Parthenon Law: A Self-Evolving Legal-Agent Framework

The article introduces \textsc{Parthenon}, a self-evolving legal-agent framework designed to enhance the performance of legal-domain large language models (LLMs) by addressing key challenges in the deployment of legal agents. It features a large-scale empirical study with $12,510$ agent trajectories demonstrating that while model accuracy improves with stronger models, matter completion remains inadequate. The framework incorporates a learning loop that allows agents to refine their skills and knowledge based on past performance, facilitating continuous improvement without altering model weights, which is crucial for practitioners aiming to build reliable legal AI systems.