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The paper introduces LEAN, a loss-engineered regularization method that reshapes a trained model's weight-sensitivity distribution, shrinking the sensitive tail 5-13x and enabling models to survive hundreds of bit-flip attacks while storing ImageNet ResNet-50 at a 3.29-bit payload (2.4x smaller than INT8).