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Existing unlearning studies that rely on label manipulation or task-gradient reversal often deliver limited unlearning effectiveness. Moreover, they can undermine the original learning objective and typically do not guarantee equivalence to standard unlearning by retraining.\n  In this paper, we propose \\textbf{ManiF-SMC} (\\textbf{Mani}fold \\textbf{F}orgetting with \\textbf{S}elf \\textbf{M}ode \\textbf{C}onnectivity), motivated by the observation that a model retrained on the remaining data tends to classify erased samples by their semantic similarity to the retained data. We begin with systematically recasting the approximate unlearning as pushing each erased sample away from its original learned manifold representation centroid toward its nearest semantic neighbors in the retained data. This reformulation aligns unlearning with retraining behavior and","title":"Approximate Machine Unlearning through Manifold Representation Forgetting Guided by Self Mode Connectivity","url":"https://arxiv.org/abs/2605.22871","vendor":"arxiv_cs_ai"},"summary":"arXiv:2605.22871v1 Announce Type: cross \nAbstract: Machine unlearning is a fundamental mechanism that enforces the right to be forgotten. Existing unlearning studies that rely on label manipulation or task-gradient reversal often deliver limited unlearning effectiveness. Moreover, they can undermine the original learning objective and typically do not guarantee equivalence to standard unlearning by retraining.\n  In this paper, we propose \\textbf{ManiF-SMC} (\\textbf{Mani}fold \\textbf{F}orgetting with \\textbf{S}elf \\textbf{M}ode \\textbf{C}onnectivity), motivated by the observation that a model retrained on the remaining data tends to classify erased samples by their semantic similarity to the retained data. We begin with systematically recasting the approximate unlearning as pushing each erased sample away from its original learned manifold representation centroid toward its nearest semantic neighbors in the retained data. 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