TLDR
随着 LLM 智能体处理涉及多样化信息需求的复杂任务，信息检索的重要性日益凸显。由于检索依赖通过索引键表征每个文档的索引结构，检索质量在很大程度上取决于这些索引键在多大程度上有效暴露文档所含知识。然而有效的索引表征会因检索环境而异，使任何固定的优化策略都难以稳定表现。但将索引演化适配到其检索环境在很大程度上仍由人驱动，需要人类去诊断检索失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 fa