LLM Reasoning

LLM: Large Language Model

Follow this topic Ask about this topic

Momentum

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

Jul 13Week of Sep 28

Latest papers 650

All topics

Semiotic logical hexagon theory for LLM logical reasoning
Jul 24, 2026Yunyao Zhang, Xinglang Zhang, Zeliang Chen +2Logical Reasoning LLM Reliability
aHuazhong University of Science and Technology, , Wuhan, 430074, Hubei, China
Euclid-MCP: A Model Context Protocol Server for Deterministic Logical Reasoning via Prolog
Jul 23, 2026Bartolomeo BoglioloLogic Programming LLM Reliability
AdaKP: Online Adaptive Knowledge-Point Selection for Reasoning-Oriented Reinforcement Learning
Jul 23, 2026Zibin Meng, Zhenyu Zhao, Chunqiang RunKnowledge Augmentation for Language Models RL for Language Model Reasoning
The Hong Kong University of Science and Technology · University of the Chinese Academy of Sciences
Notes to Self: Can LLMs Benefit from Experiential Abstractions?
Jul 22, 2026Chang Liu, Xinyu Li, Artur DubrawskiKnowledge Augmentation for Language Models LLM Reasoning
Auton Lab, Carnegie Mellon University
PoTRE: Test-Time Reasoning inspired by Cognitive Heterogeneity
Jul 22, 2026Anmol Kankariya, Sercan Ö. ArıkTest-Time Scaling Language Model Ensembles
Applied AI, Google Cloud
CUSUM-Shaped Inference-Time Monitoring and Targeted Re-Decoding for Quantized Small Language Model Reasoning
Jul 22, 2026El Hassane Ettifouri, Ayoub Belfatmi, Mahaman Sanoussi Yahaya Alassan +1Inference-Time Guidance LLM Reasoning
Novelis Research, Paris, France
Are you Talking Logic to Me? Assessing Language Models Syllogistic Reasoning Capabilities
Jul 22, 2026Hanna Abi Akl, Fabien Gandon, Catherine Faron +1Knowledge Representation Structured Reasoning
Université Côte d’Azur, Inria, CNRS, I3S, Sophia Antipolis, France · Data ScienceTech Institute, Paris, France
Reasoning Error from Known Fact: Step-Level Self-Consistency Group Relative Policy Optimization for LLM
Jul 21, 2026Xiaomeng Hu, Jiaqi Hu, Hao Chen +4RL for Language Model Reasoning Hallucination in Language Models
Zhejiang University
CASE: Causal Alignment and Structural Enforcement for Improving Chain-of-Thought Faithfulness
Jul 21, 2026Ziming Wang, Yinghua Yao, Changwu Huang +2CoT Faithfulness Faithfulness of Language Model Explanations
Department of Computer Science and Engineering of Southern University of Science and Technology, Shenzhen, China · Center for Frontier AI Research, Agency for Science, Technology and Research (A*STAR), Singapore · School of AI and Liberal Arts, Beijing Normal-Hong Kong Baptist University, Zhuhai, China +1
Reasoning Fine-Tuning Induces Persistent Latent Policy States
Jul 20, 2026Abir Harrasse, Michael Lan, Hunar Batra +2Dynamical Systems Latent Dynamics Modeling
Martian · University of Oxford · University of Utah
Relay-Bench: Evaluating LLMs on Multi-Domain Reasoning Chains
Jul 20, 2026Liam SwayneLLM Evaluation LLM Reasoning
Can We Break LLMs Out of Self-Loops? Fine-Grained Reasoning Control with Activation Steering
Jul 20, 2026Sheldon Yu, Tong Yu, Xunyi Jiang +6Overthinking in Language Models Causal Interventions in Language Models
UC San Diego · Adobe Research · University of New South Wales
Solver-Hard Is Not Model-Hard: A Hardness-Controlled Diagnostic for LLM Constraint Reasoning
Jul 19, 2026Lucky VermaLLM Evaluation Constraint Satisfaction
Independent Researcher
Committed Before Reasoning: Behavioral Reproduction and Preliminary Activation-Level Evidence of Answer Pre-Commitment in an Open-Weight LLM
Jul 17, 2026Heejin JoCoT Faithfulness LLM Reasoning
Loop the Loopies!
Jul 17, 2026Zitian Gao, Yilong Chen, Yihao Xiao +4Mixture-of-Experts Language Models Recurrent Transformers
IQuest Research
Better Starts, Better Ends: Bootstrapped Iterative Self-Reasoning Distillation for Compressed Reasoning
Jul 17, 2026Leichao Dong, Dongxu Zhang, Yiding Sun +4CoT Distillation Efficient Language Model Reasoning
1Xi’an Jiaotong University · 2Peking University
T^2MLR: Transformer with Temporal Middle-Layer Recurrence
Jul 16, 2026Ziyang Cai, Xingyu Zhu, Yihe Dong +2Recurrent Transformers LLM Reasoning
Princeton Language and Intelligence, Department of Computer Science Princeton University
Answer-Conditioned Chains of Thought Degrade Verifiable-Reasoning Distillation in Large Language Models
Jul 16, 2026Jungseob Lee, Seungyoon Lee, Suhyune Son +4CoT Distillation Language Model Distillation
1Korea University · 2Zoom Communications · 3Yonsei University
Controlled Reformulation Testing for Logical Consistency in Large Language Models
Jul 16, 2026Alexander Gu, Alan ChenLLM Evaluation Reasoning Consistency in Language Models
University of Texas at Austin
Implicit Reasoning Steering via Concept Chaining
Jul 15, 2026Xiao Ye, Sanika Chavan, Yuxi Huang +4Adversarial Attacks on LLMs Reasoning Consistency in Language Models
School of Computing and Augmented Intelligence, Arizona State University · Bellini College of AI, Cybersecurity, and Computing, University of South Florida, Tampa, Florida, USA · Department of Computer Science, University of California, Davis
Post-Training Shifts Confidence: A Three-Stage Analysis of How SFT, RL, and OPD Shape Pre-, Intra-, and Post-CoT Calibration
Jul 15, 2026Shuhao Li, Guodong Du, Anhao Zhao +3Confidence Estimation in Language Models Language Model Calibration
Eastern Institute of Technology, Ningbo · The Hong Kong Polytechnic University
Consensus as Privileged Context for Label-Free Self-Distillation
Jul 15, 2026John Gkountouras, Josip Jukić, Ivan TitovSelf-Training for Language Models Language Model Distillation
1ILLC, University of Amsterdam · 2ILCC, University of Edinburgh
Learning Mechanistic Reasoning for Chemical Reactions with Large Language Models
Jul 14, 2026Xingyu Dang, Haocheng Tang, Junmei Wang +1LLM Reasoning
Department of Computer Science, Princeton University, Princeton, NJ, 08540, United States · School of Pharmacy, University of Pittsburgh, Pittsburgh, PA, 15261, United States · Khoury College of Computer Science, Northeastern University, Boston, MA, 02115, United States +2
LLMs Can See the Smoke but not the Fire: Evaluating Abductive Reasoning with Elenchos
Jul 14, 2026Julius Steiglechner, Lucas Mahler, Gabriele LohmannLLM Evaluation LLM Reasoning
Dept. of Biomedical Magnetic Resonance, University Hospital Tübingen, Tübingen, Germany · Max-Planck-Institute for Biological Cybernetics, Magnetic Resonance Center, Tübingen, Germany
Beyond Sally-Anne: Evaluating Theory of Mind in LLMs using Epistemic Schelling Points
Jul 13, 2026Roberta Rocca, Sami Boukortt, Geoff Keeling +1LLM Evaluation Theory of Mind
1Google
ProgramTab: Boosting Table Reasoning of LLMs via Programmatic Paradigm
Jul 13, 2026Pei Guo, Enjie Liu, Yunzhi Tan +6Tabular ML LLM Reasoning
♠Big Data and AI Platform Department, Tencent, China · ♢Institute of Computer Science and Technology, Soochow University, China
Diagnosing and Mitigating Thinking Collapse in On-Policy Self-Distillation
Jul 12, 2026Keqin Peng, Chen Li, Yuanxin Ouyang +2On-Policy Self-Distillation LLM Reasoning
Beihang University · Hong Kong Polytechnic University · Alibaba Group
When Reasoning Hurts Legal Drafting: The Verbalization Bottleneck in Patent Claim Generation
Jul 11, 2026Lekang Jiang, Wenjun Sun, Stephan GoetzLLM Prompting CoT Reasoning
University of Cambridge · National Science Library, Chinese Academy of Sciences; Department of Information Resources Management, School of Economics and Management, University of Chinese Academy of Sciences.
Structured Thoughts For Improved Reasoning And Context Pruning
Jul 11, 2026Zain Sarwar, Supriyo Chakraborty, Berkcan Kapusuzoglu +5CoT Reasoning Efficient Language Model Reasoning
University of Chicago · Capital One
SPARK: Susceptibility-Guided Profiling and Steering of Latent Reasoning States in Large Language Models
Jul 11, 2026Dongxu Zhang, Yiding Sun, Zihao Guo +5LLM Interpretability Activation Steering
Xi’an Jiaotong University · Peking University · Tencent

Jul 24, 2026, cs.AIJ/K move · Enter open · S save

Semiotic logical hexagon theory for LLM logical reasoning

Yunyao Zhang, Xinglang Zhang, Zeliang Chen, Junqing Yu+1

aHuazhong University of Science and Technology, , Wuhan, 430074, Hubei, China

Logical Reasoning LLM Reliability LLM Reasoning

Open paper AskPDF arXiv

Large language models (LLMs) have become powerful tools for language understanding and logical reasoning. However, they still make mistakes when a problem requires both understanding meaning and following logic. A key reason is that natural-language statements often carry implicit semantic relations before any formal reasoning begins. If these hidden meanings are not properly organized, the model may reach incorrect conclusions even when the subsequent reasoning process appears logically valid. Existing methods improve reasoning through decomposition, symbolic translation, external solvers, or self-verification, but pay comparatively less attention to the semantic structure on which reasoning depends. In this paper, we further investigate how semantic organization influences logical reasoning in LLMs. To this end, we propose HexLogicAgent, a framework that first organizes the meaning of natural-language statements and then guides logical reasoning through structured verification. In our investigation, we also make two observations. First, incomplete semantic representations, rather than deductive inference itself, are a major source of logical reasoning failures in LLMs. Second, explicitly modeling the complete structure of semantic opposition substantially delays the degradation of reasoning performance as logical complexity increases. Experiments on challenging logical reasoning benchmarks demonstrate that HexLogicAgent consistently improves reasoning reliability across multiple LLMs. The core idea is supported by a logical hexagon theory, which explains why a complete structure of opposing meanings is necessary for reliable reasoning.

Momentum

Related topics

Latest papers 650