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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SCRuB: Social Concept Reasoning under Rubric-Based Evaluation
May 7, 2026Jamelle Watson-Daniels, Himaghna Bhattacharjee, Skyler Wang +11LLM Evaluation Rubric-Based Evaluation
1Meta · 2McGill University · 3Handshake AI +1
Measuring Black-Box Confidence via Reasoning Trajectories: Geometry, Coverage, and Verbalization
May 7, 2026Marc Boubnovski Martell, Josefa Lia Stoisser, Kaspar Märtens +4Confidence Estimation in Language Models CoT Reasoning
1Novo Nordisk · University of Oxford
Rethinking RL for LLM Reasoning: It's Sparse Policy Selection, Not Capability Learning
May 7, 2026Ömer Faruk Akgül, Rajgopal Kannan, Willie Neiswanger +1RL for Language Model Reasoning Efficient Language Model Reasoning
University of Southern California · 2DEVCOM ARL
Post-Reasoning: Improving the Performance of Non-Thinking Models at No Cost
May 7, 2026Richmond Sin Jing Xuan, Rishabh Bhardwaj, Soujanya PoriaLLM Prompting Language Model Post-Training
Nanyang Technological University
Novelty-based Tree-of-Thought Search for LLM Reasoning and Planning
May 7, 2026Leon Hamm, Zlatan AjanovicNovelty Search Tree of Thoughts
1RWTH Aachen
Logic-Regularized Verifier Elicits Reasoning from LLMs
May 7, 2026Xinyu Wang, Changzhi Sun, Lian Cheng +4LLM Evaluation LLM Answer Verification
Department of Computer Science and Technology, East China Normal University · Institute of Artificial Intelligence (TeleAI), China Telecom
Hypothesis generation and updating in large language models
May 7, 2026Hua-Dong XiongLogical Reasoning Bayesian Inference
School of Psychological and Brain Sciences Georgia Tech
ReFlect: An Effective Harness System for Complex Long-Horizon LLM Reasoning
May 7, 2026Fan HuangLLM Self-Correction LLM Reliability
Indiana University Bloomington
Decodable but Not Corrected by Fixed Residual-Stream Linear Steering: Evidence from Medical LLM Failure Regimes
May 7, 2026Ming LiuLanguage Model Steering Medical LLMs
Amazon
Formalizing Latent Thoughts: Four Axioms of Thought Representation in LLMs
May 7, 2026Fahd Seddik, Fatemeh FardLLM Evaluation LLM Interpretability
Department of Computer Science University of British Columbia Kelowna, BC, Canada
G-SHARE: A Guideline-Based Structured Reasoning Framework for Human-Factor Event Diagnosis
May 7, 2026Xingyu Xiao, Mao Du, Jiejuan Tong +2Structured Reasoning LLM Reasoning
Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing, 100084, China · Fujian Fuqing Nuclear Power Co., Ltd., Fujian, 350318, China · National Key Laboratory of Human Factors Engineering, Beijing, 100094, China
Agentic Retrieval-Augmented Generation for Financial Document Question Answering
May 6, 2026Yang Shu, Yingmin Liu, Zequn XieFinancial QA Agentic RAG
College of Computer Science and Technology, Zhejiang University, Hangzhou, 310027, China
How Does Thinking Mode Change LLM Moral Judgments? A Controlled Instant-vs-Thinking Comparison Across Five Frontier Models
May 6, 2026Sai Sourabh MadurLLM Evaluation Moral Reasoning in Language Models
Independent researcher (ex-Meta)
NoisyCausal: A Benchmark for Evaluating Causal Reasoning Under Structured Noise
May 5, 2026Zhi Xu, Yun FuStructural Causal Models Causal Reasoning in Language Models
Northeastern University
The Counterexample Game: Iterated Conceptual Analysis and Repair in Language Models
May 5, 2026Daniel Drucker, Kyle MahowaldLLM Self-Correction LLM Evaluation
Department of Philosophy The University of Texas at Austin · Department of Linguistics The University of Texas at Austin
SERE: Structural Example Retrieval for Enhancing LLMs in Event Causality Identification
May 5, 2026Zhifeng Hao, Zhongjie Chen, Junhao Lu +5Causal Reasoning in Language Models Causal Reasoning
School of Computer Science, Guangdong University of Technology · College of Mathematics and Computer, Shantou University · Huawei Noah’s Ark Lab +1
When to Think, When to Speak: Learning Disclosure Policies for LLM Reasoning
May 5, 2026Jiaqi Wei, Xuehang Guo, Pengfei Yu +5LLM Serving LLM Reasoning
Zhejiang University · College of William and Mary · University of Illinois Urbana-Champaign +4
Do LLMs have core beliefs?
May 5, 2026Anna Sokol, Marianna B. Ganapini, Nitesh V. ChawlaCognitive Modeling LLM Reasoning
University of Notre Dame · University of North Carolina at Charlotte
Measuring AI Reasoning: A Guide for Researchers
May 4, 2026Munachiso Samuel Nwadike, Zangir Iklassov, Kareem Ali +2Reasoning Evaluation LLM Reasoning
MBZUAI, Department of Natural Language Processing · MBZUAI, Department of Machine Learning
The Model Knows, the Decoder Finds: Future Value Guided Particle Power Sampling
May 4, 2026Tu Nguyen, Matthieu Zimmer, Rasul Tutunov +2Language Model Decoding LLM Inference Acceleration
1Huawei Heisenberg Research Center · 2Huawei Noah’s Ark Lab · 3UCL Centre for Artificial Intelligence
The Reasoning Trap: An Information-Theoretic Bound on Closed-System Multi-Step LLM Reasoning
May 3, 2026Kwan Soo ShinReasoning Evaluation Evidence-Grounded Reasoning
PolymathMinds AI Lab
Evaluating LLMs on Large-Scale Graph Property Estimation via Random Walks
May 2, 2026Sunil Kumar Maurya, Xin LiuLLM Evaluation LLM Reasoning with Graphs
University of Tokyo Tokyo, Japan · AIST Tokyo, Japan
Verbal-R3: Verbal Reranker as the Missing Bridge between Retrieval and Reasoning
May 2, 2026Sangkwon Park, Donghun Kang, Jisoo Mok +1Retrieval-Augmented Generation LLM Reranking
Department of Electrical and Computer Engineering, Seoul National University · DGIST · Interdisciplinary Program in Artificial Intelligence, Seoul National University +1
Reasoning emerges from constrained inference manifolds in large language models
May 2, 2026Yanbiao Ma, Fei Luo, Linfeng Zhang +10LLM Inference LLM Interpretability
Renmin University of China · Tsinghua University · Xiaomi EV +1
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
When LLMs Stop Following Steps: A Diagnostic Study of Procedural Execution in Language Models
May 1, 2026Sailesh Panda, Pritam Kadasi, Abhishek Upperwal +1LLM Evaluation Instruction Following
Indian Institute of Technology Gandhinagar · Soket AI
Decouple before Integration: Test-time Synthesis of SFT and RLVR Task Vectors
May 1, 2026Chaohao Yuan, Chenghao Xiao, Yu Rong +2Task Arithmetic Language Model Merging
Department of Systems Engineering and Engineering Management, Chinese University of Hong Kong, Hong Kong, China · DAMO Academy, Alibaba Group, Hangzhou, China · Hupan Lab, Hangzhou, China +1
Impact of Task Phrasing on Presumptions in Large Language Models
May 1, 2026Kenneth J. K. OngPrompt Sensitivity LLM Prompting
AI.DA STC · ST Engineering, Singapore
Assert, don't describe: Linguistic features that shift LLM reasoning about animal welfare
Apr 30, 2026Jasmine Brazilek, Harper DunnMoral Reasoning in Language Models Language Model Post-Training
Compassion Aligned Machine Learning (CaML) · Independent researcher
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

May 7, 2026, cs.AIJ/K move · Enter open · S save

SCRuB: Social Concept Reasoning under Rubric-Based Evaluation

Jamelle Watson-Daniels, Himaghna Bhattacharjee, Skyler Wang, Brandon Handoko+10

1Meta · 2McGill University · 3Handshake AI+1

LLM Evaluation Rubric-Based Evaluation Social Reasoning LLM Reasoning

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While many studies of Large Language Model (LLM) reasoning capabilities emphasize mathematical or technical tasks, few address reasoning about social concepts: the abstract ideas shaping social norms, culture, and institutions. This understudied capability is essential for modern models acting as social agents, yet no systematic evaluation methodology targets it. We introduce SCRuB (Social Concept Reasoning under Rubric-Based Evaluation), a framework designed for this setting of task indeterminacy. Our goal is to measure the degree to which a model reasons about social concepts with the depth and critical rigor of a human expert. SCRuB proceeds in three phases: prompt construction from established sources, response generation by experts and models, and comparative evaluation using a five-dimensional critical thinking rubric. To enable generalization of the pipeline, we introduce a Panel of Disciplinary Perspectives ensemble validated against independent expert judges. We release SCRuBEval (n=4,711 evaluation prompts) and SCRuBAnnotations (300 expert-authored responses and 150 expert comparative judgments from 45 PhD-level scholars). Our results show that frontier models consistently outperform human experts across all five rubric dimensions. Across 1,170 pairwise comparisons, expert judges ranked a model response first in 80.8% of judgments and preferred model responses overall 74.4% of the time. Ultimately, this study provides the first expert-grounded demonstration of evaluation saturation for social concept reasoning: the single-turn exam-style format has reached its ceiling for models and humans alike.

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