Large Reasoning Models

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Large reasoning models study how language models can solve difficult problems by allocating computation across intermediate steps, tools, and alternative solution paths. Common approaches include reinforcement learning with verifiable rewards, supervised reasoning-trace distillation, tool use, search over sequential or parallel paths, and latent recurrent computation. Evaluation spans mathematics, coding, science question answering, logic, and multimodal tasks, measuring not only accuracy but also compute use, robustness, and the quality of intermediate reasoning. A central tension is that extra thinking can help on hard problems but also produce loops, errors, and safety risks, so researchers are developing adaptive stopping and compression while testing whether reasoning traces reliably reflect how answers are formed.

Latest papers

339 papers25 papers in the last four weeks, up 4% on the four weeks before. 0.1% of all new papers.

Reasoning Structure of Large Language Models
Jun 2, 2026Frédéric Berdoz, Luca A. Lanzendörfer, Fabian Farestam +1Logical Reasoning LLM Evaluation
1ETH Zurich, Switzerland.
Bridging Auxiliary Constraints to Resolve Instruction Following in Large Reasoning Models
Jun 2, 2026Zhengyi Zhao, Shubo Zhang, Huimin Wang +7LLM Prompting Instruction Following
The Chinese University of Hong Kong · Tencent Jarvis Lab · Westlake University +2
ThoughtFold: Folding Reasoning Chains via Introspective Preference Learning
Jun 2, 2026Ziyan Liu, Xueda Shen, Yuzhe Gu +7Efficient Language Model Reasoning Preference Optimization
Shanghai Artificial Intelligence Laboratory · University of Science and Technology of China · MMLab, The Chinese University of Hong Kong
Thinking Past the Answer: Evaluating Harmful Overthinking in Large Reasoning Models
Jun 1, 2026Simone Caldarella, Davide Talon, Rahaf Aljundi +2LLM Evaluation Overthinking in Language Models
University of Trento · 3Fondazione Bruno Kessler · 2Toyota Motor Europe
A Primer in Post-Training Reasoning Data: What We Know About How It Works
Jun 1, 2026Yaoming Li, Guangxiang Zhao, Qilong Shi +3RL Post-Training LLM Post-Training
State Key Laboratory of Multimedia Information Processing, School of Computer Science, Peking University · Qiyuan Tech · Tsinghua University
Extreme Low-Bit Inference in Reasoning Models: Failure Modes and Targeted Recovery
Jun 1, 2026Ekaterina Alimaskina, Darya Rudas, Denis Shveykin +3LLM Quantization Repetition in Language Generation
BRAIn Lab, Moscow, Russia
An Enigma of Artificial Reason: Investigating the Production-Evaluation Gap in Large Reasoning Models
May 31, 2026Mingzhong Sun, Teresa Yeo, Armando Solar-Lezama +1Reasoning Evaluation LLM Interpretability
1NUS Department of Computer Science · 4Singapore-MIT Alliance for Research and Technology (SMART) · 2MIT EECS +1
Reasoning4Sciences: Bridging Reasoning Language Models to All Scientific Branches
May 31, 2026Teddy Ferdinan, Bartłomiej Koptyra, Mikołaj Langner +42AI for Science Scientific Reasoning
Quantized Reasoning Models Think They Need to Think Longer, but They Do Not
May 29, 2026Sanae Lotfi, Polina Kirichenko, Steven Li +1Logit Adjustment Overthinking in Language Models
1FAIR at Meta · 2Meta AI
Unlocking Fine-Grained Translation Quality Estimation in LRMs through Mutually Boosting Implicit and Explicit Reasoning
May 29, 2026Renfei Dang, Xinye Wang, Zhejian Lai +5MT Evaluation Large Reasoning Models
National Key Laboratory for Novel Software Technology, Nanjing University · Huawei Translation Services Center, Beijing, China
AdaptR1: Reinforcement Learning Based Adaptive Interleaved Thinking in Multi-hop Question Answering
May 29, 2026Yuxin Wang, Jiahao Lu, Qifeng Wu +5Multi-Hop QA Adaptive Reasoning in Language Models
1Computer Science, Fudan University · Institute of Modern Languages and Linguistics, Fudan University · 3Shanghai Innovation Institute +1
SLAT: Segment-Level Adaptive Trimming for Efficient CoT Reasoning
May 29, 2026Jian Yao, Xiongcai Luo, Ran Cheng +1RL for Language Model Reasoning Efficient Language Model Inference
Department of Data Science and Artificial Intelligence, The Hong Kong Polytechnic University · Bytedance · The Hong Kong Polytechnic University-Daya Bay Technology and Innovation Research Institute, Huizhou,Guangdong Province, China.
ReasonOps: Operator Segmentation for LLM Reasoning Traces
May 28, 2026Daniel Lee, Owen Queen, James ZouLLM Reasoning Reasoning Trace Analysis
Stanford University
CosmicFish-HRM: Adaptive Reasoning via Hierarchical Recurrent Mechanisms in Compact Language Models
May 27, 2026Venkat Akhil LakkapragadaSmall Language Models LLM Reasoning
Mistyoz AI Hyderabad, India
Reasoning that Travels: Dissecting How Chain-of-Thought Transfers Across Models
May 27, 2026Xinyuan Cheng, Beiduo Chen, Philipp Mondorf +1LLM Prompting LLM Reasoning
UMaiNLP, Center for Information and Language Processing, LMU Munich, Germany · ÆMunich Center for Machine Learning, Germany
Mitigating Adaptive Attacks against Reasoning Models with Activation Consistency Training
May 27, 2026Avidan Shah, Jannik Brinkmann, Rico AngellPrompt Injection Defense Adversarial Robustness of Language Models
New York University · Technical University Clausthal
HRBench: Benchmarking and Understanding Thinking-Mode Switch Strategies in Hybrid-Reasoning LLMs
May 27, 2026Yansong Ning, Mianpeng Liu, Jingwen Ye +2LLM Evaluation Adaptive Inference
AI Thrust, The Hong Kong University of Science and Technology (Guangzhou) · AIPD, Tencent
Bridging the Detection-to-Abstention Gap in Reasoning Models under Insufficient Information
May 27, 2026Renjie Gu, Jiaxu Li, Yihao Wang +8LLM Abstention Large Reasoning Models
1Fudan University · 4Tsinghua University · 2Ant Group +1
The Shape of Overthinking: Backtracking Bursts in Long Reasoning Traces
May 27, 2026Navid Rezazadeh, Arash Gholami DavoodiReasoning Evaluation Efficient Language Model Inference
University of California, Irvine · Carnegie Mellon University
Beyond Input Understanding: Diagnosing Multilingual Mathematical Reasoning with Directed Acyclic Trace Graphs
May 26, 2026Jiaqiao Zhang, Zhuojun Li, Raoyuan Zhao +5Mathematical Reasoning Low-Resource Language Processing
Southwest University · LMU Munich & MCML
Using Probabilistic Programs to Train Inductive Reasoning in Large Language Models
May 26, 2026Liyi Zhang, Akshay K. Jagadish, Brenden M. Lake +1Language Model Calibration LLM Uncertainty Estimation
Department of Computer Science Princeton University · Princeton AI Lab Princeton University · Department of Psychology and Computer Science Princeton University
In LLM Reasoning, there is Irrationality on top of Value Misalignment
May 26, 2026Kejiang Qian, Fengxiang HeLLM Alignment LLM Reasoning
University of Edinburgh
Beyond a Single Direction: Chain-of-Thought Disrupts Simple Steering of Refusal
May 26, 2026Kia-Jüng Yang, Dominik Meier, Jiachen Zhao +2Reasoning Trace Analysis LLM Refusal Behavior
University of Göttingen, Germany · LKA NRW, Germany · Northeastern University, Boston, MA, USA.
Harmony in Diversity: Multi-domain Contrastive Policy Optimization for Large Reasoning Models
May 25, 2026Zongji Yu, Wenshui Luo, Yiliu Sun +5Reinforcement Learning RL for Language Model Reasoning
Shanghai Jiao Tong University · Shanghai AI Laboratory · Shandong University
Better, Faster: Harnessing Self-Improvement in Large Reasoning Models
May 24, 2026Qihuang Zhong, Liang Ding, Juhua Liu +3Overthinking in Language Models RL for Language Model Reasoning
1Nanyang Technological University, Singapore · 2Alibaba Group · School of Computer Science, Wuhan University, China +1
Reasoning as an Attack Surface: Adaptive Evolutionary CoT Jailbreaks for LLMs
May 23, 2026Jianan Li, Simeng Qin, Xiaojun Jia +5Adversarial Attacks on LLMs LLM Jailbreak Attacks
Northeastern University · The University of Hong Kong, Hong Kong, China · BraneMatrix AI, China +7
ChaosBench-Logic v2: Evaluating LLM Logical Reasoning over Dynamical Systems at Scale
May 23, 2026Noel ThomasLLM Evaluation Language Model Robustness
Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE
Limited Marginal Benefit of Reasoning-Heavy LLM Deployment in ESG Narrative Scoring: A 4-Model Consensus Study on Japanese Listed Firms
May 22, 2026Hiroyuki KokubuLLM Evaluation Language Model Ensembles
Kansai University
PathCal: State-Aware Reflection-Marker Calibration for Efficient Reasoning
May 21, 2026Lingyu Jiang, Zirui Li, Lingyu Xu +7Efficient Language Model Reasoning Efficient Language Model Inference
Tohoku University · Texas A&M University · Worcester Polytechnic Institute
Reasoning-Trace Collapse: Evaluating the Loss of Explicit Reasoning During Fine-Tuning
May 20, 2026Lukas Twist, Helen Yannakoudakis, Jie M. ZhangSupervised Fine-Tuning Reasoning Evaluation
King’s College London London, UK

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Reasoning Structure of Large Language Models

Frédéric Berdoz, Luca A. Lanzendörfer, Fabian Farestam, Roger Wattenhofer

1ETH Zurich, Switzerland.

Logical Reasoning LLM Evaluation LLM Reasoning with Graphs Structured Reasoning LLM Reasoning Large Reasoning Models

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Large reasoning models (LRMs) are often evaluated using metrics such as final-answer accuracy or token count. However, identical scores on these metrics can hide fundamentally different reasoning structures. To address this limitation, we introduce a scalable LRM benchmark of logic puzzles and a pipeline that converts unstructured traces into verifiable reasoning graphs of claims and dependencies. This turns reasoning into a structured, measurable object whose topology can be quantitatively analyzed. Building on this, we define a reasoning efficiency metric that quantifies how concentrated the model's logical flow is. Our analysis on open-source reasoning models shows that structural measurements separate behaviors that token count and accuracy conflate, providing a practical tool for diagnosing failure modes and comparing how reasoning scales with puzzle difficulty.