Conditional Intervention Selection for Efficient Activation Steering

Activation steering adapts frozen models by intervening on internal representations, yet conventional relevance scores do not reveal whether neurons remain useful when intervened on jointly. We formulate sparse steering-site localization as budgeted conditional selection and introduce SCOUT, which constructs a relevance-ranked working set, accepts neurons according to their measured utility conditioned on the current selection, and calibrates the resulting displacement into static neuron-wise scaling for single-forward deployment. Across the evaluated model scales, SCOUT achieves positive mean accuracy gains of up to 3.44 percentage points while selecting only 11–16 neurons on average. This corresponds to an approximately 84–99.6% reduction in intervention sites relative to baselines using 100 to 4096 sites. Selection ablations further show that conditional utility consistently improves over relevance-only and independent-utility alternatives. These results establish conditional intervention selection as an accurate and highly sparse approach to activation steering.