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arXiv — cs.AI preprintsInternational21 October 2026

Joint Constraint Aggregation for Qualifier-Sensitive Retrieval-Augmented QA

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arXiv:2610.27114v1 Announce Type: cross Abstract: Retrieval-augmented QA can fail even when the correct answer is in the retrieved evidence. This can happen when a competing answer satisfies some, but not all, of the question's requirements and is mentioned frequently in the retrieved documents, which can make it appear to be the best-supported answer. To test whether this retrieval skew actually affects the decision, we manipulate it directly: increasing the prevalence of passages that favor the competing answer makes the model more likely to select it. This points to a decision problem that
— arXiv — cs.AI preprints

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