@LiorOnAI: Most world models predict what happens next. Sora predicts pixels, JEPA compresses observations. NEO tries to figure ou…
Summary
NEO is a new type of world model that learns to discover reusable building blocks of explanation from raw observations without supervision or language, selected as an ICML 2026 oral presentation.
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Cached at: 07/01/26, 06:13 PM
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.
Sungjin Ahn (@SungjinAhn_): 🚀 We introduce Neural Theorizer (NEO) — a new type of world model that learns to theorize the world from observation, without language or LLM supervision.
Selected as an ICML 2026 oral presentation — 0.7% of submitted papers.
The paper asks:
“What does it mean to understand
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