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) = Cᵇeta * nᵍamma / Hᵃlpha (with fitted exponents alpha = 1. 42, beta = 0. 31, gamma = 0. 47) and the sharp collapse boundary Hcrit (C, n) = (Cᵇeta * nᵍamma) ^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.