Beyond Spread: Distinct Organizational Pathways in Predictive JEPA

Existing analyses of geometric regularization in predictive JEPA often emphasize endpoint properties such as feature spread, isotropy, or collapse avoidance. We argue that these endpoint summaries are too coarse to explain how different regularizers shape predictive representations during training. Motivated by observations from a shell-based neighborhood regularization strategy, we compare shell-based neighborhood regularization (shell_context_knn), whitening decorrelation, VICReg-style regularization (vicreg_like), and a repaired SIGReg-like Gaussian matching configuration (sigreg_like) in a controlled PatchJEPA setting. We observe consistent differences in linear probe accuracy, effective rank, and trajectory dynamics across methods. In particular, shell and whitening reach similar endpoint probe performance through visibly different training trajectories, while SIGReg exhibits stronger context-prediction coupling but weaker structural expansion and lower final probe. We further show that these pathway differences remain visible in a joint rank-coupling state space, persist under low-cost hyperparameter perturbations, and respond sharply to mid-training intervention. When the regularizer is switched at step 100, context-prediction coupling rapidly follows the new regularizer style, whereas final probe remains only partially plastic. These patterns motivate an organizational view of regularization in which fast coupling variables and slower predictive-structural variables separate in time. In our controlled setting, these pathways provide a diagnostic observational framework that complements spread-based summaries for understanding downstream behavior.