Representation geometry explains why forgotten knowledge is easily relearned, motivating an MCU unlearning method that targets minor components
This work studies, from a representation geometry perspective, why unlearned models rapidly recover forgotten knowledge under relearning attacks, finding that existing unlearning methods mainly optimize along dominant components while leaving minor components largely unchanged, that modifications in dominant components are easily reversed during relearning whereas minor components resist such reversal, and proposes Minor Component Unlearning (MCU), which explicitly targets minor components and is validated on three datasets to substantially improve resistance to relearning attacks.