We propose a retrieval-augmented LLM system that automatically identifies and rewrites problematic industrial contract revisions, achieving high accuracy even in low-resource legal settings.
We introduce gradient-norm–weighted averaging methods for distributed deep learning that pull workers toward flatter regions of the loss landscape. The proposed model-level and layer-level variants converge in both convex and non-convex settings and empirically achieve faster training, better optima, and reduced communication compared to prior work.