add README.md (architecture diagram + Mermaid source)

README.md

@@ -77,29 +77,9 @@ The cascade architecture (A gate + B specialist) is the result of **421 autonomo
 
 ## Architecture
 
-```mermaid
-flowchart TB
-    INPUT["Input<br/>(drug_a, drug_b)<br/>e.g. warfarin + ibuprofen"]
-    ENCODE["encode_pair → 193-dim ternary feature vector<br/>• 64 BLAKE2b-128 hash trits per drug (x2 = 128 bits)<br/>• 26 ATC pharmacology flag bits per drug (x2 = 52 bits)<br/>• 13 pair-derived DDI rule bits"]
-    A["A Bundle (gate, 256-hidden)<br/>193 → 256 → 5<br/>ternary weights, Q16.16 biases<br/>bundle_id: 1f0f8859...<br/>50,949 params, 118 KB<br/>100% recall: contra (44/44), major (4/4)<br/>0 contra FP, 0 major FP"]
-    B["B Bundle (tier-2 specialist, 64-hidden)<br/>193 → 64 → 5<br/>ternary weights, Q16.16 biases<br/>bundle_id: 5f7ed5f6...<br/>~12,300 params, 30 KB<br/>trained on non-contra subset (95 samples)<br/>100% recall: serious (69/69), moderate (22/22)"]
-    DISPATCH["Cascade Dispatcher<br/>if A predicts contraindicated → contraindicated<br/>else → B's constrained argmax over moderate / serious / major<br/>composite weights_id = a_id + b_id (129 chars)"]
-    OUT["BitNetResult<br/>severity_name in none, moderate, serious, major, contraindicated<br/>logits_q16: 5x Q16.16 fixed-point logits<br/>feature_hash: SHA-256 over 193-dim feature vector<br/>repro_hash: SHA-256 over feature_hash + logits + severity + weights_id<br/>weights_id: composite a_id + b_id<br/>= bit-identical replay primitive, verifiable on any chip, decades later"]
-
-    INPUT --> ENCODE
-    ENCODE --> A
-    ENCODE --> B
-    A --> DISPATCH
-    B --> DISPATCH
-    DISPATCH --> OUT
-
-    style INPUT fill:#EFF6FF,stroke:#2563eb,color:#1e3a8a
-    style ENCODE fill:#F0FDFA,stroke:#0F766E,color:#134E4A
-    style A fill:#FEF2F2,stroke:#dc2626,color:#7f1d1d
-    style B fill:#EFF6FF,stroke:#2563eb,color:#1e3a8a
-    style DISPATCH fill:#FEF3C7,stroke:#d97706,color:#7c2d12
-    style OUT fill:#F0FDF4,stroke:#16a34a,color:#14532d
-```
+![ClinicalMem BitNet b1.58 — A+B cascade architecture](architecture.svg)
+
+> *Source diagram (Mermaid): [`architecture.mmd`](architecture.mmd). Rendered to SVG with `@mermaid-js/mermaid-cli` v11.x and committed alongside the model so the architecture renders correctly on every viewer (HF model cards, GitHub, GitLab, NPM, etc.) without depending on a Mermaid runtime.*
 
 > **Source**: BitNet b1.58 architecture from Ma, Wang, Ma, et al. ([arXiv:2402.17764](https://arxiv.org/abs/2402.17764)). This is a clean-room Python implementation with **pure-integer Q16.16 fixed-point arithmetic** — no `torch` runtime dep, no GPU required. Training used PyTorch + Straight-Through Estimator on H200 SXM (RunPod).