Syndrome, Synergy, and Safety: Structured Reasoning and Knowledge-Driven Alignment for TCM Prescription Generation

The paper introduces a four‑stage framework—SFT, PG‑CoT, Dynamic, and K‑RL—to improve large language models for Traditional Chinese Medicine prescription generation. It addresses three key gaps: lack of auditable reasoning (SR Gap), failure to adjust prescriptions over time (LA Gap), and non‑enforcement of absolute contraindication rules (SC Gap). Experiments on 12 fine‑tuned models and 6 zero‑shot baselines show that the framework, particularly a 7B Mistral model, outperforms zero‑shot GPT‑5 on all three TCM evaluation metrics.

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• llms
• reinforcement-learning
• fine-tuning
• efficiency
• safety