The practice of Traditional Chinese Medicine (TCM) requires profound expertise and extensive clinical experience. While Large Language Models (LLMs) offer significant potential in this domain, current TCM-oriented LLMs suffer two critical limitations: (1) a rigid consultation framework that fails to conduct comprehensive and patient-tailored interactions, often resulting in diagnostic inaccuracies; and (2) treatment recommendations generated without rigorous syndrome differentiation, which deviates from the core diagnostic and therapeutic principles of TCM. To address these issues, we develop \textbf{JingFang (JF)}, an advanced LLM-based multi-agent system for TCM that facilitates the implementation of AI-assisted TCM diagnosis and treatment. JF integrates various TCM Specialist Agents in accordance with authentic diagnostic and therapeutic scenarios of TCM, enabling personalized medical consultations, accurate syndrome differentiation and treatment recommendations. A \textbf{Multi-Agent Collaborative Consultation Mechanism (MACCM)} for TCM is constructed, where multiple Agents collaborate to emulate real-world TCM diagnostic workflows, enhancing the diagnostic ability of base LLMs to provide accurate and patient-tailored medical consultation. Moreover, we introduce a dedicated \textbf{Syndrome Differentiation Agent} fine-tuned on a preprocessed dataset, along with a designed \textbf{Dual-Stage Recovery Scheme (DSRS)} within the Treatment Agent, which together substantially improve the model's accuracy of syndrome differentiation and treatment. Comprehensive evaluations and experiments demonstrate JF's superior performance in medical consultation, and also show improvements of at least 124% and 21.1% in the precision of syndrome differentiation compared to existing TCM models and State of the Art (SOTA) LLMs, respectively.

翻译：中医实践需要深厚的专业知识和丰富的临床经验。尽管大语言模型在该领域展现出巨大潜力，但目前面向中医的大语言模型存在两个关键局限：（1）僵化的问诊框架无法进行全面的、针对患者个体的交互，常导致诊断不准确；（2）生成的诊疗建议缺乏严格的辨证论治过程，偏离了中医诊疗的核心原则。为解决这些问题，我们开发了 **JingFang (JF)**，一个基于大语言模型的高级中医多智能体系统，旨在推动人工智能辅助中医诊疗的实现。JF 根据真实的中医诊疗场景，集成了多种中医专科智能体，能够进行个性化的医疗问诊、准确的辨证分型及治疗推荐。我们构建了一个 **中医多智能体协同问诊机制**，其中多个智能体协同工作以模拟真实世界的中医诊断流程，从而增强基础大语言模型的诊断能力，提供精准且个体化的医疗咨询。此外，我们引入了一个基于预处理数据集微调的专用 **辨证智能体**，并在治疗智能体中设计了一个 **双阶段恢复方案**，二者共同显著提升了模型的辨证准确性与治疗方案的合理性。全面的评估与实验表明，JF 在医疗问诊方面表现卓越，并且在辨证精确度上，相较于现有的中医模型和前沿大语言模型，分别取得了至少 124% 和 21.1% 的提升。