# Role Activation

Multi-agent collaboration is now sized by how competent the backbone is on the current query: 93.81% average across six benchmarks, and on higher-competence backbones average accuracy rises from 87.22 to 89.97 while token cost per query drops 36.26%.

Multi-agent collaboration is now sized by how competent the backbone is on the current query: 93.81% average across six benchmarks, and on higher-competence backbones average accuracy rises from 87.22 to 89.97 while token cost per query drops 36.26%.

This work analyzes "assistant bias" in LLM user simulators at the level of model activations, extracting a user role vector by contrasting how the model represents user versus assistant perspectives on the same dialogue, and finds that the user direction is identifiable in activations, elicits user-like behaviors, and captures characteristics distinct from assistant traits, while user-role activation associates with simulation realism and steering strengthens it but can also exaggerate user behaviors and override individual user profiles.

This work analyzes "assistant bias" in LLM user simulators at the level of model activations, extracting a user role vector by contrasting how the model represents user versus assistant perspectives on the same dialogue, and finds that the user direction is identifiable in activations, elicits user-like behaviors, and captures characteristics distinct from assistant traits, while user-role activation associates with simulation realism and steering strengthens it but can also exaggerate user behaviors and override individual user profiles.