Governed Continual Training Regimes

Governed Continual Training Regimes (GCTR) is a framework for changing models, adapters, routers, semantic substrate, and deployment standing without treating evolution as an ambient side effect of optimization. It extends Governed Training Regimes from admissible parameter transitions to admissible evolution of a model participant inside a living semantic system. The central design heuristic is: do not put the changing world in the weights when an inspectable, updateable substrate can carry it. Current facts, sources, warrants, registries, and revocations normally belong in governed substrate. Bounded behavioral adaptations may belong in adapters or routing policy. General participant competence may require periodic consolidation. Recurrent frontier maneuvers may be crystallized into deterministic semantic operations. This is a routing presumption, not a literal prohibition: base models inevitably encode world regularities, and a single event may require coordinated changes across several layers. GCTR trains a model to be a better semantic scout: to retrieve, offload, challenge, refuse, preserve evidence boundaries, propose candidate structure, and recognize missing closure. It does not make a model, receipt, scorecard, benchmark, or crystallization process sovereign. Model evolution remains a governed transition with declared authority, scope, evidence, privacy, rollback, standing, temporal validity, and revision. This revision makes the operative vocabulary explicit; separates target state from governance history; expands the formal system state beyond model parameters; treats hot, warm, cool, and cold freshness as non-exclusive routing profiles; adds layer-specific forgetting claims; binds receipts to limited proof roles; integrates the universal GSM and realization funnel; and supplies countertests and author decisions. No external literature verification, training, evaluation, or implementation was performed.