Geometric Stability: The Missing Axis of Representations
The research introduces Geometric Stability as a new metric to assess neural network representations beyond simple similarity or alignment measures. Shesha quantifies this stability by measuring the self-consistency of pairwise distances when using complementary random subsets of feature dimensions. This reveals structural reliability, showing that models optimized for transferability can have poor geometric recovery, highlighting a crucial blind spot in current evaluation methods.