Streaming video understanding often involves time-sensitive scenarios where models need to answer exactly when the supporting visual evidence appears: answering before the evidence reflects speculation, answering after it has passed reduces real-time utility. To capture this behavior, we introduce a readiness-aware formulation of streaming video understanding with the Answer Readiness Score (ARS), a timing-aware objective with asymmetric early and late penalties. When combined with correctness, ARS defines an effective accuracy that measures not just whether a model is right, but whether it answers at the appropriate moment. Building on this formulation, we introduce StreamReady, a framework to unify temporal reasoning with on-time answering through a lightweight readiness mechanism that decides if sufficient evidence has been observed before responding. To evaluate this capability, we further introduce ProReady-QA, a benchmark with annotated answer evidence windows and proactive multi-turn questions across local and global contexts. StreamReady achieves superior performance on ProReady-QA, and consistently outperforms prior methods across eight additional streaming and offline long-video benchmarks, demonstrating robust and broadly generalizable video understanding capability.
StreamingCoT: A Dataset for Temporal Dynamics and Multimodal Chain-of-Thought Reasoning in Streaming VideoQA
Henan Institute of Advanced Technology, Zhengzhou University（河南高级技术研究所，郑州大学）；Institute of Automation, CAS（自动化研究所，中国科学院）；UCAS（中国科学院大学）；Peng Cheng Laboratory（鹏城实验室）
Dynamic Hub-and-Spoke Memory for Streaming Video Understanding
Northeastern University（东北大学）；University of Alabama at Birmingham（阿拉巴马大学伯明翰分校）；Tulane University（杜兰大学）；University of Virginia（弗吉尼亚大学）；Adobe Research（奥多比研究院）
StreamArena: Toward Continuous, Interactive, and Long-Horizon Agentic Streaming Video Understanding
The Hong Kong University of Science and Technology（香港科技大学）；Xiaohongshu Inc.（小红书公司）；The University of Hong Kong（香港大学）；The Chinese University of Hong Kong（香港中文大学）
SPARE: Shared Perception and Adaptive Retrieval of Evidence for Streaming Video Understanding
Xiamen University（厦门大学）；Beijing Institute of Technology（北京理工大学）；Fudan University（复旦大学）；Shanghai Jiao Tong University（上海交通大学）；University of Hong Kong（香港大学）；China Telecom（中国电信）
SVBench: A Benchmark with Temporal Multi-Turn Dialogues for Streaming Video Understanding
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Kuaishou Technology（快手科技）；Zhengzhou University（郑州大学）；ShanghaiTech University（上海科技大学）；Peng Cheng Laboratory（鹏城实验室）
StreamScout: Learning When to Look Deeper for Streaming Video Understanding
Carnegie Mellon University（卡内基梅隆大学）；University of Rochester（罗切斯特大学）；Northwestern University（西北大学）；University of California, San Diego（加利福尼亚大学圣迭戈分校）；TikTok（字节跳动旗下TikTok）
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）
LiveVLM: Efficient Online Video Understanding via Streaming-Oriented KV Cache and Retrieval
School of Computer Science, Shanghai Jiao Tong University（上海交通大学计算机科学学院）；College of Intelligent Robotics and Advanced Manufacturing, Fudan University（复旦大学智能机器人与先进制造学院）；Guizhou Provincial Laboratory of Big Data, College of Computer Science and Technology, Guizhou University（贵州大数据省实验室，贵州大学计算机科学与技术学院）
StreamOV: Streaming Omni-Video Understanding via Evidence-Guided Memory and Response Triggering
Shanghai Innovation Institute（上海创新研究院）；Fudan University（复旦大学）；Nanjing University（南京大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Huazhong University of Science and Technology（华中科技大学）
Learning Streaming Video Representation via Multitask Training
School of Artificial Intelligence, Shanghai Jiao Tong University（上海交通大学人工智能学院）；Shanghai Innovation Institute（上海创新研究院）；Fudan University（复旦大学）；Shanghai AI Laboratory（上海人工智能实验室）
Scaling the Long Video Understanding of Multimodal Large Language Models via Visual Memory Mechanism
Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University（厦门大学多媒体可信感知与高效计算教育部重点实验室）；National University of Defense Technology（国防科技大学）
Eyes Wide Open: Ego Proactive Video-LLM for Streaming Video
ShanghaiTech University（上海科技大学）；School of Computer Science and Engineering, Sun Yat-sen University（中山大学计算机科学与工程学院）；School of Computing and Data Science, The University of Hong Kong（香港大学计算科学与数据科学学院）
APM-Bench: Benchmarking Cross-session Persistent Memory for Egocentric Streaming Video Assistants
Shanghai Jiao Tong University（上海交通大学）；Eastern Institute of Technology, Ningbo（宁波东方理工大学）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Zhongguancun Academy（中关村学院）；Dalian University of Technology（大连理工大学）；Beihang University（北京航空航天大学）；Hong Kong Polytechnic University（香港理工大学）
