Useful Directions and Useful Partners: Auditing Paired Gradient Subspaces Keywords

Pairing related inputs can suggest directions for adapting a classifier. To measure what the original matching contributes, we hold each input's loss gradient and label fixed, shuffle partners within labels, and refit the subspace of gradient differences. Random directions provide a different comparison. We show that identical gains over random references can accompany positive, zero or negative pairing effects on retained target-gradient energy. This ambiguity remains even when random directions lie in the space where gradients can occur. Across visual and multilingual dialogue settings, energy advantages over this restricted random reference are common. Evidence that the original partners improve energy retention is selective, and accuracy gains are rarer. Compared with support-matched random directions, dialogue adaptation also incurs class-balanced prediction costs that ordinary accuracy can hide. The audit separates the quality of the directions, the contribution of the original partners, and the value of updating along those directions.