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This paper introduces Statistically Meaningful Geometry (SMG), a geometric framework for modeling over-parameterized learning systems as infinite-dimensional non-parametric Orlicz fiber bundles. It proposes that under out-of-distribution stimuli, the system undergoes a gauge symmetry break, leading to the emergence of new causal axes that can distinguish genuine scientific discovery from hallucinations.