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SEAM is a generator-agnostic framework that audits global consistency of explanations in scientific machine learning, detecting incompatible local explanations even when predictions are locally accurate and attributing failures to specific channels and overlaps. The paper presents theory and experiments across PDE systems, neural operators, and four open datasets.