让AI自己给自己建索引
搜索引擎的索引一直靠人调：检索效果不好，得人来诊断、改策略、重新处理文档。这篇让索引自己进化——系统自动找出哪里没检索到、只改出问题的那些索引键、改完先验证再更新，还能主动模拟未来可能出现的查询，提前把索引长出来。在多个语料和检索器上，它都比现有优化方法更稳。它不是你明天能用上的东西，但指向一个方向：AI 系统开始自己维护自己的基础设施，而不是等人来修。
📄 原文摘要(英文)
Information retrieval is increasingly important as LLM agents tackle complex tasks involving diverse information needs. Because retrieval relies on an index that represents each document through index keys, retrieval quality depends heavily on how effectively these keys expose the knowledge contained in each document. However, effective index representations vary across retrieval environments, making it difficult for any fixed optimization strategy to perform consistently. Yet evolving an index to its retrieval environment remains largely human-driven, requiring humans to diagnose retrieval failures, refine the optimization strategy, and reprocess the index accordingly. We propose SELF-INDEX, a framework that enables an index to self-evolve without human intervention. Its Optimizer autonomously diagnoses retrieval shortfalls, selectively revises the responsible index keys, and validates each revision before updating the index. Beyond reacting to observed retrieval demands, SELF-INDEX proactively explores additional demands through a Query Simulator, allowing the index to evolve beyond the queries already available for optimization. Across diverse corpora and retrievers, SELF-INDEX consistently improves retrieval performance while outperforming existing index optimization methods. We further show that these benefits extend to downstream applications, improving the effectiveness and efficiency of search agents and helping agent memory systems retrieve useful past interactions.