Yann LeCun's new paper asks when LeJEPA truly learns hidden world variables, and finds Gaussian structure is the key.
Means LeJEPA can only reliably learn the real hidden causes behind what it sees when those causes are shaped like a balanced Gaussian cloud.
The paper proves that, when the true hidden variables are independent Gaussian variables and the paired views come from a stable noisy process, the best LeJEPA solution must recover those variables up to a rotation or flip.
The paper gives a math reason for when a self-supervised AI model is really learning the structure of the world, not just making useful features that happen to work on a test.
----
Link – arxiv. org/abs/2605.26379
Title: "When Does LeJEPA Learn a World Model?"
