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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TRACES: Tagging Reasoning Steps for Adaptive Cost-Efficient Early-Stopping
Apr 22, 2026Yannis Belkhiter, Seshu Tirupathi, Giulio Zizzo +1LLM Inference Efficiency 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
Beyond Meta-Reasoning: Metacognitive Consolidation for Self-Improving LLM Reasoning
Apr 19, 2026Ziqing Zhuang, Linhai Zhang, Jiasheng Si +2Continual Learning for LLMs Meta-Learning
Southeast University · King’s College London · Qilu University of Technology (Shandong Academy of Sciences) +1
Efficient Test-Time Scaling via Temporal Reasoning Aggregation
Apr 19, 2026Jiakun Li, Xingwei He, Kefan Li +3Test-Time Scaling Efficient Language Model Inference
School of Computer Science and Engineering, Beihang University · 3Hangzhou Innovation Institute, Beihang University · 2Qingdao Research Institute, Beihang University
Probabilistic Programs of Thought
Apr 19, 2026Poorva Garg, Renato Lui Geh, Daniel Israel +3LLM-Based Program Synthesis Efficient Language Model Reasoning
University of California Los Angeles · Allen Institute for AI
Do LLMs Hold Their Values? MANTA: A Multi-Turn Adversarial Benchmark for Animal Welfare Reasoning
Apr 18, 2026Isabella Luong, Joyee Chen, Arturs Kanepajs +5LLM Evaluation Moral Reasoning in Language Models
SPAR · Compassion Aligned Machine Learning · NUS +2
MCPO: Mastery-Consolidated Policy Optimization for Large Reasoning Models
Apr 18, 2026Zhaokang Liao, Yingguo Gao, Yi Yang +2RL for Language Model Reasoning Group Relative Policy Optimization
Ant Group · Zhejiang University
The Cognitive Penalty: Ablating System 1 and System 2 Reasoning in Edge-Native SLMs for Decentralized Consensus
Apr 18, 2026Syed Muhammad Aqdas RizviSmall Language Models Adversarial Robustness of Language Models
Independent Researcher Karachi, Pakistan
Integrating Graphs, Large Language Models, and Agents: Reasoning and Retrieval
Apr 17, 2026Hamed Jelodar, Samita Bai, Mohammad Meymani +3LLM Reasoning with Graphs Graph Retrieval-Augmented Generation
Canadian Institute for Cybersecurity, Faculty of Computer Science, University of New Brunswick, Fredericton, NB E3B 9W4, Canada
DPrivBench: Benchmarking LLMs' Reasoning for Differential Privacy
Apr 17, 2026Erchi Wang, Pengrun Huang, Eli Chien +4Differential Privacy LLM Reasoning
Halıcıo˘glu Data Science Institute, UC San Diego · Department of Computer Science and Engineering, UC San Diego · Department of Electrical Engineering, National Taiwan University +2
Structured Abductive-Deductive-Inductive Reasoning for LLMs via Algebraic Invariants
Apr 17, 2026Sankalp Gilda, Shlok GildaLogical Reasoning Abductive Reasoning
DeepThought Solutions · Department of Computer Science · University of Florida

Apr 22, 2026, cs.CLJ/K move · Enter open · S save

TRACES: Tagging Reasoning Steps for Adaptive Cost-Efficient Early-Stopping

Yannis Belkhiter, Seshu Tirupathi, Giulio Zizzo, John D. Kelleher

IBM Research Europe · Trinity College Dublin · Dublin, Ireland+1

LLM Inference Efficiency Early Stopping Efficient Language Model Reasoning Efficient Language Model Inference LLM Reasoning Large Reasoning Models+1

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The field of Language Reasoning Models (LRMs) has been very active over the past few years with advances in training and inference techniques enabling LRMs to reason longer, and more accurately. However, a growing body of studies show that LRMs are still inefficient, over-generating verification and reflection steps. Additionally, the high-level role of each reasoning step and how different step types contribute to the generation of correct answers, is largely underexplored. To address this challenge, we introduce TRACES (Tagging of the Reasoning steps enabling Adaptive Cost-Efficient early-Stopping), a lightweight framework that tags reasoning steps in real-time, and enable adaptive, cost-efficient early stopping of large-language-model inferences. Building on this framework we monitor reasoning behaviors during inferences, and we find that LRMs tend to shift their reasoning behavior after reaching a correct answer. We demonstrate that the monitoring of the specific type of steps can produce effective interpretable early stopping criteria. We evaluate the TRACES framework on three mathematical reasoning benchmarks, namely, MATH500, GSM8K, AIME and two knowledge and reasoning benchmarks, MMLU and GPQA respectively. We achieve 20 to 50% token reduction while maintaining comparable accuracy to standard generation.

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