Think, Then Verify: A Hypothesis-Verification Multi-Agent Framework for Long Video Understanding

• URL: http://arxiv.org/abs/2603.04977v1
• Date: Thu, 05 Mar 2026 09:16:07 GMT
• Title: Think, Then Verify: A Hypothesis-Verification Multi-Agent Framework for Long Video Understanding
• Authors: Zheng Wang, Haoran Chen, Haoxuan Qin, Zhipeng Wei, Tianwen Qian, Cong Bai,
• Abstract summary: VideoHV-Agent is a framework that reformulates video question answering as a structured hypothesis-verification process.<n>We show that VideoHV-Agent achieves state-of-the-art accuracy while providing enhanced interpretability, improved logical soundness, and lower computational cost.
• Score: 25.82963105515627
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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: https://github.com/Haorane/VideoHV-Agent.

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