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.

Evaluating Reasoning Models for Queries with Presuppositions
May 4, 2026Rose Sathyanathan, Kinshuk Vasisht, Danish PruthiLLM Evaluation Language Model Robustness
Indian Institute of Science Bengaluru, KA, India
Distilling Long-CoT Reasoning through Collaborative Step-wise Multi-Teacher Decoding
May 4, 2026Taewon Yun, Jisu Shin, Jeonghwan Choi +2CoT Distillation Efficient Language Model Reasoning
KAIST · UNIST
Towards Understanding Specification Gaming in Reasoning Models
May 4, 2026Kei Nishimura-Gasparian, Robert McCarthy, David LindnerReward Hacking RL for Language Model Reasoning
1MATS · University College London, London, UK · 3Google Deep- Mind.
Spatiotemporal Hidden-State Dynamics as a Signature of Internal Reasoning in Large Language Models
May 3, 2026Kotaro Furuya, Takahito TanimuraTransformer Interpretability LLM Evaluation
Research and Development Group, Hitachi, Ltd. 1-280 Higashi-Koigakubo, Kokubunji-shi, Tokyo 185-8601, Japan
Only Say What You Know: Calibration-Aware Generation for Long-Form Factuality
May 3, 2026Wen Luo, Guangyue Peng, Liang Wang +7Long-Form Document Generation LLM Hallucination Mitigation
State Key Laboratory of Multimedia Information Processing, School of Computer Science, Peking University Research Asia · MSRA · Microsoft
When Less is Enough: Efficient Inference via Collaborative Reasoning
May 1, 2026Yilei Chen, Sharut Gupta, Yannis Paschalidis +2Efficient Language Model Reasoning Efficient Language Model Inference
Boston University · MIT CSAIL · Biohub
Retrieval-Augmented Reasoning for Chartered Accountancy
Apr 30, 2026Jatin Gupta, Akhil Sharma, Saransh Singhania +1Retrieval-Augmented Generation Financial QA
Dept. of Computer Science and Engineering Sharda University, Greater Noida, India · Sharda University, Greater Noida, India
State Stream Transformer (SST) V2: Parallel Training of Nonlinear Recurrence for Latent Space Reasoning
Apr 30, 2026Thea AvissRecurrent Neural Networks Recurrent Transformers
Fifth Dimension
Path-Lock Expert: Separating Reasoning Mode in Hybrid Thinking via Architecture-Level Separation
Apr 29, 2026Shouren Wang, Wang Yang, Chuang Ma +7Mixture-of-Experts Language Models LLM Reasoning
1Case Western Reserve University · 2NII LLMC, Japan · 3Michigan State University
When to Retrieve During Reasoning: Adaptive Retrieval for Large Reasoning Models
Apr 29, 2026Dongxin Guo, Jikun Wu, Siu Ming YiuRetrieval-Augmented Generation Retrieval-Augmented Reasoning
The University of Hong Kong Hong Kong, China · Brain Investing Limited Hong Kong, China · Stellaris AI Limited Hong Kong, China
When to Vote, When to Rewrite: Disagreement-Guided Strategy Routing for Test-Time Scaling
Apr 29, 2026Zhimin Lin, Yixin Ji, Jinpeng Li +5Test-Time Scaling LLM Routing
School of Computer Science and Technology, Soochow University · Department of Foundation Model, 2012 Labs, Huawei · 3Harbin Institute of Technology, Shenzhen (HITSZ)
Stabilizing Efficient Reasoning with Step-Level Advantage Selection
Apr 27, 2026Han Wang, Xiaodong Yu, Jialian Wu +4RL for Language Model Reasoning Efficient Language Model Reasoning
UNC Chapel Hill · Advanced Micro Devices, Inc.
UpstreamQA: A Modular Framework for Explicit Reasoning on Video Question Answering Tasks
Apr 25, 2026Jason Nguyen, Ameet Rao, Alexander Chang +2Multimodal Large Language Models Video QA
Lincoln North Star High School · The Charter School of Wilmington · Greenwich High School +2
TRACES: Tagging Reasoning Steps for Adaptive Cost-Efficient Early-Stopping
Apr 22, 2026Yannis Belkhiter, Seshu Tirupathi, Giulio Zizzo +1Reasoning Trace Analysis Early Stopping
IBM Research Europe · Trinity College Dublin · Dublin, Ireland +1
Binary Agreement for Efficient Unsupervised Confidence Calibration of Reasoning LLMs
Apr 21, 2026Thomas Zollo, Jimmy Wang, Richard ZemelConfidence Estimation in Language Models Language Model Calibration
Columbia University
How Much Thinking is Enough? Quantifying and Understanding Redundancy in LLM Reasoning
Apr 21, 2026Zhiyuan Zhai, Xinkai You, Wenjing Yan +1LLM Inference Efficiency RL for Language Model Reasoning
Fudan University · The Chinese University of Hong Kong
More Thinking, More Bias: Length-Driven Position Bias in Reasoning Models
Apr 21, 2026Xiao WangMultiple-Choice Question Answering CoT Reasoning
When Safety Fails Before the Answer: Benchmarking Harmful Behavior Detection in Reasoning Chains
Apr 21, 2026Ishita Kakkar, Enze Zhang, Rheeya Uppaal +1AI Safety Evaluation LLM Safety Evaluation
Department of Computer Sciences, University of Wisconsin-Madison
Reasoning Structure Matters for Safety Alignment of Reasoning Models
Apr 21, 2026Yeonjun In, Wonjoong Kim, Sangwu Park +1LLM Safety Alignment Large Reasoning Models
KAIST
Fine-Tuning Small Reasoning Models for Quantum Field Theory
Apr 21, 2026Nathaniel S. Woodward, Zhiqi Gao, Yurii Kvasiuk +3Physical Reasoning LLM Fine-Tuning
Department of Physics, University of Wisconsin-Madison · Department of Computer Science, University of Wisconsin-Madison · Perimeter Institute for Theoretical Physics
Reasoning Models Know What's Important, and Encode It in Their Activations
Apr 20, 2026Yaniv Nikankin, Martin Tutek, Tomer Ashuach +2LLM Interpretability LLM Reasoning
Technion · University of Zagreb, FER · MIT +1
Playing Psychic: Using Thought Trees to Predict Reasoning Models Accuracy on Coding Tasks
Apr 18, 2026Jiaxin Fang, Runyuan He, Sahil Bhatia +2Structured Reasoning Code Generation Evaluation
University of California, Berkeley
x1: Learning to Think Adaptively Across Languages and Cultures
Apr 18, 2026Yangfan Ye, Xiaocheng Feng, Xiachong Feng +8Multilingual Language Models Cross-Lingual Reasoning in Language Models
Harbin Institute of Technology · Peng Cheng Laboratory · The University of Hong Kong +1
Step-GRPO: Internalizing Dynamic Early Exit for Efficient Reasoning
Apr 18, 2026Benteng Chen, Weida Wang, Shufei Zhang +2RL for Language Model Reasoning LLM Inference Acceleration
East China Normal University · Shanghai AI Laboratory · Fudan University +1
RankGuide: Tensor-Rank-Guided Routing and Steering for Efficient Reasoning
Apr 17, 2026Jiayi Tian, Yupeng Su, Ryan Solgi +2LLM Routing Efficient Language Model Inference
University of California, Santa Barbara · Intel Labs
Cut Your Losses! Learning to Prune Paths Early for Efficient Parallel Reasoning
Apr 17, 2026Jiaxi Bi, Tongxu Luo, Wenyu Du +2LLM Inference Acceleration Large Reasoning Models
The Chinese University of Hong Kong, Shenzhen · Shenzhen Loop Area Institute · DualityRL
CiPO: Counterfactual Unlearning for Large Reasoning Models through Iterative Preference Optimization
Apr 17, 2026Junyi Li, Yongqiang Chen, Ningning DingMachine Unlearning LLM Unlearning
The Hong Kong University of Science and Technology (Guangzhou) · The Chinese University of Hong Kong
Reasoning-targeted Jailbreak Attacks on Large Reasoning Models via Semantic Triggers and Psychological Framing
Apr 17, 2026Zehao Wang, Lanjun WangAdversarial Prompt Generation Jailbreak Attacks
College of Intelligence and Computing, Tianjin University, Tianjin 300350, China. · School of New Media and Communication, Tianjin University, Tianjin 300072, China. · Shanghai Key Laboratory of Data Science,[cs.LG] Shanghai 200438, China.
LACE: Lattice Attention for Cross-thread Exploration
Apr 16, 2026Yang Li, Zirui Zhang, Yang Liu +1Parallel Reasoning in Language Models LLM Inference
Department of Computer Science Rutgers University · Amazon AGI Labs
Think Multilingual, Not Harder: A Data-Efficient Framework for Teaching Reasoning Models to Code-Switch
Apr 16, 2026Eleanor M. Lin, David JurgensMultilingual Language Models Code-Switching
University of Michigan

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Evaluating Reasoning Models for Queries with Presuppositions

Rose Sathyanathan, Kinshuk Vasisht, Danish Pruthi

Indian Institute of Science Bengaluru, KA, India

LLM Evaluation Language Model Robustness Large Reasoning Models Language Model Reasoning Evaluation

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Millions of users turn to AI models for their information needs. It is conceivable that a large number of user queries contain assumptions that may be factually inaccurate. Prior work notes that large language models (LLMs) often fail to challenge such erroneous assumptions, and can reinforce users' misinformed opinions. However, given the recent advances, especially in model's reasoning capabilities, we revisit whether large reasoning models (LRMs) can reason about the underlying assumptions and respond to user queries appropriately. We construct queries with varying degrees of presuppositions spanning health, science, and general knowledge, and use it to evaluate several widely-deployed models When compared to non-reasoning models, we find that reasoning models achieve a slightly higher accuracy (2-11%), but they still fail to challenge a large fraction (26-42%) of false presuppositions. Further, reasoning models remain susceptible to how strongly the presupposition is expressed.