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.

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

Attention-Guided Reward for Reinforcement Learning-based Jailbreak against Large Reasoning Models
May 19, 2026Zheng Lin, Zhenxing Niu, Haoxuan Ji +2Adversarial Attacks on LLMs LLM Jailbreak Attacks
Xidian University · Xi’an Jiaotong University
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.
Monitoring the Internal Monologue: Probe Trajectories Reveal Reasoning Dynamics
May 18, 2026Maciej Chrabąszcz, Aleksander Szymczyk, Marcin Sendera +2Language Model Safety Evaluation LLM Interpretability
NASK - National Research Institute, Poland · Faculty of Electronics and Information Technology, Warsaw University of Technology, Poland · Faculty of Mathematics and Computer Science, Jagiellonian University, Poland +3
Entropy-Gradient Inversion: Moving Toward Internal Mechanism of Large Reasoning Models
May 18, 2026Junyao Yang, Chen Qian, Kun Wang +4LLM Interpretability RL for Language Model Reasoning
National University of Singapore · Renmin University of China · Nanyang Technological University +1
Stop When Reasoning Converges: Semantic-Preserving Early Exit for Reasoning Models
May 17, 2026Dehai Min, Giovanni Vaccarino, Huiyi Chen +3Large Reasoning Models CoT Reasoning
University of Illinois Chicago · Politecnico di Milano · University of Illinois Urbana-Champaign +1
Auditing Reasoning-Trace Memorization Claims after Unlearning with Head-Conditioned Canaries
May 17, 2026Yanhang Li, Zhichao Fan, Zexin ZhuangMemorization in Language Models LLM Auditing
Northeastern University, USA · University of Illinois Urbana-Champaign, USA · Southern Methodist University, USA
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
Effort as Ceiling, Not Dial: Reasoning Budget Does Not Modulate Cognitive Cost Alignment Between Humans and Large Reasoning Models
May 16, 2026Yueqing Hu, Tianhong WangComputational Cognitive Modeling Inference-Time Scaling
Institute of Neuroscience, Chinese Academy of Sciences, Shanghai, China · School of Philosophy, Anhui University, Hefei, China
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
Reasoning Models Don't Just Think Longer, They Move Differently
May 14, 2026Anders Gjølbye, Lars Kai Hansen, Sanmi KoyejoLLM Interpretability Representation Geometry in Language Models
Technical University of Denmark · Stanford University
Natural Synthesis: Outperforming Reactive Synthesis Tools with Large Reasoning Models
May 14, 2026Frederik Schmitt, Matthias Cosler, Niklas Metzger +4Program Synthesis Formal Verification
1CISPA Helmholtz Center for Information Security · 2Technical University of Munich
PolitNuggets: Benchmarking Agentic Discovery of Long-Tail Political Facts
May 13, 2026Yifei ZhuAI Agent Benchmarks Multilingual IR
The University of Hong Kong
Inducing Overthink: Hierarchical Genetic Algorithm-based DoS Attack on Black-Box Large Language Reasoning Models
May 13, 2026Shuqiang Wang, Wei Cao, Jiaqi Weng +4Overthinking in Language Models Adversarial Attacks on LLMs
The State Key Laboratory of Blockchain and Data Security, Zhejiang University · Alibaba Group
What properties of reasoning supervision are associated with improved downstream model quality?
May 13, 2026Mikołaj Langner, Dzmitry Pihulski, Jan Eliasz +5Training Data Selection LLM Reasoning
Wroclaw Tech, 50-370 Wrocław, Poland
STOP: Structured On-Policy Pruning of Long-Form Reasoning in Low-Data Regimes
May 13, 2026Chenjun Xu, Zhennan Zhou, Zhan Su +3LLM Pruning LLM Fine-Tuning
University of Washington · University of Montreal
Nice Fold or Hero Call: Learning Budget-Efficient Thinking for Adaptive Reasoning
May 12, 2026Zhaomeng Zhou, Lan Zhang, Junyang Wang +2RL for Language Model Reasoning Efficient Language Model Reasoning
University of Science and Technology of China · The Chinese University of Hong Kong
LEAD: Length-Efficient Adaptive and Dynamic Reasoning for Large Language Models
May 10, 2026Songtao Wei, Yi Li, Zhikai Li +7RL for Language Model Reasoning Efficient Language Model Reasoning
University of Texas at Dallas · Emory University · University of Texas at Arlington +2
Decomposing and Steering Functional Metacognition in Large Language Models
May 9, 2026Yanshi Li, Xueru Bai, Shuman Liu +2LLM Evaluation LLM Interpretability
Shopee Singapore, Singapore
Internalizing Safety Understanding in Large Reasoning Models via Verification
May 9, 2026Yi Zhang, Yuxin Chen, Leheng Sheng +4LLM Safety Alignment LLM Safety
University of Science and Technology of China · National University of Singapore · Shanghai Artificial Intelligence Laboratory
Reasoning Compression with Mixed-Policy Distillation
May 9, 2026Han Yang, Mingyan Wu, Bailan He +4Language Model Distillation Efficient Language Model Inference
Technical University of Munich · GESIS – Leibniz Institute for the Social Sciences · Northeastern University +5
Hint Tuning: Less Data Makes Better Reasoners
May 9, 2026Siqi Fan, Minghao Li, Xiaoqian Ma +6Overthinking in Language Models LLM Fine-Tuning
University of Electronic Science and Technology of China · Xiaohongshu Inc. · National University of Singapore
Reason to Play: Behavioral and Brain Alignment Between Frontier LRMs and Human Game Learners
May 8, 2026Botos Csaba, Sreejan Kumar, Austin Tudor David Andrews +6Game-Playing Agents Human Behavior Simulation
University of Oxford · Columbia University · New York University +2
ExpThink: Experience-Guided Reinforcement Learning for Adaptive Chain-of-Thought Compression
May 8, 2026Tingcheng Bian, Yuzhe Zhang, Jing Jin +5Reward Shaping RL for Language Model Reasoning
1Baidu Inc. · 2Shenzhen University · 3Peking University +2
Rethinking Dense Sequential Chains: Reasoning Language Models Can Extract Answers from Sparse, Order-Shuffling Chain-of-Thoughts
May 8, 2026Yi-Chang Chen, Feng-Ting Liao, Da-shan Shiu +1CoT Reasoning Causal Interventions in Language Models
MediaTek Research · Artificial Intelligence Center of Research Excellence, National Taiwan University
ReasonLab: A Controlled and Auditable Evaluation of Prompting Techniques for Multiple-Choice QA
May 7, 2026Inder Preet, Shuxin Lin, Dhaval PatelLLM Evaluation Language Model Generation Evaluation
IBM, Dublin · IBM, New York
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
Teaching Thinking Models to Reason with Tools: A Full-Pipeline Recipe for Tool-Integrated Reasoning
May 7, 2026Qianjia Cheng, Yuchen Zhang, Zhilin Wang +9Tool-Integrated Reasoning RL for Language Model Reasoning
Zhejiang University · Shanghai AI Laboratory · Peking University +4
Chain of Risk: Safety Failures in Large Reasoning Models and Mitigation via Adaptive Multi-Principle Steering
May 7, 2026Xiaomin Li, Jianheng Hou, Zheyuan Deng +6LLM Safety LLM Safety Evaluation
Harvard University · University of Southern California · Brown University +6
ZAYA1-8B Technical Report
May 6, 2026Robert Washbourne, Rishi Iyer, Tomas Figliolia +15Mixture-of-Experts Language Models RL for Language Model Reasoning
Zyphra San Francisco, CA
Rose-SQL: Role-State Evolution Guided Structured Reasoning for Multi-Turn Text-to-SQL
May 5, 2026Le Zhou, Feng Yao, Fengcai Qiao +3Schema Linking Text-to-SQL
College of Systems Engineering, National University of Defense Technology · College of Advanced Interdisciplinary Studies, National University of Defense Technology · Institute of Automation, Chinese Academy of Sciences +1

May 19, 2026J/K move · Enter open · S save

Attention-Guided Reward for Reinforcement Learning-based Jailbreak against Large Reasoning Models

Zheng Lin, Zhenxing Niu, Haoxuan Ji, Yuzhe Huang+1

Xidian University · Xi’an Jiaotong University

Adversarial Attacks on LLMs LLM Jailbreak Attacks Large Reasoning Models

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Large Reasoning Models (LRMs) have demonstrated remarkable capabilities in solving complex problems by generating structured, step-by-step reasoning content. However, exposing a model's internal reasoning process introduces additional safety risks; for example, recent studies show that LRMs are more vulnerable to jailbreak attacks than standard LLMs. In this paper, we investigate jailbreak attacks on LRMs and reveal that the attack success rate (ASR) is closely correlated with LRMs' attention patterns. Specifically, successful jailbreaks tend to assign lower attention to harmful tokens in the input prompt, while allocating higher attention to those tokens in the reasoning content. Motivated by this finding, we propose a novel jailbreak method for LRMs that leverages reinforcement learning (RL) to enhance attack effectiveness, explicitly incorporating attention signals into the reward function design. In addition, we introduce diverse persuasion strategies to enrich the RL action space, which consistently improves the ASR. Extensive experiments on five open-source and closed-source LRMs across three benchmarks demonstrate that our method achieves substantially higher ASR, outperforming existing approaches in terms of effectiveness, efficiency, and transferability.