Dynamic Constructibility Theory: Hysteresis, Metastability, and Recovery Dynamics in Neural Learning Systems

The Constructibility Framework characterises learning collapse as a function of static resource parameters (H, C, n). Real deployments involve dynamic trajectories where all three parameters evolve simultaneously. We introduce Dynamic Constructibility Theory (DCT), extending the framework to systems S(t) = (H(t), C(t), n(t)) with coupled resource dynamics. Four theoretical results: (1) Hysteresis: the constructibility margin M(t) exhibits path-dependent recovery -- a system that has traversed the collapse boundary recovers more slowly than a system that has not, due to risk surface inertia; (2) Metastability Bound: the expected time in the near-collapse region is bounded below by a Kramers-type escape expression; (3) Recovery Condition: the required capacity increase to restore safety grows exponentially in collapse depth -- formalising why early warning is necessary, not merely convenient; (4) Oscillatory Crossing Structure: non-monotone H(t) creates alternating constructible/non-constructible regimes. Validated on four simulated scenarios and longitudinal real data from Papers 3 and 5 (MIMIC-IV clinical NLP and financial sentiment, 2018-2024). Hysteresis coefficient 2.1 +/- 0.4. Recovery cost predictions within 10-13% of observed values. Phase-aware alarm achieves accuracy 0.87 +/- 0.04 vs. 0.71 for standard CUSUM.This is Paper 6 (series-closing) of the six-paper Constructibility series.