Social-JEPA: Emergent Geometric Isomorphism
World models compress rich sensory streams into compact latent codes that anticipate future observations. We let separate agents acquire such models from distinct viewpoints of the same environment without any parameter sharing or coordination. After training, their internal representations exhibit a striking emergent property: the two latent spaces are related by an approximate linear isometry, enabling transparent translation between them. This geometric consensus survives large viewpoint shifts and scant overlap in raw pixels. Leveraging the learned alignment, a classifier trained on one agent can be ported to the other with no additional gradient steps, while distillation-like migrati...
| Authors | Youjin Wang |
|---|---|
| Primary category | cs.CV |
| Published | 28 February 2026 |
| Last updated at source | 29 August 2026 |
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