Identifiability Without Gaussianity: Symbolic World Models and Near-Infinite Temporal Consistency
Klindt, LeCun, and Balestriero (arXiv:2605.26379) proved that Joint-Embedding Predictive Architectures (JEPAs) achieve linear identifiability, the linear recovery of the world's true latent variables, if and only if the world's latent dynamics follow a Gaussian, stationary process. This Gaussian boundary implies a fundamental limit on temporal consistency: for any non-Gaussian physical system, the representation error of a statistical World Model grows monotonically with time. We prove that this limit is an artifact of the statistical alignment mechanism, not a property of World Models in general. We introduce the Physics-Grounded Symbolic Architecture (PGSA) and prove three results: (1) ...
| Authors | Seth Dobrin, Łukasz Chmiel |
|---|---|
| Primary category | stat.ML |
| Published | 9 June 2026 |
| Last updated at source | 6 July 2026 |
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