arXiv:2611.12957v1 Announce Type: new Abstract: VLM-based agents solve complex tasks by iteratively interacting with environments, and their effectiveness depends heavily on how the environment exposes information and provides tools for acting on it. However, constructing an effective environment for complex multimodal document question answering remains challenging: documents usually span many pages, with multimodal contents distributed across hierarchical sections and linked by cross-modal associations, while existing methods organize documents into structured representations that address only the information side of the environment and lack tools aligned with the structure, limiting agents' ability to fully exploit it. We observe that file systems naturally combine structure organization with aligned tools: directories and files encode document structure, while file operations provide rich tools to access file contents. Building on this insight, we propose DocFS, a file-system-based agentic framework that reorganizes raw documents as file systems, where directories mirror the section hierarchy and explicit links connect text with figures, tables, and equations. Based on well-established file system tools such as navigation, search, and reading, agents can iteratively explore the document structure, locate relevant evidence, and understand fine-grained local information. Paired with Qwen3.5-Plus, DocFS achieves state-of-the-art performance on MMLongBench-Doc and FinRAGBench-V, outperforming the strongest baselines by 2.9 and 4.3 percentage points, respectively, and is up to 10.3x faster with up to 10.1x fewer tokens than the strongest baseline during reasoning.