Abstract
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. We make our code publicly available at: this https URL.
Abstract (translated)
长视频理解具有挑战性，原因在于其密集的视觉冗余、长时间的时序依赖关系以及基于检索和链式思考代理易于积累语义漂移和相关驱动错误的趋势。我们主张，针对长视频进行推理不应从反应式的检索开始，而应首先明确任务定义：模型必须先描述候选答案在视频中成立所必需满足的情况。这一“思考前寻找”的原则激发了VideoHV-Agent框架的诞生，该框架将视频问答问题重新构建为结构化的假设验证过程。基于视频摘要，“思考者”会将答案候选人重写成可检验的假设，“法官”则推导出一种区分性线索以明确必须检查哪些证据，“验证者”通过使用局部化、细致的视频内容来落实和测试该线索，而“回答者”则整合经过验证的证据以生成最终的答案。在三个长视频理解基准上的实验表明，VideoHV-Agent实现了最先进的准确率，并提供了增强的可解释性、改进了逻辑严谨性和降低了计算成本。我们的代码已公开发布在：[此链接](this https URL)。
URL
https://arxiv.org/abs/2603.04977
