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Self-Evolving Search Index — arXiv:2609.19656

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.19656
License (as archived): CC0-1.0
Archived: 2026-10-10T03:23:31.001Z
SHA-256 of the archived text: be1e4b09e5aea86ed53717b7875ecca7e0feb78ac36c3cfb32a93911d36f49bb
Stable URL of this page: https://metraipolis.com/documento/dc_59990b00a76b

How to cite

Self-Evolving Search Index — arXiv:2609.19656 — arXiv (en), https://arxiv.org/abs/2609.19656 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_59990b00a76b (Metraipolis archive dc_59990b00a76b, sha256 be1e4b09e5aea86ed53717b7875ecca7e0feb78ac36c3cfb32a93911d36f49bb)

BibTeX

@misc{dc59990b00a76b,
  title = {Self-Evolving Search Index — arXiv:2609.19656},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.19656},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_59990b00a76b; sha256 be1e4b09e5aea86ed53717b7875ecca7e0feb78ac36c3cfb32a93911d36f49bb},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.19656. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-18 in cs.AI).
Categorie arXiv: Information Retrieval (cs.IR) ; Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2609.19656 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-10T03:04:13.845Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-09-18?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).