Alpha-TCM makes two contributions under a central thesis: auditable systems should separate domain knowledge, case evidence, probabilistic inference, and downstream authority.
First contribution
First, it proposes a nine-layer TCM-oriented architecture covering semantic definitions, multimodal acquisition, quality control, provenance-bearing knowledge, probabilistic inference, longitudinal updating, constrained decisions, language interaction, and human governance. Each layer specifies its information contract, failure boundary, dependencies, and validation status. This is a system-design proposal, not a claim of complete clinical implementation.
Second contribution
Second, a controlled synthetic prototype instantiates Layers 1-5 to audit the knowledge-evidence interface.
- Four conditional-Gaussian encoders process synthetic tongue images, pulse waveforms, inquiry fields, and text, while a frozen bridge predicts a five-dimensional synthetic outcome.
- A versioned contextual prior is compiled independently of the renderer.
- Fusion removes duplicated reference-prior information before adding context.
- Equal-information controls, prior regularization, graded contamination, and structural corruption test contextual benefits, Bayesian specificity, and conflict detection.
- Across ten paired seeds and four training sizes, contextual knowledge improved mean absolute error over marginal committee fusion at 600 and 1,200 cases, but not over equal-information neural controls.
- Fully incorrect context harmed performance at every size, while prior effects were inconsistent and conflict scores did not track case-level damage.
- The results support contextual structure in low-data synthetic settings, not a specifically Bayesian advantage.
- The prototype establishes no claims about TCM theory or clinical, diagnostic, therapeutic, causal, longitudinal, or transfer validity.