Segmentation Vision-Language Models (VLMs) have significantly advanced grounded visual understanding, yet they remain prone to pixel-grounding hallucinations, producing masks for incorrect objects or for objects that are entirely absent. Existing evaluations rely almost entirely on text- or label-based perturbations, which check only whether the predicted mask matches the queried label. Such evaluations overlook the spatial footprint and severity of hallucination and therefore fail to reveal vision-driven hallucinations, which are more challenging and more prevalent. To address this gap, we formalize the task of Counterfactual Segmentation Reasoning (CSR), where a model must segment the referenced object in the factual image and abstain in its counterfactual counterpart. To support this task, we curate HalluSegBench, the first large-scale benchmark to diagnose referring and reasoning expression segmentation hallucinations using controlled visual counterfactuals, alongside new evaluation metrics that measure hallucination severity and disentangle vision- and language-driven failure modes. We further introduce RobustSeg, a segmentation VLM trained with counterfactual fine-tuning (CFT) to learn when to segment and when to abstain. Experimental results confirm RobustSeg reduces hallucinations by 30%, while improving segmentation performance on FP-RefCOCO(+/g).
ClinHallu: A Benchmark for Diagnosing Stage-Wise Hallucinations in Medical MLLM Reasoning
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；DAMO Academy, Alibaba Group（阿里巴巴达摩院）；Hupan Lab（湖畔实验室）；Zhejiang University（浙江大学）
Detecting Clinical Hallucinations in LVLMs via Counterfactual Visual Grounding Uncertainty
School of Intelligent Science and Technology, Nanjing University（南京大学智能科学与技术学院）；National Institute of Healthcare Data Science at Nanjing University（南京大学医疗数据科学国家研究院）；School of Biomedical Engineering, Nanjing University（南京大学生物医学工程学院）
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）
TwinICL: Diagnosing Multimodal In-Context Learning through Paired Counterfactuals
University of California, Los Angeles（加州大学洛杉矶分校）；National Taiwan University（国立台湾大学）；Texas A&M University（德克萨斯A&M大学）；NVIDIA AI Technology Center（英伟达AI技术中心）；Arena Intelligence Inc（Arena Intelligence公司）
AHA: Aligning Large Audio-Language Models for Reasoning Hallucinations via Counterfactual Hard Negatives
Arizona State University（亚利桑那州立大学）；Clemson University（克莱姆斯大学）；Washington University in St.Louis（圣路易斯华盛顿大学）；Rice University（Rice 大学）；Morgan Stanley（摩根大通）
Diagnosing Hallucination Risk in AI Surgical Decision-Support: A Sequential Framework for Sequential Validation
Orthopaedic Centre, The University of Hong Kong - Shenzhen Hospital（香港大学深圳医院骨科中心）；Translational Medicine Centre, The University of Hong Kong - Shenzhen Hospital（香港大学深圳医院转化医学中心）；Department of Orthopaedics & Traumatology, Li Ka Shing Faculty of Medicine, The University of Hong Kong（香港大学李嘉诚医学院骨科与创伤外科部）
CDH-Bench: A Commonsense-Driven Hallucination Benchmark for Evaluating Visual Fidelity in Vision-Language Models
Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies, Institute of Cyberspace Security, School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen（广东省新型安全智能技术重点实验室，网络空间安全研究院，计算机科学与技术学院，哈尔滨工业大学（深圳））
CheXPO: Preference Optimization for Chest X-ray VLMs with Counterfactual Rationale
The Key Laboratory of Smart Human-Computer Interaction（智能人机交互关键实验室）；Wearable Technology of Shaanxi Province, Xidian University, Xi'an, China（陕西 wearable 技术, 西安电子科技大学, 中国）；Nanjing University of Science and Technology（南京理工大学）
