[
    {
        "id": "osp-21732",
        "type": "article-journal",
        "title": "Storage Is Not Strategy: State-Conditioned Support Control for LLM Unlearning",
        "author": [
            {
                "family": "Qian",
                "given": "Tianhao"
            },
            {
                "family": "Hong",
                "given": "Ziming"
            },
            {
                "family": "Gao",
                "given": "Chongyang"
            },
            {
                "family": "Chen",
                "given": "Kezhen"
            },
            {
                "family": "Wang",
                "given": "Lixu"
            }
        ],
        "URL": "https://omanscience.com/ar/articles/storage-is-not-strategy-state-conditioned-support-control-for-llm-unlearning",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "abstract": "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 (Static-IV). We then introduce selective dynamic intervention re-ranking (DIR-R), 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 Static-IV, 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."
    }
]