Multi - agent Cooperative Mechanisms for Legal Adjudication: The Crucial Role of Automatic Prompt Optimization

Abstract

In legal AI, prompt engineering unlocks legal knowledge by bridging experts and LLMs. These prompts, often not following human language conventions, boost LLMs' NLP performance with minimal data, yet task-specific optimization remains challenging. Current soft-prompt methods lack interpretability and cross-linguistic adaptability, while general optimization approaches prove time-consuming and fail to integrate legal reasoning. This paper proposes LAC-APO, a multi-agent collaborative framework based on Toulmin's model that incorporates judges' implicit knowledge. Through Logical combing - Knowledge injection - Error induction - Prompt adjustment process, it systematically converts implicit expertise into explicit LLM outputs. Experiments show LAC-APO outperforms manual optimization and existing competitiveness prompt optimization methods, enhancing efficiency while maintaining legal reasoning integrity.