The Universal Weight Subspace Hypothesis
Researchers tested thousands of diverse deep neural networks across various tasks and models to find a common structure in their weights. They discovered a 'Universal Subspace': most networks converge onto a low-dimensional set of principal directions in weight space, regardless of the specific task or architecture. This means many different AI models share fundamental geometric patterns that can be used for efficient model merging and adaptation.