Artificial intelligence systems demonstrate strong capabilities in optimisation and pattern recognition but remain vulnerable to instability during training, including divergence, loss spikes, and collapse. Existing stabilisation methods operate within optimisation frameworks and are applied reactively. This paper introduces admissibility control as a structural mechanism for stabilising AI systems prior to collapse. Using the Pressure-Flow Language Extension (PFL-X), training dynamics are expressed as flows of input, evaluation, and continuation under constraint. Instability is identified as high-density evaluation (>>O<<), and stabilisation is achieved through hinge-mediated deviation and fallback to admissible configurations. The framework is domain-neutral and operates prior to optimisation, providing a structural control layer for AI training systems.
Andrew John Paton (Tue,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: