v4.3.0 — Model Lifecycle

What's new in v4.3.0

From "best experiment" to governed, versioned, production-tracked model. Turing now manages the full model lifecycle — incremental updates without retraining, a formal registry with promotion gates, and enhanced model cards with fairness analysis.

/turing:update — Incremental Model Update

Add new data to an existing model without starting from scratch. Model-specific strategies: continued boosting for XGBoost/LightGBM (add N rounds), fine-tuning with replay buffer for neural networks (configurable old-data ratio to prevent catastrophic forgetting), and partial_fit/warm_start for scikit-learn. Automatically checks for forgetting — if accuracy on old data degrades beyond tolerance, warns and offers rollback.

/turing:registry — Model Registry

Track which model is production, staging, candidate, or archived. Four-stage lifecycle with automated promotion gates:

• candidate → staging: requires regression check PASS + seed study
• staging → production: requires audit PASS + calibration check
• Demotion and archiving with reason tracking
• Full promotion/demotion history with timestamps and gate results

Enhanced /turing:card

• New --include fairness flag: demographic parity and equal opportunity metrics across protected groups
• Registry status section: shows current stage, version, gates passed
• Both integrated automatically when data is available

Integration

• Registry status and update history appear in /turing:brief research briefing
• All commands registered in router, installer, verifier, and scaffold

Numbers

Metric	v4.2.0	v4.3.0	Delta
Tests	1740	1876	+136
Commands	69	71	+2
Scripts	88	90	+2

One phase remaining: 29 (Operational Intelligence).