Abstract: To address the challenges of automated judicial document generation for criminal cases,including limited accuracy in sentencing prediction,limited adaptability to complex case circumstances,and insufficient compliance with formal writing stan-dards,this paper proposes a multi-agent collaborative automated generation method for judicial documents(MAC-AG).Following an “understanding-planning-execution-generation” pipeline,MAC-AG is built on a collaborative framework composed of four specialized agents.The case fact analysis agent,powered by a general-purpose large language model(LLM),is responsible for factual determination tasks such as case element extraction,dispute focus identification,and case type classification.The case classification agent incorporates retrieval-augmented generation(RAG) to retrieve relevant legal provisions and similar cases,thereby supporting charge determination and simple-complex case routing.The judgment prediction agent employs a deep learning regression model to calibrate the LLM’s preliminary judgment predictions,improving the accuracy of sentence term and fine prediction.The document generation agent,built on a domain-supervised fine-tuned LLM,ensures that the generated text conforms to judicial writing conventions and formatting requirements.Experiments conducted on JuDGE,a benchmark for judicial document generation in the Chinese legal system,the results show that:1)MAC-AG consistently improves the document generation performance of seven baseline LLMs,with Qwen3-8B@MAC-AG achieving the most significant gains in sentencing prediction,charge determination,legal article citation,and semantic quality,demonstrating the generalizability of the proposed method;2)Compares with the state-of-the-art MRAG method,Qwen3-8B@MAC-AG improves the F1 score for charge prediction by 2.5%,the F1 score for legal article citation by 11.4%,and overall semantic similarity of judicial documents by 14.01%;3)Ablation experiments confirm that each agent is indispensable,as removing any component leads to a marked performance decline,highlighting the effectiveness of task decoupling and inter-agent collaboration;and 4)In human evaluation,Qwen3-8B@MAC-AG achieves an average score of 4.79/5.00,outperforming baseline methods across six dimensions:reasoning,logical consistency,norm compliance,completeness,readability,and value balancing.Overall,this study provides a reproducible multi-agent collaboration paradigm for judicial intelligence and substantially improves the accuracy of sentencing prediction,the rigor of legal reasoning,and the practical applicability of automated judicial document generation.

Key words: Multi-agent collaboration, Judicial document generation, Criminal cases, Digital court, Chain-of-Thought, Retrieval-augmented generation