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LeCun’s Joint-Embedding Predictive Architecture (JEPA) and its recent implementation in LeWorldModel represent an elegant and efficient step toward stable world-model learning from raw pixels. However, the core regularization mechanism; SIGReg (Spherical Isotropic Gaussian Regularizer), contains a subtle but fundamental architectural flaw: it bounds all data to a statistical center point and thereby reinforces bad values (noise, hallucinations, outliers) rather than rejecting or damping them. This short note formalizes that flaw mathematically, contrasts it with a superior active-regulation approach, and provides a long-context, reproducible simulation (10,000 steps) that demonstrates the difference in practice. Two concrete, lightweight fixes are proposed: one general and one that stays strictly within the JEPA/LeWorldModel paradigm. *DISCLAIMER* - The LeWorldModel-themed fix (entropy-gated latent damping) is offered as a constructive contribution that stays entirely within the existing JEPA/LeWorldModel paradigm and can be implemented immediately. The general/RHEA-style fix remains under RHEA Core Public Grant v2.1 and is not public-domain.