Vision-language models (VLMs) can ingest only a limited number of video frames, making frame selection a practical necessity. But do current Video QA benchmarks genuinely require temporal frame selection, or can most questions be answered regardless of which frames are shown? We introduce Frame Selection Sensitivity (FSS), a per-sample diagnostic that measures how much VLM accuracy changes when the most relevant frames are replaced with the least relevant ones. Across six benchmarks and eight VLMs, we find that a large majority of samples are frame-agnostic: only a minority are genuinely sensitive to frame choice. Combining FSS with a Language Independence Score (LIS) reveals that merely 5.5--31% of samples are Temporally Sensitive. We construct TempCore, compact evaluation subsets that isolate these temporal samples from existing benchmarks, and will release code and per-sample annotations upon publication.
SurgViVQA: Temporally-Grounded Video Question Answering for Surgical Scene Understanding
Dipartimento di Elettronica, Informazione e Bioingegneria (DEIB)（电子、信息与生物工程系）；Politecnico di Milano（米兰理工大学）；IRCCS Humanitas Research Hospital（IRCCS人类itas研究医院）；UCL Hawkes Institute and Department of Computer Science（UCL Hawkes研究所和计算机科学系）；University College London（伦敦大学学院）；University of Manchester（曼彻斯特大学）
Towards Temporal Compositional Reasoning in Long-Form Sports Videos
School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；MAIS, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所MAIS）
ChronoQG: Towards a Temporally Expressive and Hop-Bounded Benchmark for Temporal Knowledge Graph Question Generation
Nankai University（南开大学）；Peking University（北京大学）；University of Electronic Science and Technology of China（电子科技大学）；Shanghai University of Finance and Economics（上海财经大学）
MedFrameQA: A Multi-Image Medical VQA Benchmark for Clinical Reasoning
University of Pennsylvania（宾夕法尼亚大学）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；UC Santa Cruz（加州大学圣克鲁兹分校）；Harvard University（哈佛大学）；UC Berkeley（加州大学伯克利分校）；Emory University（埃默里大学）；UC San Francisco（旧金山加州大学）
MedScope: Incentivizing "Think with Videos" for Clinical Reasoning via Coarse-to-Fine Tool Calling
College of Health Science and Technology, Shanghai Jiao Tong University School of Medicine, Shanghai, China（上海交通大学医学院健康科学与技术学院）；Fudan University, Shanghai, China（复旦大学）；Shanghai Innovation Institute, Shanghai, China（上海创新研究院）；Tsinghua University, Beijing, China（清华大学）；Shanghai Artificial Intelligence Laboratory, Shanghai, China（上海人工智能实验室）；The Chinese University of Hong Kong, Hong Kong, China（香港中文大学）；Ruijin Hospital, Shanghai Jiaotong University, Shanghai, China（上海交通大学瑞金医院）
Czech Institute of Informatics, Robotics and Cybernetics at the Czech Technical University in Prague（布拉格捷克理工大学捷克信息学、机器人学与控制论研究所）；Inria, École normale supérieure, CNRS, PSL Research University（法国国家信息与自动化研究所、巴黎高等师范学院、法国国家科学研究中心、PSL大学）
本文提出了grounded video caption generation新任务，构建了GROC测试集和HowToGround训练集，并开发了VideoGround模型，通过统一视频字幕生成与目标定位，在时间一致的边界框基础上实现了该任务的最新技术水平。
MedStreamBench: A Time-Aware Benchmark for Streaming and Proactive Medical Video Understanding
College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；Department of Electrical and Computer Engineering, Carnegie Mellon University（卡内基梅隆大学电气与计算机工程系）
Large-scale Pre-training for Grounded Video Caption Generation
Czech Institute of Informatics, Robotics and Cybernetics at the Czech Technical University in Prague（捷克信息技术、机器人与自动化研究所（捷克技术大学））；Inria, École normale supérieure, CNRS, PSL Research University（法国国家信息与自动化研究所、法国高等师范学校、法国国家科学研究中心、巴黎-萨克勒大学）
GraphThinker: Reinforcing Temporally Grounded Video Reasoning with Event Graph Thinking
Queen Mary University of London（伦敦玛丽女王大学）；Samsung AI Centre Cambridge（剑桥三星人工智能中心）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Nanyang Technological University（南洋理工大学）
HeiCo-FOCUS: A Clinically Grounded Dataset for Long-Context Video Understanding
German Cancer Research Center (DKFZ) Heidelberg（德国癌症研究中心（DKFZ）海德堡）；Heidelberg University（海德堡大学）；National Center for Tumor Diseases (NCT)（国家肿瘤疾病中心（NCT））；Helmholtz Imaging（亥姆霍兹影像）；Stanford University（斯坦福大学）；University of Pennsylvania（宾夕法尼亚大学）
