Title:Does RAG Know When Retrieval Is Wrong? Diagnosing Context Compliance under Knowledge Conflict

Abstract:The Context-Compliance Regime in Retrieval-Augmented Generation (RAG) occurs when retrieved context dominates
the final answer even when it conflicts with the model's parametric knowledge. Accuracy alone does not reveal
how retrieved context causally shapes answers under such conflict. We introduce Context-Driven Decomposition
(CDD), a belief-decomposition probe that operates at inference time and serves as an intervention mechanism for
controlled retrieval conflict. Across Epi-Scale stress tests, TruthfulQA misconception injection, and cross-
model reruns, CDD exposes three patterns. P1: context compliance is measurable in an upper-bound adversarial
setting, where Standard RAG reaches 15.0% accuracy on TruthfulQA misconception injection (N=500). P2:
adversarial accuracy gains transfer across model families: CDD improves accuracy on Gemini-2.5-Flash and on
Claude Haiku/Sonnet/Opus, but rationale-answer causal coupling does not transfer. CDD reaches 64.1% mistake-
injection causal sensitivity on Gemini-2.5-Flash, while sensitivities for all three Claude variants fall in the
[-3%, +7%] range, suggesting that the Claude-side accuracy gains operate through a mechanism distinct from the
explicit conflict-resolution trace. P3: explicit conflict decomposition improves robustness under temporal
drift and noisy distractors, with CDD reaching 71.3% on temporal shifts and 69.9% on distractor evidence on the
full Epi-Scale adversarial benchmark. These three patterns identify context-compliance as a structural axis
along which standard RAG can be probed and intervened on, distinct from retrieval-quality or single-method
robustness questions, and motivate releasing Epi-Scale for systematic study across model families and retrieval
pipelines.

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