LeCun用数学证明了AI学到世界内部表征的条件，方法简单但约束明确，对理解智能系统本质很有启发。
LeCun的新论文提出了LeJEPA方法，通过预测相关视图来恢复真实隐变量。该方法要求隐变量服从高斯动力学，否则会失效。在2D到1024维空间测试中，利用LeJEPA学习到的空间进行规划与真实世界规划匹配。论文为AI内部世界模型的正确性提供了数学证明。
Yann LeCun's new paper just proved when AI truly l…
Yann LeCun's new paper just proved when AI truly learns the world.
Most AI systems learn an internal picture of the world.
Nobody could prove that picture is correct.
LeCun's new research finally provides that proof.
It studies LeJEPA, a method that trains models to predict related views.
The result is recovering the true hidden variables behind raw pixels.
This works up to a simple rotation, nothing scrambled.
The trick is elegant.
Linear features stay stable across nearby views, while distortions fade fast.
So the objective is forced to keep only real structure.
One sharp condition makes it work:
> Hidden variables follow Gaussian dynamics > Any other distribution breaks it > Training loss tracks recovery quality
They tested it from tiny 2D cases to 1024-dimensional spaces.
Planning inside this learned space matches planning in reality.
What happens when robot data refuses to stay Gaussian?