@LiorOnAI
Most world models predict what happens next. Sora predicts pixels, JEPA compresses observations. NEO tries to figure out why something happened instead. Example: show it a shape moving left then down, and instead of just reconstructing that motion, it learns "left" and "down" as separate reusable building blocks then reuses them elsewhere. Instead of one big black-box model, NEO searches for a short "program" made of simple reusable steps that explains what it saw. The interesting bit isn't that it learns programs. It's that it discovers the building blocks of explanation on its own, no labels, no hand-coded symbols, just raw observation.