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structural constraints, excessive representationalvariance causes the model to collapse to trivial solutions.The recent LeWorldModel (LeWM) shows that this issue can be alleviated bysimply constraining latent embeddings with an isotropic Gaussian prior.However, latent representations inherently lie on low-dimensional manifoldswithin a high-dimensional ambient space, and enforcing an isotropic Gaussianprior directly in this ambient space introduces an overly strong bias.In this work, we propose ame, which seeks a favorable operatingpoint on the bias-variance frontier by applying Gaussian constraints inmultiple random subspaces rather than in the originalembedding space.This design relaxes the global constraint whil","title":"Sub-JEPA: Subspace Gaussian Regularization for Stable End-to-End World Models","url":"https://arxiv.org/abs/2605.09241","vendor":"arxiv_cs_ai"},"summary":"arXiv:2605.09241v1 Announce Type: cross \nAbstract: Joint-Embedding Predictive Architectures (JEPAs) provide a 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