The Constructibility Framework: Collapse Boundaries, Early Warning, and Dynamic Resource Trajectories in Neural Learning Systems. A Six-Paper Research Monograph

This monograph collects and unifies the six-paper Constructibility research programme (2026), which addresses a fundamental question in the deployment of neural learning systems: can learning collapse be predicted, monitored, and characterised before accuracy failure occurs -- and under what resource conditions can collapse be prevented or reversed?The Constructibility Framework characterises learning collapse as a function of three resource parameters: corruption intensity H, model capacity C, and dataset size n. The central result is the power-law effective capacity constraint L(S) = C^beta * n^gamma / H^alpha (with fitted exponents alpha = 1.42, beta = 0.31, gamma = 0.47) and the sharp collapse boundary H_crit(C,n) = (C^beta * n^gamma)^{1/alpha}.The six papers develop this foundation through: (1) static boundary characterisation with empirical scaling law (AUC 0.91); (2) margin-based early warning M(t) = log L(S)(t) - log R(S)(t) with provable lead-time guarantees; (3) proxy-calibrated monitoring under natural distribution shift (AUC 0.84); (4) multi-agent cascade and synchronization theory (AUC 0.86); (5) fully unsupervised zero-shot deployment monitoring on real longitudinal data (AUC 0.79); (6) Dynamic Constructibility Theory covering hysteresis, metastability, and recovery conditions under time-varying (H(t), C(t), n(t)).Three additional chapters extend the framework into Constructibility Survival Theory (Cox hazard model; concordance index 0.82), Constructibility Control Theory (viability kernel and feedback intervention), and a formal identifiability analysis showing that only the effective stress phi(t) = H(t)^alpha is identifiable from observables -- retroactively stabilising the theoretical foundations.