Visual Attention Reasoning via Hierarchical Search and Self-Verification
Wei Cai, Jian Zhao, Yuchen Yuan, Tianle Zhang, Ming Zhu, Haichuan Tang, Xuelong Li
备注：
The paper is withdrawn by the authors after discovering a flaw in the theoretical derivation presented in the Method section. This incorrect step leads to conclusions that are not supported by the corrected derivation. The authors plan to reconstruct the argument and will release an updated version once the issue is fully resolved
Multimodal Large Language Models (MLLMs) frequently hallucinate due to their reliance on fragile, linear reasoning and weak visual grounding. We propose Visual Attention Reasoning (VAR), a reinforcement learning framework that reformulates reasoning as a hierarchical search with self-verification. VAR enforces traceable evidence grounding by generating explicit bounding boxes, guided by a novel reward function combining geometric precision and semantic sufficiency. Furthermore, it replaces linear Chain-of-Thought with a tree-search policy capable of backtracking to correct logical errors. Theoretical analysis validates the framework's reliability, and extensive experiments demonstrate that VAR significantly outperforms state-of-the-art methods on complex hallucination and safety benchmarks.
SVSR: A Self-Verification and Self-Rectification Paradigm for Multimodal Reasoning
South China Agricultural University（华南农业大学）；University of Glasgow（格拉斯哥大学）；University of Electronic Science and Technology of China（电子科技大学）；Monash University（莫纳什大学）；University of Science and Technology of China（中国科学技术大学）；National University of Defense Technology（国防科技大学）；Renmin University of China（中国人民大学）；South China Normal University（华南师范大学）
A Closer Look at the Self-Verification Abilities of Large Language Models in Logical Reasoning
Institute for Artificial Intelligence, Tsinghua University（清华大学人工智能研究院）；Beijing National Research Center for Information Science and Technology（北京信息科学与技术国家研究中心）；Tencent AI Lab（腾讯AI实验室）
Fact in Fragments: Deconstructing Complex Claims via LLM-based Atomic Fact Extraction and Verification
Key Laboratory of Trustworthy Distributed Computing and Service (MoE)（可信分布式计算与服务重点实验室）；Beijing Academy of Artificial Intelligence（北京人工智能研究院）；Jinan University（暨南大学）；Department of Gastric Surgery, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital(Fujian Branch of Fudan University Shanghai Cancer Center)（福建医科大学胃外科临床肿瘤学院、福建肿瘤医院（复旦大学上海肿瘤中心福建分部））
NCV: A Node-Wise Consistency Verification Approach for Low-Cost Structured Error Localization in LLM Reasoning
Beijing University of Posts and Telecommunications（北京邮电大学）；Shanghai Jiao Tong University（上海交通大学）；Ant Group（蚂蚁集团）；Tsinghua University（清华大学）；Inner Mongolia Research Institute of SJTU（内蒙古大学SJTU研究所）
RAG-Star: Enhancing Deliberative Reasoning with Retrieval Augmented Verification and Refinement
Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学高瓴人工智能学院）；Wuhan University of Science and Technology（武汉科技大学）；National University of Singapore（新加坡国立大学）；Northeastern University at Qinhuangdao（东北大学秦皇岛分校）；BOSS Zhipin（BOSS直聘）
Forward-Backward Reasoning in Large Language Models for Mathematical Verification
Southern University of Science and Technology（南方科技大学）；Hong Kong University of Science and Technology（香港科技大学）；Huawei Noah’s Ark Lab（华为诺亚方舟实验室）；Peking University（北京大学）
Yale University（耶鲁大学）；The Chinese University of Hong Kong（香港中文大学）；Northeastern University（东北大学）；Penn State University（宾夕法尼亚州立大学）
Numerical reasoning over hybrid data containing both textual and tabular content (e.g., financial reports) has recently attracted much attention in the NLP community. However, existing question answering (QA) benchmarks over hybrid data only include a single flat table in each document and thus lack examples of multi-step numerical reasoning across multiple hierarchical tables. To facilitate data analytical progress, we construct a new large-scale benchmark, MultiHiertt, with QA pairs over Multi Hierarchical Tabular and Textual data. MultiHiertt is built from a wealth of financial reports and …
Reflect to Inform: Boosting Multimodal Reasoning via Information-Gain-Driven Verification
University of Science and Technology of China（中国科学技术大学）；Huawei Foundation Model Department（华为基础模型部）；The Chinese University of Hong Kong（香港中文大学）；Hong Kong University of Science and Technology（香港科技大学）
THOR: A Theta-Gamma Hierarchical Oscillatory Reasoning Framework for Multi-hop QA
Suzhou Institute for Advanced Research, University of Science and Technology of China（中国科学技术大学苏州高等研究院）；School of Biomedical Engineering, Division of Life Sciences and Medicine, University of Science and Technology of China（中国科学技术大学生命科学与医学部生物医学工程学院）
