Query-Aware Source-Risk Triage for Retrieval-Augmented Generation
Summary
The paper proposes a query-aware triage layer for retrieval-augmented generation (RAG) pipelines to handle the material relationship of sources to queries, using scoring and routing methods evaluated on synthetic data to define risk-coverage targets.
View Cached Full Text
Cached at: 09/16/26, 09:00 AM
# Query-Aware Source-Risk Triage for Retrieval-Augmented Generation Source: [https://arxiv.org/abs/2609.16564](https://arxiv.org/abs/2609.16564) [View PDF](https://arxiv.org/pdf/2609.16564) > 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 20,000\-row scenario with synthetic domain identifiers supports controlled workload analysis\. An oracle page gate defines a risk\-coverage target for a future learned classifier\. The evaluation shows why page\-level frequency cannot substitute for family\-level exposure and quantifies how calibration changes scenario activation\. Annotation reliability remains unmeasured, and synthetic rankings omit real retrieval dynamics\. The result is an auditable triage method and validation plan, not an estimate of deployed review workload, live\-Web prevalence, or downstream answer\-quality gains\. ## Submission history From: Lu Yi \[[view email](https://arxiv.org/show-email/e4c89fb0/2609.16564)\] **\[v1\]**Tue, 15 Sep 2026 03:01:27 UTC \(775 KB\)
Similar Articles
Source-Aware Reranking for Retrieval-Augmented Generation: A Reliability Prior Approach
This paper introduces a source-aware reranking method for RAG that incorporates domain-informed source reliability priors, improving Precision@5 from 0.48 to 0.72 on a 120-document health corpus and reducing adversarial document retrieval.
Claim-Selective Certification for High-Risk Medical Retrieval-Augmented Generation
This paper proposes claim-selective certification for high-risk medical retrieval-augmented generation (RAG), decomposing responses into verifiable claims and scoring them against evidence to produce actions (full, partial, conflict, abstain) using an intent-aware selector, achieving low unsupported-claim risk and high action accuracy.
RAG-CT: Mitigating Privacy Risks on Retrieval-Augmented Generation Systems via Scanning Prompt Distribution
RAG-CT is a novel defense method that identifies malicious queries by analyzing entropy and margin distributions to mitigate privacy risks in Retrieval-Augmented Generation systems, significantly reducing PII leakage.
Retrieval-augmented generation solves a problem most teams don't actually have
The article argues that retrieval-augmented generation (RAG) is often misapplied in AI systems, where the real issue is context curation rather than retrieval. It suggests that RAG is only truly beneficial for large, frequently changing corpora.
SelfGraphRAG: Bridging the Supervision Gap in Graph-Based RAG with Synthetic QA Generation
SelfGraphRAG introduces a framework that generates synthetic question-answer pairs from knowledge graphs to address the supervision gap in graph-based retrieval-augmented generation, enhancing retrieval precision and reasoning performance.