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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Deep Reasoning in General Purpose Agents via Structured Meta-Cognition
May 12, 2026Dean Light, Michael Theologitis, Kshitish Ghate +7Structured Reasoning Agentic Reasoning
University of Washington Seattle, WA, USA
DGPO: Beyond Pairwise Preferences with Directional Consistent Groupwise Optimization
May 11, 2026Mengyi Deng, Zhiwei Li, Xin Li +4LLM Alignment Reasoning Consistency in Language Models
1Information Hub, The Hong Kong University of Science and Technology (Guangzhou), China · 2The Hong Kong University of Science and Technology, Hong Kong SAR
SLASH the Sink: Sharpening Structural Attention Inside LLMs
May 11, 2026Yiming Liu, Bin Lu, Xinbing Wang +2LLM Reasoning with Graphs LLM Interpretability
Shanghai Jiao Tong University, Shanghai, China · Institute of Geographical Science and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
Teaching LLMs to See Graphs: Unifying Text and Structural Reasoning
May 11, 2026Dario VajdaLLM Reasoning with Graphs Text-Attributed Graph Learning
Faculty of Computer and Information Science University of Ljubljana
Breaking the Reward Barrier: Accelerating Tree-of-Thought Reasoning via Speculative Exploration
May 11, 2026Shuzhang Zhong, Haochen Huang, Shengxuan Qiu +3Inference-Time Search Tree of Thoughts
Institute for Artificial Intelligence, Peking University · School of Integrated Circuits, Peking University · 3ByteDance Seed
FERA: Uncertainty-Aware Federated Reasoning for Large Language Models
May 11, 2026Ruhan Wang, Chengkai Huang, Zhiyong Wang +6LLM Reasoning Federated Learning
Indiana University · The University of New South Wales · The Chinese University of Hong Kong +2
TeleResilienceBench: Quantifying Resilience for LLM Reasoning in Telecommunications
May 11, 2026Pranshav Gajjar, Emmanuel Ojo, Vijay K ShahLLM Evaluation LLM Reasoning
¹NextG Wireless Lab, North Carolina State University
Exploration-Driven Optimization for Test-Time Large Language Model Reasoning
May 11, 2026Changhao Li, Yuchen Zhuang, Chenxiao Gao +4RL for Language Models Inference-Time Optimization
Georgia Institute of Technology
Absurd World: A Simple Yet Powerful Method to Absurdify the Real-world for Probing LLM Reasoning Capabilities
May 10, 2026Ryan Albright, Golam Md Muktadir, Zarif Ikram +3LLM Evaluation Language Model Robustness
The Nueva School, San Mateo, USA · University of Southern California · Notre Dame College, Dhaka, Bangladesh +2
Efficient LLM Reasoning via Variational Posterior Guidance with Efficiency Awareness
May 10, 2026Zizhao Chen, Yuying Li, Siting Lin +1LLM Inference Efficiency Efficient Language Model Reasoning
Guangdong University of Foreign Studies, Guangzhou, China.
Not All Thoughts Need HBM: Semantics-Aware Memory Hierarchy for LLM Reasoning
May 10, 2026Aojie Yuan, Tianqi Shen, Dajun ZhangKV-Cache Offloading KV Caching
University of Southern California · †University of Wisconsin–Madison
LoopUS: Recasting Pretrained LLMs into Looped Latent Refinement Models
May 10, 2026Taekhyun Park, Yongjae Lee, Dohee Kim +1Recurrent Transformers LLM Post-Training
Department of Data Science Pusan National University Busan, Republic of Korea · Department of Industrial Engineering Pusan National University Busan, Republic of Korea · Department of Artificial Intelligence Engineering Changwon National University Changwon, Republic of Korea
An Execution-Verified Multi-Language Benchmark for Code Semantic Reasoning
May 10, 2026Yikun Li, Jinfeng Jiang, Ting Zhang +7LLM Evaluation Code Language Models
Singapore Management University · Monash University · Nanjing University of Science and Technology +1
RuPLaR : Efficient Latent Compression of LLM Reasoning Chains with Rule-Based Priors From Multi-Step to One-Step
May 10, 2026Xiaocheng Luo, Kang Wang, Zaifu Zhan +2LLM Compression CoT Compression
School of Computer Science and Technology, Soochow University, Suzhou, 215006, Jiangsu, PR China · Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, 55455, MN, USA
Memorize Theorems, Not Instances: Probing SFT Generalization through Mathematical Reasoning
May 10, 2026Ruiying Peng, Mengyu Yang, Jing Lei +3Supervised Fine-Tuning Memorization in Language Models
Tsinghua Shenzhen International Graduate School · Huawei Technologies
Do LLMs Experience an Internal Polylogue? Investigating Reasoning through the Lens of Personas
May 9, 2026Nils A. Herrmann, Leander Girrbach, Kirill Bykov +1Language Model Steering LLM Interpretability
1Technical University of Munich · 2Helmholtz Munich · 3Munich
Evaluating Pragmatic Reasoning in Large Language Models: Evidence from Scalar Diversity
May 9, 2026Ye-eun ChoLLM Evaluation Pragmatic Reasoning in Language Models
Sungkyunkwan University Seoul, South Korea
Where Larger Models Excel: The Primacy of Constraint-Guided Reasoning
May 9, 2026Guan-Yi Lin, Hen-Hsen HuangLanguage Model Scaling Laws LLM Reasoning
National Chengchi University Taipei, Taiwan · Academia Sinica Taipei, Taiwan
Training with Harnesses: On-Policy Harness Self-Distillation for Complex Reasoning
May 9, 2026Zhengyang Zhao, Lu Ma, Wentao ZhangOn-Policy Self-Distillation Language Model Distillation
Peking University
Mid-Training with Self-Generated Data Improves Reinforcement Learning in Language Models
May 8, 2026Aswin RRV, Jacob Dineen, Divij Handa +4Synthetic Data Generation RL for Language Model Reasoning
Arizona State University · Google Cloud AI Research · Google DeepMind
VecCISC: Improving Confidence-Informed Self-Consistency with Reasoning Trace Clustering and Candidate Answer Selection
May 8, 2026James Petullo, Sonny George, Dylan Cashman +1LLM Inference Efficiency Inference-Time Scaling
Computer Science Department, Brandeis University
Abductive Reasoning with Probabilistic Commonsense
May 8, 2026Joseph Cotnareanu, Chiara Roverato, Han Zhou +3Abductive Reasoning Neuro-Symbolic Reasoning
McGill University · International Laboratory on Learning Systems · Mila - Quebec Artificial Intelligence Institute +1
Reliable Chain-of-Thought via Prefix Consistency
May 8, 2026Naoto Iwase, Yuki Ichihara, Mohammad Atif Quamar +1Self-Consistency Decoding Reasoning Consistency in Language Models
Nagoya University · Nara Institute of Science and Technology · Mohamed bin Zayed University of Artificial Intelligence +1
MAVEN: Multi-Agent Verification-Elaboration Network with In-Step Epistemic Auditing
May 8, 2026Yinsheng Yao, Jiehao Tang, Zhaozhen Yang +1LLM Auditing Multi-Agent Debate
Tongji University Shanghai, China
Think-with-Rubrics: From External Evaluator to Internal Reasoning Guidance
May 8, 2026Jiachen Yu, Zhihao Xu, Junjie Wang +1Instruction Following Rubric-Based Evaluation
1Tsinghua University · 2Renmin University of China
Confidence-Aware Alignment Makes Reasoning LLMs More Reliable
May 8, 2026Kejia Chen, Jiawen Zhang, Yihong Wu +5LLM Alignment Direct Preference Optimization
Zhejiang University · Université de Montréal · Sun Yat-sen University +1
Implicit Compression Regularization: Concise Reasoning via Internal Shorter Distributions in RL Post-Training
May 8, 2026Chen Wang, Hexuan Deng, Yining Zhang +5Overthinking in Language Models Reinforcement Learning with Verifiable Rewards
College of Software, Nankai University · 2Zhongguancun Academy · 3Harbin Institute of Technology +4
LaTER: Efficient Test-Time Reasoning via Latent Exploration and Explicit Verification
May 8, 2026Xuan Li, Yining Wang, Yuchen Liu +7CoT Reasoning Efficient Language Model Reasoning
University of Science and Technology of China · Unisound AI Technology Co., Ltd · MAIS, Institute of Automation, Chinese Academy of Sciences
From 0-Order Selection to 2-Order Judgment: Combinatorial Hardening Exposes Compositional Failures in Frontier LLMs
May 8, 2026Hanmeng Liu, Shichao Weng, Xiulai Liu +3Logical Reasoning LLM Evaluation
Hainan University, Haikou, China · Fudan University, Shanghai, China
Structural Rationale Distillation via Reasoning Space Compression
May 8, 2026Jialin Yang, Jiankun Wang, Jiajun Wu +3CoT Distillation Structured Reasoning
University of Calgary · Calgary, Canada · University of Michigan +1

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Deep Reasoning in General Purpose Agents via Structured Meta-Cognition

Dean Light, Michael Theologitis, Kshitish Ghate, Shuyue Stella Li+6

University of Washington Seattle, WA, USA

Structured Reasoning Agentic Reasoning LLM Agents LLM Agent Planning LLM Reasoning

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Humans intuitively solve complex problems by flexibly shifting among reasoning modes: they plan, execute, revise intermediate goals, resolve ambiguity through associative judgment, and apply formal procedures to well-specified subproblems. Current LLM agents lack this flexibility, as their scaffolds hard-code such reasoning decisions in advance. These scaffolds are effective when their prescribed structure matches the task, but brittle when solving the task requires adapting the structure of reasoning itself. We introduce Deep Reasoning -- an inference-time approach for constructing task-specific scaffolds through structured meta-reasoning. Deep Reasoning uses a formal language that represents meta-reasoning as executable decompositions over associative inference, formal computation, and recursive subproblem solving, enabling decomposition principles to be encoded as in-context examples that guide test-time scaffold construction. We instantiate this approach in a general-purpose agent (DOLORES) that distributes complex tasks across more controlled reasoning threads. We evaluate it against state-of-the-art scaffolding methods across four hard benchmarks: multi-hop reasoning, long-chain question answering, long-context aggregation, and deep research-style information seeking. DOLORES outperforms all evaluated scaffolds across three model sizes and two model families, improving over the strongest evaluated scaffold baseline by 24.8% on average. DOLORES distributes cognition across structured, lower-load reasoning threads, thereby reducing premature termination and hallucinations. This advantage can even bridge the scaling gap, with an 8B version surpassing all evaluated 32B baselines from the same family in more than half the settings. These results point toward future agentic systems that treat scaffolding as adaptive reasoning, constructing the structure each task requires just-in-time.

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