发表机构
University of California, Davis; Zhejiang University; The University of Hong Kong（加利福尼亚大学戴维斯分校; 浙江大学; 香港大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究长文档问答中智能体获取文档路径自行决定读取内容的模式，通过对照研究，在InfiniteBench上对比原始文档导航、Agent Skills包设计与经典混合检索器，发现不同情况下各方法表现不同，渐进式披露在语料库大时作用关键。
AI 中文摘要
长文档问答通常要在将整个文档加载到上下文窗口和附加单独的检索器之间做出选择。智能体人工智能提出了一个更广泛的选项，即给智能体文档路径并让它决定如何以及读取什么内容。Agent Skills作为一种将专业知识打包成智能体按需加载的文件夹的标准，提供了一种现成的机制：渐进式披露，它仅暴露查询所需的内容，从简短描述到具体段落。从业者迅速采用这种模式进行书籍长度的理解任务，但支持此类选择的证据一直是传闻。我们对这种模式进行了首次对照研究，在InfiniteBench上，针对三种智能体框架和三个模型家族，将原始文档导航和几种Agent Skills包设计与经典混合检索器进行比较。在单本书籍的情况下，收益取决于框架，当智能体对原始文档导航不佳时收益很大，而当强大的智能体框架已经自行划分和检索时收益接近零。当扩展到跨多本书籍的任务时，原始文档导航失效，而一级渐进式披露退化更慢并领先。第二个更深的路由级别毫无帮助，有时甚至会直接破坏准确性，所以一个级别就足够了。渐进式披露带来的是上下文，而非智能：当强大的智能体可以自行定位正确段落时它是多余的，而一旦语料库变得太大而无法通过阅读导航时它是决定性的。
英文摘要
Long-document question answering usually forces a choice between loading the whole document into the context window and bolting on a separate retriever. Agentic AI suggests a broader option, giving the agent the document path and letting it decide how and what to read. Agent Skills, a standard for packaging expertise into folders an agent loads on demand, supply a ready mechanism: progressive disclosure, which exposes only what a query needs, from a short description down to the specific passages. Practitioners rapidly adopted this pattern for book-length understanding tasks, but the evidence to support such choices has been anecdotal. We run the first controlled study of the pattern, comparing raw-document navigation and several designs of Agent Skills packs against a classical hybrid retriever across three agent harnesses and three model families on InfiniteBench. On a single book, the gain depends on the harness, running large when the agent navigates the raw document poorly but near zero when a strong agent harness already divides and retrieves on its own. When scaling up to tasks that span many books, raw-document navigation collapses while one-level progressive disclosure degrades more slowly and pulls ahead. A second, deeper routing level never helps and sometimes breaks accuracy outright, so one level is enough. Progressive disclosure buys context, not intelligence: it is redundant while a strong agent can locate the right passages itself, and decisive once the corpus grows too large to navigate by reading.
