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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How Well Do LLMs Perform on the Simplest Long-Chain Reasoning Tasks: An Empirical Study on the Equivalence Class Problem
May 7, 2026Chun Zheng, Lianlong Wu, Bingqian Li +2LLM Evaluation LLM Reasoning
University of Science and Technology of China · University of Oxford
Extracting Search Trees from LLM Reasoning Traces Reveals Myopic Planning
May 7, 2026Sixing Chen, Ji-An Li, Saner Cakir +3LLM Planning LLM Interpretability
Department of Psychology New York University · Generality Inc.
When Does Critique Improve AI-Assisted Theoretical Physics? SCALAR: Structured Critic--Actor Loop for Agentic Reasoning
May 7, 2026Vasilis Niarchos, Constantinos Papageorgakis, Alexander G. Stapleton +1AI for Science AI Agent Evaluation
Department of Physics, CCTP and ITCP, University of Crete, 71303, Greece · Centre for Theoretical Physics, Department of Physics and Astronomy, Queen Mary University of London, London E1 4NS, United Kingdom · Theoretical Physics Department, CERN, Geneva, Switzerland +1
Can RL Teach Long-Horizon Reasoning to LLMs? Expressiveness Is Key
May 7, 2026Tianle Wang, Zhaoyang Wang, Guangchen Lan +4Logical Reasoning RL for Language Model Reasoning
Purdue University · UNC Chapel Hill · Georgia Tech +1
SCRuB: Social Concept Reasoning under Rubric-Based Evaluation
May 7, 2026Jamelle Watson-Daniels, Himaghna Bhattacharjee, Skyler Wang +11LLM Evaluation Rubric-Based Evaluation
1Meta · 2McGill University · 3Handshake AI +1
Measuring Black-Box Confidence via Reasoning Trajectories: Geometry, Coverage, and Verbalization
May 7, 2026Marc Boubnovski Martell, Josefa Lia Stoisser, Kaspar Märtens +4Confidence Estimation in Language Models CoT Reasoning
1Novo Nordisk · University of Oxford
Rethinking RL for LLM Reasoning: It's Sparse Policy Selection, Not Capability Learning
May 7, 2026Ömer Faruk Akgül, Rajgopal Kannan, Willie Neiswanger +1RL for Language Model Reasoning Efficient Language Model Reasoning
University of Southern California · 2DEVCOM ARL
Post-Reasoning: Improving the Performance of Non-Thinking Models at No Cost
May 7, 2026Richmond Sin Jing Xuan, Rishabh Bhardwaj, Soujanya PoriaLLM Prompting Language Model Post-Training
Nanyang Technological University
Novelty-based Tree-of-Thought Search for LLM Reasoning and Planning
May 7, 2026Leon Hamm, Zlatan AjanovicNovelty Search Tree of Thoughts
1RWTH Aachen
Logic-Regularized Verifier Elicits Reasoning from LLMs
May 7, 2026Xinyu Wang, Changzhi Sun, Lian Cheng +4LLM Evaluation LLM Answer Verification
Department of Computer Science and Technology, East China Normal University · Institute of Artificial Intelligence (TeleAI), China Telecom
Hypothesis generation and updating in large language models
May 7, 2026Hua-Dong XiongLogical Reasoning Bayesian Inference
School of Psychological and Brain Sciences Georgia Tech
ReFlect: An Effective Harness System for Complex Long-Horizon LLM Reasoning
May 7, 2026Fan HuangLLM Self-Correction LLM Reliability
Indiana University Bloomington
Decodable but Not Corrected by Fixed Residual-Stream Linear Steering: Evidence from Medical LLM Failure Regimes
May 7, 2026Ming LiuLanguage Model Steering Medical LLMs
Amazon
Formalizing Latent Thoughts: Four Axioms of Thought Representation in LLMs
May 7, 2026Fahd Seddik, Fatemeh FardLLM Evaluation LLM Interpretability
Department of Computer Science University of British Columbia Kelowna, BC, Canada
G-SHARE: A Guideline-Based Structured Reasoning Framework for Human-Factor Event Diagnosis
May 7, 2026Xingyu Xiao, Mao Du, Jiejuan Tong +2Structured Reasoning LLM Reasoning
Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing, 100084, China · Fujian Fuqing Nuclear Power Co., Ltd., Fujian, 350318, China · National Key Laboratory of Human Factors Engineering, Beijing, 100094, China
Agentic Retrieval-Augmented Generation for Financial Document Question Answering
May 6, 2026Yang Shu, Yingmin Liu, Zequn XieFinancial QA Agentic RAG
College of Computer Science and Technology, Zhejiang University, Hangzhou, 310027, China
How Does Thinking Mode Change LLM Moral Judgments? A Controlled Instant-vs-Thinking Comparison Across Five Frontier Models
May 6, 2026Sai Sourabh MadurLLM Evaluation Moral Reasoning in Language Models
Independent researcher (ex-Meta)
NoisyCausal: A Benchmark for Evaluating Causal Reasoning Under Structured Noise
May 5, 2026Zhi Xu, Yun FuStructural Causal Models Causal Reasoning in Language Models
Northeastern University
The Counterexample Game: Iterated Conceptual Analysis and Repair in Language Models
May 5, 2026Daniel Drucker, Kyle MahowaldLLM Self-Correction LLM Evaluation
Department of Philosophy The University of Texas at Austin · Department of Linguistics The University of Texas at Austin
SERE: Structural Example Retrieval for Enhancing LLMs in Event Causality Identification
May 5, 2026Zhifeng Hao, Zhongjie Chen, Junhao Lu +5Causal Reasoning in Language Models Causal Reasoning
School of Computer Science, Guangdong University of Technology · College of Mathematics and Computer, Shantou University · Huawei Noah’s Ark Lab +1
When to Think, When to Speak: Learning Disclosure Policies for LLM Reasoning
May 5, 2026Jiaqi Wei, Xuehang Guo, Pengfei Yu +5LLM Serving LLM Reasoning
Zhejiang University · College of William and Mary · University of Illinois Urbana-Champaign +4
Do LLMs have core beliefs?
May 5, 2026Anna Sokol, Marianna B. Ganapini, Nitesh V. ChawlaCognitive Modeling LLM Reasoning
University of Notre Dame · University of North Carolina at Charlotte
Measuring AI Reasoning: A Guide for Researchers
May 4, 2026Munachiso Samuel Nwadike, Zangir Iklassov, Kareem Ali +2Reasoning Evaluation LLM Reasoning
MBZUAI, Department of Natural Language Processing · MBZUAI, Department of Machine Learning
The Model Knows, the Decoder Finds: Future Value Guided Particle Power Sampling
May 4, 2026Tu Nguyen, Matthieu Zimmer, Rasul Tutunov +2Language Model Decoding LLM Inference Acceleration
1Huawei Heisenberg Research Center · 2Huawei Noah’s Ark Lab · 3UCL Centre for Artificial Intelligence
The Reasoning Trap: An Information-Theoretic Bound on Closed-System Multi-Step LLM Reasoning
May 3, 2026Kwan Soo ShinReasoning Evaluation Evidence-Grounded Reasoning
PolymathMinds AI Lab
Evaluating LLMs on Large-Scale Graph Property Estimation via Random Walks
May 2, 2026Sunil Kumar Maurya, Xin LiuLLM Evaluation LLM Reasoning with Graphs
University of Tokyo Tokyo, Japan · AIST Tokyo, Japan
Verbal-R3: Verbal Reranker as the Missing Bridge between Retrieval and Reasoning
May 2, 2026Sangkwon Park, Donghun Kang, Jisoo Mok +1Retrieval-Augmented Generation LLM Reranking
Department of Electrical and Computer Engineering, Seoul National University · DGIST · Interdisciplinary Program in Artificial Intelligence, Seoul National University +1
Reasoning emerges from constrained inference manifolds in large language models
May 2, 2026Yanbiao Ma, Fei Luo, Linfeng Zhang +10LLM Inference LLM Interpretability
Renmin University of China · Tsinghua University · Xiaomi EV +1
When Less is Enough: Efficient Inference via Collaborative Reasoning
May 1, 2026Yilei Chen, Sharut Gupta, Yannis Paschalidis +2Efficient Language Model Reasoning Efficient Language Model Inference
Boston University · MIT CSAIL · Biohub
When LLMs Stop Following Steps: A Diagnostic Study of Procedural Execution in Language Models
May 1, 2026Sailesh Panda, Pritam Kadasi, Abhishek Upperwal +1LLM Evaluation Instruction Following
Indian Institute of Technology Gandhinagar · Soket AI

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How Well Do LLMs Perform on the Simplest Long-Chain Reasoning Tasks: An Empirical Study on the Equivalence Class Problem

Chun Zheng, Lianlong Wu, Bingqian Li, Lvting Liu+1

University of Science and Technology of China · University of Oxford

LLM Evaluation LLM Reasoning Large Reasoning Models

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Large Language Models (LLMs) have achieved great improvements in recent years. Nevertheless, it still remains unclear how good LLMs are for reasoning tasks, especially for long-chain ones. In this paper, we evaluate LLMs' performance on the simplest yet long-chain reasoning task, namely the Equivalence Class Problem (ECP), i.e., determining whether two variables are equal given a set of randomly generated equivalence relations. We consider both reasoning and non-reasoning representative LLMs over a large variety of problem instances, ranging over different numbers of variables, connectivity probabilities, prompts, and other factors. The experimental results show that non-reasoning LLMs fail ECP, while reasoning models are significantly better but still struggle to completely solve this problem. Interestingly, considering various connectivity probabilities with a fixed number of variables, we observe that, for non-reasoning models, the hardest problem instances coincide with the phase transition point of ln n/(n-1), suggesting the chaos of the problem; in contrast, for reasoning models, the hardest ones coincide with the biggest diameter, suggesting the reasoning difficulty of the problem.

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