Storage Is Not Strategy: State-Conditioned Support Control for LLM Unlearning

Many localized large language model (LLM) unlearning methods select a small parameter subset from a localization signal and keep it fixed during optimization. The parameters most associated with a target, however, need not be the best ones to update, and candidate interventions can change value as optimization proceeds. In a controlled experiment, a storage-localization score reaches an area under the receiver operating characteristic curve (AUROC) of $0.981$, yet storage identity agrees with the better intervention on only $17/36$ targets, while low-rank adaptation (LoRA) wins $35/36$. We introduce Intervention Score, which ranks editable groups by the predicted effect of the actual unlearning update while accounting for collateral damage, and use it to form the static intervention-value baseline (). We then introduce selective dynamic intervention re-ranking (), which revisits that subset only when a calibrated probe justifies the comparison. On the Natural-TOFU dataset, our method has positive descriptive margins in $19/20$ comparisons between methods and objectives, although several are near zero. On the LACUNA localization-precision benchmark, our mean terminal utility is higher in all six negative preference optimization (NPO) and SimNPO comparisons: NPO margins range from $+0.431$ to $+0.848$, and SimNPO margins range from $+0.503$ to $+0.571$. The gradient-difference (GradDiff) objective reveals substantial field dependence. Relative to , the primary four-field GradDiff evaluation has six wins, six ties, and no losses, with mean and median paired gains of $+0.165$ and $+0.0025$. The evidence supports separating localization, initial intervention selection, and checkpoint-dependent support revision.