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This article introduces a novel optimization framework that enables the training of extremely deep neural networks without relying on standard backpropagation, addressing a fundamental bottleneck in current machine learning architectures. By leveraging augmented Lagrangian methods and predictive coding, the approach offers a significant theoretical shift toward more scalable and potentially hardware-efficient model training paradigms.