Emerging joint-embedding predictive architectures (JEPAs) are moving beyond representation learning toward latent world models that support prediction, planning, and control. These models do not simply map inputs to features; they build internal worlds that can be used for reasoning about future states and actions.
According to the abstract, however, these latent worlds naturally lack a certain quality—the text is truncated, but the title and framing make clear the focus is interpretability. The paper positions interpretability as a key challenge for JEPAs, suggesting that understanding what these latent worlds encode is essential for trusting and deploying them.
The proposed direction is to move from decoding individual representations to understanding the latent world as a whole. This shift implies that interpretability research must adapt to the new structure of world models, where the object of study is not a single feature but an entire internal environment.