The ability to distinguish subtle differences between visually similar images is essential for diverse domains such as industrial anomaly detection, medical imaging, and aerial surveillance. While comparative reasoning benchmarks for vision-language models (VLMs) have recently emerged, they primarily focus on images with large, salient differences and fail to capture the nuanced reasoning required for real-world applications. In this work, we introduce VLM-SubtleBench, a benchmark designed to evaluate VLMs on subtle comparative reasoning. Our benchmark covers ten difference types - Attribute, State, Emotion, Temporal, Spatial, Existence, Quantity, Quality, Viewpoint, and Action - and curate paired question-image sets reflecting these fine-grained variations. Unlike prior benchmarks restricted to natural image datasets, our benchmark spans diverse domains, including industrial, aerial, and medical imagery. Through extensive evaluation of both proprietary and open-source VLMs, we reveal systematic gaps between model and human performance across difference types and domains, and provide controlled analyses highlighting where VLMs' reasoning sharply deteriorates. Together, our benchmark and findings establish a foundation for advancing VLMs toward human-level comparative reasoning.
EasyLens: A Training-Free Plug-and-Play Subtle-Lesion Representation Amplifier for Medical Vision-Language Models
Jilin University（吉林大学）；School of Computer Science, The University of Sydney（悉尼大学计算机科学学院）；ByteDance（字节跳动）；Institute of Translational Medicine, Shanghai Jiao Tong University（上海交通大学转化医学研究院）
Max Planck Institute for Informatics（马克斯·普朗克院信息研究所）；Saarland Informatics Campus（萨尔兰州信息校园）；Saarland University（萨尔兰州大学）；The University of Edinburgh（爱丁堡大学）；Samsung AI Center, Cambridge（三星AI中心，剑桥）
Deep Expert Injection for Anchoring Retinal VLMs with Domain-Specific Knowledge
Beijing Institute of Technology, Beijing, China（北京理工大学）；National University of Singapore, Singapore（新加坡国立大学）；Tsinghua University, Beijing, China（清华大学）；The Hong Kong Polytechnic University, Hong Kong（香港理工大学）
BioMedVR: Confusion-Aware Mixture-of-Prompt Experts for Biomedical Visual Reprogramming
Guangdong Institute of Intelligence Science and Technology（广东智能科技研究院）；Zhejiang University（浙江大学）；Southeast University（东南大学）；The Hong Kong Polytechnic University（香港理工大学）；Institute of Science Tokyo（东京科学大学）；Shanghai Jiao Tong University（上海交通大学）
Discovering Failure Modes in Vision-Language Models using RL
Mila – Québec AI Institute（魁北克AI研究所）；Université de Montréal（蒙特利尔大学）；Michigan State University（密歇根州立大学）；McGill University（麦吉尔大学）；Canada CIFAR AI Chair（加拿大CIFAR人工智能主席）
Better Eyes, Better Thoughts: Why Vision Chain-of-Thought Fails in Medicine
Department of Computer Science, City University of Hong Kong (Dongguan)（香港城市大学（东莞）计算机科学系）；College of Computing and Data Science (CCDS), Nanyang Technological University（南洋理工大学计算与数据科学学院）；School of Computer Science, Northwestern Polytechnical University（西北工业大学计算机科学学院）
See-in-Pairs: Reference Image-Guided Comparative Vision-Language Models for Medical Diagnosis
Electrical and Computer Engineering Department, The University of British Columbia（不列颠哥伦比亚大学电气与计算机工程系）；Vector Institute（向量研究所）；ETH Zurich（苏黎世联邦理工学院）；Renmin University of China（中国人民大学）；National University of Singapore（新加坡国立大学）
"I See What You Did There": Can Large Vision-Language Models Understand Multimodal Puns?
Zhejiang University（浙江大学）；Beihang University（北京航空航天大学）；Palo Alto Networks；Hangzhou Dianzi University（杭州电子科技大学）；Ningbo Global Innovation Center, Zhejiang University（浙江大学宁波全球创新中心）；OPPO Research Institute（OPPO研究院）；Qilu University of Technology（齐鲁工业大学）
MMBU: A Massive Multi-modal Biomedical Understanding Benchmark to Probe the Perception Capabilities of Vision-Language Models
Stanford University（斯坦福大学）；University of Wisconsin–Madison（威斯康星大学麦迪逊分校）；Instituto Tecnológico de Monterrey（蒙特雷技术学院）；Monash University（墨尔本大学）；University of Cambridge（剑桥大学）；Shanghai Jiao Tong University（上海交通大学）；Shandong University（山东大学）
Tool-IQA: Augmenting Image Quality Assessment with Simple Tools
National University of Singapore（新加坡国立大学）；OPPO Research Institute（OPPO研究院）；Nanyang Technical University（南洋理工大学）；Duke University（杜克大学）；City University of Hong Kong（香港城市大学）；The Hong Kong Polytechnic University（香港理工大学）
Structured-Condensed Prompt Tuning in Vision-Language Models for Fine-grained Image Recognition
School of Information Science and Technology, Northwest University（西北大学信息科学与技术学院）；Laboratory of Intelligent Information Processing, Institute of Computing Technology（中国科学院计算技术研究所智能信息处理重点实验室）
Zero-shot segmentation of skin tumors in whole-slide images with vision-language foundation models
Instituto Universitario de Investigación en Tecnología Centrada en el Ser Humano (HUMAN-Tech), Universitat Politécnica de Valencia（人类中心技术大学研究机构（HUMAN-Tech）、西班牙巴塞罗那理工大学）；Artikode Intelligence S.L.（Artikode Intelligence公司）
