arXiv: when synthetic data augmentation actually helps imbalanced classification

arXiv·low signal

A theoretical paper (2606.26053) derives conditions under which synthetic data augmentation improves score-based imbalanced classification, an area where practice has outrun theory. The result gives agent and ML builders a principled signal for when generating synthetic minority-class data will help versus quietly hurt. Single-source theory result, useful as a sanity check on a common data-pipeline habit.