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SAGE collects real module inputs from a small retain proxy, extracts their dominant activation geometry, and solves a source-anchored optimization ob","title":"SAGE: Retain-Aware Post-Hoc Sanitization of Final Unlearning Vector","url":"https://arxiv.org/abs/2606.18309","vendor":"arxiv_cs_ai"},"summary":"arXiv:2606.18309v1 Announce Type: cross \nAbstract: Large Language Model (LLM) unlearning aims to remove undesirable knowledge or behaviors while preserving retained capabilities. Current unlearning methods all involve a trade-off between unlearning and retention. We have found that the retention activation bias can also be used to quantify the damage an unlearning method inflicts on retention, without considering the specific implementation of the unlearning process. This allows us to restore retention performance for any unlearning method using a post-hoc approach. 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