DeltaStreamer 提出流式视频理解智能体框架
DeltaStreamer: Towards Human-Like Agentic Streaming Video Understanding
这篇论文做了个能边看视频边记笔记的智能体，回答问题前还会回头重看历史帧补证据，在 OVO-Bench 和 StreamingBench 上分数都超过了现有方法。
DeltaStreamer 是一个面向流式视频理解的智能体框架，模仿人类观察、记忆和调查的方式。它在查询到来前持续记录视觉变化到结构化记忆中，保留事件演变过程。查询到来后，由查询驱动规划器引导记忆检索和定向帧回放，找回此前遗漏的视觉细节。在 OVO-Bench 和 StreamingBench 上，该方法整体得分分别为 66.07% 和 59.53%，超过代表性流式视频理解方法。
DeltaStreamer: Towards Human-Like Agentic Streaming Video Understanding
We propose DeltaStreamer, a novel agentic framework for streaming video understanding inspired by how humans observe, remember, and investigate. Streaming video understanding requires a system to retain evidence before knowing which details future queries will need. Existing approaches often obscure event transitions and omit relevant details when compressing video history, and cannot recover missing visual evidence. DeltaStreamer addresses these limitations by combining change-aware memory with query-driven planning and targeted evidence gathering with replay. Before a query arrives, it continuously observes the stream and explicitly records visual changes in structured memory, preserving how events unfold rather than only what is present. When a query arrives, a query-driven planner identifies the required evidence and guides memory retrieval and targeted replay. By revisiting historical frames, the agent can recover visual details omitted during observation and ground its answer in evidence beyond the retained descriptions. This connects continuous observation with active investigation tailored to each question. Experiments on OVO-Bench and StreamingBench demonstrate improvements over representative streaming video understanding methods, with overall scores of 66.07% and 59.53%, respectively. Project page: https://bbbbchan.github.io/DeltaStreamer/
