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This paper introduces Predictive Memory Localization (PML), a method that forecasts selective intervention outcomes from internal model signals, distinguishing calibrated target movement from semantic-neighbor and capability damage. Experiments across 3,000 records and nine datasets show improved selective-path prediction and risk-aware intervention decisions.