arXiv Artificial Intelligence

Query-Aware Source-Risk Triage for Retrieval-Augmented Generation

1 hour ago 16.09.2026 07:00 👁 2 views
Query-Aware Source-Risk Triage for Retrieval-Augmented Generation

Quick summary

arXiv:2609.16564v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) pipelines may omit a source's material relationship to the query. We study a pre-generation triage layer that treats this relationship as query dependent. The method routes canonical query families for enhanced review and assigns retrieved pages to pass, contextualize, exclude, or review. It combines a four-dimension page score, rank-discounted family aggregation, intent-preserving query mutations, and a family-held-out router. A single-coded pilot of 200 real URLs supplies provisional calibration anchors; a 2

Key takeaways

  • arXiv:2609.16564v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) pipelines may omit a source's material relationship to the query.
  • We study a pre-generation triage layer that treats this relationship as query dependent.
  • The method routes canonical query families for enhanced review and assigns retrieved pages to pass, contextualize, exclude, or review.

Why it matters

The value of this work lies as much in how it was tested as in the claim itself. Sample design, baselines, uncertainty and replication help separate a laboratory result from real-world impact.

Kaynak sitede devamını oku: arXiv Artificial Intelligence ↗