Erased, but Not Gone: Output Forgetting Is Not True Forgetting
arXiv:2606.25001v2 Announce Type: replace-cross Abstract: Machine unlearning (MU) is commonly judged by output forgetting, such as low forget-set accuracy or reduced membership inference. But if output-level success can coexist with retraining-inconsistent residuals in representation space, what kind of forgetting are current evaluations actually certifying? We study this question through retraining-consistent representation forgetting, using a population of retrained models (i.e., trained from…