LLM Reasoning

LLM: Large Language Model

This field asks how language models can carry out multi-step inference across mathematics, coding, logic, science, and real-world tasks, and how to tell robust reasoning from pattern matching. Common approaches include chain-of-thought prompting, self-consistency and tree search at inference time, tool use and symbolic verification, and post-training with supervised traces, reinforcement learning from verifiable rewards, or process feedback. Researchers are also exploring latent and recurrent computation to reduce reliance on long written traces, alongside benchmarks that test compositionality, robustness, and long-horizon execution. A central tension is that more computation can improve accuracy but raise cost, while plausible explanations and correct final answers do not by themselves establish faithful, reliable reasoning.

Latest papers

931 papers105 papers in the last four weeks, up 27% on the four weeks before. 0.6% of all new papers.

Thinking Without Words: Efficient Latent Reasoning with Abstract Chain-of-Thought
Apr 24, 2026Keshav Ramji, Tahira Naseem, Ramón Fernandez AstudilloEfficient Language Model Reasoning Implicit Reasoning in Language Models
IBM Research AI
Large Language Models Decide Early and Explain Later
Apr 24, 2026Ayan Datta, Zhixue Zhao, Bhuvanesh Verma +3Efficient Language Model Reasoning LLM Inference Acceleration
IIIT Hyderabad · University of Sheffield, UK · Goethe University, Frankfurt am Main, Germany
Calibration Drift Under Reasoning: How Chain-of-Thought Budgets Induce Overconfidence in Large Language Models
Apr 24, 2026Prakul Sunil Hiremath, Harshit R. HiremathConfidence Estimation in Language Models CoT Reasoning
Department of Computer Science and Engineering Visvesvaraya Technological University, Belagavi · Department of Computer Science and Business System SG Balekundri Institute of Technology, Belagavi
Thinking with Reasoning Skills: Fewer Tokens, More Accuracy
Apr 23, 2026Guangxiang Zhao, Qilong Shi, Xusen Xiao +3Efficient Language Model Reasoning Efficient Language Model Inference
Qiyuan Tech · Tsinghua University · The University of Hong Kong +1
To See the Unseen: on the Generalization Ability of Transformers in Symbolic Reasoning
Apr 23, 2026Nevena Lazić, Liam Fowl, András György +1Logical Reasoning Decoder-Only Language Models
Process Supervision via Verbal Critique Improves Reasoning in Large Language Models
Apr 23, 2026Hao-Yuan ChenInference-Time Scaling Process Supervision
Mindify AI Research · University of London · Senate House, Malet Street, London WC1E 7HU, United Kingdom
CoFEE: Reasoning Control for LLM-Based Feature Discovery
Apr 23, 2026Maximilian Westermann, Ben Griffin, Aaron Ontoyin Yin +6LLM Prompting Automated Feature Engineering
University of Oxford · Vela Research · Amazon
Reasoning Primitives in Hybrid and Non-Hybrid LLMs: Do Architectural Differences Yield Advantages in State-Tracking and Recall?
Apr 23, 2026Shivam Rawat, Lucie Flek, Florian Mai +1CoT Reasoning LLM Memory
Lamarr Institute for Machine Learning and Artificial Intelligence · Rheinische Friedrich-Wilhelms-Universität Bonn
On Reasoning Behind Next Occupation Recommendation
Apr 23, 2026Shan Dong, Palakorn Achananuparp, Hieu Hien Mai +3Large Language Model-Based Recommendation Language Model Post-Training
Singapore Management University · Columbia University
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
Where Reasoning Breaks: Logic-Aware Path Selection by Controlling Logical Connectives in LLMs Reasoning Chains
Apr 22, 2026Seunghyun Park, Yuanyuan LeiInference-Time Search RL for Language Model Reasoning
Independent Researcher · University of Florida Gainesville, FL, United States
LLM StructCore: Schema-Guided Reasoning Condensation and Deterministic Compilation
Apr 22, 2026Serhii ZabolotniiHealthcare Clinical Information Extraction
Cherkasy State Business College2026 Cherkasy, Ukraine
Early-Stage Product Line Validation Using LLMs: A Study on Semi-Formal Blueprint Analysis
Apr 22, 2026Viet-Man Le, Thi Ngoc Trang Tran, Sebastian Lubos +2Software Engineering LLM Evaluation
Graz University of Technology Graz, Austria
Self-Awareness before Action: Mitigating Logical Inertia via Proactive Cognitive Awareness
Apr 22, 2026Fulong Fan, Peilin Liu, Fengzhe Liu +2Abductive Reasoning Structured Reasoning
School of Software, Jilin University · School of Computer Science, Jilin University
HiPO: Hierarchical Preference Optimization for Adaptive Reasoning in LLMs
Apr 22, 2026Darsh Kachroo, Arjun Prasaath Anbazhagan, Adriana Caraeni +2LLM Alignment Direct Preference Optimization
Vellore Institute of Technology, Chennai · Northwestern University · University of Massachusetts Amherst +2
Differentiable Conformal Training for LLM Reasoning Factuality
Apr 22, 2026Nathan Hittesdorf, Marco Salzetta, Lu ChengLLM Hallucination Mitigation Conformal Prediction
Department of Computer Science, University of Illinois at Chicago, Chicago, United States · Department of Physics, University of Illinois at Urbana-Champaign, Urbana, United States
Separable Pathways for Causal Reasoning: How Architectural Scaffolding Enables Hypothesis-Space Restructuring in LLM Agents
Apr 21, 2026John Alderete, Sebastian Benthal, Connie Xu +1Causal Discovery LLM Agents
Amigo AI · Simon Fraser University · International Computer Science Institute
Discovering a Shared Logical Subspace: Steering LLM Logical Reasoning via Alignment of Natural-Language and Symbolic Views
Apr 21, 2026Feihao Fang, My T. Thai, Yuanyuan LeiSymbolic Reasoning Neuro-Symbolic Reasoning
University of Illinois Urbana-Champaign, Champaign, IL · Computer & Information Science and Engineering, University of Florida, Gainesville, FL
CoDA: Towards Effective Cross-domain Knowledge Transfer via CoT-guided Domain Adaptation
Apr 21, 2026Jianzhi Yan, Le Liu, Buzhou Tang +3Domain Adaptation Cross-Domain Transfer Learning
Harbin Institute of Technology, Shenzhen, 518100, China · Pengcheng Laboratory, Shenzhen, 518100, China
Do LLMs Game Formalization? Evaluating Faithfulness in Logical Reasoning
Apr 21, 2026Kyuhee Kim, Auguste Poiroux, Antoine BosselutFormal Verification Lean Theorem Proving
EPFL
How Do Answer Tokens Read Reasoning Traces? Self-Reading Patterns in Thinking LLMs for Quantitative Reasoning
Apr 21, 2026Haoyang Chen, Yi Liu, Jianzhi Shao +3LLM Interpretability Mathematical Reasoning
State Key Laboratory for Novel Software Technology, Nanjing University, China · Alibaba Group, China · Ant Group, China
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
AI scientists produce results without reasoning scientifically
Apr 20, 2026Martiño Ríos-García, Nawaf Alampara, Chandan Gupta +5AI Agent Reliability Scientific Reasoning
Laboratory of Organic and Macromolecular Chemistry (IOMC), Friedrich Schiller University Jena, Humboldstr. 10, 07743 Jena, Germany · Department of Civil Engineering, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India · School of Interdisciplinary Research, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India +3
Knowing When to Quit: A Principled Framework for Dynamic Abstention in LLM Reasoning
Apr 20, 2026Hen Davidov, Nachshon Cohen, Oren Kalinsky +4RL for Language Model Reasoning LLM Abstention
University of Oxford. Work done partly during an internship at amazon. · 2Amazon.
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
Process Reward Models Meet Planning: Generating Precise and Scalable Datasets for Step-Level Rewards
Apr 20, 2026Raffaele Pisano, Roberto NavigliProcess Reward Models Synthetic Data Generation
Babelscape · Sapienza University of Rome
SPREG: Structured Plan Repair with Entropy-Guided Test-Time Intervention for Large Language Model Reasoning
Apr 20, 2026Xuan Wang, Yu Ming, Xinhao Zhong +4LLM Self-Correction Classifier-Free Guidance
1Meituan
KnowledgeBerg: Evaluating Systematic Knowledge Coverage and Compositional Reasoning in Large Language Models
Apr 19, 2026Xiao Zhang, Qianru Meng, Yongjian Chen +2LLM Evaluation Compositional Reasoning
University of Groningen · LIACS, Leiden University
Beyond Fine-Tuning: In-Context Learning and Chain-of-Thought for Reasoned Distractor Generation
Apr 19, 2026Elaf Alhazmi, Quan Z. Sheng, Wei Emma ZhangLLM Prompting In-Context Learning
School of Computing, Macquarie University, Australia · College of Engineering and Computing in Al-Lith, Umm Al-Qura University, Saudi Arabia · School of Computer and Mathematical Sciences, Adelaide University, Australia
Self-Consistency from Only Two Samples: CoT-PoT Ensembling for Efficient LLM Reasoning
Apr 19, 2026Raman Saparkhan, Majd Hawasly, Md Rizwan Parvez +1LLM Inference Efficiency Self-Consistency Decoding
Carnegie Mellon University Qatar · Qatar Computing Research Institute

Apr 24, 2026J/K move · Enter open · S save

Thinking Without Words: Efficient Latent Reasoning with Abstract Chain-of-Thought

Keshav Ramji, Tahira Naseem, Ramón Fernandez Astudillo

IBM Research AI

Efficient Language Model Reasoning Implicit Reasoning in Language Models Efficient Language Model Inference Language Model Post-Training LLM Reasoning Latent Reasoning+1

Open paper AskPDF arXiv

While long, explicit chains-of-thought (CoT) have proven effective on complex reasoning tasks, they are costly to generate during inference. Non-verbal reasoning methods have emerged with shorter generation lengths by leveraging continuous representations, yet their performance lags behind verbalized CoT. We propose

\textbf{Abstract Chain-of-Thought}

, a discrete latent reasoning post-training mechanism in which the language model produces a short sequence of tokens from a reserved vocabulary in lieu of a natural language CoT, before generating a response. To make previously unseen ''abstract'' tokens useful, we introduce a policy iteration-style warm-up loop that alternates between (i.) bottlenecking from a verbal CoT via masking and performing supervised fine-tuning, and (ii.) self-distillation by training the model to generate abstract tokens from the prompt alone via constrained decoding with the codebook. After warm-up, we optimize the generation of abstract sequences with warm-started reinforcement learning under constrained decoding. Abstract-CoT achieves up to

11.6\times

fewer reasoning tokens while demonstrating comparable performance across mathematical reasoning, instruction-following, and multi-hop reasoning, and generalizes across language model families. We also find an emergent power law distribution over the abstract vocabulary, akin to those seen in natural language, that evolves across the training phases. Our findings highlight the potential for post-training latent reasoning mechanisms that enable efficient inference through a learned abstract reasoning language.