Temporal Stabilization Trajectories in Learned Representations: A Minimal Measurement Framework for Representation Reorganization During Learning

Recent work on learned representations has focused primarily on static observables such as geometric similarity, invariance, robustness, and final performance. However, comparatively less attention has been devoted to the temporal organization of representation reorganization during learning itself. This paper introduces a minimal measurement framework for studying dynamic stabilization trajectories in trained systems. Rather than proposing a theory of cognition or biological learning, the framework defines operational observables intended to evaluate whether representation trajectories contain reproducible structure beyond static representation metrics. We introduce a representation reorganization observable measuring local representation change between successive optimization states during learning. The framework is explicitly methodological and falsifiable: it does not assume universal scaling laws, critical phenomena, or neurobiological equivalence. The central question investigated is whether temporal stabilization trajectories contain predictive information not captured by final accuracy or static representation similarity alone. The proposed framework therefore aims to provide a controlled experimental instrument for detecting, testing, or rejecting structured stabilization behavior in learned representations.