AI 开始自己给自己建索引了
搜索引擎的命根子是索引——每篇文档靠一组关键词被找到。过去这组关键词怎么定，得靠人反复试错：搜不到就人工诊断、改策略、重新处理。这篇让索引自己进化：系统自动找出哪些关键词拖了后腿、只改该改的，改完先验证再更新；还会主动模拟用户可能问的新问题，提前把索引补全，而不是只等现有查询暴露短板。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，但它指向一个趋势：AI 正在把「调优自己」这件事从人手里接过去，连检索这种最底层的基建也开始自我维护。
📄 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.