Branch-JEPA: Finite-Support Predictive Distributions for JEPA World Models
Joint-embedding predictive architectures (JEPAs) learn dynamics by predicting future observations in representation space. Yet most JEPA world models return one latent successor, even when hidden intent, partial observation, or stochastic dynamics make several futures plausible. We introduce Branch-JEPA, which replaces this point-valued transition with a context-weighted finite set of latent successors. Every branch is decoded independently, and the complete set is retained at inference. The architecture supports two complementary training regimes: specialization for recovering separated successors and full-set Energy-Score training for distributional fidelity. In a locked five-seed evalu...
| Authors | Zhi Song, Ximing Xing, Zhenchao Tang et al. |
|---|---|
| Primary category | cs.AI |
| Published | 6 July 2026 |
| Last updated at source | 3 August 2026 |
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