Memory-Coupled Unlearning for Data-Absent Identity Forgetting

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arXiv:2610.14148v1 Announce Type: new Abstract: Machine unlearning aims to reduce the influence of designated training data from a trained model while preserving its utility on retained data. Identity-level unlearning is particularly challenging when only limited target-specific information is available when a forgetting request is processed. In this work, we propose Memory-Coupled Unlearning (MCU), a framework that augments a host model with a Modern Hopfield Network (MHN) serving as a contr