I knew close to nothing about JEPA before reading your post, but i was under the impression this was supposed to be a contender for LLM's limitations, a way to do things "properly".

However, based on this article, i have the feeling that on the contrary, JEPA is filling a super niche problem, and is pretty much only trying to improve robotic motions in space.

Which of the two ideas is it ? Can JEPA technics be applied to a much more wide range of problems ? (like LLM and transformers)

JEPA can be applied to text as well in the sense you can use it to build text embedding models, freeze your JEPA text embedder, then train a decoder over the top of that.

OF course your decoder model could end up doing a lot of the heavy lifting if your decoder loss was based on text reconstruction methods (which almost all LLMs use for eval purposes)

For certain tasks, you do need pixel/token level reconstruction fidelity to do a lot of things. Like image generation or text generation.

For other tasks, having a coherent representation of the world that can be computed quickly and intuitively is more salient. I suspect the human mind works more like JEPA than like an autoregressive LLM. Human children train on far less data to achieve extreme fidelity on a lot of tasks generative reconstruction loss based AI/ML techniques struggle with.