给AI一张地图，它找资料不再瞎翻
现在的AI在大量文档里找答案，就像一个人走进没目录的档案室：翻到一份文件，不知道它跟别的文件什么关系，只能每次从头再翻一遍，既费钱又容易漏。这篇论文给AI画了张“实体地图”：先把所有文档里反复出现的同一个东西（比如一个项目、一个人名）自动归拢成一个“实体页”，每页都链到所有提到它的文件，AI顺着这张网走，就能把散在各处的证据串起来。在7个模型、3个数据集上，它比原来的“瞎翻”方式找到的证据更多、答案更准，平均花的token还更少。它不是你明天就能用上的产品，但指向一个明确方向：AI检索的下一步，不是让模型更聪明，而是先把资料库整理成它能走的路。
📄 原文摘要(英文)
Answering questions and completing tasks over large document collections often requires connecting evidence spread across multiple documents, such as a project's approval recorded in one, its requirements in another, and its latest status in a third. Recent LLM agents approach this by iteratively searching the full corpus rather than reading only a fixed set of top-ranked documents. However, when the corpus is exposed only as a flat collection of files, a relevant document gives no indication of how it relates to others, so the agent must rediscover these relationships for every query, often missing complementary evidence while simultaneously consuming substantial additional tokens. To address this, we introduce CorpusMap, a navigation layer that organizes the corpus around its recurring entities, which are identifiable from the documents themselves and can link a single document to many others across sources. Specifically, CorpusMap represents each recurring entity as an Entity Page that aggregates information about it and links to every document that refers to it, forming a graph between entities and documents that the agent can traverse to gather otherwise disconnected evidence. Moreover, since CorpusMap is constructed offline by resolving mentions of the same entity across documents, its links are shared across queries rather than rediscovered repeatedly at inference time. Using 7 different models with 3 benchmark datasets, we show that CorpusMap improves both evidence discovery and answer quality over raw-corpus agentic search while using fewer tokens on average, and further outperforms 4 alternative navigation layers, suggesting that entities serve as effective anchors for navigating large document collections.