Extending language models to video introduces two challenges: representation, where existing methods rely on lossy approximations, and long-context, where caption- or agent-based pipelines collapse video into text and lose visual fidelity. To overcome this, we introduce \textbf{VideoAtlas}, a task-agnostic environment to represent video as a hierarchical grid that is simultaneously lossless, navigable, scalable, caption- and preprocessing-free. An overview of the video is available at a glance, and any region can be recursively zoomed into, with the same visual representation used uniformly for the video, intermediate investigations, and the agent's memory, eliminating lossy text conversion end-to-end. This hierarchical structure ensures access depth grows only logarithmically with video length. For long-context, Recursive Language Models (RLMs) recently offered a powerful solution for long text, but extending them to visual domain requires a structured environment to recurse into, which \textbf{VideoAtlas} provides. \textbf{VideoAtlas} as a Markov Decision Process unlocks Video-RLM: a parallel Master-Worker architecture where a Master coordinates global exploration while Workers concurrently drill into assigned regions to accumulate lossless visual evidence. We demonstrate three key findings: (1)~logarithmic compute growth with video duration, further amplified by a 30-60\% multimodal cache hit rate arising from the grid's structural reuse. (2)~environment budgeting, where bounding the maximum exploration depth provides a principled compute-accuracy hyperparameter. (3)~emergent adaptive compute allocation that scales with question granularity. When scaling from 1-hour to 10-hour benchmarks, Video-RLM remains the most duration-robust method with minimal accuracy degradation, demonstrating that structured environment navigation is a viable and scalable paradigm for video understanding.

翻译：将语言模型扩展至视频领域面临两大挑战：表征方面，现有方法依赖有损近似；长上下文方面，基于描述或代理的流水线将视频压缩为文本并丢失视觉保真度。为此，我们提出\textbf{VideoAtlas}——一种任务无关环境，将视频表示为同时具备无损性、可导航性、可扩展性且无需描述与预处理的分层网格。用户可一览视频全局概览，任意区域支持递归缩放，同一视觉表示统一用于视频、中间探索过程及代理记忆，从端到端消除有损文本转换。这种分层结构确保访问深度仅随视频长度对数增长。针对长上下文问题，递归语言模型（RLM）近期为长文本处理提供了强大方案，但将其扩展至视觉领域需要结构化环境支持递归操作，而\textbf{VideoAtlas}恰好提供此条件。作为马尔可夫决策过程，\textbf{VideoAtlas}解锁了Video-RLM：一种并行主从架构，其中主代理协调全局探索，从代理并行深入分配区域以累积无损视觉证据。我们证明三项核心发现：(1) 计算量随视频时长呈对数增长，并通过网格结构复用产生的30-60\%多模态缓存命中率进一步放大；(2) 环境预算控制，约束最大探索深度提供了原则性的计算-精度超参数；(3) 随问题粒度缩放的自适应计算分配涌现。从1小时扩展至10小时基准测试时，Video-RLM仍保持最强的时长鲁棒性且精度退化最小，表明结构化环境导航是视频理解中可行且可扩展的范式。