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.

翻译：视频的密集时序特性为自动化分析带来了深刻挑战。尽管现有方法借助强大的视觉-语言模型，但其推理与感知之间存在固有脱节：它们依赖静态的预处理信息，无法随理解过程的演进主动从视频中获取原始证据。为此，我们提出LensWalk——一种灵活的智能体框架，使大语言模型推理器能够主动控制自身的视觉观察过程。LensWalk构建了紧密的"推理-规划-观察"循环：智能体在每一步动态指定待观察视频的时间范围与采样密度。通过一套基于视觉-语言模型、由上述参数化的多功能工具，智能体可执行线索广域扫描、聚焦特定片段进行事实提取，并跨多时刻拼接证据实现整体验证。这种设计支持渐进式、按需的证据采集，直接服务于智能体动态演进的推理链。无需任何模型微调，LensWalk在多种模型方案上实现了显著的即插即用性能提升，在LVBench与Video-MME等具有挑战性的长视频基准测试中将准确率提升超过5%。我们的分析表明：赋予智能体控制"如何观看"的能力，是解锁更准确、鲁棒且可解释的视频推理的关键。