Beyond Cooperative Simulators: Generating Realistic User Personas for Robust Evaluation of LLM Agents
The research introduced Persona Policies (PPol), an evolutionary program search framework to create diverse, human-like user personas for testing LLM agents. Evolved programs achieved significant fitness gains by optimizing Python generators to produce realistic behavioral variations, leading to more robust and reliable agent evaluations.