PRISM: Progressive Prescription Reasoning via Integrated Hierarchical Latent and Role-aware Symptom-herb Modeling for Prescription Recommendation

Traditional Chinese Medicine (TCM) prescription recommendation aims to generate effective herb combinations from complex symptom patterns. However, existing approaches suffer from three limitations: insufficient hierarchical modeling of symptom-herb relationships, failure to simulate hierarchical prescription construction, and lack of interpretable reasoning trajectories. In this paper, we propose PRISM (Progressive Prescription Reasoning via Integrated Hierarchical Latent and Role-aware Symptom-herb Modeling), a progressive reasoning framework for TCM prescription recommendation. PRISM introduces hierarchical symptom-herb graph reasoning to capture multi-level clinical dependencies, adaptive main-auxiliary prescription reasoning to model progressive prescription construction, and progressive latent prescription states to provide interpretable reasoning trajectories. Extensive experiments on widely used TCM prescription datasets demonstrate that PRISM consistently outperforms existing baselines. The source code and datasets are publicly available at https://anonymous.4open.science/r/PRISM-A452/.