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Introduces energy conservation as a hard physical constraint on inter-module information flow in modular neural networks, enforcing exact preservation of activation energy at module boundaries to attenuate error propagation. Experiments on CIFAR-10 and a robotic pipeline show significant improvements in noise robustness.