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Distance Is Not Enough: Forget-Retain Alignment Gap Predicts LLM Relearning Robustness
Machine unlearning aims to make a model forget specific data, yet unlearned LLMs often fail to stay unlearned: brief fine-tuning can revive removed knowledge. Existing robustness predictors rely on global weight-space displacement, but distance alone can be misleading when random or destructive upda
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