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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PathCal: State-Aware Reflection-Marker Calibration for Efficient Reasoning
May 21, 2026Lingyu Jiang, Zirui Li, Shuo Xing +6Efficient Language Model Reasoning Efficient Language Model Inference
1Tohoku University · 2Texas A&M University · 3Worcester Polytechnic Institute
Think Thrice Before You Speak: Dual knowledge-enhanced Theory-of-Mind Reasoning for Persuasive Agents
May 21, 2026Minghui Ma, Bin Guo, Runze Yang +7Theory of Mind Conversational Agents
Northwestern Polytechnical University, Xi’an, China · Peking University, Beijing, China · Harbin Engineering University, Harbin, China
LANG: Reinforcement Learning for Multilingual Reasoning with Language-Adaptive Hint Guidance
May 21, 2026Yuchun Fan, Bei Li, Peiguang Li +9Multilingual Language Models Reinforcement Learning
NLP Lab, School of Computer Science and Engineering, Northeastern University, Shenyang, China · Meituan Inc. 3NiuTrans Research, Shenyang, China
Search-E1: Self-Distillation Drives Self-Evolution in Search-Augmented Reasoning
May 21, 2026Zihan Liang, Yufei Ma, Ben Chen +4Tool-Augmented Language Model Agents On-Policy Self-Distillation
Unified Data Selection for LLM Reasoning
May 21, 2026Xiaoyuan Li, Yubo Ma, Chengpeng Li +6Training Data Selection LLM Training
University of Science and Technology of China · Alibaba Group · National University of Singapore
Tailoring Teaching to Aptitude: Direction-Adaptive Self-Distillation for LLM Reasoning
May 21, 2026Hongbin Zhang, Chaozheng Wang, Kehai Chen +4On-Policy Self-Distillation Mathematical Reasoning
Institute of Computing and Intelligence, Harbin Institute of Technology, Shenzhen, China · Keeta AI, Meituan, Beijing, China · Peng Cheng Laboratory, Shenzhen, China
ArborKV: Structure-Aware KV Cache Management for Scaling Tree-based LLM Reasoning
May 21, 2026Yeqiu Chen, Ziyan Liu, Zhenxin Huang +3Inference-Time Search Tree of Thoughts
University of Science and Technology of China
Manifold-Guided Attention Steering
May 20, 2026Ian Li, Kapilesh Guruprasad, Raunak Sengupta +3Language Model Steering Self-Attention
University of California, San Diego
DeepWeb-Bench: A Deep Research Benchmark Demanding Massive Cross-Source Evidence and Long-Horizon Derivation
May 20, 2026Sixiong Xie, Zhuofan Shi, Haiyang Shen +8LLM Evaluation LLM Grounding
Peking University
Self-Verified Distillation: Your Language Model Is Secretly Its Own Synthetic Data Pipeline
May 20, 2026Tony Lee, Percy LiangSelf-Training for Language Models Language Model Self-Assessment
Stanford University
Tracing the ongoing emergence of human-like reasoning in Large Language Models
May 20, 2026Paolo Morosi, Nikoleta Pantelidou, Fritz Günther +2Logical Reasoning Pragmatic Reasoning in Language Models
Departament de Filologia Catalana, Universitat Autònoma de Barcelona, Barcelona 08193, Spain. · Institut für Psychologie, Humboldt-Universitat zu Berlin, Berlin 10099, Germany. · Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona 08010, Spain
Strategy-Induct: Task-Level Strategy Induction for Instruction Generation
May 20, 2026Po-Chun Chen, Hen-Hsen Huang, Hsin-Hsi ChenLLM Prompting Few-Shot Prompting
Department of Computer Science and Information Engineering, National Taiwan University, Taiwan · Institute of Information Science, Academia Sinica, Taiwan · AI Research Center (AINTU), National Taiwan University, Taiwan
When Do LLMs Reason? A Dynamical Systems View via Entropy Phase Transitions
May 20, 2026Wei Xia, Haoqing Wang, Zhi-Hong Deng +1Language Model Decoding CoT Reasoning
Samsung Research, Beijing, China · State Key Laboratory of General Artificial Intelligence, School of Intelligence Science and Technology, Peking University
CopT: Contrastive On-Policy Thinking with Continuous Spaces for General and Agentic Reasoning
May 19, 2026Dachuan Shi, Hanlin Zhu, Xiangchi Yuan +4Agentic Reasoning Efficient Language Model Inference
Georgia Tech · UC Berkeley · Stanford University +1
Pseudocode-Guided Structured Reasoning for Automating Reliable Inference in Vision-Language Models
May 19, 2026Weicong Ni, Tianbao Jiang, Linlin WangVLM Robustness Object Hallucination in VLMs
All authors are with East China Normal University, Shanghai, China.
LLMEval-Logic: A Solver-Verified Chinese Benchmark for Logical Reasoning of LLMs with Adversarial Hardening
May 19, 2026Ming Zhang, Qiyuan Peng, Yinxi Wei +13Logical Reasoning LLM Evaluation
Institute of Trustworthy Embodied Artificial Intelligence, Fudan University · 2Hunyuan Team, Tencent · School of Philosophy, Fudan University
Beyond Fixed Budgets: Characterizing the Inelasticity and Limitations of Tree-of-Thought Reasoning Strategies
May 19, 2026Atkia Mahila, Avinash Maurya, M. Mustafa Rafique +1LLM Evaluation Inference-Time Search
Rochester Institute of Technology Rochester, USA · Argonne National Laboratory Lemont, USA
Taming the Thinker: Conditional Entropy Shaping for Adaptive LLM Reasoning
May 19, 2026Shuyu Wei, Jian Sun, Delai Qiu +6RL for Language Model Reasoning LLM Reasoning
Beijing Key Laboratory of Traffic Data Mining and Embodied Intelligence, Beijing Jiaotong University · Unisound AI Technology Co., Ltd.
Diagnosing Multi-step Reasoning Failures in Black-box LLMs via Stepwise Confidence Attribution
May 19, 2026Xiaoou Liu, Tiejin Chen, Dengjia Zhang +3Uncertainty Quantification Confidence Estimation in Language Models
1Arizona State University · 2Johns Hopkins University · 3Purdue +1
QSTRBench: a New Benchmark to Evaluate the Ability of Language Models to Reason with Qualitative Spatial and Temporal Calculi
May 18, 2026Anthony G. Cohn, Robert E. BlackwellSpatial Reasoning Benchmarks LLM Evaluation
School of Computer Science, The University of Leeds, Woodhouse Lane, Leeds, LS2 9JT, UK · The Alan Turing Institute, British Library, 96 Euston Road, London, NW1 2DB, UK · Tongji University, Shanghai, China
Presupposition and Reasoning in Conditionals: A Theory-Based Study of Humans and LLMs
May 18, 2026Tara Azin, Yongan Yu, Raj Singh +1LLM Evaluation Pragmatic Reasoning in Language Models
Department of Cognitive Science, Carleton University · School of Computer Science, McGill University · Mila – Quebec AI Institute
SD-Search: On-Policy Hindsight Self-Distillation for Search-Augmented Reasoning
May 18, 2026Yufei Ma, Zihan Liang, Ben Chen +6On-Policy Self-Distillation RL for Language Model Reasoning
Kuaishou Technology
Stop When Reasoning Converges: Semantic-Preserving Early Exit for Reasoning Models
May 17, 2026Dehai Min, Giovanni Vaccarino, Huiyi Chen +3Early Stopping CoT Reasoning
University of Illinois Chicago · Politecnico di Milano · University of Illinois Urbana-Champaign +1
Self-Supervised On-Policy Distillation for Reasoning Language Models
May 17, 2026Zhiquan Tan, Yinrong HongRL for Language Model Reasoning Process Supervision
Tsinghua University · Beihang University
Scientific Logicality Enriched Methodology for LLM Reasoning: A Practice in Physics
May 16, 2026Zhaoxin Yu, Nan Xu, Kun Chen +3Scientific Reasoning Scientific QA
State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China · School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China · Beijing Wenge Technology Co., Ltd, Beijing, China
Why Do Reasoning Models Lose Coverage? The Role of Data and Forks in the Road
May 16, 2026Ngoc-Hieu Nguyen, Parshin Shojaee, Phuc Minh Nguyen +4LLM Fine-Tuning Reasoning Diversity
The Pennsylvania State University · Virginia Tech · VinUniversity
Reasoning Can Be Restored by Correcting a Few Decision Tokens
May 16, 2026Changshuo Shen, Leheng Sheng, Yuxin Chen +2Efficient Language Model Reasoning Inference-Time Compute Allocation
University of Science and Technology of China, Hefei, China · National University of Singapore, Singapore
Confidence Geometry Reveals Trace-Level Correctness in Large Language Model Reasoning
May 16, 2026Shuo Liu, Ding Liu, Shi-Ju RanConfidence Estimation in Language Models LLM Reliability
School of Computer Science and Technology, Tiangong University, Tianjin, China. · Center for Quantum Physics and Intelligent Sciences, Department of Physics, Capital Normal University, Beijing, China.
Distributional Energy-Based Models for Uncertainty-Aware Structured LLM Reasoning
May 15, 2026Shireen Kudukkil Manchingal, Abhey Kalia, Fernanda Gonçalves +1LLM Answer Verification Energy-Based Models
Oxford Dynamics · Harwell Science and Innovation Campus, United Kingdom
SGR: A Stepwise Reasoning Framework for LLMs with External Subgraph Generation
May 15, 2026Xin Zhang, Yang Cao, Baoxing Wu +2LLM Grounding Knowledge Augmentation for Language Models
School of Information Science and Engineering, Chongqing Jiaotong University · School of Computer Science and Technology, Chongqing University of Posts and Telecommunications

May 21, 2026, cs.AIJ/K move · Enter open · S save

PathCal: State-Aware Reflection-Marker Calibration for Efficient Reasoning

Lingyu Jiang, Zirui Li, Shuo Xing, Peiran Li+5

1Tohoku University · 2Texas A&M University · 3Worcester Polytechnic Institute

Efficient Language Model Reasoning Efficient Language Model Inference Inference-Time Guidance LLM Reasoning Large Reasoning Models Reasoning Trace Analysis

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The emergence of Large Reasoning Language Models (LRMs) has paved the way for tackling complex reasoning tasks through test-time scaling by generating long-form Chain-of-Thought (CoT) trajectories during inference. Meanwhile, these trajectories often contain explicit reflection markers such as wait'', but'', and ``alternatively'', signaling hesitation, revision, and the consideration of alternative explorations, respectively. Recent studies on test-time control leverage such markers as lightweight handles for steering reasoning, typically treating them as a single coarse-grained category rather than distinguishing their distinct functional roles. In this paper, we conduct type-wise suppression and fixed-prefix intervention, revealing that reflection markers differ not only in their functional roles but also in when they exert the greatest influence. Specifically, different marker classes affect accuracy and generation length in distinct ways, and marker choices are most consequential before the model settles into a stable reasoning trajectory. Motivated by these findings, we introduce PathCal, a novel training-free decoding controller that calibrates reasoning paths by distinguishing marker types and intervening only at locally uncertain states. At each decoding step, PathCal utilizes the distribution over reflection-markers to estimate local competition between maintaining the current reasoning trajectory and initiating a competing branch, and softly rebalances marker logits when competing-branch evidence becomes excessive. Experiments across six reasoning benchmarks demonstrate that PathCal achieves a better efficiency--performance trade-off, improving or preserving accuracy while reducing generation length, without relying on external verifiers or additional sampling.

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