Mitigating Sequential Reappearance in Diffusion Data-Point Unlearning

Diffusion data-point unlearning is typically evaluated immediately after each deletion, even though subsequent requests may repeatedly update the same model. We identify sequential reappearance, a failure mode in which an instance that is initially judged to be forgotten later returns to the memorized regime without reuse of the deleted data or adversarial fine-tuning. To capture this behavior, we introduce a target-level evaluation protocol that tracks whether each target is forgotten immediately, remains forgotten at the end of the sequence, or reappears during subsequent deletions. We further find that targets that later reappear exhibit greater local recovery accessibility after deletion than targets that remain forgotten. We characterize this vulnerability as recovery accessibility and show that this post-deletion local geometry is associated with subsequent reappearance risk. Motivated by this finding, we propose a recovery-guided unlearning method that iteratively identifies the most recoverable region around each target and extends the deletion objective to that region. Experiments show that our method improves forgetting persistence across long deletion sequences while maintaining competitive generation quality, highlighting the importance of evaluating diffusion unlearning beyond immediate deletion efficacy.