M6.7 - Low-Rank Theory: Intrinsic Dimension, Subspaces, and Dynamics
A rigorous study of why low-rank adaptation can work and where it fails: intrinsic-dimension measurement, matrix manifolds, SVD reconstruction, factor redundancy, initialization gradients, NTK preservation, expressive-power bounds, feature learning, and forgetting.