MM-LeJEPA: Correlated Gaussian Regularization for Multimodal Joint-Embedding Predictive Architectures

Haowen Bai, Rui Gong$, Meichen Liu, Ming-Xuan Weng, Bihan Wen +1

LeJEPA derives an isotropic Gaussian embedding target by minimizing, under its task assumptions, a Fisher information criterion that controls the task-averaged squared prediction bias. This enables unimodal self-supervised learning without reconstruction decoders, exponential-moving-average teachers, or contrastive negatives. However, its isotropic task prior does not account for the cross-modal structure of multimodal tasks that read shared content. We introduce MM-LeJEPA by extending LeJEPA's task assumptions to multimodal learning. Specifically, we model multimodal tasks as strength-weighted combinations of components that read independent information from the two modalities and components that read the same content from both. Under explicit gradient-moment, correspondence, and symmetry assumptions, we derive a correlated Gaussian target that minimizes a weighted Fisher information functional under a fixed total variance constraint. An analytic transform adapts Sketched Isotropic Gaussian Regularization (SIGReg) to this target, yielding a two-term objective that combines distributional regularization with local-to-global prediction. The extension adds a single scalar…