VideoSeek: Long-Horizon Video Agent with Tool-Guided Seeking
Jingyang Lin, Jialian Wu, Jiang Liu, Ximeng Sun, Ze Wang, Xiaodong Yu, Jiebo Luo, Zicheng Liu, Emad Barsoum

TL;DR
VideoSeek introduces a tool-guided, logic flow-based video agent that efficiently seeks critical evidence in videos, reducing computational costs while improving accuracy on reasoning benchmarks.
Contribution
It presents a novel long-horizon video agent that actively seeks evidence using video logic flow, significantly reducing frame usage and enhancing reasoning capabilities.
Findings
Achieves 10.2 points improvement on LVBench over GPT-5
Uses 93% fewer frames than prior methods
Maintains or improves understanding with fewer frames
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…
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Taxonomy
TopicsMultimodal Machine Learning Applications · Explainable Artificial Intelligence (XAI) · Reinforcement Learning in Robotics
