提出视频浏览智能体，高效处理网页视频信息探索。
Video-Browser: Towards Agentic Open-web Video Browsing
- 分层感知策略：先用元数据筛选，必要时才调用高成本视觉分析。
- 相比直接视觉推理，准确率提升37.5%，令牌消耗降低58.3%。
- 适合需要精准视频内容理解的研究者和开发者。
自主智能体正推动信息获取从被动检索转向主动、开放式的网络研究。然而，网页中最动态且信息密集的模态——视频，仍面临显著的模态鸿沟。本文首次形式化了「智能体视频浏览」任务，并引入 Video-BrowseComp 基准，评估需强制依赖视频的开放式智能体浏览任务。我们发现，现有方法难以兼顾大规模视频探索与细粒度视觉验证的需求：直接视觉推理（如 RAG）虽增强感知能力，但上下文开销过高；文本摘要虽高效，却常遗漏关键视觉细节。为此，我们提出 Video-Browser，采用金字塔式感知机制，通过廉价元数据过滤，仅在必要时调用昂贵的视觉感知。实验表明，该方法相较直接视觉推理实现 37.5% 的相对性能提升，同时将令牌消耗降低 58.3%，为可验证的开放网络视频研究奠定基础。代码与基准已开源。
原文摘要 · Abstract (English)
The evolution of autonomous agents is redefining information seeking, transitioning from passive retrieval to proactive, open-ended web research. However, a significant modality gap remains in processing the web's most dynamic and information-dense modality: video. In this paper, we first formalize the task of Agentic Video Browsing and introduce Video-BrowseComp, a benchmark evaluating open-ended agentic browsing tasks that enforce a mandatory dependency on videos. We observe that current paradigms struggle to reconcile the scale of open-ended video exploration with the need for fine-grained visual verification. Direct visual inference (e.g., RAG) maximizes perception but incurs prohibitive context costs, while text-centric summarization optimizes efficiency but often misses critical visual details required for accurate grounding. To address this, we propose Video-Browser, a novel agent leveraging Pyramidal Perception, filtering with cheap metadata and zooming in with expensive visual perception only when necessary. Experiments demonstrate that our approach achieves a 37.5% relative improvement while reducing token consumption by 58.3% compared to Direct visual inference, establishing a foundation for verifiable open-web video research. We open-source all codes, benchmark at {https://anonymous.4open.science/r/VideoBrowser} and {https://github.com/chrisx599/Video-Browser}.
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