Symphony：受认知启发的多智能体系统用于长视频理解
Symphony: A Cognitively-Inspired Multi-Agent System for Long-Video Understanding
提出基于VLM的视觉定位方法评估视频片段与问题的相关性，并设计带反思的多智能体协作机制，可复用于长视频问答中的关键片段定位与多步推理。
原文依据 Symphony provides a VLM-based grounding approach to analyze LVU tasks and assess the relevance of video segments
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论尽管多模态大语言模型（MLLM）智能体发展迅速且应用广泛，它们在长视频理解（LVU）任务上仍面临困难，这类任务具有信息密度高和时序跨度长的特点。近期关于LVU智能体的研究表明，简单的任务分解与协作机制不足以应对长链推理任务。此外，通过基于嵌入的检索直接缩减时间上下文可能会丢失复杂问题的关键信息。本文提出Symphony，一种多智能体系统，以缓解这些局限。通过模拟人类认知模式，Symphony将LVU分解为细粒度子任务，并引入由反思增强的深度推理协作机制，有效提升了推理能力。此外，Symphony提供了一种基于视觉语言模型（VLM）的视觉定位方法，用于分析LVU任务并评估视频片段的相关性，显著增强了定位具有隐含意图和大时间跨度的复杂问题的能力。实验结果表明，Symphony在LVBench、LongVideoBench、VideoMME和MLVU上取得了最优性能，在LVBench上相比先前的最优方法提升了5.0%。代码见 https://github.com/Haiyang0226/Symphony。
英文原文摘要 1316 字符
Despite rapid developments and widespread applications of MLLM agents, they still struggle with long-form video understanding (LVU) tasks, which are characterized by high information density and extended temporal spans. Recent research on LVU agents demonstrates that simple task decomposition and collaboration mechanisms are insufficient for long-chain reasoning tasks. Moreover, directly reducing the time context through embedding-based retrieval may lose key information of complex problems. In this paper, we propose Symphony, a multi-agent system, to alleviate these limitations. By emulating human cognition patterns, Symphony decomposes LVU into fine-grained subtasks and incorporates a deep reasoning collaboration mechanism enhanced by reflection, effectively improving the reasoning capability. Additionally, Symphony provides a VLM-based grounding approach to analyze LVU tasks and assess the relevance of video segments, which significantly enhances the ability to locate complex problems with implicit intentions and large temporal spans. Experimental results show that Symphony achieves state-of-the-art performance on LVBench, LongVideoBench, VideoMME, and MLVU, with a 5.0% improvement over the prior state-of-the-art method on LVBench. Code is available at https://github.com/Haiyang0226/Symphony.