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

2026-03-05

AI Summary

This work addresses the challenges of long-form video understanding, which include high visual redundancy, extensive temporal spans, and the susceptibility of existing methods to cumulative semantic drift and relevance errors. To tackle these issues, the authors propose VideoHV-Agent, a novel framework that introduces a “hypothesis-verification” reasoning paradigm. Within this paradigm, a Thinker generates testable hypotheses, a Judge extracts discriminative evidence, a Verifier assesses this evidence against fine-grained local content, and an Answer module synthesizes verified clues into a final response. This multi-agent collaborative mechanism enables structured, interpretable reasoning while significantly enhancing logical rigor and reducing computational overhead. Experiments demonstrate state-of-the-art performance on three long-video question-answering benchmarks.

Technology Category

• Computer Vision: Video Understanding & Activity Analysis
• Knowledge Representation and Reasoning: Argumentation
• Reasoning under Uncertainty: Other Foundations of Reasoning under Uncertainty

Application Category

• Search and Retrieval-Augmented AI: Agentic search
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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.

Problem

• long video understanding
• semantic drift
• temporal dependencies
• visual redundancy
• reasoning errors

Innovation

• hypothesis-verification
• multi-agent framework
• long video understanding
• semantic drift mitigation
• structured reasoning