{"slug": "docatlas-long-document-understanding-as-mutable-state-interaction", "title": "DocAtlas: Long-Document Understanding as Mutable-State Interaction", "summary": "DocAtlas, a new system for long-document understanding that treats the process as mutable-state interaction, achieves 71.4% on MMLongBench-Doc with GPT-5.4, surpassing the human-expert reference of 65.8%. A Qwen3.5-4B VLM trained with end-to-end reinforcement learning in the DocAtlas environment reaches 63.7%, compared with a 54.4% direct-input baseline, showing that mutable document-harness design can improve compact document agents by a large margin.", "body_md": "arXiv:2608.07527v1 Announce Type: new\nAbstract: Long-document understanding requires models to find and combine evidence across many pages, layouts, tables, figures, and charts. Existing retrieval-augmented systems usually select evidence from a static index before generation, while recent agentic systems add multi-turn tool use but often rely on frozen proprietary backbones whose behavior is set by prompts. We present DocAtlas, a system that treats long-document understanding as a mutable-state information-seeking process. We instantiate DocAtlas as a mutable document harness: an external environment that determines what document information is searched, read, stored, reviewed, and shown to the model at each step. Given a document and question, the harness exposes search, reading, note-taking, and review tools, maintains a hierarchical tree and note store, and updates both as the agent records evidence. DocAtlas combines self-improving retrieval, selective evidence access, and active working memory under a fixed context budget. The same harness supports inference-time use with large VLMs and end-to-end reinforcement learning for compact VLM agents. With GPT-5.4, DocAtlas reaches 71.4\\% on MMLongBench-Doc, exceeding the human-expert reference of 65.8\\%. A Qwen3.5-4B VLM trained with end-to-end RL in the DocAtlas environment reaches 63.7\\%, compared with a 54.4\\% direct-input baseline, showing that mutable document-harness design can improve compact document agents by a large margin.", "url": "https://wpnews.pro/news/docatlas-long-document-understanding-as-mutable-state-interaction", "canonical_source": "https://arxiv.org/abs/2608.07527", "published_at": "2026-08-11 04:00:00+00:00", "updated_at": "2026-08-11 04:09:54.077748+00:00", "lang": "en", "topics": ["artificial-intelligence", "machine-learning", "large-language-models", "ai-research"], "entities": ["DocAtlas", "MMLongBench-Doc", "GPT-5.4", "Qwen3.5-4B"], "alternates": {"html": "https://wpnews.pro/news/docatlas-long-document-understanding-as-mutable-state-interaction", "markdown": "https://wpnews.pro/news/docatlas-long-document-understanding-as-mutable-state-interaction.md", "text": "https://wpnews.pro/news/docatlas-long-document-understanding-as-mutable-state-interaction.txt", "jsonld": "https://wpnews.pro/news/docatlas-long-document-understanding-as-mutable-state-interaction.jsonld"}}