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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NOLLI: A Difficulty-Calibrated Puzzle Benchmark for Diagnosing the English-Korean Performance Gap
Aug 5, 2026Dasol Choi, Joonyong Park, Daegon Yu +3Multilingual Language Model Evaluation Benchmark Validity
AIM Intelligence · HAERAE LAB · KT Corp. +3
Test-Time Scaling in Reasoning LLMs: Inference Regimes, Evaluation, and Reproducibility
Aug 4, 2026Mohsen Hariri, Weicong Chen, Nahal Shahini +11LLM Evaluation Test-Time Scaling
Case Western Reserve University
LoopMTP: A looped transformer guided by latent multi-token prediction
Aug 4, 2026Behzad Shomali, Markus Frey, David Berghaus +2Multi-Token Prediction Recurrent Transformers
Lamarr Institute · University of Bonn · Fraunhofer IAIS
GradCuit: Credit-Assigned Gradient Flow Enables Robust and Interpretable Test-Time Latent Reasoning
Aug 3, 2026Zhaoxin Yu, Qi Shen, Hengli Li +4Test-Time Optimization LLM Interpretability
NLCo Lab, Beijing Institute for General Artificial Intelligence · School of Artificial Intelligence, Beijing University of Posts and Telecommunications · School of Artificial Intelligence for Science, Peking University
Beyond the Mean: Multi-Moment Policy Optimization for LLM Reasoning
Aug 3, 2026Yijun Zhang, Yule Xie, Jiaxin Ding +4RL for Language Model Reasoning Policy Optimization
Shanghai Jiao Tong University
HPFA: Hypergraph-Based Paired Failure Attribution for LLM Reasoning
Aug 3, 2026Runchuan Zhu, Hongbin Lai, Bowen Jiang +4LLM Reasoning Reasoning Trace Analysis
National University of Singapore · Peking University · University of Texas at Austin +1
AdaThinkV: Adaptive Thinking for Token-Efficient Video Reasoning
Aug 3, 2026Jingqi Tian, Haoji Zhang, Lin Chen +7Efficient VLM Inference RL for Language Model Reasoning
Tsinghua Shenzhen International Graduate School, Tsinghua University · Alibaba Group · Peking University
Does Accuracy Equal Evidence? Reasoning Faithfulness under KV Cache Compression
Aug 3, 2026Mengting Ai, Jingrui He, Yue GuoCoT Faithfulness Reasoning Evaluation
University of Illinois Urbana-Champaign
Recursive Vision Language Models for General Symbolic Reasoning
Aug 2, 2026Omid Nejati Manzari, Guillaume Lajoie, Hassan RivazSymbolic Reasoning Implicit Reasoning in Language Models
Concordia University, Montreal, Canada · Mila — Quebec AI Institute · Université de Montréal
Cognitive Demand Steering for Adaptive Meta-Reasoning in Large Language Models
Aug 2, 2026John Scoville, Shengzhuang Chen, Yejin Bang +2Metacognition in Language Models LLM Reasoning
Thomson Reuters Foundational Research · Imperial College London
BLADE: Boundary-Expanded and Layer-Adaptive Dynamic Exit for Efficient LLM Reasoning
Jul 31, 2026Keshu Fu, Keqin Peng, Jun Bai +6Efficient Language Model Reasoning Efficient Language Model Inference
School of Computer Science and Engineering, Beihang University · Beijing Institute for General Artificial Intelligence, BIGAI · Academy for Advanced Interdisciplinary Studies, Peking University +1
Would You Walk to the Car Wash? Revealing the Salience Bias of Large Language Models in Commonsense Reasoning
Jul 30, 2026Zheng Wu, Chenhao Xue, Shijie Zheng +3Implicit Reasoning in Language Models LLM Reasoning
School of Computer Science, Shanghai Jiao Tong University · ByteDance Inc
Lightning OPD 2.0: Mitigating Style Bias in Cross-Teacher On-Policy Distillation for Large Reasoning Models
Jul 30, 2026Yecheng Wu, Song Han, Han CaiLanguage Model Distillation On-Policy Distillation
NVIDIA
Not All Tokens Deserve Equal Credit: Counterfactual Sensitivity Credit Reallocation for Long-CoT Reasoning
Jul 30, 2026Qiangqiang He, Zhongheng Wu, ZiJian WangToken-Level Credit Assignment RL for Language Model Reasoning
State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China · Institute of Wireless Communications Technology, Shanghai Jiao Tong University, Shanghai, China
ARES: Adaptive Reasoning-Effort Steering for PPA- and Cost-Aware RTL Optimization with LLM Agents
Jul 30, 2026Stef Cuyckens, Mihaela Jivanescu, Jun Yin +2Inference-Time Optimization Large Language Model-Guided Optimization
KU Leuven Leuven, Belgium · Nokia Bell Labs Antwerp, Belgium
LoRA Scaffolded Policy Optimization (LSPO): A Sampling-Time Low-Rank Scaffold for Recovering Reinforcement-Learning Gradient on Zero-Reward Cliff Prompts
Jul 30, 2026Ken DingReinforcement Learning RL for Language Model Reasoning
NVIDIA
Reasoning Consensus: Structural Ensembling of LLM Reasoning via Weighted DAG Aggregation
Jul 30, 2026Amruta Parulekar, Jinu Lee, Dilek Hakkani-Tür +1LLM Reasoning with Graphs LLM Interpretability
University of Illinois Urbana-Champaign
Strategy, Not Payoffs: A Behavioural Embedding of Normal-Form Games
Jul 30, 2026Joshua Caiata, Sreepriya Pulyassary, Xiang Li +1LLM Fine-Tuning Strategic Reasoning Risks in Language Models
University of Waterloo
Benchmarking LLM Competence on Logical Inference over Probability Operators
Jul 29, 2026Nayera Hasan, Jack Greff, Alvin GrissomProbabilistic Logic LLM Evaluation
Haverford College
ReCo: Reweighting GRPO Against Distributional Concentration
Jul 29, 2026Junoh Park, Junseo Hwang, Wonguk Cho +1Reasoning Diversity RL for Language Model Reasoning
Graduate School of Data Science, Seoul National University
Knowledge before Reasoning: EC-Reason-Bench, a Training-Free Diagnostic Benchmark for LLM Enzyme Classification
Jul 29, 2026Linyu Li, Zhi Jin, Yichi Zhang +6Retrieval-Augmented Generation LLM Evaluation
School of Computer Science, Peking University, Beijing, China · College of Computer Science and Technology, Zhejiang University, Hangzhou, China · School of Computer Science, Tibet University, Lhasa, China
Do Diagrams Help Large Language Models Reason? Evidence from Syllogistic Reasoning
Jul 26, 2026Risako Ando, Koji MineshimaLLM Evaluation LLM Reasoning
Keio University, 2-15-45 Mita, Minato-ku, Tokyo 108-8345, Japan
Reasoning or Memorization: Can LLMs Understand and Generate Chinese Xiehouyu Riddles?
Jul 26, 2026Hai Hu, Siyuan Song, Chongtian Shao +3LLM Evaluation Memorization in Language Models
1The Hong Kong Polytechnic University · 2The University of Texas at Austin · 3Shanghai Jiao Tong University
Confidently Wrong: Exception Chain Collapse in Frontier LLM Rule Evaluation
Jul 25, 2026Paul Simpson, John Kozak, Lisa DoakeLLM Reliability Neuro-Symbolic AI
Aethis (aethis.ai)
SymStep: Symbolic Step Verification for Logical Reasoning
Jul 25, 2026Aida Usmanova, Rui Gao, Dilshod Azizov +2Symbolic Reasoning Structured Reasoning
Leuphana University of Lüneburg · Mohamed bin Zayed University of Artificial Intelligence
J-CoT: Chain-of-Thought in J-Space
Jul 24, 2026Junde Wu, Jiayuan Zhu, Fengling Liu +2LLM Reasoning Latent Reasoning
University of Oxford · Stanford University
Semiotic logical hexagon theory for LLM logical reasoning
Jul 24, 2026Yunyao Zhang, Xinglang Zhang, Zeliang Chen +2Logical Reasoning LLM Reliability
aHuazhong University of Science and Technology, , Wuhan, 430074, Hubei, China
Euclid-MCP: A Model Context Protocol Server for Deterministic Logical Reasoning via Prolog
Jul 23, 2026Bartolomeo BoglioloLogic Programming LLM Reliability
AdaKP: Online Adaptive Knowledge-Point Selection for Reasoning-Oriented Reinforcement Learning
Jul 23, 2026Zibin Meng, Zhenyu Zhao, Chunqiang RunKnowledge Augmentation for Language Models RL for Language Model Reasoning
The Hong Kong University of Science and Technology · University of the Chinese Academy of Sciences
Notes to Self: Can LLMs Benefit from Experiential Abstractions?
Jul 22, 2026Chang Liu, Xinyu Li, Artur DubrawskiKnowledge Augmentation for Language Models LLM Reasoning
Auton Lab, Carnegie Mellon University

Aug 5, 2026, cs.CLJ/K move · Enter open · S save

NOLLI: A Difficulty-Calibrated Puzzle Benchmark for Diagnosing the English-Korean Performance Gap

Dasol Choi, Joonyong Park, Daegon Yu, Soo Yong Kim+2

AIM Intelligence · HAERAE LAB · KT Corp.+3

Multilingual Language Model Evaluation Benchmark Validity LLM Reasoning Cross-Lingual Transfer

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We introduce NOLLI, a procedurally generated English-Korean puzzle benchmark designed to diagnose where Korean performance gaps arise. It comprises 15 puzzle types (25 tasks; 7,500 items), with every instance seed-regenerable, verified to have a unique solution, and scored deterministically. Rather than equating harder with bigger, we calibrate difficulty behaviorally, tuning each generator until a fixed reference model lands in target accuracy bands. Its three-level design combines matched direct translations, script adaptations over Hangul jamo (sub-syllabic letters), and Korean-only tasks grounded in Korean culture or orthography. We evaluate 15 frontier, open-weight, and Korean-developed models; among the 12 above a 3% overall-accuracy floor, matched English-Korean accuracy is statistically equivalent within a +/- 10 pp margin (TOST), suggesting little cost from presentation language alone. Writing-system-intensive tasks show sharper gaps: Korean Cipher falls behind English by up to 68.7 pp, whereas Cryptarithmetic over the same jamo shows no systematic penalty, and Jamo Composition accuracy predicts Korean Cipher accuracy. These contrasts are diagnostic rather than causal, consistent with difficulty in multi-step sub-syllabic execution. Korean-only tasks separate rule-application deficits, which vary in sign, from a Kinship deficit positive in all 12. Finally, a salient size measure fails to grow from Easy to Hard in 7 of 15 types, making structural size an unreliable proxy for empirical difficulty.

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