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Accuracy alone does not reveal\n  how retrieved context causally shapes answers under such conflict. We introduce Context-Driven Decomposition\n  (CDD), a belief-decomposition probe that operates at inference time and serves as an intervention mechanism for\n  controlled retrieval conflict. Across Epi-Scale stress tests, TruthfulQA misconception injection, and cross-\n  model reruns, CDD exposes three patterns. P1: context compliance is measurable in an upper-bound adversarial\n  setting, where Standard RAG reaches 15.0% accuracy on TruthfulQA misconception injection (N=500). P2:\n  adversarial accuracy gains transfer across model families: CDD improves accuracy on Gemini-2.5-Flash and on\n  Claude Haiku/Sonnet/Opus, but rationale-answer causal coupling d","title":"Does RAG Know When Retrieval Is Wrong? Diagnosing Context Compliance under Knowledge Conflict","url":"https://arxiv.org/abs/2605.14473","vendor":"arxiv_cs_ai"},"summary":"arXiv:2605.14473v1 Announce Type: cross \nAbstract: The Context-Compliance Regime in Retrieval-Augmented Generation (RAG) occurs when retrieved context dominates\n  the final answer even when it conflicts with the model's parametric knowledge. Accuracy alone does not reveal\n  how retrieved context causally shapes answers under such conflict. We introduce Context-Driven Decomposition\n  (CDD), a belief-decomposition probe that operates at inference time and serves as an intervention mechanism for\n  controlled retrieval conflict. Across Epi-Scale stress tests, TruthfulQA misconception injection, and cross-\n  model reruns, CDD exposes three patterns. P1: context compliance is measurable in an upper-bound adversarial\n  setting, where Standard RAG reaches 15.0% accuracy on TruthfulQA misconception injection (N=500). 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