Stabilization as an Empirical Constraint on Representation: A Minimal Falsification Framework in Machine Learning and Cognitive Systems

We propose a minimal empirical framework for evaluating stabilization as a measurable constraint on representations in learning systems. Building on a previously defined stabilization functional, we operationalize stabilization in terms of representational coherence under transformations induced by learning dynamics, perturbations, and re-encodings. We derive testable hypotheses in machine learning settings, including continual learning, robustness under distribution shift, and representation compression. The framework does not assume a theory of cognition or a specific architectural model, but instead investigates whether stabilization correlates systematically with robustness, compressibility, and degradation under sequential updates. The goal is to provide a falsifiable and domain-agnostic evaluation of stabilization as a structural property influencing representational viability in learning systems.