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This paper introduces a physics-inspired framework for structural attribution in cyber-physical IoT systems, using an undirected energy-based representation to provide dependency-aware explanations without requiring a directed causal graph. Experiments on an industrial IoT testbed demonstrate higher attribution accuracy, robustness, and scalability compared to existing graph-based methods.