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.

Reasoning Patterns for Efficient AI Coding: An Empirical Study
Oct 26, 2026Zhe Yin, Yuling Shi, Xiaodong Gu +1Efficient Language Model Reasoning Large Reasoning Models
Shanghai Jiao Tong University, China
CALM: Consistency-Aware Logit Masking in Decoding Space for Mitigating Overthinking of Large Reasoning Models
Oct 19, 2026Shengjing Liu, Feiyang Li, Yuxuan Yang +2Overthinking in Language Models Efficient Language Model Reasoning
College of Computer Science and Software Engineering, Shenzhen University
ARBOR: Locating Reasoning Failures in Long Chains of Thought through Reasoning State Tracking
Oct 15, 2026Huihan Li, Siyuan Wang, Naga Vamsi Ramana Dinavahi +2Reasoning Trace Analysis Large Reasoning Models
University of Southern California · The Chinese University of Hong Kong · New York University
Are Large Reasoning Models Good Everyday Reasoners? A Comparison with Non-Reasoning Models on Logical Reasoning Tasks
Oct 15, 2026Kentaro Ozeki, Risako Ando, Takanobu Morishita +3Language Model Reasoning Evaluation LLM Reasoning
Keio University
Entertain, then Cancel: How Thinking Makes Reasoning Models Read Some and Or Literally
Oct 15, 2026Zhizhuo Kou, Jian Yang, Pengfei Hao +4Pragmatic Reasoning in Language Models Large Reasoning Models
1The Hong Kong University of Science and Technology · 2The Hong Kong University of Science and Technology (Guangzhou) · Peking University
Beyond Correctness: Compute-Responsive Data Selection for Reasoning Model Post-Training
Oct 14, 2026Liang He, Xiangyu Li, Yuxin Wang +4Training Data Selection Language Model Post-Training
To Reason but Not to Stop: Understanding and Recovering Pruned Large Reasoning Models
Oct 14, 2026Minh-Anh Nguyen, Nguyen T. Ha Lan, Xiyan Fu +2Repetition in Language Generation LLM Reasoning
Nanyang Technological University, Singapore · VinUniversity, Vietnam
Efficiently Learning To Reason or Not to Reason: Root-token Policy Optimization for Adaptive Thinking
Oct 13, 2026Taehyeon Kim, Hyunsoo Lee, Youngsoo Jang +1Adaptive Reasoning in Language Models Efficient Language Model Reasoning
LG AI Research · UNIST · University of Illinois Chicago
From Candidate Tokens to Final Outcomes: Decoding Relative Long-Horizon Candidate Value from Reasoning Model Representations
Oct 11, 2026Yixin Yang, Yue Wang, Fandong Meng +1LLM Reasoning Large Reasoning Models
State Key Laboratory of Multimedia Information Processing, School of Computer Science, Peking University · WeChat AI, Tencent Inc., China
RECAP: Calibrating Low-Bit Reasoning Models on the History They Read
Oct 11, 2026Chi Ruan, Yujie Chen, Zixiang Zhao +2LLM Quantization Post-Training Quantization
SpinEntropy · ETH Zurich · PolyU
REDaCT: Collapse-Free Unlearning in Large Reasoning Models
Oct 8, 2026Joshua Maccoby, Wendy Hui WangMachine Unlearning LLM Unlearning
Stevens Institute of Technology Hoboken, New Jersey, USA
Learning to Plan by Looking Back: Hindsight Hierarchies for Training Reasoning Models
Oct 8, 2026Lars Simon, Holger Eble, Manuel RadonsLanguage Model Self-Improvement Large Reasoning Models
Bundesdruckerei GmbH Berlin, Germany
RL-ARC: Calibrating Large Reasoning Models via Reasoning-guided Uncertainty
Oct 8, 2026Gukhyeon Lee, SangKeun LeeLanguage Model Calibration LLM Uncertainty Estimation
Department of Artificial Intelligence, Korea University, Seoul, Republic of Korea · Department of Computer Science and Engineering, Korea University, Seoul, Republic of Korea
The Long Road to the Same Answer: Cognitive Bias Under Escalating Reasoning Budgets in Large Language Models
Oct 7, 2026Obada KraishanCognitive Bias in Language Models Large Reasoning Models
College of Media and Communication Texas Tech University Lubbock, TX, USA
Monitorability Disposition in Large Reasoning Models
Oct 4, 2026Shahriar Golchin, Marc WetterAI Agent Monitoring LLM Auditing
Scale AI · Applied Machine Learning Research, Labelbox
Reasoning Models Are Accurate but Unsound on Identification
Oct 2, 2026Arman Behnam, Binghui WangQuery Identifiability Causal Identifiability
Department of Computer Science Illinois Institute of Technology Chicago, IL, USA
Interpreting Reasoning of Large Language Models via Partial Information Decomposition
Sep 30, 2026Barproda Halder, Qiuyi Zhang, Sanghamitra DuttaLLM Interpretability Efficient Language Model Reasoning
University of Maryland, College Park · Elorian AI
When a Kindergartener Solves Calculus: Measuring Capability Leakage in Role-Prompted Reasoning Models
Sep 30, 2026Pakhapoom Sarapat, Saksorn Ruangtanusak, Kunat Pipatanakul +1Large Language Model-Based Role-Play Simulation LLM Security
SCB DataX · Nanyang Technological University
Diversity Combining for Multi-Path LLM Reasoning
Sep 30, 2026Guangsheng Yu, Litianyi Zhang, Qin Wang +5Inference-Time Optimization Self-Consistency Decoding
University of Technology Sydney · The University of Sydney · CSIRO +2
When Reasoning Goes Astray: Attention Dynamics of Uncontrolled Reasoning
Sep 30, 2026Yuanhe Zhang, Ziwei Wang, Jie Ren +6Language Model Steering Overthinking in Language Models
Beijing University of Posts and Telecommunications · Wuhan University · Nanyang Technological University +1
$S^3$ : Spectral Null-Space Swap Makes Reasoning Models Efficient
Sep 29, 2026Hongbo Ma, Sansheng Cao, Jiajun Fan +2Efficient Language Model Reasoning Model Merging
University of Illinois Urbana-Champaign · Tsinghua University · Dartmouth College +1
Hidden Reasoning Must Leak, but Need Not Be Readable: Fundamental Opportunities and Limits for Chain-of-Thought Monitoring
Sep 29, 2026Mohammadali Mohammadkhani, Madhava Krishna, Yash Sarrof +1CoT Monitoring Large Reasoning Models
Saarland University, Saarland Informatics Campus · Zuse School ELIZA
MetaCtrl: Your Large Language Models Can Reason Better and More Concisely with a Metacognitive Controller
Sep 29, 2026Zhibin Wen, Tao Han, Lei Bai +2Adaptive Reasoning in Language Models RL for Language Model Reasoning
Southern University of Science and Technology · The Hong Kong University of Science and Technology · Shanghai Artificial Intelligence Laboratory +1
Towards Mitigating Deceptive Safety Alignment in Large Reasoning Models
Sep 28, 2026Xiangyu Zhou, Saleh Zare Zade, Rafi Ibn Sultan +2Language Model Safety Evaluation Deception in Language Models
Wayne State University
Jailbreaks for Black-Box Uncertainty Quantification in Large Reasoning Models
Sep 28, 2026Lucas Biechy, Cédric Eichler, Adrien Boiret +1Language Model Calibration LLM Uncertainty Estimation
Petscraft, Inria · Université Paris-Saclay · INSA CVL +2
Efficient Reasoning via Constrained Optimization in Latent Space
Sep 28, 2026Zhinan Hou, Xingchen Li, Keyou YouOverthinking in Language Models Efficient Language Model Reasoning
Tsinghua University
Not Too Hard, Not Too Easy: Learning from Intermediate States for LLM Structured Reasoning
Sep 27, 2026Hongbo Chen, Guohua Lu, Ting Dang +1Structured Reasoning in Language Models Self-Training for Language Models
University of Auckland, Auckland, New Zealand · University of Melbourne, Melbourne, Australia
On the Pitfalls of Verbalized Confidence Priors for Calibrating Large Reasoning Models
Sep 26, 2026Shuoyuan Wang, Beier Luo, Hao Zeng +5Language Model Calibration Confidence Estimation in Language Models
Southern University of Science and Technology · Nanyang Technological University · University of Electronic Science and Technology of China +2
Monitor Jailbreaking: Evading Chain-of-Thought Monitoring Without Encoded Reasoning
Sep 25, 2026Julian SchulzCoT Monitoring Language Model Safety Evaluation
Meridian Cambridge
Prefilling the Reasoning Channel: Output-Prefix Attacks on Reasoning LLMs
Sep 24, 2026Lukáš Brůna, Robert Bridges, Adam EkAdversarial Attacks on LLMs Prompt Injection Attacks
Uppsala University · AI Sweden

Oct 26, 2026J/K move · Enter open · S save

Reasoning Patterns for Efficient AI Coding: An Empirical Study

Zhe Yin, Yuling Shi, Xiaodong Gu, Beijun Shen

Shanghai Jiao Tong University, China

Efficient Language Model Reasoning Large Reasoning Models LLM Code Generation

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Large Reasoning Models (LRMs) generate long reasoning traces that expose intermediate inference behavior, offering opportunities to study and control reasoning in AI coding. However, the underlying patterns in these traces and their potential for analyzing and guiding coding models remain poorly understood, limiting systematic characterization and inference-time control of LRM reasoning. We conduct an empirical study of reasoning patterns across four representative coding tasks. We transform reasoning traces into structured action sequences and mine recurring fragments using within-task support, cross-task contrasts, and valid--invalid support contrasts. We characterize four general reasoning patterns by coding agents: design-to-implementation for code generation, state tracing for execution reasoning, evidence--fix--recheck for debugging, and semantic mapping for translation. We further identify positive and negative patterns associated with valid and invalid reasoning traces, respectively. Positive patterns reflect verification-driven convergence, whereas negative patterns reflect repeated design revision, reflection, and knowledge recall without closure. To demonstrate their practical utility, we evaluate three pattern-based applications: task identification, pattern-guided prompting, and pattern-aware early stopping. Features capturing general reasoning patterns enable accurate task identification from reasoning traces without accessing task descriptions or final answers. Prompting guided by positive and negative patterns improves the overall accuracy of downstream tasks, while negative pattern-aware early stopping substantially reduces API-reported token usage with only a modest decrease in pass@1. All three applications also generalize to previously unseen models, showing that reasoning patterns can support both the analysis and control of LRM behavior in AI coding.