Erased, but Not Gone: Output Forgetting Is Not True Forgetting
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 scratch without the forget data) as an operational reference distribution for ideal forgetting. Across multiple unlearning methods, datasets, and models, our theoretical analysis and empirical results show that standard output-level evaluation can systema...
| Authors | Teresa Pui Yee Yong, Win Kent Ong, Chee Seng Chan |
|---|---|
| Primary category | cs.LG |
| Published | 23 June 2026 |
| Last updated at source | 9 October 2026 |
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