SELF-INDEX: a retrieval index that diagnoses and fixes its own weak spots
_reachsumit · x · 2026-09-18
The SELF-INDEX paper introduces a framework for retrieval indexes to self-evolve without human intervention.
- Motivation: as LLM agents handle complex tasks, retrieval quality hinges on how index keys expose document knowledge; optimal representations vary by environment, and human-driven diagnosis, strategy tuning, and reprocessing don't scale
- Optimizer: autonomously diagnoses retrieval shortfalls, selectively revises the responsible index keys, and validates each revision before updating
- Query Simulator: proactively explores additional demands, letting the index evolve beyond the queries already available
- Consistently improves retrieval performance across diverse corpora and retrievers, beating existing index optimization methods; benefits also extend to search agents and agent memory systems
Marked work-in-progress, with code released.
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