HoloTSH: A Neuro-Symbolic Tensor Logic for TCM Modernization via Mathematical Isomorphism and Theoretical Guarantees

The modernization of Traditional Chinese Medicine (TCM) faces a fundamental epistemological mismatch: TCM operates as a top-down theoretical system, while modern informatics relies on bottom-up inductive logic. This divide creates a "Data Wall" characterized by sparsity and inconsistent interpretability. We propose HoloTSH, a framework establishing a mathematical isomorphism between TCM diagnostics and tensor algebra. Unlike deep learning, HoloTSH: (1) resolves theoretical heterogeneity via Block Term Decomposition; (2) provides theoretical guarantees against "shrinkage bias," preserving chronic signals; and (3) encodes herbal hierarchies via Role-Aware Hypergraph Attention. Our large-scale validation (n = 1, 000) demonstrates that HoloTSH reduces chronic pathology recovery error by ∼9.4× compared to Robust PCA. Furthermore, it maintains robust performance in acute anomaly detection even under 70% missing data, serving as a deterministic reasoning kernel for future AI medical systems.