Rongxin Cheng

Self-Refine Learning in LLM Multi-Agent Systems for Legal Norm Cognition and Compliance

As large language models (LLMs) increasingly serve as autonomous agents in social simulations, ensuring their ability to understand and comply with legal norms is essential. Yet, current LLM agents frequently exhibit reward hacking (RH) behaviors by optimizing metrics at the expense of norm adherenc