The Eigenweight Engine

We demonstrate that the weight matrices of multiple large language models sharing the same architecture but trained for different purposes (code generation vs conversational instruction-following) occupy a shared low-rank manifold. By computing a joint eigenspace (via SVD) across model weights, we factor N separate models into one shared basis matrix plus N small per-model coefficient vectors.