先思考，再验证：面向长视频理解的假设验证多智能体框架
Think, Then Verify: A Hypothesis-Verification Multi-Agent Framework for Long Video Understanding
将长视频问答重构为「先提出可检验假设、再定位证据验证」的多智能体流程，为视觉语言模型处理长视频时抑制语义漂移和相关性错误提供了一种可复用的推理范式。
原文依据 This thinking-before-finding principle motivates VideoHV-Agent, a framework that reformulates video question answering as a structured hypothesis-verification process.
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论长视频理解面临密集视觉冗余、长程时序依赖，以及思维链和基于检索的智能体容易累积语义漂移与相关性驱动错误等挑战。我们认为长视频推理不应始于反应式检索，而应始于有意的任务形式化：模型必须首先阐明，对于每个候选答案，视频中必须成立什么。这一「先思考后查找」原则催生了 VideoHV-Agent，该框架将视频问答重构为结构化的假设验证过程。基于视频摘要，思考者（Thinker）将候选答案改写为可检验的假设，裁判（Judge）推导出判别性线索，指明必须核查哪些证据，验证者（Verifier）利用局部细粒度视频内容对线索进行定位与检验，答案智能体（Answer agent）整合已验证证据以生成最终答案。在三个长视频理解基准上的实验表明，VideoHV-Agent 在达到最优准确率的同时，提供了更强的可解释性、更高的逻辑严谨性以及更低的计算成本。
英文原文摘要 1156 字符
Long video understanding is challenging due to dense visual redundancy, long-range temporal dependencies, and the tendency of chain-of-thought and retrieval-based agents to accumulate semantic drift and correlation-driven errors. We argue that long-video reasoning should begin not with reactive retrieval, but with deliberate task formulation: the model must first articulate what must be true in the video for each candidate answer to hold. This thinking-before-finding principle motivates VideoHV-Agent, a framework that reformulates video question answering as a structured hypothesis-verification process.Based on video summaries, a Thinker rewrites answer candidates into testable hypotheses, a Judge derives a discriminative clue specifying what evidence must be checked, a Verifier grounds and tests the clue using localized, fine-grained video content, and an Answer agent integrates validated evidence to produce the final answer.Experiments on three long-video understanding benchmarks show that VideoHV-Agent achieves state-of-the-art accuracy while providing enhanced interpretability, improved logical soundness, and lower computational cost.