Claim-Selective Certification for High-Risk Medical Retrieval-Augmented Generation

• URL: http://arxiv.org/abs/2605.21949v1
• Date: Thu, 21 May 2026 03:29:50 GMT
• Title: Claim-Selective Certification for High-Risk Medical Retrieval-Augmented Generation
• Abstract summary: Mixed evidence may support one claim, require conditions for another, and contradict a third.<n>We study claimselective responses into verifiable claims, scored against retrieved evidence, and mapped by an intent-aware selector to full, partial, conflict, abstain<n>The resulting interface separates action-label prediction from evidence-linked claim selection under mixed evidence.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Medical RAG systems in high-risk QA settings are often evaluated through a single answer-or-abstain decision, but mixed evidence may support one claim, require conditions for another, and contradict a third. We study claim-selective certification: each response is decomposed into verifiable claims, scored against retrieved evidence, and mapped by an intent-aware selector to {full, partial, conflict, abstain}. On the primary weak-label certificate protocol, whose real-source-only dev/test rows cover the naturally occurring non-abstain actions, the full system records UCCR=0.0000, PAU=1.0000, PAU Precision=0.9901, and action accuracy=0.9204 on dev (n=314), and UCCR=0.0000, PAU=0.9967, PAU Precision=0.9739, and action accuracy=0.8997 on test (n=319). UCCR measures unsupported-claim risk within the certificate definition, and a source-missing counterfactual slice evaluates abstain under empty evidence. Shortcut controls quantify the action-label prior explained by source and intent metadata, while source/evidence-novel slices characterize transfer boundaries. The resulting interface separates action-label prediction from evidence-linked claim selection under mixed evidence.

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