# PULSE

Protein language models can be steered toward a target property without changing their weights, but existing activation steering builds the direction from the difference between the global means of property-positive and property-negative proteins, ignoring that the useful direction shifts with sequence background. PULSE first matches positive and negative proteins by representation similarity, then aggregates similarity-weighted, unit-normalized differences between matched pairs into a steering vector. On solubility, immunogenicity and toxicity optimization, PULSE beats global centroid steering in both final predicted property scores and optimization success rates, with the largest score gain on toxicity and the largest success gain on immunogenicity.

Protein language models can be steered toward a target property without changing their weights, but existing activation steering builds the direction from the difference between the global means of property-positive and property-negative proteins, ignoring that the useful direction shifts with sequence background. PULSE first matches positive and negative proteins by representation similarity, then aggregates similarity-weighted, unit-normalized differences between matched pairs into a steering vector. On solubility, immunogenicity and toxicity optimization, PULSE beats global centroid steering in both final predicted property scores and optimization success rates, with the largest score gain on toxicity and the largest success gain on immunogenicity.