Paired-unit activation steering for context-aware protein design

Activation steering in protein language models enables controllable sequence generation without changing the model weights. However, existing methods typically construct steering vectors from global differences between property-positive and property-negative protein populations, overlooking how property-improving directions vary across different protein sequence backgrounds. To address this, we propose \textit{Paired-Unit local steering of embeddings (PULSE)}, which matches positive- and negative-property proteins by maximizing their representation cosine similarity and constructs steering vectors by aggregating similarity-weighted, unit-normalized differences between matched pairs. Across three protein optimization objectives--solubility, immunogenicity, and toxicity--PULSE consistently outperforms global population-centroid steering in both final predicted property scores and optimization success rates. Further analysis reveals that conventional static steering-vector diagnostics cannot adequately explain optimization performance. To characterize steering reliability during iterative sequence editing, we introduce \textit{Local Directional Support (LDS)}, which measures how well successive representation changes align with property-improving directions from similar protein backgrounds. We show that LDS captures differences in trajectory reliability and enables predictor-free candidate prioritization, enriching successful protein designs without requiring property predictions during candidate selection. Together, these findings demonstrate the importance of context-aware steering and trajectory-level evaluation for controllable protein sequence optimization.