Hallucinations in vision language models (VLMs) are commonly treated as semantic errors, yet they often arise from partial or ambiguous visual evidence. Prior work mainly focuses on detecting or suppressing hallucinations at generation time, leaving the subsequent reasoning stage largely unexplored. In this work, we study Post Hallucination Reasoning (PHR), the stage in which hallucinated semantics enter the model's inference context and influence downstream predictions. To systematically investigate PHR, we introduce HIVE, Hallucination Inference and Verification Engine, an evaluation infrastructure that enables controlled comparisons between faithful and hallucinated captions. Across nine tasks and nine models, we observe structured modality dependent patterns: hallucinated captions often improve accuracy on vision language tasks, while text only tasks exhibit limited or unstable effects. Further analyses show that hallucinated cues broaden semantic coverage and reshape reasoning dynamics while preserving stable inference. These findings highlight that hallucinated semantics may influence downstream reasoning once they enter the model's inference context. Understanding this post hallucination stage is important for improving the reliability and interpretability of multimodal reasoning systems. Code is publicly available at https://github.com/hefengcs/HIVE.
Understanding the Role of Hallucination in Reinforcement Post-Training of Multimodal Reasoning Models
University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；University of Science and Technology of China（中国科学技术大学）；Arizona State University（亚利桑那州立大学）；Honda Research Institute, USA（本田美国研究所）
Train at Moving Edge: Online-Verified Prompt Selection for Efficient RL Training of Large Reasoning Model
Southern University of Science and Technology（南方科技大学）；The Hong Kong Polytechnic University（香港理工大学）；The Hong Kong University of Science and Technology（香港科学理工大学）；Nanyang Technological University（南洋理工大学）；Rutgers University（罗格斯大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科学理工大学（广州））
VIB-Probe: Detecting and Mitigating Hallucinations in Vision-Language Models via Variational Information Bottleneck
College of Computer Science and Artificial Intelligence, Fudan University, Shanghai, China（复旦大学计算机科学与人工智能学院，上海，中国）；Shanghai Key Laboratory of Intelligent Information Processing（上海智能信息处理重点实验室）
HalluClear: Diagnosing, Evaluating and Mitigating Hallucinations in GUI Agents
MAIS&NLPR, Institute of Automation, Chinese Academy of Sciences（自动化研究所人工智能与自然语言处理实验室，中国科学院）；School of Artificial Intelligence, UCAS（中国科学院大学人工智能学院）；Meituan（美团）
Overconfidence and Calibration in Medical VQA: Empirical Findings and Hallucination-Aware Mitigation
Johns Hopkins University, School of Medicine（约翰霍普金斯大学医学院）；Massachusetts Institute of Technology（麻省理工学院）；Microsoft Healthcare & Life Sciences（微软医疗健康与生命科学）
How to Detect and Defeat Molecular Mirage: A Metric-Driven Benchmark for Hallucination in LLM-based Molecular Comprehension
Shenzhen Graduate School, Peking University（北京大学深圳研究生院）；International Digital Economy Academy (IDEA)（国际数字经济学院（IDEA））；Pengcheng Laboratory（鹏城实验室）；Hong Kong University of Science and Technology（香港科技大学）
Hallucination Detection and Correction in Medical VLMs via Counter-Evidence Verification
College of Computer Science, Sichuan University（四川大学计算机科学学院）；Yong Loo Lin School of Medicine, National University of Singapore（新加坡国立大学杨潞龄医学院）；Key Laboratory of Data Protection and Intelligent Management, Ministry of Education, Sichuan University（四川大学数据保护与智能管理教育部重点实验室）；National Key Laboratory of Autonomous Intelligent Unmanned Systems, Beijing Institute of Technology（北京理工大学自主智能无人系统国家重点实验室）；Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局高性能计算研究所）
Do I Really Know? Learning Factual Self-Verification for Hallucination Reduction
Qatar Computing Research Institute (QCRI), Hamad Bin Khalifa University (HBKU), Doha, Qatar. \,The authors contributed equally. \,The authors jointly supervised the work
HaloProbe: Bayesian Detection and Mitigation of Object Hallucinations in Vision-Language Models
Multimodal AI Lab, Technical University of Darmstadt（达姆施塔特工业大学多模态人工智能实验室）；Department of Computer Engineering, Sharif University of Technology（谢里夫理工大学计算机工程系）
Citation-Enhanced Generation for LLM-based Chatbots
Tsinghua University（清华大学）；Institute for AI Industry Research (AIR), Tsinghua University（清华大学人工智能产业研究院）；Jiangsu Collaborative Innovation Center for Language Competence（江苏省语言能力协同创新中心）
Place Matters: Comparing LLM Hallucination Rates for Place-Based Legal Queries
School of Computing and Information Systems, The University of Melbourne, Australia（计算与信息系统学院，墨尔本大学，澳大利亚）；The Centre for Artificial Intelligence and Digital Ethics（人工智能与数字伦理中心）；Melbourne Law School, The University of Melbourne, Australia（墨尔本法学院，墨尔本大学，澳大利亚）
