R-JEPA: A Relational World Model That Predicts Before It Observes

Relational reasoning from a few examples is usually studied by naming the shared relation or scoring candidate answers, while joint embedding predictive architectures (JEPA) hold that predicting a target without negatives is the better objective. These views obscure what a relation does to a state, when that operation can stay fixed, and when predicting the state beats choosing among candidates. We argue that relational reasoning is better understood as an inference cycle in latent space, where a relation induced from example pairs predicts an unobserved state that is reused as the next input and checked against later observations. Based on this view, we propose R-JEPA, a relation reader and a transition operator that returns a state, trained by latent prediction or classification or used without learned parameters. Across knowledge graphs and systems described to language models, the predicted state follows edits of the knowledge, composes over grounded steps and ranks facts missing from a 2019Wikidata snapshot as well as the true relation does. How consistently roles form directions, measured across seven language models, decides whether the transition must be learned, and latent prediction needs no negatives, while classification with one negative per query trails it by up to 0.13. These results suggest that relational reasoning should be evaluated by prediction before observation, and that latent prediction pays where classification cannot see enough negatives.