Identifiability Without Gaussianity: Symbolic World Models and Near-Infinite Temporal Consistency

The article presents the Physics-Grounded Symbolic Architecture (PGSA), which achieves exact linear identifiability and near-infinite temporal consistency across all physical regimes, regardless of latent distribution. Unlike traditional Joint-Embedding Predictive Architectures (JEPAs), which are limited by Gaussian assumptions, the PGSA maintains per-step error bounded solely by numerical precision, enabling consistent performance even in non-Gaussian systems. This advancement is significant for practitioners as it offers a robust framework for modeling complex dynamics without the constraints of Gaussianity, enhancing the reliability of temporal predictions in AI applications.