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VideoScout Agent Learns Adaptive Pacing for Long Video Understanding

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced VideoScout, an agent designed for understanding long videos by employing a Sequential Evidence Acquisition (SEA) paradigm. This approach allows the agent to adapt its viewing pace, retain crucial evidence, revisit uncertain segments, and efficiently determine when to answer. VideoScout-66K, a dataset of over 66,000 exploration turns, was created to train the agent using a two-stage pipeline involving supervised fine-tuning and trajectory-level reinforcement learning with a composite reward. AI

IMPACT Introduces a novel agentic approach to long video analysis, potentially improving efficiency and accuracy in multimodal AI systems.

RANK_REASON The cluster contains a research paper detailing a new method and model for video understanding. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

VideoScout Agent Learns Adaptive Pacing for Long Video Understanding

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The cluster contains a research paper detailing a new method and model for video understanding. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Weixin Xu, Zhenyu Yang, Bing Wang, Shengsheng Qian, Changsheng Xu ·

    VideoScout: Learning Agentic Active Exploration with Adaptive Reasoning Pacing for Long Video Understanding

    arXiv:2609.15606v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) have achieved remarkable progress on short video understanding yet remain limited on long videos due to the limited visual context window. Prevailing approaches rely on uniform frame sampli…

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