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PAVA pairs a forget loss with a visual-attribute anchor that preserves image-grounded behavior by distilling the model's own pre-unlearning answers from the forget images alone and gives the strongest forget-retain trade-off among forget-set-only methods and remains competitive with retain-based baselines.
AIM is proposed, a two-stage method that anchors an identity-forgetting target with a universal visual prompt and then matches the vision encoder to that target under a Fisher-based constraint, which achieves competitive identity forgetting while preserving non-deleted identities, prior knowledge, and visual perception...
This work identifies batch prompting as a distinct safety failure mode, not reducible to known vulnerabilities such as in-context learning or long-context effects, and analyzes its causes from two complementary perspectives: alignment signal weakening and refusal signal dilution.
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