ml_foundation_model

We present a formally verified framework for analyzing foundation model training dynamics through the lens of generalization bounds and scaling laws. The framework establishes fundamental relationships between training loss, validation loss, generalization gap, and model size, providing rigorous guarantees for certificate-based quality metrics.