This paper considers the challenges Large Language Models (LLMs) face when reasoning over text that includes information involving uncertainty explicitly quantified via probability values. This type of reasoning is relevant to a variety of contexts ranging from everyday conversations to medical decision-making. Despite improvements in the mathematical reasoning capabilities of LLMs, they still exhibit significant difficulties when it comes to probabilistic reasoning. To deal with this problem, we introduce the Bayesian Linguistic Inference Dataset (BLInD), a new dataset specifically designed to test the probabilistic reasoning capabilities of LLMs. We use BLInD to find out the limitations of LLMs for tasks involving probabilistic reasoning. In addition, we present several prompting strategies that map the problem to different formal representations, including Python code, probabilistic algorithms, and probabilistic logical programming. We conclude by providing an evaluation of our methods on BLInD and an adaptation of a causal reasoning question-answering dataset. Our empirical results highlight the effectiveness of our proposed strategies for multiple LLMs.
Omni-R1: Towards the Unified Generative Paradigm for Multimodal Reasoning
The Hong Kong Polytechnic University（香港理工大学）；Singapore Management University（新加坡国立大学）；Shandong University（山东大学）；University of Science and Technology of China（中国科学技术大学）；Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳））
School of Informatics, Xiamen University, China（厦门大学信息学院）；WeChat AI, Tencent Inc, China（腾讯公司微信AI部门）；Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan (Xiamen University), Ministry of Culture and Tourism, China（福建省和台湾非物质文化遗产数字化保护与智能处理重点实验室）；Shanghai Artificial Intelligence Laboratory, China（上海人工智能实验室）
Generative Evaluation of Complex Reasoning in Large Language Models
Institute for Artificial Intelligence, Peking University（北京大学人工智能研究院）；Wangxuan Institute of Computer Technology, Peking University（北京大学王选计算机研究所）；Institute for AI Industry Research, Tsinghua University（清华大学人工智能产业研究院）；Computer Science Department, Stanford University（斯坦福大学计算机科学系）
Parameterized Argumentation-based Reasoning Tasks for Benchmarking Generative Language Models
Bernoulli Institute of Mathematics, Computer Science and Artificial Intelligence, University of Groningen（伯努利数学、计算机科学和人工智能研究所，格罗宁根大学）；Department of Information and Computing Sciences, Utrecht University（信息与计算科学系，乌得勒支大学）
Overview of PAN 2026: Voight-Kampff Generative AI Detection, Text Watermarking, Multi-Author Writing Style Analysis, Generative Plagiarism Detection, and Reasoning Trajectory Detection
Friedrich Schiller University Jena（耶纳弗里德里希·施勒格尔大学）；University of Applied Sciences BFI（应用科学大学BFI）；Mohamed bin Zayed University of Artificial Intelligence（马尔代夫 bin Zayed 人工智能大学）；University of Kassel（卡塞尔大学）；INSAIT, Sofia University ``St. Kliment Ohridski''（INSAIT，索菲亚大学『圣克莱门特·奥赫里迪斯』）；University of Innsbruck（因斯布鲁克大学）
PAN 2026旨在通过客观评估推进计算风格学和文本取证，涵盖生成式AI检测、文本水印、多作者写作分析、抄袭检测及推理轨迹检测等五个新旧任务。
Generative Data Augmentation for Commonsense Reasoning
Northwestern University（西北大学）；Allen Institute for Artificial Intelligence（艾伦人工智能研究所）；University of Pennsylvania（宾夕法尼亚大学）；Carnegie Mellon University（卡内基梅隆大学）；University of Washington（华盛顿大学）
PlanU: Large Language Model Reasoning through Planning under Uncertainty
Fujian Key Laboratory of Sensing and Computing for Smart Cities, School of Informatics, Xiamen University（福建智慧城市感知与计算重点实验室，信息学院，厦门大学）；Key Laboratory of Multimedia Trusted Perception and Efficient Computing, XMU（多媒体可信感知与高效计算重点实验室，XMU）；School of Computer, National University of Defense Technology（计算机学院，国防科技大学）
Partial Reasoning in Language Models: Search and Refinement Guided by Uncertainty
Advanced Knowledge Center for Immersive Technologies (AKCIT)（沉浸式技术高级知识中心）；Federal University of Goiás, Brazil（巴西戈亚斯联邦大学）；OATML, University of Oxford（牛津大学OATML）
Combinatorial Reasoning: Selecting Reasons in Generative AI Pipelines via Combinatorial Optimization
Icosa Computing Inc.（伊科萨计算公司）；NASA ARC（美国国家航空航天局艾姆斯研究中心）；USRA Research Institute for Advanced Computer Science (RIACS)（美国大学空间研究协会高级计算机科学研究所）；Hewlett Packard Labs（惠普实验室）；DataStax（DataStax公司）
本文提出完全自动化的组合推理框架 CR，将 LLM 流水线采样的理由建模为 QUBO 问题并选择子集构建思维链提示，实验表明组合优化可增强生成式 AI 推理。
RiddleBench: A New Generative Reasoning Benchmark for LLMs
Nilekani Centre at AI4Bharat（AI4Bharat的Nilekani中心）；Indian Institute of Technology Madras（印度理工学院马德拉斯分校）；IT University of Copenhagen（哥本哈根技术大学）；Microsoft（微软）；Indian Institute of Engineering, Science and Technology, Shibpur（Shibpur印度工程科学技术学院）
GRAM-R$^2$: Self-Training Generative Foundation Reward Models for Reward Reasoning
School of Computer Science and Engineering, Northeastern University（东北大学计算机科学与工程学院）；Pattern Recognition Center, WeChat AI, Tencent Inc.（腾讯公司微信AI模式识别中心）
Learning More by Training Less: Uncertainty-Guided Progressive Contraction for Efficient Reasoning Post-Training
National University of Singapore（新加坡国立大学）；Nanjing University（南京大学）；Fudan University（复旦大学）；The University of Sydney（悉尼大学）；The Chinese University of Hong Kong（香港中文大学）；Nanyang Technological University（南洋理工大学）
Generative Visual Commonsense Answering and Explaining with Generative Scene Graph Constructing
College of Artificial Intelligence, Nanjing University of Aeronautics and Astronautics（南京航空航天大学人工智能学院）；The Hong Kong Polytechnic University（香港理工大学）；The Chinese University of Hong Kong（香港中文大学）
Elucidating STEM Concepts through Generative AI: A Multi-modal Exploration of Analogical Reasoning
Carnegie Learning（卡内基学习公司）；University of Sheffield（谢菲尔德大学）；Microsoft Research（微软研究院）；University of Maryland, College Park（马里兰大学帕克分校）；University of Georgia（佐治亚大学）；New York University（纽约大学）；Carnegie Mellon University（卡内基梅隆大学）
FERA: Uncertainty-Aware Federated Reasoning for Large Language Models
Indiana University（印第安纳大学）；The University of New South Wales（新南威尔士大学）；The Chinese University of Hong Kong（香港中文大学）；University of California San Diego（加州大学圣地亚哥分校）；Adobe Research（Adobe研究院）
