LLM Reasoning

LLM: Large Language Model

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68 papers in the last four weeks, up 143% on the four weeks before. 0.7% of all new papers.

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rePIRL: Learn PRM with Inverse RL for LLM Reasoning
Feb 8, 2026Xian Wu, Kaijie Zhu, Ying Zhang +2Reinforcement Learning Process Reward Models
Meta AI · Department of Computer Science, University of California, Santa Barbara · Independent Researcher +1
GR2: Generative Reasoning Re-ranker
Feb 8, 2026Mingfu Liang, Yufei Li, Jay Xu +20RL for Language Models LLM Reranking
Meta AI
Bypassing the Rationale: Causal Auditing of Implicit Reasoning in Language Models
Feb 3, 2026Anish Sathyanarayanan, Aditya Nagarsekar, Aarush RathoreCoT Faithfulness Faithfulness of Language Model Explanations
BITS Pilani, K. K. Birla Goa Campus
Uncertainty Localization in LLM Reasoning via Embedding Perturbations
Feb 2, 2026Qihao Wen, Jiahao Wang, Yang Nan +3LLM Reliability LLM Uncertainty Estimation
University of Arizona · Michigan State University
Beyond Dense States: Sparse Transcoders as Causally Testable Operators for LLM Latent Reasoning
Feb 2, 2026Yadong Wang, Haodong Chen, Yu Tian +3Sparse Autoencoders Continuous Latent Reasoning
Nanjing University of Aeronautics and Astronautics · State Key Laboratory of Ocean Sensing, ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou, 311215, China · Security Capability Center
Continuous-Utility Direct Preference Optimization
Jan 31, 2026Muhammad Ahmed Mohsin, Muhammad Umer, Emily FoxLLM Alignment Direct Preference Optimization
Stanford University, Stanford, California, USA
Latent Chain-of-Thought as Planning: Decoupling Reasoning from Verbalization
Jan 29, 2026Jiecong Wang, Hao Peng, Zhanyi Wang +2LLM Planning Efficient Language Model Reasoning
Beihang University · Didi Chuxing
Understanding LLM Failures: A Multi-Tape Turing Machine Analysis of Systematic Errors in Language Model Reasoning
Jan 27, 2026Magnus BomanLLM Interpretability Language Modeling
Karolinska Institutet, Stockholm, Sweden
Improving LLM Reasoning with Homophily-aware Structural and Semantic Text-Attributed Graph Compression
Jan 13, 2026Zijun Di, Bin Lu, Huquan Kang +5Text-Attributed Graph Learning LLM Reasoning
Shanghai Jiao Tong University Shanghai, China
Triggering Chain-of-Thought via Latent Feature Interventions in Large Language Models
Jan 12, 2026Zhenghao He, Guangzhi Xiong, Bohan Liu +2LLM Interpretability CoT Reasoning
University of Virginia, USA
Entropy-Aware Token Rejection for Improving Speculative Decoding
Dec 29, 2025Tiancheng Su, Meicong Zhang, Guoxiu HeSpeculative Decoding LLM Inference Acceleration
School of Economics and Management East China Normal University
NRR-Core: Non-Resolution Reasoning as a Computational Framework for Contextual Identity and Ambiguity Preservation
Dec 15, 2025Kei SaitoLLM Reasoning
Independent Researcher, Japan
Enhancing Large Language Model-Based Systems for End-to-End Circuit Analysis Problem Solving
Dec 10, 2025Liangliang Chen, Weiyu Sun, Huiru Xie +2LLM Answer Verification Educational Technology
School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332 USA
EgoVITA: Learning to Plan and Verify for Egocentric Video Reasoning
Nov 23, 2025Yogesh Kulkarni, Pooyan FazliEgocentric Video Understanding Video Reasoning
Arizona State University
Evaluating Implicit Biases in LLM Reasoning through Logic Grid Puzzles
Nov 8, 2025Fatima Jahara, Mark Dredze, Sharon LevyGender Bias in Language Models Language Model Bias Evaluation
Rutgers University · Johns Hopkins University
CoT-X: An Adaptive Framework for Cross-Model Chain-of-Thought Transfer and Optimization
Nov 7, 2025Ziqian Bi, Yinzhi Wang, Tianyang Wang +6Efficient Language Model Reasoning LLM Inference Acceleration
1Purdue University, USA · 2Baruch College, City University of New York, USA · 3The Ohio State University, USA +6
Mind the Gap... or Not? How Translation Errors and Evaluation Details Skew Multilingual Results
Nov 7, 2025Jan-Thorsten Peter, David Vilar, Tobias Domhan +2Mathematical Reasoning Benchmarks Multilingual Language Model Evaluation
Google
Mitigating Hallucination in Large Language Models: A Capability-Oriented Survey on RAG, Reasoning, and Agentic Systems
Oct 28, 2025Yihan Li, Xiyuan Fu, Ghanshyam Verma +2Retrieval-Augmented Generation LLM Hallucination Mitigation
Electronic Information School, Wuhan University, Wuhan, China · School of Computing, Dublin City University, Dublin, Ireland · School of Public Health, Wuhan University, Wuhan, China +2
Learning to Reason Efficiently with Discounted Reinforcement Learning
Oct 27, 2025Alex Ayoub, Kavosh Asadi, Dale Schuurmans +2Reinforcement Learning RL for Language Model Reasoning
Amazon, University of Alberta · Amazon · University of Alberta
RLIE: Rule Generation with Logistic Regression, Iterative Refinement, and Evaluation for Large Language Models
Oct 22, 2025Yang Yang, Hua XU, Zhangyi Hu +1Logistic Regression Neuro-Symbolic Reasoning
The Hong Kong University of Science and Technology (Guangzhou), Guangzhou 511400, China · Institute of Deep Perception Technology, JITRI, Wuxi 214000, China
A Model Can Help Itself: Reward-Free Self-Training for LLM Reasoning
Oct 21, 2025Mengqi Li, Lei Zhao, Anthony Man-Cho So +2Self-Training for Language Models Mathematical Reasoning
The Chinese University of Hong Kong, Shenzhen · Shanghai Jiao Tong University · The Chinese University of Hong Kong
Make an Offer They Can't Refuse: Grounding Bayesian Persuasion in Real-World Dialogues without Pre-Commitment
Oct 15, 2025Buwei He, Yang Liu, Zhaowei Zhang +6Game Theory LLM Decision-Making
Beijing University of Posts and Telecommunications · Beijing Institute for General Artificial Intelligence · Peking University +1
Demystifying Hybrid Thinking: Can LLMs Truly Switch Between Think and No-Think?
Oct 14, 2025Shouren Wang, Wang Yang, Xianxuan Long +4LLM Reasoning
Case Western Reserve University · Meta AI
Deductive Logic in Language Models: Horizontal vs Vertical Reasoning
Oct 10, 2025Davide Maltoni, Matteo FerraraTransformer Interpretability CoT Reasoning
Department of Computer Science and Engineering, University of Bologna, Italy
oMeBench: Towards Robust Benchmarking of LLMs in Organic Mechanism Elucidation and Reasoning
Oct 9, 2025Ruiling Xu, Yifan ZhangLLM Evaluation Benchmark Design
University of Illinois Urbana-Champaign
Explore-Execute Chain: Towards an Efficient Structured Reasoning Paradigm
Sep 28, 2025Kaisen Yang, Tinghe Zhang, Rushi Shah +4LLM Inference Efficiency LLM Planning
1Qizhi Institute · 2Dept. of Computer Science, Tsinghua University · College of AI, Tsinghua University +4
Predicting LLM Reasoning Performance with Small Proxy Model
Sep 25, 2025Woosung Koh, Juyoung Suk, Sungjun Han +2Language Model Pretraining LLM Evaluation
Trillion Labs · KAIST AI · ▽Trillion Labs, ♠KAIST AI
Vis-CoT: A Human-in-the-Loop Framework for Interactive Visualization and Intervention in LLM Chain-of-Thought Reasoning
Sep 1, 2025Kaviraj Pather, Elena Hadjigeorgiou, Arben Krasniqi +4LLM Interpretability Human-in-the-Loop AI
Department of Computer Science & Engineering University of Mauritius R´eduit 80837, Mauritius · Department of Electrical & Computer Engineering Cyprus University of Technology 3036 Limassol, Cyprus · Faculty of Electrical & Computer Engineering University of Prishtina ”Hasan Prishtina” 10000 Prishtina, Kosovo +4
PiCSAR: Probabilistic Confidence Selection And Ranking for Reasoning Chains
Aug 29, 2025Joshua Ong Jun Leang, Zheng Zhao, Aryo Pradipta Gema +7Confidence Estimation in Language Models Best-of-N Sampling
Imperial College London · University of Edinburgh · UCL
Fast, Slow, and Tool-augmented Thinking for LLMs: A Review
Aug 17, 2025Xinda Jia, Jinpeng Li, Zezhong Wang +6LLM Tool Use LLM Reasoning
School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China · Huawei Technologies Co., Ltd., Beijing 100084, China · Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong, Hong Kong 999077, China +3

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rePIRL: Learn PRM with Inverse RL for LLM Reasoning

Xian Wu, Kaijie Zhu, Ying Zhang, Lun Wang+1

Meta AI · Department of Computer Science, University of California, Santa Barbara · Independent Researcher+1

Reinforcement Learning Process Reward Models RL for Language Model Reasoning LLM Reasoning

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Process rewards have been widely used in deep reinforcement learning to improve training efficiency, reduce variance, and prevent reward hacking. In LLM reasoning, existing works also explore various solutions for learning effective process reward models (PRM) with or without the help of an expert policy. However, existing methods either rely on strong assumptions about the expert policies (e.g., requiring their reward functions) or suffer intrinsic limitations (e.g., entropy collapse), resulting in weak PRMs or limited generalizability. In this paper, we introduce rePIRL, an inverse RL-inspired framework that learns effective PRMs with minimal assumptions about expert policies. Specifically, we design a dual learning process that updates the policy and the PRM interchangeably. Our learning algorithm has customized techniques to address the challenges of scaling traditional inverse RL to LLMs. We theoretically show that our proposed learning framework can unify both online and offline PRM learning methods, justifying that rePIRL can learn PRMs with minimal assumptions. Empirical evaluations on standardized math and coding reasoning datasets demonstrate the effectiveness of rePIRL over existing methods. We further show the application of our trained PRM in test-time training, test-time scaling, and providing an early signal for training hard problems. Finally, we validate our training recipe and key design choices via a detailed ablation study.

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