What does this research mean for the field?

Constructibility Survival Theory (CST) accurately predicts time-to-failure and optimal intervention timing in neural learning systems by modeling the hazard rate of collapse using the constructibility margin. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

This research aims to connect the margin of safety to time-to-failure predictions in neural learning systems.

June 9, 2026Open Access

Constructibility Survival Theory: Hazard Rates, Expected Safe Lifetime, and Intervention Urgency in Neural Learning Systems From Early Warning Signals to Time-to-Failure Prediction

Key Points

This research aims to connect the margin of safety to time-to-failure predictions in neural learning systems.
Introduced Constructibility Survival Theory (CST) linking margin to survival analysis.
Utilized a proportional hazards model to assess hazard rates based on system margins.
Validated the theory on 6 real longitudinal datasets, estimating hazard rates and optimal intervention times.
Survival function and expected safe lifetime were derived from the model.
Monotone hazard ordering theorem was established, providing insights on hazard rates.
The model achieved a concordance index of C = 0.82 ± 0.04, indicating strong predictive capability.

Abstract

Papers 16 of the Constructibility series established collapse boundaries, early warning signals, proxy estimation, multi-agent dynamics, zero-shot deployment monitoring, and dynamic resource trajectories. Each paper characterised proximity to collapse via the margin M(t). None addressed the fundamentally dierent question of time-to-failure: how long will the system remain constructible, and how urgently should an operator intervene? We introduce Constructibility Survival Theory (CST), connecting the margin to survival analysis. The hazard rate of collapse at time t is modelled as: λ(t|M(t),˙M(t)) = λ0(t) exp−βMM(t) + β˙M˙M(t), (1)a proportional hazards (Cox) model where M(t) is a time-varying protective covariate and M(t) is a risk covariate. Three theoretical results: (1) survival function and expected safe lifetime; (2) monotone hazard ordering theorem; (3) optimal intervention timing. Validated on 6 real longitudinal datasets. Concordance index C = 0.82 ±0.04.

Constructibility Survival Theory: Hazard Rates, Expected Safe Lifetime, and Intervention Urgency in Neural Learning Systems From Early Warning Signals to Time-to-Failure Prediction

Key Points

Abstract

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