Abstract
Video agentic models have advanced challenging video-language tasks. However, most agentic approaches still heavily rely on greedy parsing over densely sampled video frames, resulting in high computational cost. We present VideoSeek, a long-horizon video agent that leverages video logic flow to actively seek answer-critical evidence instead of exhaustively parsing the full video. This insight allows the model to use far fewer frames while maintaining, or even improving, its video understanding capability. VideoSeek operates in a think-act-observe loop with a well-designed toolkit for collecting multi-granular video observations. This design enables query-aware exploration over accumulated observations and supports practical video understanding and reasoning. Experiments on four challenging video understanding and reasoning benchmarks demonstrate that VideoSeek achieves strong accuracy while using far fewer frames than prior video agents and standalone LMMs. Notably, VideoSeek achieves a 10.2 absolute points improvement on LVBench over its base model, GPT-5, while using 93% fewer frames. Further analysis highlights the significance of leveraging video logic flow, strong reasoning capability, and the complementary roles of toolkit design.
Abstract (translated)
视频智能体模型在具有挑战性的视频-语言任务上取得了进展。然而，大多数智能体方法仍严重依赖对密集采样视频帧的贪婪式解析，导致计算成本高昂。本文提出VideoSeek，一个利用视频逻辑流主动寻找答案关键证据而非穷尽解析完整视频的长时序视频智能体。这一洞见使模型能够在保持甚至提升视频理解能力的同时，使用远少于以往的帧数。VideoSeek采用思考-行动-观察循环机制，并配备精心设计的工具包以收集多粒度视频观测。该设计支持对累积观测进行查询感知的探索，并适用于实际的视频理解与推理。在四个具有挑战性的视频理解与推理基准测试上的实验表明，VideoSeek在帧数远少于先前视频智能体及独立大语言模型的情况下，实现了强大的准确率。值得注意的是，VideoSeek在LVBench上相较于其基础模型GPT-5实现了10.2个绝对百分点的提升，而帧数减少93%。进一步分析凸显了利用视频逻辑流、强推理能力以及工具包互补设计的重要性。
URL
https://arxiv.org/abs/2603.20185
