil Verkama, Joshua Newbould, Hans Gundlach, Leonor Brito-Santana, Brian Amaro, Vivek Vajipey, Rynaa Grover, Ting Wang, Yosi Kratish, Wen-Ding Li, Sivakanth Gopi, Andrea Caciolai, Christian Schroeder de Witt, Pablo Hernández-Cámara, Emanuele Rodolà, Jules Robins, Dominic Williamson, Vincent Cheng, Brad Raynor, Hao Qi, Ben Segev, Jingxuan Fan, Sarah Martinson, Erik Y. Wang, Kaylie Hausknecht, Michael P. Brenner, Mao Mao, Christoph Demian, Peyman Kassani, Xinyu Zhang, David Avagian, Eshawn Jessica Scipio, Alon Ragoler, Justin Tan, Blake Sims, Rebeka Plecnik, Aaron Kirtland, Omer Faruk Bodur, D. P. Shinde, Yan Carlos Leyva Labrador, Zahra Adoul, Mohamed Zekry, Ali Karakoc, Tania C. B. Santos, Samir Shamseldeen, Loukmane Karim, Anna Liakhovitskaia, Nate Resman, Nicholas Farina, Juan Carlos Gonzalez, Gabe Maayan, Earth Anderson, Rodrigo De Oliveira Pena, Elizabeth Kelley, Hodjat Mariji, Rasoul Pouriamanesh, Wentao Wu, Ross Finocchio, Ismail Alarab, Joshua Cole, Danyelle Ferreira, Bryan Johnson, Mohammad Safdari, Liangti Dai, Siriphan Arthornthurasuk, Isaac C. McAlister, Alejandro José Moyano, Alexey Pronin, Jing Fan, Angel Ramirez-Trinidad, Yana Malysheva, Daphiny Pottmaier, Omid Taheri, Stanley Stepanic, Samuel Perry, Luke Askew, Raúl Adrián Huerta Rodríguez, Ali M. R. Minissi, Ricardo Lorena, Krishnamurthy Iyer, Arshad Anil Fasiludeen, Ronald Clark, Josh Ducey, Matheus Piza, Maja Somrak, Eric Vergo, Juehang Qin, Benjámin Borbás, Eric Chu, Jack Lindsey, Antoine Jallon, I. M. J. McInnis, Evan Chen, Avi Semler, Luk Gloor, Tej Shah, Marc Carauleanu, Pascal Lauer, Tran Đuc Huy, Hossein Shahrtash, Emilien Duc, Lukas Lewark, Assaf Brown, Samuel Albanie, Brian Weber, Warren S. Vaz, Pierre Clavier, Yiyang Fan, Gabriel Poesia Reis e Silva, Long, Lian, Marcus Abramovitch, Xi Jiang, Sandra Mendoza, Murat Islam, Juan Gonzalez, Vasilios Mavroudis, Justin Xu, Pawan Kumar, Laxman Prasad Goswami, Daniel Bugas, Nasser Heydari, Ferenc Jeanplong, Thorben Jansen, Antonella Pinto, Archimedes Apronti, Abdallah Galal, Ng Ze-An, Ankit Singh, Tong Jiang, Joan of Arc Xavier, Kanu Priya Agarwal, Mohammed Berkani, Gang Zhang, Zhehang Du, Benedito Alves de Oliveira Junior, Dmitry Malishev, Nicolas Remy, Taylor D. Hartman, Tim Tarver, Stephen Mensah, Gautier Abou Loume, Wiktor Morak, Farzad Habibi, Sarah Hoback, Will Cai, Javier Gimenez, Roselynn Grace Montecillo, Jakub Łucki, Russell Campbell, Asankhaya Sharma, Khalida Meer, Shreen Gul, Daniel Espinosa Gonzalez, Xavier Alapont, Alex Hoover, Gunjan Chhablani, Freddie Vargus, Arunim Agarwal, Yibo Jiang, Deepakkumar Patil, David Outevsky, Kevin Joseph Scaria, Rajat Maheshwari, Abdelkader Dendane, Priti Shukla, Ashley Cartwright, Sergei Bogdanov, Niels Mündler, Sören Möller, Luca Arnaboldi, Kunvar Thaman, Muhammad Rehan Siddiqi, Prajvi Saxena, Himanshu Gupta, Tony Fruhauff, Glen Sherman, Mátyás Vincze, Siranut Usawasutsakorn, Dylan Ler, Anil Radhakrishnan, Innocent Enyekwe, Sk Md Salauddin, Jiang Muzhen, Aleksandr Maksapetyan, Vivien Rossbach, Chris Harjadi, Mohsen Bahaloohoreh, Claire Sparrow, Jasdeep Sidhu, Sam Ali, Song Bian, John Lai, Eric Singer, Justine Leon Uro, Greg Bateman, Mohamed Sayed, Ahmed Menshawy, Darling Duclosel, Dario Bezzi, Yashaswini Jain, Ashley Aaron, Murat Tiryakioglu, Sheeshram Siddh, Keith Krenek, Imad Ali Shah, Jun Jin, Scott Creighton, Denis Peskoff, Zienab EL-Wasif, Ragavendran P, Michael Richmond, Joseph McGowan, Tejal Patwardhan, Hao-Yu Sun, Ting Sun, Nikola Zubić, Samuele Sala, Stephen Ebert, Jean Kaddour, Manuel Schottdorf, Dianzhuo Wang, Gerol Petruzella, Alex Meiburg, Tilen Medved, Ali ElSheikh, S Ashwin Hebbar, Lorenzo Vaquero, Xianjun Yang, Jason Poulos, Vilém Zouhar, Sergey Bogdanik, Mingfang Zhang, Jorge Sanz-Ros, David Anugraha, Yinwei Dai, Anh N. Nhu, Xue Wang, Ali Anil Demircali, Zhibai Jia, Yuyin Zhou, Juncheng Wu, Mike He, Nitin Chandok, Aarush Sinha, Gaoxiang Luo, Long Le, Mickaël Noyé, Michał Perełkiewicz, Ioannis Pantidis, Tianbo Qi, Soham Sachin Purohit, Letitia Parcalabescu, Thai-Hoa Nguyen, Genta Indra Winata, Edoardo M. Ponti, Hanchen Li, Kaustubh Dhole, Jongee Park, Dario Abbondanza, Yuanli Wang, Anupam Nayak, Diogo M. Caetano, Antonio A. W. L. Wong, Maria del Rio-Chanona, Dániel Kondor, Pieter Francois, Ed Chalstrey, Jakob Zsambok, Dan Hoyer, Jenny Reddish, Jakob Hauser, Francisco-Javier Rodrigo-Ginés, Suchandra Datta, Maxwell Shepherd, Thom Kamphuis, Qizheng Zhang, Hyunjun Kim, Ruiji Sun, Jianzhu Yao, Franck Dernoncourt, Satyapriya Krishna, Sina Rismanchian, Bonan Pu, Francesco Pinto, Yingheng Wang, Kumar Shridhar, Kalon J. Overholt, Glib Briia, Hieu Nguyen, David, Soler Bartomeu, Tony CY Pang, Adam Wecker, Yifan Xiong, Fanfei Li, Lukas S. Huber, Joshua Jaeger, Romano De Maddalena, Xing Han Lù, Yuhui Zhang, Claas Beger, Patrick Tser Jern Kon, Sean Li, Vivek Sanker, Ming Yin, Yihao Liang, Xinlu Zhang, Ankit Agrawal, Li S. Yifei, Zechen Zhang, Mu Cai, Yasin Sonmez, Costin Cozianu, Changhao Li, Alex Slen, Shoubin Yu, Hyun Kyu Park, Gabriele Sarti, Marcin Briański, Alessandro Stolfo, Truong An Nguyen, Mike Zhang, Yotam Perlitz, Jose Hernandez-Orallo, Runjia Li, Amin Shabani, Felix Juefei-Xu, Shikhar Dhingra, Orr Zohar, My Chiffon Nguyen, Alexander Pondaven, Abdurrahim Yilmaz, Xuandong Zhao, Chuanyang Jin, Muyan Jiang, Stefan Todoran, Xinyao Han, Jules Kreuer, Brian Rabern, Anna Plassart, Martino Maggetti, Luther Yap, Robert Geirhos, Jonathon Kean, Dingsu Wang, Sina Mollaei, Chenkai Sun, Yifan Yin, Shiqi Wang, Rui Li, Yaowen Chang, Anjiang Wei, Alice Bizeul, Xiaohan Wang, Alexandre Oliveira Arrais, Kushin Mukherjee, Jorge Chamorro-Padial, Jiachen Liu, Xingyu Qu, Junyi Guan, Adam Bouyamourn, Shuyu Wu, Martyna Plomecka, Junda Chen, Mengze Tang, Jiaqi Deng, Shreyas Subramanian, Haocheng Xi, Haoxuan Chen, Weizhi Zhang, Yinuo Ren, Haoqin Tu, Sejong Kim, Yushun Chen, Sara Vera Marjanović, Junwoo Ha, Grzegorz Luczyna, Jeff J. Ma, Zewen Shen, Dawn Song, Cedegao E. Zhang, Zhun Wang, Gaël Gendron, Yunze Xiao, Leo Smucker, Erica Weng, Kwok Hao Lee, Zhe Ye, Stefano Ermon, Ignacio D. Lopez-Miguel, Theo Knights, Anthony Gitter, Namkyu Park, Boyi Wei, Hongzheng Chen, Kunal Pai, Ahmed Elkhanany, Han Lin, Philipp D. Siedler, Jichao Fang, Ritwik Mishra, Károly Zsolnai-Fehér, Xilin Jiang, Shadab Khan, Jun Yuan, Rishab Kumar Jain, Xi Lin, Mike Peterson, Zhe Wang, Aditya Malusare, Maosen Tang, Isha Gupta, Ivan Fosin, Timothy Kang, Barbara Dworakowska, Kazuki Matsumoto, Guangyao Zheng, Gerben Sewuster, Jorge Pretel Villanueva, Ivan Rannev, Igor Chernyavsky, Jiale Chen, Deepayan Banik, Ben Racz, Wenchao Dong, Jianxin Wang, Laila Bashmal, Duarte V. Gonçalves, Wei Hu, Kaushik Bar, Ondrej Bohdal, Atharv Singh Patlan, Shehzaad Dhuliawala, Caroline Geirhos, Julien Wist, Yuval Kansal, Bingsen Chen, Kutay Tire, Atak Talay Yücel, Brandon Christof, Veerupaksh Singla, Zijian Song, Sanxing Chen, Jiaxin Ge, Kaustubh Ponkshe, Isaac Park, Tianneng Shi, Martin Q. Ma, Joshua Mak, Sherwin Lai, Antoine Moulin, Zhuo Cheng, Zhanda Zhu, Ziyi Zhang, Vaidehi Patil, Ketan Jha, Qiutong Men, Jiaxuan Wu, Tianchi Zhang, Bruno Hebling Vieira, Alham Fikri Aji, Jae-Won Chung, Mohammed Mahfoud, Ha Thi Hoang, Marc Sperzel, Wei Hao, Kristof Meding, Sihan Xu, Vassilis Kostakos, Davide Manini, Yueying Liu, Christopher Toukmaji, Jay Paek, Eunmi Yu, Arif Engin Demircali, Zhiyi Sun, Ivan Dewerpe, Hongsen Qin, Roman Pflugfelder, James Bailey, Johnathan Morris, Ville Heilala, Sybille Rosset, Zishun Yu, Peter E. Chen, Woongyeong Yeo, Eeshaan Jain, Ryan Yang, Sreekar Chigurupati, Julia Chernyavsky, Sai Prajwal Reddy, Subhashini Venugopalan, Hunar Batra, Core Francisco Park, Hieu Tran, Guilherme Maximiano, Genghan Zhang, Yizhuo Liang, Hu Shiyu, Rongwu Xu, Rui Pan, Siddharth Suresh, Ziqi Liu, Samaksh Gulati, Songyang Zhang, Peter Turchin, Christopher W. Bartlett, Christopher R. Scotese, Phuong M. Cao, Ben Wu, Jacek Karwowski, Davide Scaramuzza, Aakaash Nattanmai, Gordon McKellips, Anish Cheraku, Asim Suhail, Ethan Luo, Marvin Deng, Jason Luo, Ashley Zhang, Kavin Jindel, Jay Paek, Kasper Halevy, Allen Baranov, Michael Liu, Advaith Avadhanam, David Zhang, Vincent Cheng, Brad Ma, Evan Fu, Liam Do, Joshua Lass, Hubert Yang, Surya Sunkari, Vishruth Bharath, Violet Ai, James Leung, Rishit Agrawal, Alan Zhou, Kevin Chen, Tejas Kalpathi, Ziqi Xu, Gavin Wang, Tyler Xiao, Erik Maung, Sam Lee, Ryan Yang, Roy Yue, Ben Zhao, Julia Yoon, Sunny Sun, Aryan Singh, Ethan Luo, Clark Peng, Tyler Osbey, Taozhi Wang, Daryl Echeazu, Hubert Yang, Timothy Wu, Spandan Patel, Vidhi Kulkarni, Vijaykaarti Sundarapandiyan, Ashley Zhang, Andrew Le, Zafir Nasim, Srikar Yalam, Ritesh Kasamsetty, Soham Samal, Hubert Yang, David Sun, Nihar Shah, Abhijeet Saha, Alex Zhang, Leon Nguyen, Laasya Nagumalli, Kaixin Wang, Alan Zhou, Aidan Wu, Jason Luo, Anwith Telluri, Steven Dillmann, Zhengxiang Wang, Junyu Luo, Hugo Lunn, Artem Gazizov, Haitz Sáez de Ocáriz Borde, Ivan Trus, Morgan Hervault, Zheyu Zhang, Bo Chen, Yuchen Wu, Christopher J. Cordier, Gün Kaynar, Cansin Ayvaz, Polina Avdiunina, Johannes Brust, Xingjian Diao, K. D. Meaney, Yifan Gu, Chenyu Wang, Chenzhuo Dong, William Wright, Simon Brave, Owen Root, Jiayuan Liu, Chow Chun Lok, Tianqin Li, Shiyi Du, Dailan He, Lufeiya Liu, Sina Jamalzadegan, Anil Ramakrishna, Xuanqing Xu, Xin Qing, Xin Luo, Wenkai Li, Shi Bo, Filipp Gusev, Maximos Skandalis, Desheng Ma, Chunhui Zhang, Haoran Qiu, Allen G Hart, Rickard Brüel Gabrielsson, Ido Akov, Artem Lukoianov, Summer Yue, Alexandr Wang, Dan Hendrycks (2025)
• JEPA-T: Joint-Embedding Predictive Architecture with Text Fusion for Image Generation - Wan, Siheng; Yao, Zhengtao; Li, Zhengdao; Dong, Junhao; Li, Yanshu; Li, Yikai; Li, Linshan; Xu, Haoyan; Li, Yijiang; Dong, Zhikang; Wang, Huacan; Shen, Jifeng (2025)
• Kimi K2: Open Agentic Intelligence - Kimi Team (2025)
• Kimi Linear: An Expressive, Efficient Attention Architecture - Kimi Team, Yu Zhang, Zongyu Lin, Xingcheng Yao, Jiaxi Hu, Fanqing Meng, Chengyin Liu, Xin Men, Songlin Yang, Zhiyuan Li, Wentao Li, Enzhe Lu, Weizhou Liu, Yanru Chen, Weixin Xu, Longhui Yu, Yejie Wang, Yu Fan, Longguang Zhong, Enming Yuan, Dehao Zhang, Yizhi Zhang, T. Y. Liu, Haiming Wang, Shengjun Fang, Weiran He, Shaowei Liu, Yiwei Li, Jianlin Su, Jiezhong Qiu, Bo Pang, Junjie Yan, Zhejun Jiang, Weixiao Huang, Bohong Yin, Jiacheng You, Chu Wei, Zhengtao Wang, Chao Hong, Yutian Chen, Guanduo Chen, Yucheng Wang, Huabin Zheng, Feng Wang, Yibo Liu, Mengnan Dong, Zheng Zhang, Siyuan Pan, Wenhao Wu, Yuhao Wu, Longyu Guan, Jiawen Tao, Guohong Fu, Xinran Xu, Yuzhi Wang, Guokun Lai, Yuxin Wu, Xinyu Zhou, Zhilin Yang, Yulun Du (2025)
• Kimi k1.5: Scaling Reinforcement Learning with LLMs - Kimi Team (2025)
• Kimi-VL Technical Report - Kimi Team; Angang Du; Bohong Yin (2025)
• LIMO: Less is More for Reasoning - Yixin Ye; Zhen Huang; Yang Xiao (2025)
• LLM-JEPA: Large Language Models Meet Joint Embedding Predictive Architectures - L. Yuan et al. (2025)
• MiniMax-01: Scaling Foundation Models with Lightning Attention - MiniMax, Aonian Li, Bangwei Gong, Bo Yang, Boji Shan, Chang Liu (2025)
• MiniMax-M1: Scaling Test-Time Compute Efficiently with Lightning Attention - MiniMax; Ailin Huang; et al. (2025)
• MoBA: Mixture of Block Attention for Long-Context LLMs - Enzhe Lu, Zhejun Jiang, Jingyuan Liu, Yulun Du, Tao Jiang, Chao Hong, Shaowei Liu, Weiran He, Enming Yuan, Yuzhi Wang, Zhiqi Huang, Huan Yuan, Suting Xu, Xinran Xu, Guokun Lai, Yanru Chen, Huabin Zheng, Junjie Yan, Jianlin Su, Yuxin Wu, Neo Y. Zhang, Zhilin Yang, Xinyu Zhou, Mingxing Zhang, Jiezhong Qiu (2025)
• Muon is Scalable for LLM Training - Jingyuan Liu; Jianlin Su; Xingcheng Yao (2025)
• Native Sparse Attention: Hardware-Aligned and Natively Trainable Sparse Attention - Yuan, Jingyang, Gao, Huazuo, Dai, Damai et al. (2025)
• Next-Embedding Prediction Makes Strong Vision Learners - Xu, Sihan; Ma, Ziqiao; Chai, Wenhao; Chen, Xuweiyi; Jin, Weiyang; Chai, Joyce; Xie, Saining; Yu, Stella X. (2025)
• On the Biology of a Large Language Model - Jack Lindsey, Adly Templeton, Jonathan Marcus, Thomas Conerly, Joshua Batson, Chris Olah, et al. (2025)
• Open-Reasoner-Zero: An Open Source Approach to Scaling Up Reinforcement Learning on the Base Model - Jingcheng Hu; Yinmin Zhang; Qi Han (2025)
• OpenAI o3 and o4-mini System Card - OpenAI (o3 and o4-mini team) (2025)
• PaperBench: Evaluating AI's Ability to Replicate AI Research - Giulio Starace; Oliver Jaffe; Dane Sherburn (2025)
• Phi-4-reasoning Technical Report - Abdin, Marah, Agarwal, Sahaj, Awadallah, Ahmed et al. (2025)
• Process Reinforcement through Implicit Rewards - Ganqu Cui, Lifan Yuan, Zefan Wang, Hanbin Wang, Wendi Li, Bingxiang He, Yuchen Fan, Tianyu Yu, Qixin Xu, Weize Chen, Jiarui Yuan, Huayu Chen, Kaiyan Zhang, Xingtai Lv, Shuo Wang, Yuan Yao, Xu Han, Hao Peng, Yu Cheng, Zhiyuan Liu, Maosong Sun, Bowen Zhou, Ning Ding (2025)
• Qwen2.5-VL Technical Report - Bai, Shuai, Chen, Keqin, Liu, Xuejing et al. (2025)
• Qwen3 Technical Report - Yang, A. et al. (Qwen Team) (2025)
• Qwen3-Next Technical Report - Qwen Team (2025)
• Qwen3-VL Technical Report - Bai, S. et al. (Qwen Team) (2025)
• Reinforcement Learning for Reasoning in Large Language Models with Tulu 3 RLVR - Yiping Wang, et al. (Tulu/RLVR) (2025)
• SWE-Lancer: Can Frontier LLMs Earn $1 Million from Real-World Freelance Software Engineering? - Miserendino, Samuel, Wang, Michele, Patwardhan, Tejal et al. (2025)
• SmolLM2: When Smol Goes Big -- Data-Centric Training of a Small Language Model - Loubna Ben Allal, Anton Lozhkov, Elie Bakouch, Gabriel Martín Blázquez, Guilherme Penedo, Lewis Tunstall (2025)
• Spurious Rewards: Rethinking Training Signals in RLVR - Rulin Shao, Shuyue Stella Li, Rui Xin, Scott Geng, Yiping Wang, Sewoong Oh, Simon Shaolei Du, Nathan Lambert, Sewon Min, Ranjay Krishna, Yulia Tsvetkov, Hannaneh Hajishirzi, Pang Wei Koh, Luke Zettlemoyer (2025)
• System Card: Claude Opus 4 and Claude Sonnet 4 - Anthropic (2025)
• The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models via the Lens of Problem Complexity - Apple Inc. (Parthasarathy Ramanathan, et al.) (2025)
• The Lessons of Developing Process Reward Models in Mathematical Reasoning - Zhenru Zhang; Chujie Zheng; Yangzhen Wu (2025)
• Titans: Learning to Memorize at Test Time - Ali Behrouz, Peilin Zhong, Vahab Mirrokni (2025)
• Tulu 3: Pushing Frontiers in Open Language Model Post-Training - Hamish Ivison; Shengyi Huang; Hongyi Wang; Nouha Dziri; Luca Soldaini; Matthew E. Peters; Sasha Luccioni; Yulia Tsvetkov; Yejin Choi; Ali Farhadi; Hannaneh Hajishirzi (Ai2) (2025)
• V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning - A. Bardes; Q. Garrido; C. Russell; J. Ponce; Y. LeCun; N. Ballas; M. Assran (2025)
• gpt-oss-120b & gpt-oss-20b Model Card - OpenAI, :, Sandhini Agarwal, Lama Ahmad, Jason Ai, Sam Altman, Andy Applebaum, Edwin Arbus, Rahul K. Arora, Yu Bai, Bowen Baker, Haiming Bao, Boaz Barak, Ally Bennett, Tyler Bertao, Nivedita Brett, Eugene Brevdo, Greg Brockman, Sebastien Bubeck, Che Chang, Kai Chen, Mark Chen, Enoch Cheung, Aidan Clark, Dan Cook, Marat Dukhan, Casey Dvorak, Kevin Fives, Vlad Fomenko, Timur Garipov, Kristian Georgiev, Mia Glaese, Tarun Gogineni, Adam Goucher, Lukas Gross, Katia Gil Guzman, John Hallman, Jackie Hehir, Johannes Heidecke, Alec Helyar, Haitang Hu, Romain Huet, Jacob Huh, Saachi Jain, Zach Johnson, Chris Koch, Irina Kofman, Dominik Kundel, Jason Kwon, Volodymyr Kyrylov, Elaine Ya Le, Guillaume Leclerc, James Park Lennon, Scott Lessans, Mario Lezcano-Casado, Yuanzhi Li, Zhuohan Li, Ji Lin, Jordan Liss, Lily, Liu, Jiancheng Liu, Kevin Lu, Chris Lu, Zoran Martinovic, Lindsay McCallum, Josh McGrath, Scott McKinney, Aidan McLaughlin, Song Mei, Steve Mostovoy, Tong Mu, Gideon Myles, Alexander Neitz, Alex Nichol, Jakub Pachocki, Alex Paino, Dana Palmie, Ashley Pantuliano, Giambattista Parascandolo, Jongsoo Park, Leher Pathak, Carolina Paz, Ludovic Peran, Dmitry Pimenov, Michelle Pokrass, Elizabeth Proehl, Huida Qiu, Gaby Raila, Filippo Raso, Hongyu Ren, Kimmy Richardson, David Robinson, Bob Rotsted, Hadi Salman, Suvansh Sanjeev, Max Schwarzer, D. Sculley, Harshit Sikchi, Kendal Simon, Karan Singhal, Yang Song, Dane Stuckey, Zhiqing Sun, Philippe Tillet, Sam Toizer, Foivos Tsimpourlas, Nikhil Vyas, Eric Wallace, Xin Wang, Miles Wang, Olivia Watkins, Kevin Weil, Amy Wendling, Kevin Whinnery, Cedric Whitney, Hannah Wong, Lin Yang, Yu Yang, Michihiro Yasunaga, Kristen Ying, Wojciech Zaremba, Wenting Zhan, Cyril Zhang, Brian Zhang, Eddie Zhang, Shengjia Zhao (2025)
• gpt-oss-120b & gpt-oss-20b Model Card (gpt-oss Technical Report) - OpenAI (2025)
• rStar-Math: Small LLMs Can Master Math Reasoning with Self-Evolved Deep Thinking - Guanzhong Lyu, et al. (Tencent) (2025)
• s1: Simple test-time scaling - Niklas Muennighoff, Zitong Yang, Weijia Shi, Xiang Lisa Li, Li Fei-Fei, Hannaneh Hajishirzi (2025)
• xLLM Technical Report: Building Efficient LLM Inference Infrastructure - xLLM Team (Meituan) (2025)

2024

• $π_0$: A Vision-Language-Action Flow Model for General Robot Control - Kevin Black; Noah Brown; Danny Driess (2024)
• $τ$-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains - Shunyu Yao, Noah Shinn, Pedram Razavi, Karthik Narasimhan (2024)
• A Survey on LLM-as-a-Judge - Jiawei Gu; Xuhui Jiang; Zhichao Shi (2024)
• A Survey on Mixture of Experts in Large Language Models - Cai, Weilin, Jiang, Juyong, Wang, Fan et al. (2024)
• A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications - Pranab Sahoo; Ayush Kumar Singh; Sriparna Saha; et al. (2024)
• AI Sandbagging: Language Models can Strategically Underperform on Evaluations - Teun van der Weij; Felix Hofstätter; Ollie Jaffe (2024)
• AI achieves silver-medal standard solving International Mathematical Olympiad problems (AlphaProof) - AlphaProof and AlphaGeometry teams (DeepMind) (2024)
• AI models collapse when trained on recursively generated data - Ivan Shumailov et al. (2024)
• ARC Prize 2024: Technical Report - Francois Chollet; Mike Knoop; Gregory Kamradt (2024)
• Adam-mini: Use Fewer Learning Rates To Gain More - Yushun Zhang; Congliang Chen; Ziniu Li (2024)
• Agent-as-a-Judge: Evaluate Agents with Agents - Mingchen Zhuge; Changsheng Zhao; Dylan Ashley (2024)
• Alignment faking in large language models - Greenblatt, Ryan, Denison, Carson, Wright, Benjamin et al. (2024)
• AlphaGeometry: Solving olympiad geometry without human demonstrations - Trieu H. Trinh, Yuhuai Wu, Quoc V. Le, He He, Thang Luong (2024)
• AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation - Wu, Q.; Bansal, G.; Zhang, J.; Wu, C.; Li, B.; Zhu, E.; Jiang, L.; Zhang, X.; Zhang, S.; Liu, J.; Awadallah, A. H.; White, R. W.; Burger, D.; Wang, C. (2024)
• BitNet a4.8: 4-bit Activations for 1-bit LLMs - Hongyu Wang; Shuming Ma; Furu Wei (2024)
• Byte Latent Transformer: Patches Scale Better Than Tokens - Pagnoni, Artidoro, Pasunuru, Ram, Rodriguez, Pedro et al. (2024)
• Chameleon: Mixed-Modal Early-Fusion Foundation Models - Chameleon Team (Meta AI) (2024)
• ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools - Junyang Lin, An Yang, Jinze Bai, et al. (2024)
• Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference - Wei-Lin Chiang; Lianmin Zheng; Ying Sheng; Anastasios Nikolas Angelopoulos (2024)
• Chronos: Learning the Language of Time Series - Abdul Fatir Ansari; Lorenzo Stella; Caner Turkmen (2024)
• Claude 3 Model Card - Anthropic (2024)
• CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer - Zhuoyi Yang; Jiayan Teng; Wendi Zheng (2024)
• Connecting Joint-Embedding Predictive Architecture with Contrastive Self-supervised Learning (2024)
• Corrective Retrieval Augmented Generation - Shi-Qi Yan, Jia-Chen Gu, Yun Zhu, et al. (2024)
• DataComp-LM: In search of the next generation of training sets for language models - Jeffrey Li; Alex Fang; Georgios Smyrnis; Maor Ivgi (2024)
• DeepSeek LLM: Scaling Open-Source Language Models with Longtermism - DeepSeek-AI, :, Xiao Bi, et al. (2024)
• DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligence - Daya Guo, Qihao Zhu, Dejian Yang, et al. (2024)
• DeepSeek-Prover: Advancing Theorem Proving in LLMs through Large-Scale Synthetic Data - Huajian Xin, Daya Guo, Zhihong Shao, Zhizhou Ren et al. (2024)
• DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model - An Yang; Baosong Yang; Binyuan Hui; Bo Zheng; et al. (2024)
• DeepSeek-V3 technical report - DeepSeek-AI (2024)
• DeepSeekMath: pushing the limits of mathematical reasoning in open language models - Zhipeng Guo et al. (DeepSeek-AI) (2024)
• DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models - Dai et al. (DeepSeek-AI) (2024)
• Diffusion Models Are Real-Time Game Engines - Dani Valevski, Yaniv Leviathan, Moab Arar, Shlomi Fruchter (2024)
• DoRA: Weight-Decomposed Low-Rank Adaptation - Shih-Yang Liu, Chien-Yi Wang, Hongxu Yin, Pavlo Molchanov, Yu-Chiang Frank Wang, Kwang-Ting Cheng (2024)
• Emu3: Next-Token Prediction is All You Need - Xinlong Wang; Xiaosong Zhang; Zhengxiong Luo (2024)
• Evaluating the world model implicit in a generative model - Keyon Vafa; Justin Y. Chen; Ashesh Rambachan; Jon Kleinberg; Sendhil Mullainathan (2024)
• Executable Code Actions Elicit Better LLM Agents - Xingyao Wang, Yangyi Chen, Lifan Yuan, et al. (2024)
• Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling - Zhe Chen; Weiyun Wang; Yue Cao; Yangzhou Liu (2024)
• FlashAttention-3: Fast and Accurate Attention with Asynchrony and Low-precision - Jay Shah, Ganesh Bikshandi, Ying Zhang, Vijay Thakkar, Pradeep Ramani, Tri Dao (2024)
• From Local to Global: A Graph RAG Approach to Query-Focused Summarization - Darren Edge; Ha Trinh; Newman Cheng; Joshua Bradley (2024)
• FrontierMath: A Benchmark for Evaluating Advanced Mathematical Reasoning in AI - Elliot Glazer, Ege Erdil, Tamay Besiroglu, Diego Chicharro, Evan Chen, Alex Gunning, Caroline Falkman Olsson, Jean-Stanislas Denain, Anson Ho, Emily de Oliveira Santos, Olli Järviniemi, Matthew Barnett, Robert Sandler, Matej Vrzala, Jaime Sevilla, Qiuyu Ren, Elizabeth Pratt, Lionel Levine, Grant Barkley, Natalie Stewart, Bogdan Grechuk, Tetiana Grechuk, Shreepranav Varma Enugandla, Mark Wildon (2024)
• GLM-4: A Multilingual Large Language Model Family (release page) - GLM Team, Aohan Zeng, Bin Xu, et al. (Zhipu AI / Tsinghua) (2024)
• GPT-4o System Card - OpenAI (2024)
• Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context - Gemini Team (Google) (2024)
• Gemma Scope: Open Sparse Autoencoders Everywhere All At Once on Gemma 2 - Lieberum, Tom, Rajamanoharan, Senthooran, Conmy, Arthur et al. (2024)
• Gemma: Open Models Based on Gemini Research and Technology - Team Gemma, Google (2024)
• Generative Verifiers: Reward Modeling as Next-Token Prediction - Lunjun Zhang; Arian Hosseini; Hritik Bansal (2024)
• Genie 2: A large-scale foundation world model - DeepMind (2024)
• Genie: Generative Interactive Environments - Jake Bruce, Michael Dennis, et al. (DeepMind) (2024)
• Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models - Albert Q. Jiang, Alexandre Sablayrolles, et al. (2024)
• Grok-1 (open-weights 314B MoE release) - xAI (2024)
• HarmBench: A Standardized Evaluation Framework for Automated Red Teaming and Robust Refusal - Mantas Mazeika; Long Phan; Xuwang Yin (2024)
• HippoRAG: Neurobiologically Inspired Long-Term Memory for Large Language Models - Bernal Jiménez Gutiérrez; Yiheng Shu; Yu Gu (2024)
• How JEPA Avoids Noisy Features: The Implicit Bias of Deep Linear Self Distillation Networks - Littwin, Etai; Saremi, Omid; Advani, Madhu; Thilak, Vimal; Nakkiran, Preetum; Huang, Chen; Susskind, Joshua (2024)
• HunyuanVideo: A Systematic Framework For Large Video Generative Models - Weijie Kong, Qi Tian, Zijian Zhang, et al. (2024)
• Inference Scaling Laws: An Empirical Analysis of Compute-Optimal Inference for Problem-Solving with Language Models - Yangzhen Wu, Zhiqing Sun, Shanda Li, Sean Welleck, Yiming Yang (2024)
• Jamba: A Hybrid Transformer-Mamba Language Model - Yoav Shoham; Opher Lieber; et al. (AI21 Labs) (2024)
• Janus: Decoupling Visual Encoding for Unified Multimodal Understanding and Generation - Chengyue Wu; Xiaokang Chen; Zhiyu Wu; Yiyang Ma (2024)
• Jumping Ahead: Improving Reconstruction Fidelity with JumpReLU Sparse Autoencoders - Senthooran Rajamanoharan; Tom Lieberum; Nicolas Sonnerat (2024)
• KAN: Kolmogorov-Arnold Networks - Liu et al. (2024)
• KTO: Model Alignment as Prospect Theoretic Optimization - Ethayarajh, Kawin, Xu, Winnie, Muennighoff, Niklas et al. (2024)
• Large Language Monkeys: Scaling Inference Compute with Repeated Sampling - Bradley Brown, Jordan Juravsky, Ryan Ehrlich, Ronald Clark, Quoc V. Le, Christopher Ré, Azalia Mirhoseini (2024)
• Learning and Leveraging World Models in Visual Representation Learning - Quentin Garrido, Mahmoud Assran, Nicolas Ballas, Adrien Bardes, Laurent Najman, Yann LeCun (2024)
• Learning to Reason with LLMs - OpenAI (2024)
• Leave No Context Behind: Efficient Infinite Context Transformers with Infini-attention - Munkhdalai, T.; Faruqui, M.; Gidaris, S. (2024)
• LiveBench: A Challenging, Contamination-Limited LLM Benchmark - Colin White; Samuel Dooley; Manley Roberts (2024)
• LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code - Jain, Naman, Han, King, Gu, Alex et al. (2024)
• LoRA Learns Less and Forgets Less - Dan Biderman; Jacob Portes; Jose Javier Gonzalez Ortiz (2024)
• LoRA+: Efficient Low Rank Adaptation of Large Models - Soufiane Hayou; Nikhil Ghosh; Bin Yu (2024)
• LongRoPE: Extending LLM Context Window Beyond 2 Million Tokens - Yiran Ding; Li Lyna Zhang; Chengruidong Zhang (2024)
• M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation - Jianlv Chen; Shitao Xiao; Peitian Zhang (2024)
• MLE-bench: Evaluating Machine Learning Agents on Machine Learning Engineering - Chan, Jun Shern, Chowdhury, Neil, Jaffe, Oliver et al. (2024)
• MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark - Wang, Yubo, Ma, Xueguang, Zhang, Ge et al. (2024)
• Many-Shot In-Context Learning - Rishabh Agarwal; Avi Singh; Lei M. Zhang; Bernd Bohnet (2024)
• Mapping the Mind of a Large Language Model - Steven Bills; Nick Cammarata; Dan Mossing; Henk Tillman; Leo Gao; Gabriel Goh; Ilya Sutskever; Jan Leike; Jeff Wu; William Saunders (OpenAI) — see note (2024)
• Medusa: Simple LLM Inference Acceleration Framework with Multiple Decoding Heads - Tianle Cai; Yuliang Liu; et al. (CMU/Princeton/Together AI) (2024)
• Mixtral of experts - Albert Q. Jiang et al. (2024)
• Mixture-of-Depths: Dynamically allocating compute in transformer-based language models - David Raposo; Sam Laskin; et al. (Mila) (2024)
• MobileLLM: Optimizing Sub-billion Parameter Language Models for On-Device Use Cases - Zechun Liu, Changsheng Zhao, Forrest Iandola, Chen Lai, Yuandong Tian, Igor Fedorov (2024)
• Molmo and PixMo: Open Weights and Open Data for State-of-the-Art Vision-Language Models - Deitke, Matt, Clark, Christopher, Lee, Sangho et al. (2024)
• Movie Gen: A Cast of Media Foundation Models - Adam Polyak; Amit Zohar; Andrew Brown (2024)
• NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Models - Chankyu Lee; Rajarshi Roy; Mengyao Xu (2024)
• Nemotron-4 340B Technical Report - NVIDIA (2024)
• OLMo: Accelerating the Science of Language Models - OLMo Team (AI2), Dirk Groeneveld, et al. (2024)
• OLMoE: Open Mixture-of-Experts Language Models - Niklas Muennighoff, Luca Soldaini, Dirk Groeneveld, et al. (2024)
• ORPO: Monolithic Preference Optimization without Reference Model - Jiwoo Hong; Noah Lee; James Thorne (2024)
• OSWorld: Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environments - Xie, Tianbao, Zhang, Danyang, Chen, Jixuan et al. (2024)
• OpenAI o1 System Card - OpenAI o1 System Card authors (OpenAI) (2024)
• OpenELM: An Efficient Language Model Family with Open-Source Training and Inference Framework - S. Mehta; M. Koncel-Kedziorski; L. Tunstall; L. von Werra; S. Wolf (2024)
• OpenHands: An Open Platform for AI Software Developers as Generalist Agents - Xingyao Wang, Boxuan Li, Yufan Song, Frank F. Xu, Xiangru Tang, Mingchen Zhuge (2024)
• OpenVLA: An Open-Source Vision-Language-Action Model - Moo Jin Kim; Karl Pertsch; Siddharth Karamcheti (2024)
• PEARL: Physical World as Joint-Embedding Predictive Model - Lixiang Liu; Lebin Zhou; Vilim Stih; Yann LeCun; Mehdi Cherti (2024)
• Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone - Marah Abdin, et al. (Microsoft) (2024)
• Phi-4 Technical Report - Marah Abdin, Jyoti Aneja, Harkirat Behl, Sébastien Bubeck, Ronen Eldan, Suriya Gunasekar (2024)
• Physics of Language Models: Part 2.1, Grade-School Math and the Hidden Reasoning Process - Tian Ye, Zicheng Xu, Yuanzhi Li, Zeyuan Allen-Zhu (2024)
• Physics of Language Models: Part 2.2, How to Learn From Mistakes on Grade-School Math Problems - Tian Ye, Zicheng Xu, Yuanzhi Li, Zeyuan Allen-Zhu (2024)
• Physics of Language Models: Part 3.3, Knowledge Capacity Scaling Laws - Zeyuan Allen-Zhu, Yuanzhi Li (2024)
• Quiet-STaR: Language Models Can Teach Themselves to Think Before Speaking - Eric Zelikman, Georges Harik, Yijia Shao, Varuna Jayasiri, Nick Haber, Noah D. Goodman (2024)
• Qwen2 Technical Report - An Yang, Baosong Yang, Binyuan Hui, Bo Zheng, Bowen Yu, Chang Zhou (2024)
• Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution - Wang, Peng, Bai, Shuai, Tan, Sinan et al. (2024)
• Qwen2.5 Technical Report - Qwen Team: An Yang; Baosong Yang; Binyuan Hui; Bo Zheng; Bowen Yu; et al. (2024)
• Qwen2.5-Math Technical Report: Toward Mathematical Expert Model via Self-Improvement - An Yang; Beichen Zhang; Binyuan Hui (2024)
• R-JEPA: Robust JEPA with adversarial/feature-space objectives - Yonggan Fu, Kevin Zhao? ('Fu et al.') (2024)
• RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval - Parth Sarthi; Salman Abdullah; Aditi Tuli (2024)
• RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control - Anthony Brohan, Noah Brown, Justice Carbajal, Yevgen Chebotar et al. (Google DeepMind) (2024)
• RULER: What's the Real Context Size of Your Long-Context Language Models? - Cheng-Ping Hsieh, Simeng Sun, Samuel Kriman, Shantanu Acharya, Dima Rekesh, Fei Jia (2024)
• RankRAG: Unifying Context Ranking with Retrieval-Augmented Generation in LLMs - Yue Yu; Wei Ping; Zihan Liu (2024)
• Refined behavior and structural universality of the blow-up profile for the semilinear heat equation with non scale invariant nonlinearity - Loth Damagui Chabi, Philippe Souplet (2024)
• Refusal in Language Models Is Mediated by a Single Direction - Andy Arditi, Oscar Obeso, Aaquib Syed, Daniel Paleka, Nina Panickssery, Wes Gurnee, Neel Nanda (2024)
• Revisiting Feature Prediction for Learning Visual Representations from Video - A. Bardes; Q. Garrido; J. Ponce; A. Porrello; L. Beyer; M. Assran; Y. LeCun; N. Ballas (2024)
• Revisiting Feature Prediction for Learning Visual Representations from Video (V-JEPA) - Bardes, Ponce, LeCun et al. (2024)
• RewardBench: Evaluating Reward Models for Language Modeling - Nathan Lambert; Valentina Pyatkin; Jacob Morrison (2024)
• SAM 2: Segment Anything in Images and Videos - Nikhila Ravi; Valentin Gabeur; Yuan-Ting Hu; Ronghang Hu (2024)
• SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering - John Yang, Carlos E. Jimenez, Alexander Wettig, Kilian Lieret, Shunyu Yao, Karthik Narasimhan (2024)
• SWE-bench Multimodal: Do AI Systems Generalize to Visual Software Domains? - Yang, John, Jimenez, Carlos E., Zhang, Alex L. et al. (2024)
• SWE-bench: Can Language Models Resolve Real-World GitHub Issues? - Jimenez, C. E.; Yang, J.; Wettig, A.; Yao, S.; Pei, K.; Press, O.; Narasimhan, K. (2024)
• Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters - Snell, Lee, Xu, Kumar (2024)
• Scaling Laws for Precision - Tanishq Kumar, Zachary Ankner, Benjamin F. Spector, Blake Bordelon, Niklas Muennighoff, Mansheej Paul (2024)
• Scaling Monosemanticity: Extracting Interpretable Features from Claude 3 Sonnet - Adly Templeton, Tom Conerly, et al. (Anthropic) (2024)
• Scaling Rectified Flow Transformers for High-Resolution Image Synthesis - Patrick Esser; Sumith Kulal; Andreas Blattmann; Rahim Entezari (2024)
• Scaling Synthetic Data Creation with 1,000,000,000 Personas - Tao Ge, Xin Chan, Xiaoyang Wang, et al. (2024)
• Self-Rewarding Language Models - Weizhe Yuan, Richard Yuanzhe Pang, Kyunghyun Cho, Xian Li, Sainbayar Sukhbaatar, Jing Xu, Jason Weston (2024)
• Self-Taught Evaluators - Tianlu Wang; Ilia Kulikov; Olga Golovneva (2024)
• Sheared LLaMA: Accelerating Language Model Training via Structured Pruning - Mengzhou Xia; Tianyu Gao; et al. (Princeton/NVIDIA) (2024)
• Show-o: One Single Transformer to Unify Multimodal Understanding and Generation - Jinheng Xie; Weijia Mao; Zechen Bai (2024)
• SimPO: Simple Preference Optimization with a Reference-Free Reward - Yu Meng; Mengzhou Xia; Danqi Chen (2024)
• Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training - Evan Hubinger, Carson Denison, Jesse Mu, Mike Lambert, Meg Tong, Monte MacDiarmid, Tamera Lanham, Daniel M. Ziegler, Tim Maxwell, Newton Cheng, Adam Jermyn, Amanda Askell, Ansh Radhakrishnan, Cem Anil, David Duvenaud, Deep Ganguli, Fazl Barez, Jack Clark, Kamal Ndousse, Kshitij Sachan, Michael Sellitto, Mrinank Sharma, Nova DasSarma, Roger Grosse, Shauna Kravec, Yuntao Bai, Zachary Witten, Marina Favaro, Jan Brauner, Holden Karnofsky, Paul Christiano, Samuel R. Bowman, Logan Graham, Jared Kaplan, Sören Mindermann, Ryan Greenblatt, Buck Shlegeris, Nicholas Schiefer, Ethan Perez (2024)
• SnapKV: LLM Knows What You are Looking for Before Generation - Yuhong Li; Yingbing Huang; Bowen Yang (2024)
• Solving olympiad geometry without human demonstrations - T. R. Trinh; Y. Wu; Q. Le; H. Lee; T. Luong (2024)
• Sparse Feature Circuits: Discovering and Editing Interpretable Causal Graphs in Language Models - Samuel Marks; Can Rager; Eric J. Michaud (2024)
• T-JEPA: A Joint-Embedding Predictive Architecture for Trajectory Similarity Computation - Li, Lihuan; Xue, Hao; Song, Yang; Salim, Flora (2024)
• Taming Throughput-Latency Tradeoff in LLM Inference with Sarathi-Serve - Amey Agrawal; Nitin Kedia; Ashish Panwar (2024)
• The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery - Chris Lu, Cong Lu, Robert Tjarko Lange, et al. (2024)
• The Claude 3 model family: Opus, Sonnet, Haiku - Anthropic (2024)
• The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits - Shuming Ma; Hongyu Wang; Lingxiao Ma; Lei Wang (2024)
• The Era of 1-bit LLMs: Training Tips, Guidelines, and Analysis (BitNet b1.58) - Shuming Ma, Hongyu Wang, Lingxiao Ma, Lei Wang, Wenhui Wang, Shaohan Huang, Li Dong, Ruiping Wang, Jilong Xue, Furu Wei (2024)
• The Llama 3 Herd of Models - Llama Team, AI @ Meta: Aaron Grattafiori; Abhimanyu Dubey; Abhinav Jauhri; et al. (2024)
• The Mamba in the Llama: Distilling and Accelerating Hybrid Models - Junxiong Wang, Daniele Paliotta, Avner May, Alexander M. Rush, Tri Dao (2024)
• TinyLlama: An Open-Source Small Language Model - P. Zhang; G. Zeng; T. Wang; W. Lu (2024)
• Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality - Tri Dao; Albert Gu (2024)
• Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality (Mamba-2) - Tri Dao, Albert Gu (2024)
• Transfusion: Predict the Next Token and Diffuse Images with One Multi-Modal Model - Zhou, Chunting, Yu, Lili, Babu, Arun et al. (2024)
• Video Representation Learning with Joint-Embedding Predictive Architectures - Drozdov, Katrina; Shwartz-Ziv, Ravid; LeCun, Yann (2024)
• Video generation models as world simulators (Sora) - OpenAI (2024)
• nGPT: Normalized Transformer with Representation Learning on the Sphere - Ilya Loshchilov, Cheng-Ping Hsieh, Simeng Sun, Bryan Kian Hsiang Low (2024)

2023

• A Survey of Large Language Models - W. X. Zhao; K. Zhou; J. Li; et al. (2023)
• A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions - J. Huang; K. Zhou; W. X. Zhao; et al. (2023)
• A Survey on In-Context Learning - Dong et al. (2023)
• A Watermark for Large Language Models - John Kirchenbauer; Jonas Geiping; et al. (UMD) (2023)
• A-JEPA: Joint-Embedding Predictive Architecture for ASR - Mido Assran, Adrien Bardes, et al. (2023)
• AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration - Ji Lin, Jiaming Tang, Haotian Tang, Shang Yang, Wei-Ming Chen, Wei-Chen Wang (2023)
• AgentBench: Evaluating LLMs as Agents - Xiao Liu, Hao Yu, Hanchen Zhang, Yifan Xu, Xuanyu Lei, Hanyu Lai (2023)
• An Overview of Catastrophic AI Risks - Dan Hendrycks et al. (2023)
• Are Emergent Abilities of Large Language Models a Mirage? - Schaeffer, Miranda, Koyejo (2023)
• BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models - Junnan Li, Dongxu Li, Silvio Savarese, Steven Hoi (2023)
• Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine - Harsha Nori; Yin Tat Lee; Sheng Zhang; et al. (2023)
• Chain-of-Thought Hub: A Continuous Effort to Measure Large Language Models' Reasoning Performance - Yao Fu; Litu Ou; Mingyu Chen; et al. (2023)
• Chain-of-Verification (CoVe) reduces hallucination in large language models - Xiao Liu; Hanyu Lai; Hao Yu; Yifan Zhang; Xu Han; Yuxiao Dong; Jie Tang; et al. (2023)
• Chameleon: Learning Model Initializations Across Tasks With Different Architectures - T. Wang; S. Yue; J. Zhang; Z. Wei; Y. Li; J. Wang; Y. Qiao (2023)
• ChatDev: Communicative Agents for Software Development - Chen Qian, Wei Liu, Hongzhang Liu, et al. (2023)
• Code Llama: Open Foundation Models for Code - Baptiste Rozière, Jonas Gehring, Fabian Gloeckle, et al. (2023)
• Cognitive Architectures for Language Agents - Theodore R. Sumers, Shunyu Yao, Karthik Narasimhan, et al. (2023)
• DINOv2: Learning Robust Visual Features without Supervision - M. Oquab; T. Darcet; T. Moutakanni; H. Vo; M. Szafraniec; Y. LeCun (2023)
• Direct Preference Optimization: Your Language Model is Secretly a Reward Model - Rafailov, R.; Sharma, A.; Mitchell, E.; Manning, C. D.; Ermon, S.; Finn, C. (2023)
• Do As I Can, Not As I Say: Grounding Language in Robotic Affordances - Michael Ahn, et al. (2023)
• Dolma: an Open Corpus of 3 Trillion Tokens for Language Model Pretraining Research - Luca Soldaini, Rodney Kinney, et al. (2023)
• Efficient Memory Management for Large Language Model Serving with PagedAttention (vLLM) - Woosuk Kwon; Zhuohan Li; et al. (UC Berkeley) (2023)
• Efficient Streaming Language Models with Attention Sinks (StreamingLLM) - Guangxuan Xiao et al. (2023)
• Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task - Kenneth Li; Aspen K. Hopkins; et al. (MIT) (2023)
• Extending Context Window of Large Language Models via Positional Interpolation - Shouyuan Chen, Sherman Wong, Liangjian Chen, et al. (2023)
• Fast Inference from Transformers via Speculative Decoding - Yaniv Leviathan, Matan Kalman, Yossi Matias (2023)
• FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning - Tri Dao (2023)
• GLM-130B: An Open Bilingual Pre-trained Model - Aohan Zeng, Bin Xu, Bowen Wang, et al. (2023)
• GPQA: A Graduate-Level Google-Proof Q&A Benchmark - David Rein, Betty Li Hou, Asa Cooper Stickland, et al. (2023)
• GPT-4 technical report - OpenAI (2023)
• Gemini: A Family of Highly Capable Multimodal Models - Gemini Team, Google DeepMind (Rohan Anil, Sebastian Borgeaud, Yonghui Wu, et al.) (2023)
• Generative Agents: Interactive Simulacra of Human Behavior - Joon Sung Park et al. (2023)
• Gorilla: Large Language Model Connected with Massive APIs - S. Patil; T. Zhang; X. Wang; Y. E. Guo (2023)
• Graph of thoughts: solving elaborate problems with large language models - Maciej Besta et al. (2023)
• Graph-JEPA: Latent Graph Predictive Encoding - Yujie Feng, Xinghao Chen, Yue Zhang? (Sicheng? correct: 'Feng et al.') (2023)
• Hyena Hierarchy: Towards Larger Convolutional Language Models - Michael Poli, Stefano Massaroli, Eric Nguyen, et al. (2023)
• Image World Models: Learning from World Models by Denoising (IWM) - Quentin Garrido; Yubei Chen; Adrien Bardes; Laurent Najman; Yann LeCun (2023)
• Inference-Time Intervention: Eliciting Truthful Answers from a Language Model - Kenneth Li; Oam Patel; Fernanda Viégas; et al. (2023)
• Instruction Tuning for Large Language Models: A Survey - Shengyu Zhang, Linfeng Dong, Xiaoya Li, et al. (2023)
• Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena - Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, et al. (2023)
• LIMA: Less Is More for Alignment - Chunting Zhou; Pengfei Liu; Puxin Xu; Srini Iyer; Jiao Sun; Yuning Mao; Xian Li; Qingqing Mo; Sheng Zha; Weizhu Chen; et al. (2023)
• LLaMA: open and efficient foundation language models - Guilherme Touvron et al. (2023)
• Language Is Not All You Need: Aligning Perception with Language Models (KOSMOS-1) - Shaokun? (actual: 'Shaokun? KOSMOS-1: Shaokun?') (2023)
• Language Modeling Is Compression - Grégoire Delétang, Anian Ruoss, Paul-Ambroise Duquenne, Elliot Catt et al. (2023)
• Language Models Represent Space and Time - Wes Gurnee, Max Tegmark (2023)
• Large Language Model based Generative Agents: A Survey - Wang et al. (2023)
• Large Language Models as Optimizers - Chengrun Yang; Xuezhi Wang; Yifeng Lu; et al. (2023)
• Large language models fail on trivial alterations to theory-of-mind - Tomer Ullman (2023)
• Learning Interactive Real-World Simulators (UniSim) - Sherry Yang, Yilun Du, Kamyar Ghasemipour, Jonathan Tompson, Leslie Kaelbling, Dale Schuurmans, Pieter Abbeel (2023)
• Learning to Act without Actions (2023)
• Let's verify step by step - Hunter Lightman et al. (2023)
• Llama 2: open foundation and fine-tuned chat models - Guilherme Touvron et al. (2023)
• Lost in the Middle: How Language Models Use Long Contexts - Nelson F. Liu; Kevin Lin; John Hewitt; et al. (Stanford) (2023)
• MC-JEPA: A Joint-Embedding Predictive Architecture for Self-Supervised Learning of Motion and Content Features - Yann LeCun, Randall Balestriero, et al. / Assran & Bardes (2023)
• MC-JEPA: A Joint-Embedding Predictive Architecture for Self-Supervised Learning of Motion, Content and Appearance - A. Bardes; Q. Garrido; J. Ponce; X. Chen; M. Rabbat; Y. LeCun; M. Assran; N. Ballas (2023)
• Magicoder: Empowering Code Generation with OSS-Instruct - Yuxiang Wei, Zhe Wang, Jiawei Liu, et al. (2023)
• Mamba: linear-time sequence modeling with selective state spaces - Albert Gu, Tri Dao (2023)
• Mastering Diverse Domains through World Models - D. Hafner; K. Pasukonis; J. Ba; T. Lillicrap (2023)
• MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework - Sirui Hong, Mingchen Zhuge, Jiaqi Chen, Xiawu Zheng, Yuheng Cheng, Ceyao Zhang, Jinlin Wang, Zili Wang, Steven Ka Shing Yau, Zijuan Lin, Liyang Zhou, Chenyu Ran, Lingfeng Xiao, Chenglin Wu, Jürgen Schmidhuber (2023)
• Mistral 7B - Albert Q. Jiang et al. (2023)
• Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection - Kai Greshake; Sahar Abdelnabi; Shailesh Mishra; et al. (2023)
• OMNI: Open-endedness via Models of human Notions of Interestingness - Jenny Zhang, Joel Lehman, Kenneth Stanley, et al. (2023)
• PaLM 2 Technical Report - Rohan Anil, Andrew M. Dai, Orhan Firat, et al. (2023)
• PaLM-E: An Embodied Multimodal Language Model - Danny Driess; Fei Xia; Mehdi S. M. Sajjadi; Corey Lynch; Aakanksha Chowdhery; Brian Ichter; Ayzaan Wahid; Jonathan Tompson; Quan Vuong; Tianhe Yu; Wenlong Huang; Yevgen Chebotar; Pete Florence; et al. (2023)
• Phi-2: Textbooks Are All You Need II (phi-2 tech report) - Yuanzhi Li, Sébastien Bubeck, et al. (Microsoft) (2023)
• Physics of Language Models: Part 1, Context-Free Grammar - Z. Allen-Zhu; Y. Li (2023)
• Physics of Language Models: Part 1, Learning Hierarchical Language Structures - Zeyuan Allen-Zhu, Yuanzhi Li (2023)
• Physics of Language Models: Part 3.1, Knowledge Storage and Extraction - Zeyuan Allen-Zhu, Yuanzhi Li (2023)
• Physics of Language Models: Part 3.2, Knowledge Manipulation - Zeyuan Allen-Zhu, Yuanzhi Li (2023)
• Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling - Stephanie Biderman, Hailey Schoelkopf, et al. (2023)
• QLoRA: efficient finetuning of quantized LLMs - Tim Dettmers et al. (2023)
• Quantifying Language Models' Sensitivity to Spurious Features in Prompt Design or: How I learned to worry about prompt formatting - Madeleine Yxsarberg Laksov; Greg durrett; Eunsol Choi (2023)
• Qwen technical report - Jinze Bai et al. (2023)
• RLAIF: Scaling Reinforcement Learning from Human Feedback with AI Feedback - Lee et al. (2023)
• RWKV: reinventing RNNs for the transformer era - Bo Peng et al. (2023)
• Reflexion: Language Agents with Verbal Reinforcement Learning - Noah Shinn, Federico Cassano, Edward Berman, Ashwin Gopinath, Karthik Narasimhan, Shunyu Yao (2023)
• Representation Engineering: A Top-Down Approach to AI Transparency - Andy Zou; Long Phan; Sarah Chen; et al. (2023)
• Retentive Network: A Successor to Transformer for Large Language Models - Yutao Sun, Li Dong, Shaohan Huang, Shuming Ma, Yuqing Xia, Jilong Xue, Jianyong Wang, Furu Wei (2023)
• Retrieval-Augmented Generation for Large Language Models: A Survey - Yunfan Gao, Yun Xiong, Xinyu Gao, et al. (2023)
• Scaling Data-Constrained Language Models - Muennighoff et al. (2023)
• Scaling Laws for Reward Model Overoptimization - Leo Gao, John Schulman, Jacob Hilton (2023)
• Scaling relationship on learning mathematical reasoning with LLMs (RLAIF) - Weizhe Yuan et al. (2023)
• Segment Anything - Kirillov et al. (Meta) (2023)
• Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection - Akari Asai, Zeqiu Wu, Yizhong Wang, Avirup Sil, Hannaneh Hajishirzi (2023)
• Self-Refine: Iterative Refinement with Self-Feedback - Aman Madaan; Niket Tandon; Prakhar Gupta; et al. (2023)
• Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture - M. Assran; Q. Duval; I. Misra; P. Bojanowski; P. Vincent; M. Rabbat; Y. LeCun; N. Ballas (2023)
• Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture (I-JEPA) - Mahmoud Assran, Quentin Duval, Ishan Misra, et al. (2023)
• Sparks of artificial general intelligence: early experiments with GPT-4 - Sébastien Bubeck et al. (2023)
• Sparse Autoencoders Find Highly Interpretable Features in Language Models - Hoagy Cunningham, Aidan Ewart, et al. (2023)
• SparseGPT: Massive Language Models Can Be Accurately Pruned in One-Shot - Elias Frantar et al. (2023)
• Spotlight Attention: Robust Object-Centric Learning With a Spatial Locality Prior - Ayush Chakravarthy, Trang Nguyen, Anirudh Goyal, Yoshua Bengio, Michael C. Mozer (2023)
• TD-MPC2: Scalable, Robust World Models for Continuous Control - Nicklas Hansen, Hao Su, Xiaolong Wang (2023)
• Textbooks Are All You Need (phi-1) - Suriya Gunasekar; Yi Zhang; Jyoti Aneja; César de Souza Mendes Gomes; Microsoft Research (2023)
• The Falcon Series: Open Language Models that Beat Closed Ones - The Falcon LLM Team (2023)
• The False Promise of Imitating Proprietary LLMs - Arnav Gudibande, Eric Wallace, Charlie Snell, et al. (2023)
• The RefinedWeb Dataset for Falcon LLM: Outperforming Curated Corpora with Web Data, and Web Data Only - Guilherme Penedo, Hynek Kydlíček, Leandro von Werra, Thomas Wolf (2023)
• The Reversal Curse: LLMs trained on "A is B" fail to learn "B is A" - Lukas Berglund, Meg Tong, Max Kaufmann, et al. (2023)
• Theory of mind may have spontaneously emerged in large language models - Michal Kosinski (2023)
• Toolformer: language models can teach themselves to use tools - Timo Schick et al. (2023)
• Towards Monosemanticity: Decomposing Language Models with Dictionary Learning - Chris Olah? (actual: 'Catherine Olsson, Nelson Elhage et al., Anthropic') (2023)
• Tree of thoughts: deliberate problem solving with large language models - Shunyu Yao et al. (2023)
• Universal Self-Consistency for Large Language Model Generation - Xinyun Chen; Renat Aksitov; Uri Alon; et al. (2023)
• Universal and Transferable Adversarial Attacks on Aligned Language Models - Andy Zou; Zifan Wang; Nicholas Carlini; et al. (2023)
• Visual Instruction Tuning (LLaVA) - Haotian Liu; Chunyuan Li; Qingyang Wu; Yong Jae Lee (2023)
• Voyager: An Open-Ended Embodied Agent with Large Language Models - Guanzhi Wang, Yuqi Xie, Linxi Fan, et al. (2023)
• WebArena: A Realistic Web Environment for Building Autonomous Agents - Shuyan Zhou, Frank F. Xu, Hao Zhu, et al. (2023)
• YaRN: Efficient Context Window Extension of Large Language Models (2023)

2022

• 8-bit optimizers via block-wise quantization - Tim Dettmers et al. (2022)
• A ConvNet for the 2020s - Zhuang Liu; Hanzi Mao; Chao-Yuan Wu; Christoph Feichtenhofer; Trevor Darrell; Saining Xie (2022)
• A ConvNet for the 2020s (ConvNeXt) - Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, Saining Xie (2022)
• A Generalist Agent - W. Reed; S. Kalashnikov; R. Hafner; M. Riedmiller; T. Lillicrap (2022)
• A Path Towards Autonomous Machine Intelligence - Yann LeCun (2022)
• A Path Towards Autonomous Machine Intelligence (version 0.9.2) - Y. LeCun (2022)
• A Path Towards Autonomous Machine Intelligence Version 0.9 - Y. LeCun (2022)
• BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation - Junnan Li, Dongxu Li, Caiming Xiong, Steven Hoi (2022)
• BLOOM: a 176B-parameter open-access multilingual language model - Teven Le Scao et al. (2022)
• Beyond the imitation game: quantifying and extrapolating the capabilities of language models (BIG-bench) - Aarohi Srivastava et al. (2022)
• Chain-of-thought prompting elicits reasoning in large language models - Jason Wei et al. (2022)
• CoCa: Contrastive Captioners are Image-Text Foundation Models - Radu Soricut? (actual: 'Jiahui Yu et al., Google') (2022)
• Complexity-Based Prompting for Multi-Step Reasoning - Yao Fu; Hao Peng; Ashish Sabharwal; et al. (2022)
• Constitutional AI: harmlessness from AI feedback - Yuntao Bai et al. (2022)
• Cramming: Training a Language Model on a Single GPU in One Day - Jonas Geiping; Tom Goldstein (2022)
• Efficiently Scaling Transformer Inference - Reiner Pope; Sherjil Ozair; Alexander Van Devender; Jakub M. Czakon; Yuanzhong Xu; George Tucker; Boris Ginsburg (2022)
• Emergent Analogical Reasoning in Large Language Models - Taylor Webb; Keith J. Holyoak; Hongjing Lu (2022)
• Emergent abilities of large language models - Jason Wei et al. (2022)
• Finetuned language models are zero-shot learners (FLAN) - Jason Wei et al. (2022)
• Flamingo: a Visual Language Model for Few-Shot Learning - Jean-Baptiste Alayrac, Jeff Donahue, Pauline Luc, Antoine Miech, Iain Barr, Yana Hasson, Karel Lenc, Arthur Mensch, Katie Millican, Malcolm Reynolds, Roman Ring, Eliza Rutherford, Serkan Cabi, Tengda Han, Zhitao Gong, Sina Samangooei, Marianne Monteiro, Jacob Menick, Sebastian Borgeaud, Andrew Brock, Aida Nematzadeh, Sahand Sharifzadeh, Mikolaj Binkowski, Ricardo Barreira, Oriol Vinyals, Andrew Zisserman, Karen Simonyan (2022)
• FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness - Tri Dao; Daniel Y. Fu; Stefano Ermon; Atri Rudra; Christopher Ré (2022)
• GPT-NeoX-20B: an open-source autoregressive language model - Sid Black et al. (2022)
• GPTQ: accurate post-training quantization for generative pre-trained transformers - Elias Frantar et al. (2022)
• Galactica: A Large Language Model for Science - Ross Taylor, Marcin Kardas, Guillem Cucurull, et al. (2022)
• Goal Misgeneralization in Deep Reinforcement Learning - Laurent Orseau et al. (2022)
• Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets - Alethea Power, Yuri Burda, Harri Edwards, Igor Babuschkin, Vedant Misra (2022)
• Hierarchical Text-Conditional Image Generation with CLIP Latents - Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, et al. (2022)
• High-Resolution Image Synthesis with Latent Diffusion Models - Robin Rombach et al. (2022)
• High-Resolution Image Synthesis with Latent Diffusion Models (Stable Diffusion) - Rombach et al. (2022)
• Improving dialogue safety with human feedback (Sparrow) - Amelia Glaese et al. (2022)
• Improving language models by retrieving from trillions of tokens (RETRO) - Sebastian Borgeaud et al. (2022)
• In-context Learning and Induction Heads - Catherine Olsson, Nelson Elhage, Neel Nanda et al. (2022)
• LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale - Tim Dettmers; Mike Lewis; Younes Belkada; Luke Zettlemoyer (2022)
• LaMDA: language models for dialog applications - Romal Thoppilan et al. (2022)
• Large language models are human-level prompt engineers (APE) - Chunting Zhou et al. (2022)
• Large language models are zero-shot reasoners - Takeshi Kojima et al. (2022)
• Least-to-Most Prompting Enables Complex Reasoning in Large Language Models - Denny Zhou; Nathanael Schärli; Le Hou; et al. (2022)
• MAGE: MAsked Generative Encoder to Unify Representation Learning and Image Synthesis - Tianhong Li, Huiwen Chang, Shlok Kumar Mishra, Han Zhang, Dina Katabi, Dilip Krishnan (2022)
• Masked Autoencoders Are Scalable Vision Learners - K. He; X. Chen; S. Xie; Y. Li; P. Dollar; R. Girshick (2022)
• Masked Feature Prediction for Self-Supervised Visual Pre-Training (MaskFeat) - Yanghao Li, Hanzi Mao, Ross Girshick, Kaiming He (2022)
• Mixture-of-Experts with Expert Choice Routing - Yanqi Zhou, Tao Lei, Hanxiao Liu, Nan Du, Yanping Huang, Vincent Zhao (2022)
• No Language Left Behind: Scaling Human-Centered Machine Translation - NLLB Team, Marta R. Costa-jussà, James Cross, et al. (2022)
• OPT: open pre-trained transformer language models - Susan Zhang et al. (2022)
• Orca: A Distributed Serving System for Transformer-Based Generative Models - Gyeong-In Yu; Joo Seong Jeong; et al. (Samsung SAIL Montreal) (2022)
• PAL: Program-aided Language Models - Luyu Gao; Aman Madaan; Shuyan Zhou; et al. (2022)
• PaLI: A Jointly-Scaled Multilingual Language-Image Model - Xi Chen, Xiao Wang, Soravit Changpinyo et al. (2022)
• PaLM: scaling language modeling with Pathways - Aakanksha Chowdhery et al. (2022)
• Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding - Chitwan Saharia, William Chan, Saurabh Saxena, et al. (2022)
• Predictability and Surprise in Large Generative Models - Samuel R. Bowman et al. (2022)
• RT-1: Robotics Transformer for Real-World Control at Scale - Anthony Brohan, et al. (2022)
• RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control - Anthony Brohan, et al. (2022)
• ReAct: synergizing reasoning and acting in language models - Shunyu Yao et al. (2022)
• Rethinking the Role of Demonstrations: What Makes In-Context Learning Work? - Sewon Min, Xinxi Lyu, Ari Holtzman, Mikel Artetxe, Mike Lewis, Hannaneh Hajishirzi (2022)
• Robust Speech Recognition via Large-Scale Weak Supervision - Alec Radford; Jong Wook Kim; Tao Xu; Greg Brockman; Christine McLeavey; Ilya Sutskever (2022)
• Robust Speech Recognition via Large-Scale Weak Supervision (Whisper) - Radford et al. (OpenAI) (2022)
• STaR: Bootstrapping Reasoning With Reasoning - Eric Zelikman, Yuhuai Wu, Noah Goodman (2022)
• Scalable Diffusion Models with Transformers - William Peebles, Saining Xie (2022)
• Scaling Laws for Reward Model Overoptimization - Gao, Schulman, Hilton (2022)
• Scaling instruction-finetuned language models (Flan-PaLM/Flan-T5) - Hyung Won Chung et al. (2022)
• Self-consistency improves chain of thought reasoning in language models - Xuezhi Wang et al. (2022)
• Self-instruct: aligning language models with self-generated instructions - Yizhong Wang et al. (2022)
• SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models - Guangxuan Xiao, Ji Lin, Mickael Seznec, Hao Wu, Julien Demouth, Song Han (2022)
• Solving Quantitative Reasoning Problems With Language Models (Minerva) - Aitor Lewkowycz et al. (2022)
• Solving math word problems with process- and outcome-based feedback - Jonathan Uesato, Nate Kushman, Ramana Kumar, Francis Song, Noah Siegel, Lisa Wang (2022)
• Temporal Difference Learning for Model Predictive Control - N. Hansen; X. Wang; H. Su (2022)
• Toy Models of Superposition - Chris Olah, et al. (Anthropic) (2022)
• Training Compute-Optimal Large Language Models - J. Hoffmann; S. Borgeaud; A. Mensch; et al. (2022)
• Training Language Models to Follow Instructions with Human Feedback (InstructGPT) - Long Ouyang et al. (2022)
• Training a helpful and harmless assistant with reinforcement learning from human feedback - Yuntao Bai et al. (2022)
• Training compute-optimal large language models (Chinchilla) - Jordan Hoffmann et al. (2022)
• Training language models to follow instructions with human feedback - Long Ouyang et al. (2022)
• Transformers are Sample-Efficient World Models - Vincent Micheli, Eloi Alonso, François Fleuret (2022)
• Using DeepSpeed and Megatron to train Megatron-Turing NLG 175B - Shaden Smith et al. (2022)
• VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning - A. Bardes; J. Ponce; Y. LeCun (2022)
• What Can Transformers Learn In-Context? A Case Study of Simple Function Classes - S. Garg; D. Tsipras; P. Valiant; G. Valiant (2022)
• data2vec: A General Framework for Self-Supervised Learning - A. Baevski; Y. Zhou; A. Mohamed; M. Auli (2022)
• data2vec: A General Framework for Self-Supervised Learning in Speech, Vision and Language - A. Baevski; Y. Zhou; A. Mohamed; M. Auli (2022)

2021

• A Survey on Quantization Methods for Efficient Neural Network Inference - Jacob? Bengio (2021)
• A general language assistant as a laboratory for alignment - Amanda Askell et al. (2021)
• Aligning AI With Shared Human Values - Dan Hendrycks, Collin Burns, Steven Basart, et al. (2021)
• An Explanation of In-context Learning as Implicit Bayesian Inference - Sang Michael Xie, Aditi Raghunathan, Percy Liang, Tengyu Ma (2021)
• BEiT: BERT Pre-Training of Image Transformers - Hangbo Bao, Li Dong, Songhao Piao, Furu Wei (2021)
• Barlow Twins: Self-Supervised Learning via Redundancy Reduction - J. Zbontar; L. Jing; I. Misra; Y. LeCun; S. Deny (2021)
• Calibrate Before Use: Improving Few-Shot Performance of Language Models - Tony Z. Zhao; Eric Wallace; Shi Feng; et al. (2021)
• Do Prompt-Based Models Really Understand the Meaning of their Prompts? - Albert Webson; Ellie Pavlick (2021)
• Emerging Properties in Self-Supervised Vision Transformers - M. Caron; H. Touvron; I. Misra; H. Jegou; J. Mairal; P. Bojanowski; Y. LeCun (2021)
• Ethical and social risks of harm from Language Models - Laura Weidinger; John Mellor; Maribeth Rauh; Conor Griffin; Jonathan Uesato; Po-Sen Huang; et al. (2021)
• Evaluating Large Language Models Trained on Code (Codex) - Mark Chen; Jerry Tworek; Heewoo Jun; Greg Brockman; OpenAI (2021)
• Exploring Simple Siamese Representation Learning - X. Chen; K. He (2021)
• Exploring Simple Siamese Representation Learning (SimSiam) - Xinlei Chen, Kaiming He (2021)
• Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity - Yao Lu; Max Bartolo; Alastair Moore; et al. (2021)
• Finetuned Language Models Are Zero-Shot Learners - Jason Wei; Maarten Bosma; Vincent Y. Zhao; Kelvin Guu; Adams Wei Yu; Brian Lester; Nan Du; Andrew M. Dai; Quoc V. Le (2021)
• Finetuned Language Models are Zero-Shot Learners (FLAN) - Wei et al. (2021)
• GLaM: Efficient Scaling of Language Models with Mixture-of-Experts - Nan Du, Yanping Huang, Andrew M. Dai, Simon Tong, Dmitry Lepikhin, Yuanzhong Xu, Maxim Krikun, Yanqi Zhou, Adams Wei Yu, Orhan Firat, Barret Zoph, Liam Fedus, Maarten Bosma, Zongwei Zhou, Tao Wang, Yu Emma Wang, Kellie Webster, Marie Pellat, Kevin Robinson, Kathleen Meier-Hellstern, Toju Duke, Lucas Dixon, Kun Zhang, Quoc V Le, Yonghui Wu, Zhifeng Chen, Claire Cui (2021)
• GPT Understands, Too - Xiao Liu; Yanan Zheng; Zhengxiao Du; et al. (2021)
• Gradient Descent on Neural Networks Typically Occurs at the Edge of Stability - Cohen, J.; Kaur, S.; Li, Y.; Kolter, J. Z.; Talwalkar, A. (2021)
• Highly accurate protein structure prediction with AlphaFold - John Jumper, Richard Evans, Alexander Pritzel, et al. (2021)
• Holistic Evaluation of Language Models - Percy Liang; Rishi Bommasani; Tony Lee; Dimitris Tsipras; Yasaman Bahri; et al. (2021)
• How Many Data Points is a Prompt Worth? - Teven Le Scao; Alexander M. Rush (2021)
• HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units - W.-N. Hsu; B. Bolte; Y.-H. H. Tsai; K. Lakhotia; R. Salakhutdinov; A. Mohamed (2021)
• Improving language models by retrieving from trillions of tokens - S. Borgeaud; A. Mensch; J. Hoffmann; et al. (2021)
• Learning Transferable Visual Models From Natural Language Supervision - Alec Radford; Jong Wook Kim; Chris Hallacy; Aditya Ramesh; Gabriel Goh; Sandhini Agarwal; Girish Sastry; Amanda Askell; Pamela Mishkin; Jack Clark; Gretchen Krueger; Ilya Sutskever (2021)
• Learning Transferable Visual Models From Natural Language Supervision (CLIP) - Alec Radford et al. (2021)
• Learning to Prompt for Vision-Language Models - Kaiyang Zhou, Jingkang Yang, Chen Change Loy, et al. (2021)
• LoRA: low-rank adaptation of large language models - Edward J. Hu et al. (2021)
• Masked Autoencoders Are Scalable Vision Learners - Kaiming He, et al. (2021)
• Mastering Atari with Discrete World Models - D. Hafner; T. Lillicrap; M. Norouzi; J. Ba (2021)
• Mastering Atari with Discrete World Models (DreamerV2) - Danijar Hafner et al. (2021)
• Measuring Mathematical Problem Solving With the MATH Dataset - Dan Hendrycks et al. (2021)
• Measuring massive multitask language understanding (MMLU) - Dan Hendrycks et al. (2021)
• Multitask prompted training enables zero-shot task generalization (T0) - Victor Sanh et al. (2021)
• On the dangers of stochastic parrots: can language models be too big? - Emily M. Bender et al. (2021)
• On the linearization of infinite-dimensional random dynamical systems - Lucas Backes, Davor Dragičević (2021)
• On the opportunities and risks of foundation models - Rishi Bommasani et al. (2021)
• Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing - Pengfei Liu; Weizhe Yuan; Jinlan Fu; et al. (2021)
• Predictive Coding: a Theoretical and Experimental Review - Beren Millidge, Tommaso Salvatori, Yuhang Song, Thomas Lukasiewicz, Rafal Bogacz (2021)
• Predictive coding approximates backprop along arbitrary computation graphs - Beren Millidge, Alexander Tschantz, Christopher L. Buckley (2021)
• Prefix-Tuning: Optimizing Continuous Prompts for Generation - Xiang Lisa Li; Percy Liang (2021)
• Pretrained Transformers as Universal Computation Engines - Kevin Lu, Aditya Grover, Pieter Abbeel, et al. (2021)
• Program Synthesis with Large Language Models - Jacob Austin; Augustus Odena; Maxwell Nye; Maarten Bosma; Henryk Michalewski; David Dohan; Ellen Jiang; Carrie Cai; Michael Terry; Quoc Le; Charles Sutton (2021)
• Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm - Laria Reynolds; Kyle McDonell (2021)
• Reward is Enough - David Silver, Satinder Singh, Doina Precup, Richard S. Sutton (2021)
• RoFormer: Enhanced Transformer with Rotary Position Embedding - Jianlin Su; Yu Lu; Shengfeng Pan; Ahmed Murtadha; Bo Wen; Yunfeng Liu (2021)
• Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision (ALIGN) - Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh et al. (2021)
• Scaling language models: methods, analysis & insights from training Gopher - Jack W. Rae et al. (2021)
• Score-based generative modeling through stochastic differential equations - Yang Song; Jascha Sohl-Dickstein; Diederik P. Kingma; Abhishek Kumar; Stefano Ermon; Ben Poole (2021)
• Show your work: scratchpads for intermediate computation with language models - Maxwell Nye et al. (2021)
• SimMIM: A Simple Framework for Masked Image Modeling - Zhenda Xie, Zheng Zhang, Yue Cao, Yutao Lin, Stephen Lin, Han Hu (2021)
• SimVLM: Simple Visual-Language Model with Pretraining - Zhe Gan? (correct: 'SimVLM: Wang et al.' — authors: Ziyun Wang? actual: 'Zhe Gan, Yen-Chun Chen') (2021)
• Sparsity in Deep Learning: Pruning and Growth for Efficient Inference and Training - Torsten Hoefler; Dan Alistarh; Tal Ben-Nun; Nikoli Dryden; Alexandra Peste (2021)
• Swin Transformer: Hierarchical Vision Transformer using Shifted Windows - Liu et al. (2021)
• Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity - William Fedus; Barret Zoph; Noam Shazeer (2021)
• The Pile: An 800GB Dataset of Diverse Text for Language Modeling - Leo Gao, Stella Biderman, Sid Black, et al. (2021)
• The Power of Scale for Parameter-Efficient Prompt Tuning - Brian Lester; Rami Al-Rfou; Noah Constant (2021)
• Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation - Ofir Press; Noah A. Smith; Mike Lewis (2021)
• Transformer Feed-Forward Layers Are Key-Value Memories - Mor Geva; Roei Schuster; Jonathan Berant; Omer Levy (2021)
• TruthfulQA: measuring how models mimic human falsehoods - Stephanie Lin et al. (2021)
• Understanding Self-Supervised Learning Dynamics without Anti-Collapse Measures - Yuandong Tian, Xinlei Chen, Surya Ganguli (2021)
• WebGPT: browser-assisted question-answering with human feedback - Reiichiro Nakano et al. (2021)
• What Makes Good In-Context Examples for GPT-$3$? - Jiachang Liu; Dinghan Shen; Yizhe Zhang; et al. (2021)
• Zero-shot text-to-image generation (DALL-E) - Aditya Ramesh; Mikhail Pavlov; Gabriel Goh; Scott Grayson; Ilya Sutskever; et al. (2021)
• iBOT: Image BERT Pre-Training with Online Tokenizer - Jinghao Zhou, Chen Wei, Huiyu Wang, Wei Shen, Caiming Xiong, Yuandong Tian (2021)

2020

• A Simple Framework for Contrastive Learning of Visual Representations - Ting Chen, et al. (2020)
• A Simple Framework for Contrastive Learning of Visual Representations (SimCLR) - Ting Chen et al. (2020)
• A solution to the learning dilemma for recurrent networks of spiking neurons - Bellec, G.; Scherr, F.; Subramoney, A.; Hajek, E.; Salaj, D.; Legenstein, R.; Maass, W. (2020)
• ALBERT: A Lite BERT for Self-supervised Learning of Language Representations - Zhenzhong Lan et al. (2020)
• An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale - Alexey Dosovitskiy; Lucas Beyer; Alexander Kolesnikov; Dirk Weissenborn; Xiaohua Zhai; Thomas Unterthiner; Mostafa Dehghani; Matthias Minderer; Georg Heigold; Sylvain Gelly; Jakob Uszkoreit; Neil Houlsby (2020)
• An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale (ViT) - Dosovitskiy et al. (2020)
• AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts - Taylor Shin; Yasaman Razeghi; Robert L. Logan; et al. (2020)
• Backpropagation and the brain - Lillicrap, T. P.; Santoro, A.; Marris, L.; Akerman, C. J.; Hinton, G. (2020)
• Big Bird: Transformers for Longer Sequences - Manik Zaheer et al. (2020)
• Bootstrap Your Own Latent: A New Approach to Self-Supervised Learning - Grill, et al. (2020)
• Bootstrap Your Own Latent: A New Approach to Self-Supervised Learning (BYOL) - Jean-Bastien Grill, Florian Strub, et al. (2020)
• Climbing towards NLU: On meaning, form, and understanding in the age of data models - Emily M. Bender; Alexander Koller (2020)
• Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditing - Inioluwa Deborah Raji; Andrew Smart; Rebecca N. White; Margaret Mitchell; Timnit Gebru; Ben Hutchinson; Jamila Smith-Loud; Daniel Theron; Parker Barnes (2020)
• Critical Thinking for Language Models - Gregor Betz; Christian Voigt; Kyle Richardson (2020)
• Data-Efficient Image Recognition with Contrastive Predictive Coding (CPC v2) - Olivier J. Henaff et al. (2020)
• DeBERTa: Decoding-enhanced BERT with Disentangled Attention - Pengcheng He; Xiaodong Liu; Jianfeng Gao; Weizhu Chen (2020)
• Denoising Diffusion Probabilistic Models - Jonathan Ho et al. (2020)
• Denoising Diffusion Probabilistic Models (DDPM) - Ho, Jain, Abbeel (2020)
• Dense Passage Retrieval for Open-Domain Question Answering - Vladimir Karpukhin et al. (2020)
• Dream to Control: Learning Behaviors by Latent Imagination - D. Hafner; T. Lillicrap; I. Fischer; R. Villegas; D. Ha; H. Lee; J. Davidson (2020)
• Dream to Control: Learning Behaviors by Latent Imagination (Dreamer) - Danijar Hafner, Timothy Lillicrap, et al. (2020)
• ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators - Kevin Clark et al. (2020)
• Emerging Properties in Self-Supervised Vision Transformers (DINO) - Mathilde Caron, Hugo Touvron, et al. (2020)
• Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inference - Timo Schick; Hinrich Schütze (2020)
• Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer - Colin Raffel; Noam Shazeer; Adam Roberts; Katherine Lee; Sharan Narang; Michael Matena; Yanqi Zhou; Wei Li; Peter J. Liu (2020)
• Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer (T5) - Colin Raffel et al. (2020)
• GLU Variants Improve Transformer - Noam Shazeer (2020)
• GShard: Scaling Giant Models to Conditional Computational Resources - Dmitry Lepikhin; HyoukJoong Lee; Yuanzhong Xu; Dehao Chen; Orhan Firat; Yanping Huang; Massimiliano Krikun; Noam Shazeer; Zhifeng Chen (2020)
• GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding - Dmitry Lepikhin, HyoukJoong Lee, Yuanzhong Xu, et al. (2020)
• Hopfield Networks is All You Need - Hubert Ramsauer, Bernhard Schäfl, Johannes Lehner et al. (2020)
• Improved Baselines with Momentum Contrastive Learning - X. Chen; H. Fan; R. Girshick; K. He (2020)
• Improved protein structure prediction using potentials from deep learning - Andrew W. Senior; Richard Evans; John Jumper; James Kirkpatrick; Laurent Sifre; Tim Green; et al. (DeepMind) (2020)
• It's Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners - Timo Schick; Hinrich Schutze (2020)
• Language Models are Few-Shot Learners - T. Brown; B. Mann; N. Ryder; et al. (2020)
• Language Models are Few-Shot Learners (GPT-3) - Tom Brown et al. (2020)
• Learning to Summarize from Human Feedback - Nisan Stiennon et al. (2020)
• Linformer: Self-Attention with Linear Complexity - Sinong Wang; Belinda Z. Li; Madian Khabsa; Han Fang; Hao Ma (2020)
• Longformer: The Long-Document Transformer - Iz Beltagy, Matthew Peters, Arman Cohan (2020)
• Making Pre-trained Language Models Better Few-shot Learners - Tianyu Gao; Adam Fisch; Danqi Chen (2020)
• Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model (MuZero) - Julian Schrittwieser et al. (2020)
• Momentum Contrast for Unsupervised Visual Representation Learning - Kaiming He et al. (2020)
• Momentum Contrast for Unsupervised Visual Representation Learning (MoCo) - Kaiming He, Xinlei Chen, Saining Xie, et al. (2020)
• NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis - Ben Mildenhall, Pratul P. Srinivasan, Matthew Tancik, et al. (2020)
• REALM: Retrieval-Augmented Language Model Pre-Training - Kelvin Guu et al. (2020)
• Reformer: The Efficient Transformer - Nikita Kitaev, Lukasz Kaiser, Anselm Levskaya (2020)
• Rethinking Attention with Performers - Krzysztof Choromanski et al. (2020)
• Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks - Patrick Lewis et al. (2020)
• Scaling Laws for Autoregressive Generative Modeling - Tom Henighan, Jared Kaplan, Mor Katz, et al. (2020)
• Scaling Laws for Neural Language Models - Jared Kaplan et al. (2020)
• Self-labelling via simultaneous clustering and representation learning - Yuki M. Asano, Christian Rupprecht, Andrea Vedaldi (2020)
• Training data-efficient image transformers & distillation through attention - Hugo Touvron, Matthieu Cord, Matthijs Douze, et al. (2020)
• Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention - Angelos Katharopoulos; Apoorv Vyas; Nikolaos Pappas; François Fleuret (2020)
• Unsupervised Cross-lingual Representation Learning at Scale (XLM-R) - Alexis Conneau et al. (2020)
• Unsupervised Learning of Visual Features by Contrasting Cluster Assignments - M. Caron; I. Misra; J. Mairal; P. Goyal; P. Bojanowski; A. Joulin (2020)
• Wav2vec 2.0: A Framework for Self-Supervised Speech Representation Learning - Alexei Baevski et al. (2020)
• ZeRO: Memory Optimizations Toward Training Trillion Parameter Models - Samyam Rajbhandari et al. (2020)
• wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations - A. Baevski; H. Zhou; A. Mohamed; M. Auli (2020)
• wav2vec: Unsupervised pre-training for speech recognition - Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael Auli (2020)

2019

• Adapting Neural Networks for the Estimation of Treatment Effects - Claudia Shi; David M. Blei; Victor Veitch (2019)
• Adversarial Patch (Brown, Mane, Roy, Abbeel, Gilad-Bachrach) - Alexandereyey (2019)
• Are sixteen heads really better than one? - Paul Michel; Omer Levy; Graham Neubig (2019)
• BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension - Mike Lewis, Yinhan Liu, Naman Goyal, et al. (2019)
• Cross-lingual Language Model Pretraining - Guillaume Lample, Alexis Conneau (2019)
• Cross-lingual Language Model Pretraining (XLM) - Guillaume Lample, Alexis Conneau (2019)
• Decoupled Weight Decay Regularization - Loshchilov, I.; Hutter, F. (2019)
• DistilBERT, a Distilled Version of BERT: Smaller, Faster, Cheaper and Lighter - Victor Sanh; Lysandre Debut; Julien Chaumond; Thomas Wolf (2019)
• ERNIE: Enhanced Representation through Knowledge Integration - Yu Sun, Shuohuan Wang, Yukun Li, Shikun Feng, Xuyi Chen, Han Zhang, Xin Tian, Danxiang Zhu, Hao Tian, Hua Wu (2019)
• EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks - Mingxing Tan; Quoc V. Le (2019)
• Emergent Tool Use From Multi-Agent Autocurricula - Bowen Baker et al. (2019)
• Energy and Policy Considerations for Deep Learning in NLP - Emma Strubell, Ananya Ganesh, Andrew McCallum (2019)
• Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer (T5) - Raffel et al. (2019)
• Fast Transformer Decoding: One Write-Head is All You Need - Noam Shazeer (2019)
• Fine-Tuning Language Models from Human Preferences - Daniel Ziegler et al. (2019)
• GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding - Alex Wang et al. (2019)
• Generating Long Sequences with Sparse Transformers - Child, Gray, Radford, Wu (2019)
• Generative modeling by estimating gradients of the data distribution - Jiaming Song; Chenlin Meng; Stefano Ermon (2019)
• Grandmaster level in StarCraft II using multi-agent reinforcement learning - Oriol Vinyals et al. (2019)
• Language Models are Unsupervised Multitask Learners (GPT-2) - Alec Radford et al. (2019)
• Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model (MuZero) - Julian Schrittwieser, Ioannis Antonoglou, David Silver, et al. (2019)
• Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism - Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper, Bryan Catanzaro (2019)
• Risks from Learned Optimization - Evan Hubinger et al. (2019)
• RoBERTa: A Robustly Optimized BERT Pretraining Approach - Yinhan Liu et al. (2019)
• Root Mean Square Layer Normalization - B. Zhang; R. Sennrich (2019)
• Solving Rubik's Cube with a Robot Hand - OpenAI et al. (2019)
• SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems - Alex Wang et al. (2019)
• The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks - Jonathan Frankle; Michael Carbin (2019)
• Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context - Zihang Dai et al. (2019)
• Transformers: State-of-the-Art Natural Language Processing - Thomas Wolf et al. (2019)
• XLNet: Generalized Autoregressive Pretraining for Language Understanding - Zhilin Yang et al. (2019)

2018

• AI4People: An ethical framework for a good AI society - Luciano Floridi; Josh Cowls; Monica Beltrametti; et al. (2018)
• An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling - Shaojie Bai, J. Zico Kolter, Vladlen Koltun (2018)
• Attention is not Explanation - Jain, Wallace (2018)
• Automatic Differentiation in Machine Learning: a Survey - Baydin, A. G.; Pearlmutter, B. A.; Radul, A. A.; Siskind, J. M. (2018)
• BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding - Jacob Devlin, Ming-Wei Chang, Kenton Lee, Kristina Toutanova (2018)
• Constituency Parsing with a Self-Attentive Encoder - Nikita Kitaev, Dan Klein (2018)
• Datasheets for Datasets - Timnit Gebru; Jamie Morgenstern; Briana Vecchione; Jennifer Wortman Vaughan; Hanna Wallach; Hal Daumé III; Kate Crawford (2018)
• Deep Contextualized Word Representations (ELMo) - Matthew E. Peters; Mark Neumann; Mohit Iyyer; Matt Gardner; Christopher Clark; Kenton Lee; Luke Zettlemoyer (2018)
• Deep Reinforcement Learning that Matters - Peter Henderson et al. (2018)
• Deep clustering for unsupervised learning of visual features - Mathilde Caron, Piotr Bojanowski, Armand Joulin, Matthijs Douze (2018)
• DeepMind Control Suite - Yuval Tassa et al. (2018)
• Dendritic cortical microcircuits approximate the backpropagation algorithm - Sacramento, J.; Costa, R. P.; Bengio, Y.; Senn, W. (2018)
• Glow: Generative Flow with Invertible 1x1 Convolutions - Diederik P. Kingma, Prafulla Dhariwal (2018)
• How Powerful are Graph Neural Networks? - Keyulu Xu; Weihua Hu; Jure Leskovec; Stefanie Jegelka (2018)
• ImageNet Training in Minutes - Yang You et al. (2018)
• Improving Language Understanding by Generative Pre-Training - Alec Radford, Karthik Narasimhan, Tim Salimans, Ilya Sutskever (2018)
• Improving Language Understanding by Generative Pre-Training (GPT-1) - Alec Radford, Karthik Narasimhan, Tim Salimans, Ilya Sutskever (2018)
• Know What You Don't Know: Unanswerable Questions for SQuAD - Pranav Rajpurkar, Robin Jia, Percy Liang (2018)
• Learning Dexterous In-Hand Manipulation - OpenAI, Marcin Andrychowicz, Bowen Baker, et al. (2018)
• Model Cards for Model Reporting - Margaret Mitchell; Simone Wu; Andrew Zaldivar; Parker Barnes; Lucy Vasserman; Ben Hutchinson; Elena Spitzer; Inioluwa Deborah Raji; Timnit Gebru (2018)
• Neural Tangent Kernel: Convergence and Generalization in Neural Networks - Jacot, A.; Gabriel, F.; Hongler, C. (2018)
• Optimization Methods for Large-Scale Machine Learning - Bottou; Curtis; Nocedal (2018)
• Relational inductive biases, deep learning, and graph networks - Peter W. Battaglia, Jessica B. Hamrick, Victor Bapst et al. (DeepMind) (2018)
• Representation Learning with Contrastive Predictive Coding - Aaron van den Oord, Yazhe Li, Oriol Vinyals (2018)
• Sharp Nearby, Fuzzy Far Away: How Neural Language Models Use Context - Urvashi Khandelwal et al. (2018)
• Tensor2Tensor for Neural Machine Translation - Ashish Vaswani et al. (2018)
• The Malicious Use of Artificial Intelligence - Miles Brundage et al. (2018)
• The NarrativeQA Reading Comprehension Challenge - Tomas Kocisky et al. (2018)
• Universal Language Model Fine-tuning for Text Classification - Jeremy Howard; Sebastian Ruder (2018)
• Universal Transformers - Dehghani, Gouws, Arnab, Beyer, Vaswani (2018)
• What you can cram into a single vector: Probing sentence embeddings for linguistic properties - Alexis Conneau et al. (2018)
• World Models - D. Ha; J. Schmidhuber (2018)
• YOLOv3: An Incremental Improvement - Joseph Redmon, Ali Farhadi (2018)

2017

• #Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning - Haoran Tang et al. (2017)
• A Unified Approach to Interpreting Model Predictions - Lundberg, Lee (2017)
• Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour - Priya Goyal et al. (2017)
• An approximation of the error backpropagation algorithm in a predictive coding network with local hebbian synaptic plasticity - James C. R. Whittington, Rafal Bogacz (2017)
• Attention Is All You Need - Viktor Vaswani et al. (2017)
• Automatic Differentiation in PyTorch - Adam Paszke et al. (2017)
• Communication-Efficient Learning of Deep Networks from Decentralized Data - McMahan, Moore, Ramage, Hampson, y Arcas (2017)
• Convolutional Sequence to Sequence Learning - Jonas Albrecht et al. (Gehring et al.) (2017)
• Deep Learning Scaling Is Predictable, Empirically - Jorn-Henrik Hestness; Sharan Narang; Newsha Ardalani; Greg Diamos; Heewoo Jun; Hassan Kianinejad; Md. Mostofa Ali Patwary; Yang Yang; Yanqi Zhou (2017)
• Deep Reinforcement Learning from Human Preferences - Paul Christiano et al. (2017)
• Enriching Word Vectors with Subword Information - Bojanowski, P.; Grave, E.; Joulin, A.; Mikolov, T. (2017)
• Grad-CAM: Visual Explanations from Deep Networks - Selvaraju, et al. (2017)
• Graph Attention Networks - Petar Velickovic, et al. (2017)
• Hindsight Experience Replay - Marcin Andrychowicz et al. (2017)
• Inductive Representation Learning on Large Graphs - Will Hamilton, Zhitao Ying, Jure Leskovec (2017)
• LSTM: A Search Space Odyssey - Klaus Greff; Rupesh K. Srivastava; Jan Koutnik; Bas R. Steunebrink; Jürgen Schmidhuber (2017)
• Large Batch Training of Convolutional Networks (LARS) - Yang You et al. (2017)
• Learning Transferable Architectures for Scalable Image Recognition - Zoph, Vasudevan, Shlens, Le (2017)
• Mask R-CNN - Kaiming He, Georgia Gkioxari, Piotr Dollar, Ross Girshick (2017)
• Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm (AlphaZero) - David Silver et al. (2017)
• Neural Discrete Representation Learning - Aaron van den Oord; Oriol Vinyals; Koray Kavukcuoglu (2017)
• Neural Message Passing for Quantum Chemistry - Justin Gilmer; Samuel S. Schoenholz; Patrick F. Riley; Oriol Vinyals; George E. Dahl (2017)
• On Calibration of Modern Neural Networks - Chuan Guo, Geoff Pleiss, Yu Sun, Kilian Q. Weinberger (2017)
• One Model To Learn Them All - Lukasz Kaiser, Aidan N. Gomez, Noam Shazeer, et al. (2017)
• Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer - Noam Shazeer et al. (2017)
• Proximal Policy Optimization Algorithms - John Schulman et al. (2017)
• Regularizing and Optimizing LSTM Language Models - Stephen Merity et al. (2017)
• SearchQA: A New Q&A Dataset Augmented with Context from a Search Engine - Matthew Dunn et al. (2017)
• Self-Attention with Relative Position Representations - Peter Shaw, Jakob Uszkoreit, Ashish Vaswani (2017)
• The Off-Switch Game - Dylan Hadfield-Menell et al. (2017)
• The consciousness prior - Yoshua Bengio (2017)
• Towards Deep Learning Models Resistant to Adversarial Attacks - Madry, Makelov, Schmidt, Tsipras, Vladu (2017)
• Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks - Jun-Yan Zhu; Taesung Park; Phillip Isola; Alexei A. Efros (2017)

2016

• A Simple but Tough-to-Beat Baseline for Sentence Embeddings - Sanjeev Arora; Yingyu Liang; Tengyu Ma (2016)
• Advances in Pre-Training Distributed Word Representations - Tomas Mikolov; Armand Joulin; Edouard Grave; Piotr Bojanowski (2016)
• An overview of gradient descent optimization algorithms - Sebastian Ruder (2016)
• Ask Me Anything: Dynamic Memory Networks for Natural Language Processing - Ankit Kumar et al. (2016)
• Benchmarking Deep Reinforcement Learning for Continuous Control - Yan Duan et al. (2016)
• Big data's disparate impact - Solon Barocas; Andrew D. Selbst (2016)
• Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models - Serban, I. V.; Sordoni, A.; Bengio, Y.; Courville, A.; Pineau, J. (2016)
• Concrete Problems in AI Safety - Amodei, Olah, Steinhardt, Christiano, Schulman, Mane (2016)
• Concrete: A Continuous Relaxation of the Discrete Argument (Gumbel-Softmax) - Jang, Gu, Poole (2016)
• Conditional Image Generation with PixelCNN Decoders - Aaron van den Oord et al. (2016)
• Continuous Control with Deep Reinforcement Learning - Lillicrap, T. P.; Hunt, J. J.; Pritzel, A.; et al. (2016)
• Cooperative Inverse Reinforcement Learning - Dylan Hadfield-Menell et al. (2016)
• Coupled Generative Adversarial Networks - Liu, Tuzel (2016)
• Deep Learning with Differential Privacy - Abadi, Chu, Goodfellow, McMahan, Mironov, Talwar, Zhang (2016)
• Deep Residual Learning for Image Recognition - He, K.; Zhang, X.; Ren, S.; Sun, J. (2016)
• Deep Speech 2: End-to-End Speech Recognition in English and Mandarin - Dario Amodei, Rishita Anubhai, Eric Battenberg, Carl Case, Jared Casper, Bryan Catanzaro, et al. (2016)
• Dialog-based Language Learning - Weston, J. (2016)
• Direct feedback alignment provides learning in deep neural networks - Aurelien Nokland (2016)
• Dynamic Coattention Networks For Question Answering - Caiming Xiong, Victor Zhong, Richard Socher (2016)
• End-to-End Training of Deep Visuomotor Policies - Sergey Levine et al. (2016)
• Enriching Word Vectors with Subword Information - Piotr Bojanowski; Edouard Grave; Armand Joulin; Tomas Mikolov (2016)
• Estimating individual treatment effect: generalization bounds and algorithms - Uri Shalit; Fredrik D. Johansson; David Sontag (2016)
• Exploring the Limits of Language Modeling - Rafal Jozefowicz; Oriol Vinyals; Mike Schuster; Noam Shazeer; Yonghui Wu (2016)
• Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation - Yonghui Wu; Mike Schuster; Zhifeng Chen; Quoc V. Le; Mohammad Norouzi et al. (2016)
• Hybrid computing using a neural network with dynamic external memory - Alex Graves; Greg Wayne; Malcolm Reynolds; Tim Harley; Ivo Danihelka; Agnieszka Grabska-Barwinska; Sergio Gomez Colmenarejo; Edward Grefenstette; Tiago Ramalho; John Agapiou; Adria Puigdomenech Badia; Karl Moritz Hermann; Yori Zwols; Georg Ostrovski; Adam Cain; Helen King; Christopher Summerfield; Phil Blunsom; Julian Schrittwieser; Demis Hassabis; Dharshan Kumaran; Daan Wierstra (2016)
• Identity Mappings in Deep Residual Networks - Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun (2016)
• Image-to-Image Translation with Conditional Adversarial Networks - Isola, Zhu, Zhou, Efros (2016)
• Improved Techniques for Training GANs - Tim Salimans; Ian Goodfellow; Wojciech Zaremba; Vicki Cheung; Alec Radford; Xi Chen (2016)
• Improving Neural Machine Translation Models with Monolingual Data - Rico Sennrich, Barry Haddow, Alexandra Birch (2016)
• Improving Variational Inference with Inverse Autoregressive Flow - Kingma, Salimans, Jozefowicz, Chen, Sutskever, Welling (2016)
• Incorporating Nesterov Momentum into Adam - Timothy Dozat (2016)
• Instance Normalization: The Missing Ingredient for Fast Stylization - Ulyanov, Vedaldi, Lempitsky (2016)
• LSTM: A Search Space Odyssey - Klaus Greff, Rupesh K. Srivastava, Jan Koutnik, Bas R. Steunebrink, Jurgen Schmidhuber (2016)
• Layer Normalization - Jimmy Ba, Ryan Kiros, Geoffrey Hinton (2016)
• Learning Distributed Representations of Sentences from Unlabelled Data - Felix Hill et al. (2016)
• Learning to learn by gradient descent by gradient descent - Marcin Andrychowicz, Misha Denil, Sergio Gomez, Matthew W. Hoffman, David Pfau, Tom Schaul, Nando de Freitas (2016)
• MS MARCO: A Human Generated MAchine Reading COmprehension Dataset - Tri Nguyen et al. (2016)
• Mastering the Game of Go with Deep Neural Networks and Tree Search - Silver, D.; Huang, A.; Maddison, C. J.; et al. (2016)
• Neural Architecture Search with Reinforcement Learning - Esteban Real, et al. (2016)
• OpenAI Gym - Greg Brockman et al. (2016)
• Pixel Recurrent Neural Networks - van den Oord, Kalchbrenner, Kavukcuoglu (2016)
• Pointer Networks - Oriol Vinyals, Meire Fortunato, Navdeep Jaitly (2016)
• Random feedback weights support learning in deep neural networks - Lillicrap, T. P.; Cownden, D.; Tweed, D. B.; Akerman, C. J. (2016)
• Rethinking the Inception Architecture for Computer Vision (Inception v3) - Christian Szegedy; Vincent Vanhoucke; Sergey Ioffe; Jonathon Shlens; Zbigniew Wojna (2016)
• SQuAD: 100,000+ Questions for Machine Comprehension of Text - Pranav Rajpurkar et al. (2016)
• Safely Interruptible Agents - Laurent Orseau and Stuart Armstrong (2016)
• Semi-Supervised Classification with Graph Convolutional Networks - Barham? no (2016)
• TensorFlow: A System for Large-Scale Machine Learning - Martin Abadi et al. (2016)
• Towards Biologically Plausible Deep Learning - Bengio, Y.; Lee, D. H.; Bornschein, J.; Mesnard, T.; Lin, Z. (2016)
• Tying Word Vectors and Word Classifiers: A Loss Framework for Language Modeling - Hakan Inan, Khashayar Khosravi, Richard Socher (2016)
• Using the Output Embedding to Improve Language Models - Ofir Press, Lior Wolf (2016)
• WaveNet: A Generative Model for Raw Audio - Aäron van den Oord; Sander Dieleman; Heiga Zen; Karen Simonyan; Oriol Vinyals; Alex Graves; Nal Kalchbrenner; Andrew Senior; Koray Kavukcuoglu (2016)
• Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks - Salimans, Kingma (2016)
• Why Should I Trust You?: Explaining the Predictions of Any Classifier (LIME) - Ribeiro, Singh, Guestrin (2016)
• XGBoost: A Scalable Tree Boosting System - Tianqi Chen; Carlos Guestrin (2016)
• fastText: A Library for Efficient Text Representations and Sentence Classification - Bojanowski, Grave, Joulin, Mikolov (2016)
• zoneout: Zoneout: Regularizing RNNs by Randomly Preserving Hidden Activations - Krueger, Maharaj, Kahn, Guez, Bengio, Courville (2016)

2015

• A Neural Algorithm of Artistic Style - Gatys, Ecker, Bethge (2015)
• A Neural Conversational Model - Vinyals, O.; Le, Q. V. (2015)
• Adam: A Method for Stochastic Optimization - Kingma, D. P.; Ba, J. (2015)
• An Empirical Exploration of Recurrent Network Architectures - Jozefowicz, R.; Zaremba, W.; Sutskever, I. (2015)
• Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift - Ioffe, S.; Szegedy, C. (2015)
• DRAW: A Recurrent Neural Network For Image Generation - Karol Gregor et al. (2015)
• Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding - Song Han, Huizi Mao, William Dally (2015)
• Deep Learning - Yann LeCun, Yoshua Bengio, Geoffrey Hinton (2015)
• Deep Learning in Neural Networks: An Overview - Jurgen Schmidhuber (2015)
• Deep Unordered Composition Rivals Syntactic Methods for Text Classification - Mohit Iyyer, Varun Manjunatha, Jordan Boyd-Graber, Hal Daume III (2015)
• Deep Visual-Semantic Alignments for Generating Image Descriptions - Andrej Karpathy; Li Fei-Fei (2015)
• Deep residual learning for image recognition - Kaiming He; Xiangyu Zhang; Shaoqing Ren; Jian Sun (2015)
• Deep unsupervised learning using nonequilibrium thermodynamics - Jascha Sohl-Dickstein; Eric A. Weiss; Niru Maheswaranathan; Surya Ganguli (2015)
• Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet - Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun (2015)
• Difference target propagation - Dong-Hyun Lee, Saizheng Zhang, Asja Fischer, Yoshua Bengio (2015)
• Distilling the Knowledge in a Neural Network - Geoffrey Hinton, Oriol Vinyals, Jeff Dean (2015)
• Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning - Yarin Gal; Zoubin Ghahramani (2015)
• Effective Approaches to Attention-based Neural Machine Translation - Minh-Thang Luong; Hieu Pham; Christopher Manning (2015)
• End-To-End Memory Networks - Sukhbaatar, S.; Weston, J.; Fergus, R. (2015)
• Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks - Shaoqing Ren; Kaiming He; Ross Girshick; Jian Sun (2015)
• Fully Convolutional Networks for Semantic Segmentation - Jonathan Long; Evan Shelhamer; Trevor Darrell (2015)
• Going Deeper with Convolutions - Szegedy, C.; Liu, W.; Jia, Y.; Sermanet, P.; Reed, S.; Anguelov, D.; Erhan, D.; Vanhoucke, V.; Rabinovich, A. (2015)
• Highway Networks - Rupesh Srivastava; Klaus Greff; Jurgen Schmidhuber (2015)
• Human-level Control through Deep Reinforcement Learning - Mnih, V.; Kavukcuoglu, K.; Silver, D.; et al. (2015)
• Improving Distributional Similarity with Lessons Learned from Word Embeddings - Omer Levy; Yoav Goldberg; Ido Dagan (2015)
• Learning to Execute - Wojciech Zaremba, Ilya Sutskever (2015)
• Listen, Attend and Spell - William Chan; Navdeep Jaitly; Quoc Le; Oriol Vinyals (2015)
• Memory Networks - Weston, J.; Chopra, S.; Bordes, A. (2015)
• Neural Machine Translation by Jointly Learning to Align and Translate - Bahdanau, D.; Cho, K.; Bengio, Y. (2015)
• Neural Machine Translation of Rare Words with Subword Units - Rico Sennrich; Barry Haddow; Alexandra Birch (2015)
• On Using Very Large Target Vocabulary for Neural Machine Translation - Sebastien Jean; Kyunghyun Cho; Roland Memisevic; Yoshua Bengio (2015)
• On the Properties of Neural Machine Translation: Encoder-Decoder Approaches - Kyunghyun Cho; Bart van Merrienboer; Dzmitry Bahdanau; Yoshua Bengio (2015)
• Optimizing neural networks with Kronecker-factored approximate curvature - James Martens, Roger Grosse (2015)
• Research priorities for robust and beneficial artificial intelligence - Stuart Russell; Daniel Dewey; Max Tegmark (2015)
• Rethinking the Inception Architecture for Computer Vision (Inception-v3) - Christian Szegedy, et al. (2015)
• SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation - Vijay Badrinarayanan, Alex Kendall, Roberto Cipolla (2015)
• Show and Tell: A Neural Image Caption Generator - Oriol Vinyals et al. (2015)
• Show and Tell: Lessons learned from the 2015 MSCOCO Image Captioning Challenge - Oriol Vinyals; Quoc Le; Samy Bengio; Alexander Toshev; Sujay Sanghavi; Marc'Aurelio Ranzato (2015)
• Show, Attend and Tell: Neural Image Caption Generation with Visual Attention - Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron Courville, Ruslan Salakhutdinov, Richard Zemel, Yoshua Bengio (2015)
• Siamese neural networks for one-shot image recognition - Koch, Zemel, Salakhutdinov (2015)
• Skip-Thought Vectors - Ryan Kiros et al. (2015)
• Teaching Machines to Read and Comprehend - Karl Moritz Hermann et al. (2015)
• The Loss Surfaces of Multilayer Networks - Choromanska, A.; Henaff, M.; Mathieu, M.; Arous, G. B.; LeCun, Y. (2015)
• The Unreasonable Effectiveness of Recurrent Neural Networks - Andrej Karpathy (2015)
• Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks - Weston, J.; Bordes, A.; Chopra, S.; Rush, A. M.; van Merrienboer, B.; Joulin, A.; Mikolov, T. (2015)
• Towards Universal Paraphrastic Sentence Embeddings - John Wieting et al. (2015)
• Trust Region Policy Optimization - John Schulman et al. (2015)
• U-Net: Convolutional networks for biomedical image segmentation - Olaf Ronneberger; Philipp Fischer; Thomas Brox (2015)
• Understanding neural networks through deep visualization - Jason Yosinski, Jeff Clune, Anh Nguyen, Thomas Fuchs, Hod Lipson (2015)
• Unsupervised Learning of Visual Representations using Videos - Wang, Gupta (2015)
• Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks - Alec Radford; Luke Metz; Soumith Chintala (2015)
• Variational Dropout and the Local Reparameterization Trick - Kingma, Salimans, Welling (2015)
• Variational Inference with Normalizing Flows - Danilo Jimenez Rezende; Shakir Mohamed (2015)
• Very Deep Convolutional Networks for Large-Scale Image Recognition - Simonyan, K.; Zisserman, A. (2015)
• You Only Look Once: Unified, Real-Time Object Detection - Joseph Redmon; Santosh Divvala; Ross Girshick; Ali Farhadi (2015)

2014

• Convolutional Neural Networks for Sentence Classification - Kim, Y. (2014)
• Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps - Karen Simonyan; Andrea Vedaldi; Andrew Zisserman (2014)
• Deep Speech: Scaling up end-to-end speech recognition - Adam Hannun; Carl Case; Jared Casper; Bryan Catanzaro; Greg Diamos; Erich Elsen; Ryan Prenger; Sanjeev Satheesh; Shubho Sengupta; Adam Coates; Andrew Ng (2014)
• DeepFace: Closing the gap to human-level performance in face verification - Yaniv Taigman; Ming Yang; Marc'Aurelio Ranzato; Lior Wolf (2014)
• DeepWalk: Online Learning of Social Representations - Piotr Mioszek? actually (2014)
• Distributed Representations of Sentences and Documents - Quoc Le, Tomas Mikolov (2014)
• Don't count, predict! A systematic comparison of context-counting vs - Marco Baroni, Georgiana Dinu, German Kruszewski (2014)
• Dropout: A Simple Way to Prevent Neural Networks from Overfitting - Srivastava, N.; Hinton, G.; Krizhevsky, A.; Sutskever, I.; Salakhutdinov, R. (2014)
• Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling - Junyoung Chung; Caglar Gulcehre; Kyunghyun Cho; Yoshua Bengio (2014)
• Generative Adversarial Nets - Ian Goodfellow et al. (2014)
• Generative Adversarial Networks - Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio (2014)
• GloVe: Global Vectors for Word Representation - Pennington, J.; Socher, R.; Manning, C. D. (2014)
• Identifying and attacking the saddle point problem in high-dimensional non-convex optimization - Dauphin, Y. N.; Pascanu, R.; Gulcehre, C.; Cho, K.; Ganguli, S.; Bengio, Y. (2014)
• Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation - Cho, K.; van Merrienboer, B.; Gulcehre, C.; Bahdanau, D.; Bougares, F.; Schwenk, H.; Bengio, Y. (2014)
• Long Short-Term Memory Based Recurrent Neural Network Architectures for Large Vocabulary Speech Recognition - Hasim Sak et al. (2014)
• Neural Turing Machines - Graves, A.; Wayne, G.; Danihelka, I. (2014)
• Neural Word Embedding as Implicit Matrix Factorization - Omer Levy, Yoav Goldberg (2014)
• One Weird Trick for Parallelizing Convolutional Neural Networks - Alex Krizhevsky (2014)
• Project Adam: Building an Efficient and Scalable Deep Learning Training System - Trishul Chilimbi et al. (2014)
• Recurrent Models of Visual Attention - Volodymyr Mnih; Nicolas Heess; Alex Graves; Koray Kavukcuoglu (2014)
• Recurrent Neural Network Regularization - Wojciech Zaremba, Ilya Sutskever, Oriol Vinyals (2014)
• Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation - Ross Girshick; Jeff Donahue; Trevor Darrell; Jitendra Malik (2014)
• Sequence to Sequence Learning with Neural Networks - Sutskever, I.; Vinyals, O.; Le, Q. V. (2014)
• Stochastic Backpropagation and Approximate Inference in Deep Generative Models - Danilo Rezende; Shakir Mohamed; Daan Wierstra (2014)
• Tensor Decompositions for Learning Latent Variable Models - Anandkumar, Ge, Hsu, Kakade, Telgarsky (2014)
• Very Deep Convolutional Networks for Large-Scale Image Recognition (VGG) - Karen Simonyan, Andrew Zisserman (2014)
• Visualizing and Understanding Convolutional Networks - Matthew Zeiler; Rob Fergus (2014)

2013

• ADADELTA: An Adaptive Learning Rate Method - Zeiler (2013)
• Advances in Optimizing Recurrent Networks - Yoshua Bengio; Nicolas Boulanger-Lewandowski; Razvan Pascanu (2013)
• Auto-Encoding Variational Bayes - Diederik Kingma, Max Welling (2013)
• Building high-level features using large scale unsupervised learning - Quoc V. Le (2013)
• Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps - Simonyan, Vedaldi, Zisserman (2013)
• Distributed Representations of Words and Phrases and their Compositionality - Mikolov, T.; Sutskever, I.; Chen, K.; Corrado, G.; Dean, J. (2013)
• Efficient Estimation of Word Representations in Vector Space - Mikolov, T.; Chen, K.; Corrado, G.; Dean, J. (2013)
• Efficient Estimation of Word Representations in Vector Space (word2vec) - Tomas Mikolov, Kai Chen, Greg Corrado, Jeffrey Dean (2013)
• Evasion Attacks Against Machine Learning at Test Time - Biggio, Corona, Maiorca, Nelson, Srndic, Laskov, Giacinto, Roli (2013)
• Explaining and Harnessing Adversarial Examples - Ian Goodfellow; Jonathon Shlens; Christian Szegedy (2013)
• Generating Sequences With Recurrent Neural Networks - Graves, A. (2013)
• Grounded Compositional Semantics for Finding and Describing Images with Sentences - Richard Socher et al. (2013)
• Hybrid Speech Recognition With Deep Bidirectional LSTM - Alex Graves, Navdeep Jaitly, Abdel-rahman Mohamed (2013)
• Intriguing properties of neural networks - Christian Szegedy; Wojciech Zaremba; Ilya Sutskever; Joan Bruna; Dumitru Erhan; Ian Goodfellow; Rob Fergus (2013)
• Learning word embeddings efficiently with noise-contrastive estimation - Andriy Mnih, Koray Kavukcuoglu (2013)
• Linguistic Regularities in Continuous Space Word Representations - Tomas Mikolov, Wen-tau Yih, Geoffrey Zweig (2013)
• Maxout networks - Goodfellow, Warde-Farley, Mirza, Courville, Bengio (2013)
• On the Difficulty of Training Recurrent Neural Networks - Pascanu, R.; Mikolov, T.; Bengio, Y. (2013)
• On the Importance of Initialization and Momentum in Deep Learning - Sutskever, I.; Martens, J.; Dahl, G.; Hinton, G. (2013)
• Playing Atari with Deep Reinforcement Learning - Mnih, V.; Kavukcuoglu, K.; Silver, D.; et al. (2013)
• Recurrent Continuous Translation Models - Kalchbrenner, N.; Blunsom, P. (2013)
• Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank - Socher, R.; Perelygin, A.; Wu, J.; Chuang, J.; Manning, C. D.; Ng, A. Y.; Potts, C. (2013)
• Representation Learning: A Review and New Perspectives - Yoshua Bengio, Aaron Courville, Pascal Vincent (2013)
• Speech recognition with deep recurrent neural networks - Alex Graves et al. (2013)
• The Arcade Learning Environment: An Evaluation Platform for General Agents - Marc Bellemare et al. (2013)
• Training Recurrent Neural Networks - Sutskever, I. (2013)
• Whatever next? Predictive brains, situated agents, and the future of cognitive science - Andy Clark (2013)

2012

• A Few Useful Things to Know About Machine Learning - Pedro Domingos (2012)
• ADADELTA: An Adaptive Learning Rate Method - Matthew Zeiler (2012)
• Context-Dependent Pre-Built Deep Neural Network Models for Large Vocabulary Speech Recognition - Dahl, Yu, Deng, Acero (2012)
• Deep Neural Networks for Acoustic Modeling in Speech Recognition - Geoffrey Hinton et al. (2012)
• Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups - Geoffrey Hinton, Li Deng, Dong Yu, George Dahl, Abdel-rahman Mohamed, Navdeep Jaitly, Andrew Senior, Vincent Vanhoucke, Patrick Nguyen, Tara Sainath, Brian Kingsbury (2012)
• ImageNet Classification with Deep Convolutional Neural Networks - Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton (2012)
• Improving Deep Neural Networks for LVCSR Using Rectified Linear Units and Dropout - H. Huang et al. (Sainath et al.) (2012)
• Improving Neural Networks by Preventing Coadaptation of Feature Detectors - Geoffrey E. Hinton; Nitish Srivastava; Alex Krizhevsky; Ilya Sutskever; Ruslan Salakhutdinov (2012)
• LSTM Neural Networks for Language Modeling - Martin Sundermeyer, Ralf Schlueter, Hermann Ney (2012)
• Large Scale Distributed Deep Networks (DistBelief) - Jeffrey Dean et al. (2012)
• Large scale distributed deep networks - Jeffrey Dean et al (2012)
• Lecture 6.5 - RMSProp: Divide the gradient by a running average of its recent magnitude - Tijmen Tieleman; Geoffrey Hinton (2012)
• Multimodal Learning with Deep Boltzmann Machines - Nitish Srivastava; Ruslan Salakhutdinov (2012)
• Noise-contrastive estimation of unnormalized statistical models, with applications to natural image statistics - Michael Gutmann, Aapo Hyvarinen (2012)
• Random Search for Hyper-Parameter Optimization - James Bergstra, Yoshua Bengio (2012)

2011

• Adaptive Subgradient Methods for Online Learning and Stochastic Optimization - John Duchi, Elad Hazan, Yoram Singer (2011)
• Deep Sparse Rectifier Neural Networks - Glorot, X.; Bordes, A.; Bengio, Y. (2011)
• Extensions of Recurrent Neural Network Language Model - Tomas Mikolov; Stefan Kombrink; Lukas Burget; Jiri Cernocky; Sanjeev Khudanpur (2011)
• Hogwild!: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent - Feng Niu, Benjamin Recht et al. (2011)
• LIBSVM: A Library for Support Vector Machines - Chih-Chung Chang, Chih-Jen Lin (2011)
• Natural Language Processing (Almost) from Scratch - Collobert, R.; Weston, J.; Bottou, L.; Karlen, M.; Kavukcuoglu, K.; Kuksa, P. (2011)
• Parsing Natural Scenes and Natural Language with Recursive Neural Networks - Richard Socher, Cliff Lin, Andrew Ng, Christopher Manning (2011)
• Semi-Supervised Recursive Autoencoders for Predicting Sentiment Distributions - Richard Socher; Jeffrey Pennington; Eric H. Huang; Andrew Y. Ng; Christopher D. Manning (2011)

2010

• Deep Learning - Yoshua Bengio, Ian Goodfellow, Aaron Courville (2010)
• Deep Learning via Hessian-Free Optimization - James Martens (2010)
• Formal theory of creativity, fun, and intrinsic motivation (1990-2010) - Juergen Schmidhuber (2010)
• Noise-contrastive estimation: A new estimation principle for unnormalized statistical models - Michael Gutmann, Aapo Hyvarinen (2010)
• Object detection with discriminatively trained part-based models - Pedro F. Felzenszwalb; Ross B. Girshick; David McAllester; Deva Ramanan (2010)
• Parallelized Stochastic Gradient Descent - Martin Zinkevich et al. (2010)
• Rectified Linear Units Improve Restricted Boltzmann Machines - Vinod Nair, Geoffrey E. Hinton (2010)
• Recurrent neural network based language model - Tomas Mikolov, Martin Karafiat, Lukas Burget, Jan Cernocky, Sanjeev Khudanpur (2010)
• Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion - Vincent, P.; Larochelle, H.; Lajoie, I.; Bengio, Y.; Manzagol, P.-A. (2010)
• The free-energy principle: a unified brain theory? - Karl J. Friston (2010)
• Theano: A CPU and GPU Math Compiler in Python - James Bergstra et al. (2010)
• Understanding the Difficulty of Training Deep Feedforward Neural Networks - Glorot, X.; Bengio, Y. (2010)
• Why Does Unsupervised Pre-training Help Deep Learning? - Dumitru Erhan; Yoshua Bengio; Aaron Courville; Pierre-Antoine Manzagol; Pascal Vincent; Samy Bengio (2010)
• Word Representations: A Simple and General Method for Semi-Supervised Learning - Joseph Turian, Lev Ratinov, Yoshua Bengio (2010)

2009

• A Novel Connectionist System for Improved Unconstrained Handwriting Recognition - Alex Graves; Marcus Liwicki; Santiago Fernandez; Roman Bertolami; Horst Bunke; Jurgen Schmidhuber (2009)
• Causality: Models, Reasoning and Inference - Pearl (2009)
• Deep Boltzmann Machines - Salakhutdinov, R.; Hinton, G. E. (2009)
• ImageNet: A Large-Scale Hierarchical Image Database - Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, Li Fei-Fei (2009)
• Learning Deep Architectures for AI - Yoshua Bengio (2009)
• Matrix Factorization Techniques for Recommender Systems - Yehuda Koren; Robert Bell; Chris Volinsky (2009)
• Semantic hashing - Ruslan Salakhutdinov, Geoffrey Hinton (2009)
• The Probabilistic Relevance Framework: BM25 and Beyond - Stephen Robertson, Hugo Zaragoza (2009)

2008

• A Convergent O(n) Algorithm for Off-policy Temporal-Difference Learning with Linear Function Approximation - Richard S. Sutton, Hamid Maei, Doina Precup et al. (2008)
• A Scalable Hierarchical Distributed Language Model - Andriy Mnih, Geoffrey E. Hinton (2008)
• A unified architecture for natural language processing - Ronan Collobert; Jason Weston (2008)
• A unified architecture for natural language processing: Deep neural networks with multitask learning - Ronan Collobert, Jason Weston (2008)
• Extracting and Composing Robust Features with Denoising Autoencoders - Pascal Vincent; Hugo Larochelle; Yoshua Bengio; Pierre-Antoine Manzagol (2008)
• Introduction to Information Retrieval - Christopher Manning, Prabhakar Raghavan, Hinrich Schuetze (2008)
• Natural language processing (almost) from scratch - Ronan Collobert, Jason Weston, Leon Bottou, Michael Karlen, Koray Kavukcuoglu, Pavel Kuksa (2008)

2007

• A Correlated Topic Model of Science - David M. Blei, John D. Lafferty (2007)
• Greedy layer-wise training of deep networks - Bengio, Lamblin, Popovici, Larochelle (2007)
• Learning a Nonlinear Embedding by Preserving Class Neighbourhood Structure - Ruslan Salakhutdinov; Geoffrey Hinton (2007)
• Restricted Boltzmann Machines for Collaborative Filtering - Ruslan Salakhutdinov; Andriy Mnih; Geoffrey Hinton (2007)
• Sparse Feature Learning for Deep Belief Networks - Ranzato, M.; Boureau, Y.-L.; LeCun, Y. (2007)
• Three new graphical models for statistical language modelling - Andriy Mnih, Geoffrey Hinton (2007)

2006

• A Fast Learning Algorithm for Deep Belief Nets - Hinton, G. E.; Osindero, S.; Teh, Y.-W. (2006)
• A Tutorial on Energy-Based Learning - Y. LeCun; S. Chopra; R. Hadsell; M. Ranzato; F. Huang (2006)
• A free energy principle for the brain - Karl J. Friston (2006)
• Connectionist Temporal Classification - Alex Graves et al. (2006)
• Connectionist Temporal Classification: Labelling Unsegmented Sequence Data with RNNs - Alex Graves et al. (2006)
• Connectionist Temporal Classification: Labelling Unsegmented Sequence Data with Recurrent Neural Networks - Graves, A.; Fernandez, S.; Gomez, F.; Schmidhuber, J. (2006)
• Constructions at Work: The Nature of Generalization in Language - Adele E. Goldberg (2006)
• Dimensionality Reduction by Learning an Invariant Mapping (DrLIM) - Raia Hadsell, Sumit Chopra, Yann LeCun (2006)
• OntoNotes: A Large Training Corpus for Enhanced Processing - Ralph Weischedel et al. (2006)
• Policy Gradient in Continuous Time - Rémi Munos (2006)
• Reducing the Dimensionality of Data with Neural Networks - Hinton, G. E.; Salakhutdinov, R. R. (2006)
• The Algorithmic Foundations of Differential Privacy - Cynthia Dwork, Aaron Roth (2006)

2005

• Coarse-to-Fine n-Best Parsing and MaxEnt Discriminative Reranking - Eugene Charniak; Mark Johnson (2005)
• Continuous space language models (CSLM) - Holger Schwenk (2005)
• Hierarchical probabilistic neural network language model - Frederic Morin; Yoshua Bengio (2005)
• Learning a similarity metric discriminatively, with application to face verification - Sumit Chopra, Raia Hadsell, Yann LeCun (2005)
• Markov Logic Networks - Pedro Domingos, Matt Richardson (2005)
• Semi-Supervised Learning Literature Survey - Xiaojin Zhu (2005)
• Universal Artificial Intelligence: Sequential Decisions Based on Algorithmic Probability - Marcus Hutter (2005)

2004

• A Tutorial on Support Vector Regression - Alexander Smola; Bernhard Schölkopf (2004)
• Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data - John D. Lafferty; Andrew McCallum; Fernando C. N. Pereira (2004)
• Discriminative Language Modeling with Conditional Random Fields and the Perceptron Algorithm - Brian Roark; Murat Saraclar; Michael Collins; Mark Johnson (2004)
• Distinctive Image Features from Scale-Invariant Keypoints - David G. Lowe (2004)
• ROUGE: A Package for Automatic Evaluation of Summaries - Lin (2004)
• Statistical Machine Translation: From Single Word Models to Alignment Templates (Ph.D - Franz Josef Och (2004)
• Understanding inverse document frequency: on theoretical arguments for IDF - Stephen Robertson (2004)

2003

• A neural probabilistic language model - Yoshua Bengio, Rejean Ducharme, Pascal Vincent, Christian Jauvin (2003)
• Accurate Unlexicalized Parsing - Dan Klein; Christopher D. Manning (2003)
• Equivalence of backpropagation and contrastive Hebbian learning in a layered network - Xiaohui Xie, H. Sebastian Seung (2003)
• Fast Exact Inference with a Factored Model for Natural Language Parsing - Dan Klein; Christopher D. Manning (2003)
• Head-Driven Statistical Models for Natural Language Parsing - Michael Collins (2003)
• Learning with Local and Global Consistency - Dengyong Zhou, Olivier Bousquet, Thomas Navin Lal, Jason Weston, Bernhard Schoelkopf (2003)
• Probability Theory: The Logic of Science - Jaynes (2003)
• Shallow Parsing with Conditional Random Fields - Fei Sha; Fernando Pereira (2003)
• Statistical Phrase-Based Translation - Philipp Koehn, Franz Josef Och, Daniel Marcu (2003)

2002

• Approximately Optimal Approximate Reinforcement Learning - Sham Kakade, John Langford (2002)
• Bleu: a Method for Automatic Evaluation of Machine Translation - Kishore Papineni, Salim Roukos, Todd Ward, Wei-Jing Zhu (2002)
• Discriminative Training Methods for Hidden Markov Models: Theory and Algorithms - Michael Collins (2002)
• Discriminative Training Methods for Hidden Markov Models: Theory and Experiments with Perceptron Algorithms - Michael Collins (2002)
• Near-Optimal Reinforcement Learning in Polynomial Time - Michael Kearns, Satinder Singh (2002)
• On Discriminative vs - Andrew Y. Ng; Michael I. Jordan (2002)
• Optimizing Search Engines using Clickthrough Data - Thorsten Joachims (2002)
• Slow Feature Analysis: Unsupervised Learning of Invariances - Laurenz Wiskott, Terrence J. Sejnowski (2002)
• Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews - Peter Turney (2002)
• Thumbs up? Sentiment Classification using Machine Learning Techniques - Bo Pang, Lillian Lee, Shivakumar Vaithyanathan (2002)
• Training Products of Experts by Minimizing Contrastive Divergence - Geoffrey E. Hinton (2002)

2001

• A Study of Smoothing Methods for Language Models Applied to Information Retrieval - Chengxiang Zhai; John Lafferty (2001)
• Discovery of Inference Rules for Question Answering - Dekang Lin; Patrick Pantel (2001)
• Estimating the Support of a High-Dimensional Distribution - Bernhard Schölkopf; John C. Platt; John Shawe-Taylor; Alexander J. Smola; Robert C. Williamson (2001)
• Greedy Function Approximation: A Gradient Boosting Machine - Jerome H. Friedman (2001)
• Latent Dirichlet Allocation - David M. Blei; Andrew Y. Ng; Michael I. Jordan (2001)
• On Spectral Clustering: Analysis and an Algorithm - Andrew Y. Ng; Michael I. Jordan; Yair Weiss (2001)
• Rapid Object Detection using a Boosted Cascade of Simple Features - Paul Viola, Michael Jones (2001)
• SVMTorch: support vector machines for large-scale regression problems - Ronan Collobert; Yoshua Bengio (2001)
• The Boosting Approach to Machine Learning: An Overview - Robert E. Schapire (2001)
• The Elements of Statistical Learning - Jerome Friedman, Trevor Hastie, Robert Tibshirani (2001)

2000

• A Maximum-Entropy-Inspired Parser - Eugene Charniak (2000)
• A global geometric framework for nonlinear dimensionality reduction (Isomap) - Joshua B. Tenenbaum; Vin de Silva; John C. Langford (2000)
• A neural probabilistic language model (NIPS 2000 conference version) - Yoshua Bengio; Rejean Ducharme; Pascal Vincent; Christian Jauvin (2000)
• Causation, prediction, and search (2nd ed.) - Peter Spirtes; Clark Glymour; Richard Scheines (2000)
• Eligibility Traces for Off-Policy Policy Evaluation - Doina Precup, Richard S. Sutton, Satinder Singh (2000)
• Nonlinear dimensionality reduction by locally linear embedding (LLE) - Sam T. Roweis; Lawrence K. Saul (2000)
• Policy Gradient Methods for Reinforcement Learning with Function Approximation - Richard S. Sutton, David McAllester, Satinder Singh, Yishay Mansour (2000)
• Random forests - Leo Breiman (2000)
• Two decades of statistical language modeling: where do we go from here? - Ronald Rosenfeld (2000)

1999

• Authoritative Sources in a Hyperlinked Environment - Jon Kleinberg (1999)
• Between MDPs and semi-MDPs: A Framework for Temporal Abstraction in Reinforcement Learning - Richard S. Sutton, Doina Precup, Satinder Singh (1999)
• Comparison of Approximate Methods for Handling Hyperparameters - David J. C. MacKay (1999)
• Finite-Sample Convergence Rates for Q-Learning and Indirect Algorithms - Michael Kearns, Satinder Singh (1999)
• Learning to forget: Continual prediction with LSTM - Felix A. Gers, Juergen Schmidhuber, Fred Cummins (1999)
• Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects - Rajesh P. N. Rao, Dana H. Ballard (1999)
• Temporal Difference Learning and TD-Gammon - Tesauro (1999)
• The PageRank Citation Ranking: Bringing Order to the Web - Larry Page, Sergey Brin, Rajeev Motwani, Terry Winograd (1999)

1998

• Automatic word sense discrimination - Hinrich Schutze (1998)
• Boosting the margin: A new explanation for the effectiveness of voting methods - Robert E. Schapire; Yoav Freund; Peter Bartlett; Wee Sun Lee (1998)
• Efficient BackProp - LeCun, Y.; Bottou, L.; Orr, G. B.; Müller, K.-R. (1998)
• Gradient-based learning applied to document recognition - Yann LeCun, Leon Bottou, Yoshua Bengio, Patrick Haffner (1998)
• Making Large-Scale SVM Learning Practical - Thorsten Joachims (1998)
• Natural gradient works efficiently in learning - Shun-ichi Amari (1998)
• Reinforcement Learning: An Introduction - Richard S. Sutton, Andrew G. Barto (1998)
• Statistical Learning Theory - Vladimir N. Vapnik (1998)
• Text categorization with support vector machines: learning with many relevant features - Thorsten Joachims (1998)
• The Anatomy of a Large-Scale Hypertextual Web Search Engine - Sergey Brin; Lawrence Page (1998)
• The PageRank Citation Ranking: Bringing Order to the Web - Lawrence Page; Sergey Brin; Rajeev Motwani; Terry Winograd (1998)
• The sample complexity of pattern classification with neural networks: the size of the weights is more important than the size of the network - Peter L. Bartlett (1998)
• VC dimension of neural networks - Eduardo D. Sontag (1998)

1997

• A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting - Yoav Freund; Robert E. Schapire (1997)
• A solution to Plato's problem: The latent semantic analysis theory of acquisition, induction, and representation of knowledge - Thomas K. Landauer, Susan T. Dumais (1997)
• Bidirectional recurrent neural networks - Martin Schuster; Kuldip K. Paliwal (1997)
• Boosting the margin: a new explanation for the effectiveness of voting methods - Robert E. Schapire; Yoav Freund; Peter Bartlett; Wee Sun Lee (1997)
• Flat minima - Sepp Hochreiter, Juergen Schmidhuber (1997)
• Flat minima (PDF located for previously null entry) - Sepp Hochreiter; Juergen Schmidhuber (1997)
• Information Extraction from Speech and Language Documents - Frederick Jelinek (1997)
• Long short-term memory - Sepp Hochreiter, Juergen Schmidhuber (1997)
• Multitask Learning - Rich Caruana (1997)

1996

• A Maximum Entropy Approach to Natural Language Processing - Adam L. Berger; Stephen A. Della Pietra; Vincent J. Della Pietra (1996)
• An empirical study of smoothing techniques for language modeling - Stanley F. Chen; Joshua T. Goodman (1996)
• Bagging predictors - Leo Breiman (1996)
• Emergence of Simple-Cell Receptive Field Properties by Learning a Sparse Code for Natural Images - Bruno Olshausen, David Field (1996)
• Exploiting tractable substructures in intractable networks - Lawrence K. Saul; Michael I. Jordan (1996)
• Generalization in Reinforcement Learning: Successful Examples Using Sparse Coarse Coding - Richard S. Sutton (1996)
• Producing high-dimensional semantic spaces from lexical co-occurrence - Kevin Lund, Curt Burgess (1996)
• Reinforcement Learning: A Survey - Leslie Pack Kaelbling; Michael L. Littman; Andrew W. Moore (1996)
• Sequential Minimal Optimization: A Fast Algorithm for Training Support Vector Machines - John C. Platt (1996)
• The Nonlinear PCA Learning Rule in Independent Component Analysis - Erkki Oja (1996)
• The Power of Amnesia: Learning Probabilistic Models with State Memory - Dana Ron; Yoram Singer; Naftali Tishby (1996)
• Why did TD-Gammon Work? - Gerald Tesauro (1996)

1995

• A decision-theoretic generalization of on-line learning and an application to boosting (AdaBoost) - Yoav Freund; Robert E. Schapire (1995)
• Causal diagrams for empirical research - Judea Pearl (1995)
• Facing Up to the Problem of Consciousness - Chalmers (1995)
• Improved Backing-Off for M-gram Language Modeling - Reinhard Kneser; Hermann Ney (1995)
• Support-vector networks - Corinna Cortes, Vladimir Vapnik (1995)
• The wake-sleep algorithm for unsupervised neural networks - Geoffrey E. Hinton; Peter Dayan; Brendan J. Frey; Radford M. Neal (1995)
• Unsupervised Word Sense Disambiguation Rivaling Supervised Methods - David Yarowsky (1995)

1994

• Efficient Memory-Based Reinforcement Learning for Robot Control - Andrew W. Moore, Jeff Schneider, K. Deng (1994)
• Fast Exact Multiplication by the Hessian - Pearlmutter, B. A. (1994)
• Fooled by Locality: Recurrent Networks for Long-Term Dependencies - John F. Kolen (1994)
• Learning long-term dependencies with gradient descent is difficult - Yoshua Bengio, Patrice Simard, Paolo Frasconi (1994)
• Mixture Density Networks - Christopher M. Bishop (1994)

1993

• A Program for Aligning Sentences in Bilingual Corpora - Kenneth Church (1993)
• Building a Large Annotated Corpus of English: The Penn Treebank - Mitchell P. Marcus; Beatrice Santorini; Mary Ann Marcinkiewicz (1993)
• C4.5: Programs for Machine Learning - J. Ross Quinlan (1993)
• Introduction to the Special Issue on Computational Linguistics Using Large Corpora - Kenneth W. Church; Robert L. Mercer (1993)
• Learning and Development in Neural Networks: The Importance of Starting Small - Jeffrey L. Elman (1993)
• Memory-Based Reinforcement Learning: Efficient Computation with Prioritized Sweeping - Andrew W. Moore, Christopher G. Atkeson (1993)
• Prioritized Sweeping: Reinforcement Learning with Less Data and Less Real Time - Andrew W. Moore, Christopher G. Atkeson (1993)
• Reinforcement Learning for Robots Using Neural Networks - Long-Ji Lin (1993)
• Signature Verification using a Siamese Time Delay Neural Network - Jane Bromley, Isabelle Guyon, Yann LeCun, Eduard Sackinger, Roopesh Shah (1993)
• The Mathematics of Statistical Machine Translation - Peter Brown et al. (1993)
• The Mathematics of Statistical Machine Translation: Parameter Estimation - Peter F. Brown; Stephen A. Della Pietra; Vincent J. Della Pietra; Robert L. Mercer (1993)

1992

• A Simple Rule-Based Part of Speech Tagger - Eric Brill (1992)
• A Training Algorithm for Optimal Margin Classifiers - Bernhard E. Boser; Isabelle M. Guyon; Vladimir N. Vapnik (1992)
• A self-organizing neural network that discovers surfaces in random-dot stereograms - Suzanna Becker, Geoffrey E. Hinton (1992)
• Class-Based n-gram Models of Natural Language - Peter F. Brown; Vincent J. Della Pietra; P. V. deSouza; J. C. Lai; Robert L. Mercer (1992)
• Learning complex, extended sequences using the principle of history compression - Juergen Schmidhuber (1992)
• Learning to control fast-weight memories: An alternative to dynamic recurrent networks - Juergen Schmidhuber (1992)
• Practical Issues in Temporal Difference Learning - Gerald Tesauro (1992)
• Q-learning - Watkins; Dayan (1992)
• Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning - Ronald J. Williams (1992)
• Temporal Difference Learning and TD-Gammon - Gerald Tesauro (1992)

1991

• A Possibility for Implementing Curiosity and Boredom in Model-Building Neural Controllers - Juergen Schmidhuber (1991)
• Adaptive Mixtures of Local Experts - Jordan C., R. Jacobs, M. Jordan, S. Nowlan, G. Hinton (1991)
• Curious Model-Building Control Systems - Jurgen Schmidhuber (1991)
• Distributed representations, simple recurrent networks, and grammatical structure - Jeffrey L. Elman (1991)
• Fast, Robust Adaptive Control by Learning Only Forward Models - Andrew W. Moore, Jeff Schneider (1991)
• Intelligence without representation - Rodney A. Brooks (1991)
• Reactive Reinforcement Learning: Complexity and Cooperation - Steven D. Whitehead, Dana H. Ballard (1991)
• Reinforcement Learning Architectures for Animats - Richard S. Sutton (1991)
• Structural risk minimization for character recognition - Isabelle Guyon, Vladimir Vapnik, Bernhard Boser, Leon Bottou, Sara A. Solla (1991)
• The Parti-Game Algorithm for Variable Resolution Reinforcement Learning in Multidimensional State-Spaces - Andrew W. Moore (1991)
• The Zero-Frequency Problem: Estimating the Probabilities of Novel Events in Adaptive Text Compression - Ian H. Witten; Timothy C. Bell (1991)
• Untersuchungen zu dynamischen neuronalen Netzen - Sepp Hochreiter (1991)
• Untersuchungen zu dynamischen neuronalen Netzen (diploma thesis) - Sepp Hochreiter (1991)

1990

• A Statistical Approach to Machine Translation - Peter F. Brown; John Cocke; Stephen A. Della Pietra; Vincent J. Della Pietra; Fredrick Jelinek; John D. Lafferty; Robert L. Mercer; Paul S. Roossin (1990)
• A Study of Cooperative Behavior and Learning in Multi-Agent Systems - Steven D. Whitehead (1990)
• Backpropagation Through Time: What It Does and How to Do It - Paul J. Werbos (1990)
• Efficient Memory-Based Learning for Robot Control (PhD thesis) - Andrew W. Moore (1990)
• Finding structure in time - Jeffrey L. Elman (1990)
• Handwritten digit recognition with a back-propagation network - Yann LeCun; Bernhard Boser; John S. Denker; Richard E. Howard; Wayne Hubbard; Lawrence D. Jackel (1990)
• Indexing by Latent Semantic Analysis - Scott Deerwester; Susan T. Dumais; George W. Furnas; Thomas K. Landauer; Richard Harshman (1990)
• Integrated Architecture for Learning, Planning and Reacting (Dyna) - Richard S. Sutton (1990)
• Making the World Differentiable: Self-Supervised Fully Recurrent Neural Networks (TR FKI-126) - J. Schmidhuber (1990)
• Mapping Part-Whole Hierarchies into Connectionist Networks - Geoffrey E. Hinton (1990)
• Optimal Brain Damage - Yann LeCun; John S. Denker; Sara A. Solla (1990)
• Recursive Distributed Representations - Jordan B. Pollack (1990)
• The Strength of Weak Learnability - Robert E. Schapire (1990)

1989

• A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition - Lawrence R. Rabiner (1989)
• A learning algorithm for continually running fully recurrent neural networks - Ronald J. Williams; David Zipser (1989)
• Acoustic-Phonetic Based Speech Recognition - Victor Zue (1989)
• Approximation by superpositions of a sigmoidal function - George Cybenko (1989)
• Backpropagation applied to handwritten zip code recognition - Yann LeCun, Bernhard Boser, John S. Denker, Donnie Henderson, Richard E. Howard, Wayne Hubbard, Lawrence D. Jackel (1989)
• Catastrophic Interference in Connectionist Networks: The Sequential Learning Problem - Michael McCloskey; Neal J. Cohen (1989)
• Fast learning in networks of locally-tuned processing units - John Moody; Christian J. Darken (1989)
• Generalization and Network Design Strategies - Yann LeCun (1989)
• Learnability and the Vapnik-Chervonenkis Dimension - A. Blumer, A. Ehrenfeucht, D. Haussler, M. Warmuth (1989)
• Learning from Delayed Rewards (PhD thesis) - Christopher J. C. H. Watkins (1989)
• Multilayer feedforward networks are universal approximators - Kurt Hornik, Maxwell Stinchcombe, Halbert White (1989)
• Phoneme recognition using time-delay neural networks - Alexander Waibel, Toshiyuki Hanazawa, Geoffrey Hinton, Kiyohiro Shikano, Kevin J. Lang (1989)
• Self-Organization and Associative Memory (3rd ed.) - Teuvo Kohonen (1989)
• What Size Net Gives Valid Generalization? - Eric B. Baum, David Haussler (1989)

1988

• A Stochastic Parts Program and Noun Phrase Parser for Unrestricted Text - Kenneth Church (1988)
• A Theoretical Framework for Back-Propagation - Yann LeCun (1988)
• ALVINN: An Autonomous Land Vehicle in a Neural Network - Dean A. Pomerleau (1988)
• Connectionism and Cognitive Architecture: A Critical Analysis - Jerry A. Fodor; Zenon W. Pylyshyn (1988)
• Generalization of Backpropagation with Application to a Recurrent Gas Market Model - Paul J. Werbos (1988)
• Learning to Predict by the Methods of Temporal Differences - Richard S. Sutton (1988)
• Multivariable Functional Interpolation and Adaptive Networks - D. S. Broomhead; David Lowe (1988)
• On Language and Connectionism: Analysis of a Parallel Distributed Processing Model of Language Acquisition - Steven Pinker; Alan Prince (1988)
• On the Proper Treatment of Connectionism - Paul Smolensky (1988)
• Perceptrons: Expanded Edition - Marvin Minsky; Seymour Papert (1988)
• Predicting the secondary structure of globular proteins using neural network models - Ning Qian, Terrence J. Sejnowski (1988)
• Probabilistic Reasoning in Intelligent Systems - Pearl (1988)
• Probabilistic reasoning in intelligent systems: Networks of plausible inference - Judea Pearl (1988)
• The ART of Adaptive Pattern Recognition by a Self-Organizing Neural Network - Gail A. Carpenter; Stephen Grossberg (1988)
• The cascade-correlation learning architecture (CMU-CS-90-100) - Scott E. Fahlman; Christian Lebiere (1988)
• Training a 3-Node Neural Network is NP-Complete - Avrim Blum; Ronald L. Rivest (1988)

1987

• A Learning Rule for Asynchronous Perceptrons with Feedback in a Combinatorial Environment - Luis B. Almeida (1987)
• An Empirical Study of Learning Speed in Back-Propagation Networks - Scott E. Fahlman (1987)
• An Introduction to Computing with Neural Nets - Richard P. Lippmann (1987)
• Estimation of probabilities from sparse data for the language model component of a speech recognizer - Slava M. Katz (1987)
• Generalization of back-propagation to recurrent neural networks - Fernando J. Pineda (1987)
• Genetic Algorithms in Search, Optimization and Machine Learning - David Goldberg (1987)
• How Learning Can Guide Evolution - Geoffrey E. Hinton, Steven J. Nowlan (1987)
• Kernel Estimates of a Distribution Function - R. S. Wenocur; B. D. Ripley (1987)
• Learning How the World Works: Specifications for Predictive Networks in Real Time - Paul J. Werbos (1987)
• Learning the Past Tenses of English Verbs: Implicit Rules or Parallel Distributed Processing? - David E. Rumelhart; James L. McClelland (1987)
• Parallel Networks that Learn to Pronounce English Text (NETtalk) - T. Sejnowski, C. Rosenberg (1987)
• The Intentional Stance - Daniel C. Dennett (1987)

1986

• A Robust Layered Control System for a Mobile Robot - Rodney A. Brooks (1986)
• A general framework for parallel distributed processing - David E. Rumelhart, Geoffrey E. Hinton, James L. McClelland (1986)
• Induction of Decision Trees - J. R. Quinlan (1986)
• Information Processing in Dynamical Systems: Foundations of Harmony Theory - Paul Smolensky (1986)
• Learning distributed representations of concepts - Geoffrey E. Hinton (1986)
• Learning internal representations by error propagation - David E. Rumelhart, Geoffrey E. Hinton, Ronald J. Williams (1986)
• Learning representations by back-propagating errors - David E. Rumelhart, Geoffrey E. Hinton, Ronald J. Williams (1986)
• Neurophilosophy: Toward a Unified Science of the Mind-Brain - Paul M. Churchland (1986)
• Parallel Distributed Processing Handbook - PDP Research Group (1986)
• Parallel Distributed Processing, Vol - David E. Rumelhart, James L. McClelland, PDP Research Group (1986)
• Parallel Distributed Processing, Volume 1 - David E. Rumelhart, James L. McClelland (1986)
• Parallel Distributed Processing, Volume 1: Foundations - David E. Rumelhart; James L. McClelland; PDP Research Group (1986)
• Parallel Distributed Processing, Volume 2: Psychological and Biological Models - James L. McClelland; David E. Rumelhart; PDP Research Group (1986)
• Serial Order: A Parallel Distributed Processing Approach (TR 8604) - Michael I. Jordan (1986)
• Serial order: A parallel distributed processing approach - Michael I. Jordan (1986)
• The Society of Mind - Minsky (1986)

1985

• A Learning Algorithm for Boltzmann Machines - David H. Ackley; Geoffrey E. Hinton; Terrence J. Sejnowski (1985)
• Boltzmann Machines and Artificial Intelligence - John J. Hopfield (ed. report) (1985)
• Learning representations by back-propagating errors - David E. Rumelhart; Geoffrey E. Hinton; Ronald J. Williams (1985)
• Learning-Logic (Invention Report S81-64, File 1) - Parker, D. B. (1985)
• NETtalk: A Parallel Network That Learns to Read Aloud - Terrence J. Sejnowski, Charles R. Rosenberg (1985)
• Parallel Networks that Learn to Pronounce English Text - Terrence J. Sejnowski; Charles R. Rosenberg (1985)
• Une procedure d'apprentissage pour reseau a neurones a seuil (A learning procedure for a threshold neural network) - Yann LeCun (1985)
• Une procedure d'apprentissage pour reseau a seuil asymetrique (A learning procedure for an asymmetric threshold network) - Yann LeCun (1985)
• Une procédure d'apprentissage pour réseau a seuil asymétrique (A learning procedure for an asymmetric threshold network) - Yann Le Cun (1985)
• Une procédure d'apprentissage pour réseau à seuil asymétrique - LeCun, Y. (1985)

1984

• A Theory of the Learnable - Leslie G. Valiant (1984)
• Classification and Regression Trees (CART) - Leo Breiman; Jerome H. Friedman; Richard A. Olshen; Charles J. Stone (1984)
• Computation and Cognition: Toward a Foundation for Cognitive Science - Zenon W. Pylyshyn (1984)
• Rule-Based Expert Systems: The MYCIN Experiments of the Stanford Heuristic Programming Project - Bruce G. Buchanan, Edward H. Shortliffe (eds.) (1984)
• Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images - Stuart Geman; Donald Geman (1984)
• Temporal Credit Assignment in Reinforcement Learning - Richard S. Sutton (1984)

1983

• A Method for Solving the Convex Programming Problem with Convergence Rate O(1/k^2) - Yurii Nesterov (1983)
• Neocognitron: A Hierarchical Neural Network Capable of Visual Pattern Recognition - Kunihiko Fukushima (1983)
• Neuronlike Adaptive Elements That Can Solve Difficult Learning Control Problems - Andrew G. Barto; Richard S. Sutton; Charles W. Anderson (1983)
• Optimal Perceptual Inference - Geoffrey E. Hinton; Terrence J. Sejnowski (1983)
• Optimization by Simulated Annealing - Scott Kirkpatrick; C. D. Gelatt; M. P. Vecchi (1983)

1982

• Neural Networks and Physical Systems with Emergent Collective Computational Abilities - John J. Hopfield (1982)
• Reverend Bayes on Inference Engines: A Distributed Hierarchical Approach - Judea Pearl (1982)
• Self-Organized Formation of Topologically Correct Feature Maps - Teuvo Kohonen (1982)
• Vision: A computational investigation into the human representation and processing of visual information - David Marr (1982)

1981

• An Interactive Activation Model of Context Effects in Letter Perception: Part 1 - McClelland; Rumelhart (1981)
• Landmark Learning: An Illustration of Associative Search - Andrew G. Barto; Richard S. Sutton (1981)
• The Correlation Theory of Brain Function - Christoph von der Malsburg (1981)

1980

• A Theory of Syntactic Recognition for Natural Language - Mitchell P. Marcus (1980)
• A fast Monte-Carlo test for primality - Robert M. Solovay; Volker Strassen (1980)
• An Algorithm for Suffix Stripping - Martin F. Porter (1980)
• Minds, Brains, and Programs - John R. Searle (1980)
• Neocognitron: A Self-Organizing Neural Network Model - Kunihiko Fukushima (1980)
• Neocognitron: A Self-Organizing Neural Network Model for a Mechanism of Pattern Recognition Unaffected by Shift in Position - Kunihiko Fukushima (1980)

1979

• Bootstrap methods: Another look at the jackknife - Efron, B. (1979)
• Discovering rules by induction from large collections of examples (ID3) - J. Ross Quinlan (1979)
• Neural Network Model for a Mechanism of Pattern Recognition Unaffected by Shift in Position - Neocognitron - Kunihiko Fukushima (1979)
• The Creative Mind: Myths and Mechanisms - Margaret A. Boden (1979)
• What Computers Can't Do - Dreyfus (1979)

1978

• A Truth Maintenance System - Jon Doyle (1978)
• Cognitive Systems Based on Adaptive Algorithms - John H. Holland; Judith S. Reitman (1978)
• Modeling by shortest data description - Jorma Rissanen (1978)
• Planning and Acting - Drew McDermott (1978)
• Rational Decision-Making in Business Organizations (Nobel Memorial Lecture) - Herbert A. Simon (1978)
• Resolving Pronoun References - Jerry R. Hobbs (1978)

1977

• A universal algorithm for sequential data compression - Jacob Ziv; Abraham Lempel (1977)
• Automated Theory Formation in Mathematics - Douglas B. Lenat (1977)
• Distinctive Features, Categorical Perception, and Probability Learning: Some Applications of a Neural Model - James A. Anderson; Jack W. Silverstein; Stephen A. Ritz; Rand S. Jones (1977)
• Maximum Likelihood from Incomplete Data via the EM Algorithm - A. P. Dempster; N. M. Laird; Donald B. Rubin (1977)
• Review of the ARPA Speech Understanding Project - Dennis Klatt (1977)
• Scripts, Plans, Goals, and Understanding: An Inquiry into Human Knowledge Structures - Roger C. Schank; Robert P. Abelson (1977)
• Speech Understanding Systems: Final Report of a Study Group - Allen Newell et al. (1977)
• The Ubiquity of Discovery - Douglas B. Lenat (1977)

1976

• Adaptive Pattern Classification and Universal Recoding, I - Stephen Grossberg (1976)
• Computer Power and Human Reason: From Judgment to Calculation - Joseph Weizenbaum (1976)
• Computer Science as Empirical Inquiry: Symbols and Search - Allen Newell; Herbert A. Simon (1976)
• Continuous speech recognition by statistical methods - Frederick Jelinek (1976)
• Early processing of visual information - David Marr (1976)
• Relevance Weighting of Search Terms - Stephen Robertson; Karen Sparck Jones (1976)
• Riemann's hypothesis and tests for primality - Gary L. Miller (1976)
• Taylor Expansion of the Accumulated Rounding Error - Seppo Linnainmaa (1976)

1975

• A Multi-Level Organization for Problem Solving Using Many Diverse Cooperating Sources of Knowledge - Lee D. Erman; Victor R. Lesser (1975)
• A Vector Space Model for Automatic Indexing of Communications - Gerard Salton; A. Wong; C. S. Yang (1975)
• A spreading-activation theory of semantic processing - Allan M. Collins; Elizabeth F. Loftus (1975)
• Adaptation in Natural and Artificial Systems - John H. Holland (1975)
• Cognitive representations of semantic categories - Eleanor Rosch (1975)
• Cognitron: A Self-Organizing Multilayered Neural Network - Kunihiko Fukushima (1975)
• How To Use What You Know - Carl Hewitt (1975)
• Inductive Inference Theory: A Unified Approach to Problems in Prediction and Decision - Ray J. Solomonoff (1975)
• Logic and conversation - H. P. Grice (1975)
• Scripts, Plans, and Knowledge - Roger C. Schank; Robert P. Abelson (1975)
• The DRAGON system - An overview - James K. Baker (1975)
• The Nonlinear Nature of Plans - Earl D. Sacerdoti (1975)

1974

• A Framework for Representing Knowledge - Marvin Minsky (1974)
• Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences - Paul J. Werbos (1974)
• Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences (PhD thesis) - Paul J. Werbos (1974)
• Information-theoretic limitations of formal systems - Gregory J. Chaitin (1974)
• The Existence of Persistent States in the Brain - W. Little (1974)
• The Roots of Backpropagation: From Ordered Derivatives to Neural Networks (PhD thesis) - Paul J. Werbos (1974)

1973

• A Universal Modular ACTOR Formalism for Artificial Intelligence - Carl Hewitt; Peter Bishop; Richard Steiger (1973)
• Artificial Intelligence - A Personal View - David Marr (1973)
• Artificial Intelligence: A General Survey (the Lighthill Report) - James Lighthill (1973)
• MARGIE: Memory, Analysis, Response Generation and Inference on English - Roger C. Schank (1973)
• The proper treatment of quantification in ordinary English - Richard Montague (1973)
• Universal sequential search problems - Leonid A. Levin (1973)

1972

• A Maximization Technique Occurring in the Statistical Analysis of Probabilistic Functions of Markov Chains - Leonard E. Baum; Ted Petrie; George Soules; Norman Weiss (1972)
• A Simple Neural Network Generating an Interactive Memory - J. Anderson (1972)
• A Statistical Interpretation of Term Specificity and Its Application in Retrieval - Karen Sparck Jones (1972)
• Associatron: A Model of Associative Memory - Kazuo Nakano (1972)
• Characteristics of Random Nets of Analog Neuron-Like Elements - Shun-ichi Amari (1972)
• Characteristics of Randomly Connected Threshold-Element Networks and Neural Systems - Shun-ichi Amari (1972)
• Citation Analysis as a Tool in Journal Evaluation - Eugene Garfield (1972)
• Conceptual dependency: A theory of natural language understanding - Roger C. Schank (1972)
• Correlation Matrix Memories - Teuvo Kohonen (1972)
• Learning patterns and pattern sequences by self-organizing nets of threshold elements - Amari, S. (1972)
• Procedures as a representation for data in a computer program for understanding natural language - Terry Winograd (1972)
• Reducibility Among Combinatorial Problems - Richard M. Karp (1972)
• The Complexity of Theorem-Proving Procedures - Stephen A. Cook (1972)
• Understanding Natural Language - Terrence Winograd (1972)

1971

• Finding the Conceptual Content and Intention in an Utterance - Roger C. Schank (1971)
• On the uniform convergence of relative frequencies of events to their probabilities - V. N. Vapnik; A. Ya. Chervonenkis (1971)
• Polynomial Theory of Complex Systems - Alexey G. Ivakhnenko; V. G. Lapa (1971)
• Procedural Embedding of Knowledge In Planner - Carl Hewitt (1971)
• Procedures as a Representation for Data in a Computer Program for Understanding Natural Language - Terry Winograd (1971)
• STRIPS: A New Approach to the Application of Theorem Proving to Problem Solving - Richard E. Fikes; Nils J. Nilsson (1971)

1970

• Learning Structural Descriptions From Examples - Patrick H. Winston (1970)
• Representation of the Cumulative Rounding Error of an Algorithm as a Taylor Expansion of the Local Rounding Errors - Seppo Linnainmaa (1970)
• Transition Network Grammars for Natural Language Analysis - William A. Woods (1970)

1969

• Applied Optimal Control: Optimization, Estimation, and Control - Arthur E. Bryson; Yu-Chi Ho (1969)
• Non-Holographic Associative Memory - David J. Willshaw, Colin von der Malsburg (1969)
• Perceptrons: An Introduction to Computational Geometry - Marvin Minsky; Seymour Papert (1969)
• Some Philosophical Problems from the Standpoint of Artificial Intelligence - John McCarthy, Patrick J. Hayes (1969)
• The Sciences of the Artificial - Herbert A. Simon (1969)

1968

• A Theory of Adaptive Pattern Classifiers - Shun-Ichi Amari (1968)
• Matter, Mind and Models - Marvin L. Minsky (1968)
• Semantic Memory - M. Ross Quillian (1968)

1967

• A Theory of Adaptive Pattern Classifiers - Shun-ichi Amari (1967)
• Computational Analysis of Present-Day American English - Henry Kucera; W. Nelson Francis (1967)
• Language Identification in the Limit - E. Mark Gold (1967)
• Nearest Neighbor Pattern Classification - Thomas Cover; Peter Hart (1967)
• Some Studies in Machine Learning Using the Game of Checkers II - Recent Progress - Arthur L. Samuel (1967)

1966

• ELIZA - A Computer Program for the Study of Natural Language Communication Between Man and Machine - Joseph Weizenbaum (1966)
• Language and Machines: Computers in Translation and Linguistics (ALPAC Report) - ALPAC Committee (John Pierce, ed.) (1966)
• On the length of programs for computing finite binary sequences - Gregory J. Chaitin (1966)
• Statistical Inference for Probabilistic Functions of Finite State Markov Chains - Leonard E. Baum; Ted Petrie (1966)

1965

• Alchemy and Artificial Intelligence - Hubert L. Dreyfus (1965)
• An Algorithm for the Machine Calculation of Complex Fourier Series - James W. Cooley, John W. Tukey (1965)
• Aspects of the theory of syntax - Noam Chomsky (1965)
• Cybernetic Predicting Devices (cybernetics predicting devices) - Alexey G. Ivakhnenko (1965)
• Embodiments of Mind - W. McCulloch (1965)
• Fuzzy Sets - Lotfi A. Zadeh (1965)
• Learning Machines: Foundations of Trainable Pattern-Classifying Systems - Nils J. Nilsson (1965)
• Three approaches to the quantitative definition of information - A. N. Kolmogorov (1965)

1964

• A Formal Theory of Inductive Inference, Parts I & II - Ray J. Solomonoff (1964)
• A Formal Theory of Inductive Inference, Parts I and II - Ray J. Solomonoff (1964)
• Artificial Intelligence and Intelligence - Thomas Marill (1964)
• Inference and Disputed Authorship: The Federalist - Frederick Mosteller; David L. Wallace (1964)
• Some methods of speeding up the convergence of iteration methods - B. T. Polyak (1964)
• Theoretical Foundations of the Potential Function Method in Pattern Recognition Learning - M. A. Aizerman, E. M. Braverman, L. I. Rozonoer (1964)

1963

• Computers and Thought - Edward A. Feigenbaum, Julian Feldman (eds.) (1963)
• Computers as Tools and as Metaphors - Ulric Neisser (1963)
• Matter, Mind and Models - Marvin Minsky (1963)
• Pattern Recognition by Machine - O. G. Selfridge, U. Neisser (1963)
• Situations, Actions, and Causal Laws - John McCarthy (1963)
• The Structure of a Semantic Theory - Katz; Fodor (1963)

1962

• Analysis of a Four-Layer Series-Coupled Perceptron - H. D. Block, B. W. Knight, F. Rosenblatt (1962)
• Generalization and Information Storage in Networks of Adaline Neurons - Bernard Widrow (1962)
• Pattern-Recognizing Control Systems - Bernard Widrow, Smith (1962)
• Principles of Neurodynamics: Perceptrons and the Theory of Brain Mechanisms - Frank Rosenblatt (1962)
• Receptive Fields, Binocular Interaction and Functional Architecture in the Cat's Visual Cortex - David H. Hubel; Torsten N. Wiesel (1962)
• The Perceptron: A Model for Brain Functioning - Neal J. Block (1962)

1961

• A Pattern Recognition Program That Generates, Evaluates and Adjusts Its Own Operators - L. Uhr, C. Vossler (1961)
• Die Lernmatrix - Karl Steinbuch (1961)
• Gradient Theory of Optimal Flight Paths - Henry J. Kelley (1961)
• Pattern Recognition and Reading by Machine - Charles Bledsoe, I. Browning (1961)
• Possible principles underlying the transformation of sensory messages - Horace B. Barlow (1961)
• Principles of Neurodynamics: Perceptrons and the Theory of Brain Mechanisms - Frank Rosenblatt (1961)
• Steps Toward Artificial Intelligence - Marvin L. Minsky (1961)

1960

• A New Approach to Linear Filtering and Prediction Problems - Kalman (1960)
• Adaptive Switching Circuits - Bernard Widrow; Marcian E. Hoff (1960)
• An Adaptive 'ADALINE' Neuron Using Chemical 'Memistors' - Bernard Widrow, Marcian E. Hoff Jr. (1960)
• Gradient Theory of Optimal Flight Paths - Henry J. Kelley (1960)
• Man-Computer Symbiosis - J. C. R. Licklider (1960)
• Minds and Machines - Putnam (1960)
• On Self-Organizing Systems and Their Environments - Heinz von Foerster (1960)
• Pattern Recognition by Machine (Stanford Stacks) - Oliver Selfridge, Ulric Neisser (1960)
• Recursive Functions of Symbolic Expressions and Their Computation by Machine (LISP) - John McCarthy (1960)
• Recursive Functions of Symbolic Expressions and Their Computation by Machine, Part I - John McCarthy (1960)
• The present status of automatic translation of languages - Yehoshua Bar-Hillel (1960)

1959

• A Guide to the General Problem-Solver Program (GPS) - Allen Newell, J. C. Shaw, Herbert A. Simon (1959)
• A Review of B - Noam Chomsky (1959)
• Finite Automata and Their Decision Problems - Michael O. Rabin, Dana Scott (1959)
• Pandemonium: A Paradigm for Learning - Oliver G. Selfridge (1959)
• Programming Computers to Play Games - A. L. Samuel (1959)
• Programs with Common Sense - McCarthy (1959)
• Some Studies in Machine Learning Using the Game of Checkers - Arthur L. Samuel (1959)
• What the Frog's Eye Tells the Frog's Brain - J. Y. Lettvin; H. R. Maturana; W. S. McCulloch; W. H. Pitts (1959)

1958

• A Course in Modern Linguistics - Charles F. Hockett (1958)
• Elements of a Theory of Human Problem Solving - Allen Newell, J. C. Shaw, Herbert A. Simon (1958)
• Mathematics and Logic for Digital Devices - James T. Culbertson (1958)
• Perception and Communication - Donald E. Broadbent (1958)
• Personal Knowledge: Towards a Post-Critical Philosophy - Michael Polanyi (1958)
• Programs with Common Sense - John McCarthy (1958)
• The Computer and the Brain - John von Neumann (1958)
• The Design of an Automatic Russian-English Technical Dictionary - Anthony G. Oettinger (1958)
• The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain - Frank Rosenblatt (1958)
• Toward a Wider Use of Man's Better Capacities - J. C. R. Licklider (1958)

1957

• A synopsis of linguistic theory 1930-1955 - J. R. Firth (1957)
• Dynamic Programming - Bellman (1957)
• Empirical Explorations of the Logic Theory Machine: A Case Study in Heuristic Programming - A. Newell; J. C. Shaw; H. A. Simon (1957)
• General Problem Solver (report P-870) - Allen Newell, J. C. Shaw, Herbert A. Simon (1957)
• Papers in Linguistics 1934-1951 - John Rupert Firth (1957)
• Report on a General Problem-Solving Program - Allen Newell; J. C. Shaw; Herbert A. Simon (1957)
• Syntactic Structures - Noam Chomsky (1957)
• The Perceptron - A Perceiving and Recognizing Automaton (Report 85-601-1) - F. Rosenblatt (1957)
• The Perceptron: A Perceiving and Recognizing Automaton (Project PARA) - Frank Rosenblatt (1957)
• The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain - Frank Rosenblatt (1957)

1956

• An Inductive Inference Machine - Ray J. Solomonoff (1956)
• An Introduction to Cybernetics - W. Ross Ashby (1956)
• Automata Studies - Marvin Minsky (ed.) et al. (1956)
• Conditional Probability Machines and Conditional Reflexes - Albert M. Uttley (1956)
• Pattern Recognition and Modern Computers - Oliver G. Selfridge (1956)
• Probabilistic Logics and the Synthesis of Reliable Organisms from Unreliable Components - John von Neumann (1956)
• Representation of Events in Nerve Nets and Finite Automata - Stephen C. Kleene (1956)
• The Logic Theory Machine: A Complex Information Processing System - Allen Newell; Herbert A. Simon (1956)
• The Magical Number Seven, Plus or Minus Two - George A. Miller (1956)
• Three Models for the Description of Language - Noam Chomsky (1956)

1955

• A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence - John McCarthy; Marvin Minsky; Nathaniel Rochester; Claude Shannon (1955)
• Citation Indexes for Science: A New Dimension in Documentation through Association of Ideas - Eugene Garfield (1955)
• Heuristic Problem Solving: The Next Advance in Operations Research - Allen Newell; Herbert A. Simon (1955)
• Pattern Recognition and Modern Computers - Oliver G. Selfridge (1955)

1954

• Distributional Structure - Zellig S. Harris (1954)
• Neural Nets and the Brain Model Problem (PhD dissertation) - Marvin Minsky (1954)
• Simulation of Self-Organizing Systems by Digital Computer - B. G. Farley; W. A. Clark (1954)
• Solvable and Unsolvable Problems - Alan M. Turing (1954)
• Some informational aspects of visual perception - Fred Attneave (1954)

1953

• Computers and Automata - Claude E. Shannon (1953)
• Digital Computers Applied to Games - Alan M. Turing (1953)
• The Living Brain - W. Grey Walter (1953)
• The Population Frequencies of Species and the Estimation of Population Parameters - Irving John Good (1953)

1952

• Cybernetics: Circular Causal and Feedback Mechanisms in Biological and Social Systems, Transactions V-VIII (1946-1953) - Josiah Macy Jr. Foundation (eds.) (1952)
• Design for a Brain - W. Ross Ashby (1952)
• Design for a Brain: The Origin of Adaptive Behaviour - W. Ross Ashby (1952)
• Stochastic estimation of the maximum of a regression function - J. Kiefer, J. Wolfowitz (1952)
• The Behavior of Simple Machines - John McCarthy (1952)
• The Chemical Basis of Morphogenesis - Alan M. Turing (1952)

1951

• A Stochastic Approximation Method - Herbert Robbins; Sutton Monro (1951)
• Cybernetics: or Control and Communication in the Animal and the Machine - Norbert Wiener (1951)
• Methods in Structural Linguistics - Zellig S. Harris (1951)
• Neural Nets and the Brain Model Problem (SNARC) - Marvin Minsky (1951)
• Prediction and Entropy of Printed English - Claude E. Shannon (1951)
• The General and Logical Theory of Automata (Cerebral Mechanisms lectures 1948, pub - John von Neumann (1951)
• Whirlwind Digital Computer and Related Reports - Jay W. Forrester; Robert R. Everett (1951)

1950

• Computing Machinery and Intelligence - Alan M. Turing (1950)
• Probability and the Weighing of Evidence - Irving John Good (1950)
• Programming a Computer for Playing Chess - Claude E. Shannon (1950)
• The Human Use of Human Beings - Norbert Wiener (1950)
• The Human Use of Human Beings: Cybernetics and Society - Norbert Wiener (1950)

1949

• Communication Theory of Secrecy Systems - Claude E. Shannon (1949)
• Extrapolation, Interpolation, and Smoothing of Stationary Time Series - Norbert Wiener (1949)
• Human Behavior and the Principle of Least Effort - George Kingsley Zipf (1949)
• The Mind of Mechanical Man - Geoffrey Jefferson (1949)
• The Monte Carlo Method - Nicholas Metropolis, Stanislaw Ulam (1949)
• The Organization of Behavior: A Neuropsychological Theory - Donald O. Hebb (1949)
• Translation - Weaver (1949)
• Universal High-Speed Digital Computers: A Small-Scale Experimental Machine - T. Kilburn; F. C. Williams (1949)

1948

• A Mathematical Theory of Communication - Claude E. Shannon (1948)
• A Symbolic Analysis of Relay and Switching Circuits - Claude Shannon (1948)
• Cybernetics; or, Control and Communication in the Animal and the Machine - Norbert Wiener (1948)
• Intelligent Machinery - Alan M. Turing (1948)
• Intelligent Machinery (NPL Report) - A. M. Turing (1948)
• Mathematical Biophysics: Physico-Mathematical Foundations of Biology - Nicolas Rashevsky (1948)

1947

• How We Know Universals: The Perception of Auditory and Visual Forms - Warren S. McCulloch; Walter H. Pitts (1947)
• Preliminary Discussion of the Logical Design of an Electronic Computing Instrument - Arthur W. Burks; Herman H. Goldstine; John von Neumann (1947)

1946

• A Manual of Operation for the Automatic Sequence Controlled Calculator (Harvard Mark I) - Staff of the Computation Laboratory (1946)
• Mathematical Methods of Statistics - Harald Cramer (1946)
• Preliminary Description of the EDVAC - J. Presper Eckert; John W. Mauchly (1946)

1945

• A Heterarchy of Values Determined by the Topology of Nervous Nets - Warren S. McCulloch (1945)
• As We May Think - Vannevar Bush (1945)
• First Draft of a Report on the EDVAC - John von Neumann (1945)
• Plankalkül: The First High-Level Programming Language (1942-45 drafts) - Konrad Zuse (1945)
• Proposed Electronic Calculator - Alan M. Turing (1945)
• Sequential Tests of Statistical Hypotheses - Abraham Wald (1945)

1944

• The Theory of Games and Economic Behavior - John von Neumann; Oskar Morgenstern (1944)
• Theory of Games and Economic Behavior - John von Neumann; Oskar Morgenstern (1944)

1943

• A Logical Calculus of the Ideas Immanent in Nervous Activity - Warren S. McCulloch; Walter H. Pitts (1943)
• Behavior, Purpose and Teleology - Arturo Rosenblueth, Norbert Wiener, Julian Bigelow (1943)
• Formal Reductions of the General Combinatorial Decision Problem - Emil Post (1943)

1942

• Computing Machine for the Solution of Large Systems of Linear Equations - Atanasoff, John V. (1942)
• The Statistical Physiology of Nervous Activity / early cybernetics milieu - Warren S. McCulloch; Walter H. Pitts (1942)

1941

• Extrapolation, Interpolation, and Smoothing of Stationary Time Series (Radiation Lab report era) - Norbert Wiener (1941)
• The Calculi of Lambda-Conversion - Church, Alonzo (1941)

1940

• A Symbolic Analysis of Relay and Switching Circuits - Claude E. Shannon (1940)
• An Algebra for Theoretical Genetics - Claude E. Shannon (1940)

1939

• Systems of Logic Based on Ordinals - Turing, Alan M. (1939)

1938

• A Symbolic Analysis of Relay and Switching Circuits - Claude E. Shannon (1938)
• Mathematical Biophysics: Physico-Mathematical Foundations of Biology - Nicolas Rashevsky (1938)

1937

• La prevision: ses lois logiques, ses sources subjectives - B. de Finetti (1937)
• Proposed Automatic Calculating Machine - Aiken, Howard H. (1937)

1936

• A Note on the Entscheidungsproblem - Alonzo Church (1936)
• An Unsolvable Problem of Elementary Number Theory - Alonzo Church (1936)
• Finite Combinatory Processes — Formulation 1 - Emil L. Post (1936)
• General Recursive Functions of Natural Numbers - Stephen C. Kleene (1936)
• On Computable Numbers, with an Application to the Entscheidungsproblem - Alan M. Turing (1936)

1935

• The Design of Experiments - Ronald A. Fisher (1935)
• Untersuchungen ueber das logische Schliessen - Gentzen, Gerhard (1935)

1934

• Functionality in Combinatory Logic - Curry, Haskell B. (1934)
• Grundlagen der Mathematik, Band I - Hilbert, David; Bernays, Paul (1934)

1933

• Grundbegriffe der Wahrscheinlichkeitsrechnung - A. N. Kolmogorov (1933)

1932

• Mathematische Grundlagen der Quantenmechanik - John von Neumann (1932)
• Remembering: A Study in Experimental and Social Psychology - Frederic C. Bartlett (1932)

1931

• The Differential Analyzer: A New Machine for Solving Differential Equations - Bush, Vannevar (1931)
• Truth and Probability - Frank P. Ramsey (1931)
• Uber formal unentscheidbare Satze der Principia Mathematica und verwandter Systeme I - Kurt Godel (1931)
• Über formal unentscheidbare Sätze der Principia Mathematica und verwandter Systeme I - Kurt Gödel (1931)

1930

• Die Vollständigkeit der Axiome des logischen Funktionenkalküls - Kurt Gödel (1930)
• Die formalen Regeln der intuitionistischen Logik - Arend Heyting (1930)
• On a Problem of Formal Logic - F. P. Ramsey (1930)
• Recherches sur la theorie de la demonstration - Jacques Herbrand (1930)
• The Genetical Theory of Natural Selection - R. A. Fisher (1930)

1929

• Brain Mechanisms and Intelligence - K. S. Lashley (1929)
• Brain Mechanisms and Intelligence: A Quantitative Study of Injuries to the Brain - Karl S. Lashley (1929)
• Gestalt Psychology - Wolfgang Köhler (1929)

1928

• Die Grundlagen der Mathematik - David Hilbert (1928)
• Lectures on Conditioned Reflexes - Ivan P. Pavlov (1928)
• The Basis of Sensation: The Action of the Sense Organs - Edgar Douglas Adrian (1928)
• Zur Theorie der Gesellschaftsspiele - John von Neumann (1928)

1927

• Conditioned Reflexes: An Investigation of the Physiological Activity of the Cerebral Cortex - Ivan P. Pavlov (1927)
• Principia Mathematica to *56 (2nd ed.) - Alfred North Whitehead; Bertrand Russell (1927)

1926

• Truth and Probability - F. P. Ramsey (1926)

1925

• Behaviorism - John B. Watson (1925)
• Statistical Methods for Research Workers - R. A. Fisher (1925)

1924

• A propos d'un traite de probabilites (the 'infinite monkey' theorem) - Emile Borel (1924)

1923

• Begruendung der elementaren Arithmetik durch die rekurrierende Denkweise ohne Anwendung scheinbarer Veraenderlichen mit unendlichem Ausdehnungsbereich - Thoralf Skolem (1923)
• Einige Bemerkungen zur axiomatischen Begrundung der Mengenlehre - Thoralf Skolem (1923)

1922

• Philosophical Transactions of the Royal Society Vol - R. A. Fisher; Royal Society (1922)
• Zu den Grundlagen der Cantor-Zermeloschen Mengenlehre - Fraenkel, Abraham A. (1922)

1921

• Introduction to a General Theory of Elementary Propositions - Emil L. Post (1921)
• The Analysis of Mind - Bertrand Russell (1921)
• Tractatus Logico-Philosophicus - Wittgenstein, Ludwig (1921)

1920

• Logisch-kombinatorische Untersuchungen über die Erfüllbarkeit oder Beweisbarkeit mathematischer Sätze - Thoralf Skolem (1920)

1919

• Introduction to Mathematical Philosophy - Bertrand Russell (1919)

1918

• Invariante Variationsprobleme (Invariant Variation Problems) - Emmy Noether (1918)
• Invariante Variationsprobleme (Invariant Variation Problems, English trans.) - Emmy Noether (1918)

1917

• Mysticism and Logic - Bertrand Russell (1917)

1916

• Cours de linguistique generale (Course in General Linguistics) - Ferdinand de Saussure (1916)
• Essays in Experimental Logic - John Dewey (1916)

1915

• Ueber Moeglichkeiten im Relativkalkuel - Leopold Loewenheim (1915)

1914

• Neue Grundlagen der Logik, Arithmetik und Mengenlehre - Gyula Koenig (Julius Koenig) (1914)

1913

• Intuitionism and Formalism - L. E. J. Brouwer (1913)
• Principia Mathematica, Volume III - Whitehead, A. N.; Russell, B. (1913)

1912

• Principia Mathematica, Volume II - Whitehead, A. N.; Russell, B. (1912)
• The Problems of Philosophy - Bertrand Russell (1912)

1911

• Animal Intelligence: Experimental Studies - Edward L. Thorndike (1911)

1910

• Principia Mathematica Vol I - A. N. Whitehead; B. Russell (1910)
• Principia Mathematica Volume I - A. N. Whitehead; B. Russell (1910)
• Principia Mathematica vol - A. N. Whitehead; Bertrand Russell (1910)

1909

• Mécanique Statistique et Irréversibilité - Borel, Émile (1909)

1908

• Biometrika Vol - Student (W. S. Gosset); K. Pearson (ed.) (1908)
• De onbetrouwbaarheid der logische principes - L. E. J. Brouwer (1908)
• Mendelian Proportions in a Mixed Population (Hardy) / Ueber den Nachweis der Vererbung beim Menschen (Weinberg) - Godfrey Harold Hardy; Wilhelm Weinberg (1908)
• Untersuchungen ueber die Grundlagen der Mengenlehre I - Ernst Zermelo (1908)
• Untersuchungen über die Grundlagen der Mengenlehre I - Ernst Zermelo (1908)

1907

• L'Évolution créatrice - Henri Bergson (1907)

1906

• Rasprostranenie zakona bol'shih chisel na velichiny, zavisyaschie drug ot druga (Extension of the law of large numbers to dependent quantities) - A. A. Markov (1906)
• Rasprostranenie zakona bolshikh chisel na velichiny, zavisyashchie drug ot druga - Andrei Markov (1906)
• The Integrative Action of the Nervous System - Charles Scott Sherrington (1906)

1905

• La valeur de la science - Henri Poincaré (1905)
• On Denoting - Bertrand Russell (1905)

1904

• Beweis, dass jede Menge wohlgeordnet werden kann - Ernst Zermelo (1904)
• On a Criterion Which May Serve to Test Various Theories of Inheritance - Karl Pearson (1904)

1903

• The Principles of Mathematics - Bertrand Russell (1903)

1902

• Elementary Principles in Statistical Mechanics - J. Willard Gibbs (1902)

1901

• On lines and planes of closest fit to systems of points in space - Karl Pearson (1901)

1900

• Mathematische Probleme (Hilbert's 23 Problems) - David Hilbert (1900)

1899

• Grundlagen der Geometrie - David Hilbert (1899)

1898

• Grundzuege einer allgemeinen Theorie der linearen Integralgleichungen - David Hilbert (1898)

1897

• Beiträge zur Begründung der transfiniten Mengenlehre (Zweiter Artikel) - Georg Cantor (1897)
• Contributions to the Analysis of the Sensations - Ernst Mach (1897)
• The Calculus of Equivalent Statements (First Paper) - Hugh MacColl (1897)

1896

• Symbolic Logic - Charles L. Dodgson (1896)
• The Regenerated Logic - Charles Sanders Peirce (1896)

1895

• Beiträge zur Begründung der transfiniten Mengenlehre (Erster Artikel) - Georg Cantor (1895)
• Grundlagen der Geometrie - David Hilbert (1895)

1894

• Symbolic Logic (2nd ed.) - John Venn (1894)
• The Asymmetrical Probability Curve - F. Y. Edgeworth (1894)

1893

• Formulaire de mathematiques - Giuseppe Peano (1893)
• Grundgesetze der Arithmetik, Bd - Gottlob Frege (1893)

1891

• Ueber eine elementare Frage der Mannigfaltigkeitslehre - Georg Cantor (1891)

1890

• Arithmetices principia - Giuseppe Peano (1890)
• The Principles of Psychology, Vol - William James (1890)
• Vorlesungen über die Algebra der Logik (Vol - Ernst Schröder (1890)

1889

• Arithmetices principia nova methodo exposita - Giuseppe Peano (1889)
• Babbage's Calculating Engines (ed - Charles Babbage (1889)
• Calcul des probabilites - Joseph Bertrand (1889)

1888

• The Logic of Chance, 3rd ed. - John Venn (1888)
• Was sind und was sollen die Zahlen? - Richard Dedekind (1888)

1887

• The Common Sense of the Exact Sciences - William Kingdon Clifford (1887)

1886

• A Memoir on the Theory of Mathematical Form - A. B. Kempe (1886)
• Beiträge zur Analyse der Empfindungen - Ernst Mach (1886)
• The Game of Logic - Lewis Carroll (1886)

1885

• Memory: A Contribution to Experimental Psychology - Ebbinghaus (1885)
• On the Algebra of Logic: A Contribution to the Philosophy of Notation - Charles S. Peirce (1885)

1884

• Die Grundlagen der Arithmetik - Gottlob Frege (1884)

1883

• Grundgesetze / Begriffsschrift topics - Gottlob Frege (1883)
• Grundlagen einer allgemeinen Mannigfaltigkeitslehre - Georg Cantor (1883)
• On a New Algebra of Logic - O. H. Mitchell (1883)
• The Science of Mechanics: A Critical and Historical Exposition - Ernst Mach (1883)

1881

• Symbolic Logic - John Venn (1881)
• Symbolic Logic (microform) - John Venn (1881)

1880

• On the Algebra of Logic - Charles Sanders Peirce (1880)
• Studies in Deductive Logic: A Manual for Students - William Stanley Jevons (1880)

1879

• Begriffsschrift - Gottlob Frege (1879)
• Begriffsschrift, eine der arithmetischen nachgebildete Formelsprache des reinen Denkens - Gottlob Frege (1879)

1878

• How to Make Our Ideas Clear - Charles S. Peirce (1878)

1876

• The Logic of Chance (2nd/3rd ed - John Venn (1876)

1874

• Grundzuege der physiologischen Psychologie (Principles of Physiological Psychology) - Wilhelm Wundt (1874)
• The Principles of Science - W. Stanley Jevons (1874)
• The Principles of Science: A Treatise on Logic and Scientific Method - William Stanley Jevons (1874)
• Ueber eine Eigenschaft des Inbegriffes aller reellen algebraischen Zahlen - Georg Cantor (1874)

1872

• A Budget of Paradoxes - Augustus De Morgan (1872)
• Stetigkeit und irrationale Zahlen - Richard Dedekind (1872)

1871

• The Descent of Man - Charles Darwin (1871)

1870

• Elementary Lessons in Logic: Deductive and Inductive - William Stanley Jevons (1870)
• On the Mechanical Performance of Logical Inference - W. Stanley Jevons (1870)

1869

• Hereditary Genius: An Inquiry into Its Laws and Consequences - Francis Galton (1869)

1868

• The Substitution of Similars - W.S. Jevons (1868)

1867

• On an Improvement in Boole's Calculus of Logic - Charles S. Peirce (1867)
• On the Doctrine of Continuity Applied to Anthropology - William Thomson (Kelvin) (1867)

1866

• The Logic of Chance - John Venn (1866)
• The Logic of Chance: An Essay on the Foundations and Province of the Theory of Probability - John Venn (1866)

1865

• Passages from the Life of a Philosopher - Charles Babbage (1865)

1864

• Pure Logic: or the Logic of Quality apart from Quantity - William Stanley Jevons (1864)

1863

• On the Sensations of Tone - Hermann von Helmholtz (1863)

1859

• A Treatise on Differential Equations - George Boole (1859)
• On the Origin of Species - Charles Darwin (1859)

1858

• A Memoir on the Theory of Matrices - Arthur Cayley (1858)

1855

• A Treatise on the Calculus of Finite Differences - George Boole (1855)
• The Principles of Psychology - Herbert Spencer (1855)
• The Senses and the Intellect - Alexander Bain (1855)

1854

• An Investigation of the Laws of Thought - George Boole (1854)
• An Investigation of the Laws of Thought, on Which Are Founded the Mathematical Theories of Logic and Probabilities - George Boole (1854)

1853

• Lectures on Quaternions - William Rowan Hamilton (1853)

1852

• An Investigation of the Laws of Thought - George Boole (1852)

1848

• The Calculus of Logic - George Boole (1848)

1847

• Formal Logic - Augustus De Morgan (1847)
• Formal Logic: or, The Calculus of Inference - Augustus De Morgan (1847)
• Méthode générale pour la résolution des systèmes d'équations simultanées - Cauchy, A.-L. (1847)
• The Mathematical Analysis of Logic - George Boole (1847)
• The Mathematical Analysis of Logic, Being an Essay Towards a Calculus of Deductive Reasoning - George Boole (1847)

1844

• Die Lineale Ausdehnungslehre - Hermann Grassmann (1844)

1843

• Sketch of the Analytical Engine Invented by Charles Babbage - Lovelace, Ada; Babbage, Charles; Menabrea, L. F. (1843)

1842

• Sketch of the Analytical Engine Invented by Charles Babbage, with Notes by the Translator - L. F. Menabrea; Ada Lovelace (notes/translator) (1842)

1837

• Wissenschaftslehre - Bernard Bolzano (1837)

1822

• Theorie Analytique de la Chaleur - Joseph Fourier (1822)

1666

• Dissertatio de Arte Combinatoria - Gottfried Wilhelm Leibniz (1666)

1517

• Ars Magna - Ramon Llull (1517)