CGUS: Conditional Gate and Up-Branch Steering for Personality Composition

Personality steering helps tailor large language models to different users and roles. However, directions extracted in isolation may not match the activation states encountered during composition, limiting fine-grained control. We propose Conditional Gate and Up-Branch Steering (CGUS), a framework with two complementary modules. The gate module identifies and edits sparse activations associated with broad personality domains and their facets. The conditional vector module extracts steering directions under specified gate interventions and adds them to the parallel up-projection branch, matching vector construction to the intervened state. Together, the modules support combining facet components on a shared personality controller, with high and low targets constructed separately against neutral references. We use local IPIP-300 questionnaires to assess broad-personality and facet steering, and external language-model ratings to assess personality expression and fluency on PersonalityBench scenarios. Preliminary results suggest improved broad-personality expression.