Abstract
The dense, temporal nature of video presents a profound challenge for automated analysis. Despite the use of powerful Vision-Language Models, prevailing methods for video understanding are limited by the inherent disconnect between reasoning and perception: they rely on static, pre-processed information and cannot actively seek raw evidence from video as their understanding evolves. To address this, we introduce LensWalk, a flexible agentic framework that empowers a Large Language Model reasoner to control its own visual observation actively. LensWalk establishes a tight reason-plan-observe loop where the agent dynamically specifies, at each step, the temporal scope and sampling density of the video it observes. Using a suite of versatile, Vision-Language Model based tools parameterized by these specifications, the agent can perform broad scans for cues, focus on specific segments for fact extraction, and stitch evidence from multiple moments for holistic verification. This design allows for progressive, on-demand evidence gathering that directly serves the agent's evolving chain of thought. Without requiring any model fine-tuning, LensWalk delivers substantial, plug-and-play performance gains on multiple model recipes, boosting their accuracy by over 5\% on challenging long-video benchmarks like LVBench and Video-MME. Our analysis reveals that enabling an agent to control how it sees is key to unlocking more accurate, robust, and interpretable video reasoning.
Abstract (translated)
视频密集的时序特性给自动化分析带来了深刻挑战。尽管使用了强大的视觉-语言模型，当前视频理解方法仍受限于推理与感知之间的固有脱节：它们依赖静态的预处理信息，无法在理解演进过程中主动从视频中寻求原始证据。为解决这一问题，我们提出了LensWalk——一个灵活的智能体框架，使大语言模型推理器能够主动控制自身的视觉观察。LensWalk建立了紧密的推理-规划-观察循环，智能体在每一步动态指定其观察视频的时间范围和采样密度。通过一套由这些参数控制的多功能视觉-语言模型工具，智能体能够进行广泛扫描以寻找线索，聚焦特定片段提取事实，并整合多时刻证据进行整体验证。这一设计支持了渐进式、按需的证据收集，直接服务于智能体不断演进的思维链。LensWalk无需任何模型微调，即可在多种模型配置上实现显著的即插即用性能提升，在LVBench和Video-MME等具有挑战性的长视频基准上将准确率提高了超过5%。我们的分析表明，使智能体能够自主控制观察方式是实现更准确、更稳健、更具可解释性的视频推理的关键。
URL
https://arxiv.org/abs/2603.24558
