Predictive Memory Localization Forecasts AI Model Intervention Paths

Activation steering allows localized representations within AI models to be used as control directions, but it's often unclear if these directions operate selectively without causing unintended side effects. This research introduces Predictive Memory Localization (PML), a novel approach that reframes memory localization to predict the specific intervention path, rather than just identifying a location. PML focuses on forecasting how an intervention will selectively move a target concept while minimizing damage to semantic neighbors or overall model capabilities. PML distinguishes between desired target movement and collateral damage, comparing static localization methods with a strength-disjoint, low-dose causal response. A comprehensive study involving 3,000 records across nine datasets and fourteen domains, generating 210,000 path-strength evaluations, demonstrated PML's effectiveness. It found that geometry-derived directions achieved significantly better target-any and clean-any outcomes compared to random interventions. Crucially, responses at low intervention strengths (e.g., |α|=0.1) proved to be the strongest signal for predicting outcomes at higher, disjoint strengths. A predictor-driven selector, based on PML, improved utility and reduced semantic-neighbor damage while avoiding most dense scan evaluations. PML consistently yielded high macro AUROC scores across different base models, transforming memory localization into a reliable forecast for selective, risk-aware interventions.