Pebblous Data Communication Team · 한국어
A world model is an attempt to let AI represent the world not as a flat stream of pixels or tokens, but as an internal model of how things move and influence one another. When a self-driving car anticipates where a pedestrian will step next, when a robot mentally simulates the outcome before grasping an object, when a video generation model paints physically plausible scenes one after another — all of it rests on an internal model of the world. That is why world models have become the single thread running through fields as far apart as autonomous driving, robotics, and video generation.
Intriguingly, the idea splits into two paths. One is the path of understanding the world. Approaches like Yann LeCun's JEPA and DeepMind's Dreamer try to learn how the world works in an abstract representation space, rather than reconstructing every pixel. The other is the path of predicting and generating the future. Approaches like Sora and Genie simulate the world by directly producing the scenes that come next. Both go by the name "world model," but their goals and methods differ — and reading them as two distinct currents brings the current landscape of AI research into much sharper focus.
This hub gathers the five articles Pebblous has written on world models in one place. From a five-level primer for readers new to the concept, to a comprehensive survey mapping the whole terrain, a deep dive into JEPA, a comparison of three approaches, and the limits that VLM and VLA have run into — the pieces are arranged to flow naturally from introduction to depth.
Start here. The single concept of a world model explained across five levels of difficulty, from elementary school to wizard. Grasp the intuition through analogy, then add depth step by step.
A map of the entire world-model terrain at a glance. A survey covering the split between the understanding track and the generative track, the lineage of major models, and the flow of research. Start here if you want the big picture first.
The path Yann LeCun chose instead of generative AI: JEPA. A technical deep dive into learning the world in an abstract representation space rather than reconstructing every pixel.
Jeff Hawkins, Yann LeCun, and Fei-Fei Li — three world models, three approaches, set side by side. A look at how they take different paths toward the same goal.
Seeing and understanding are not the same. This piece examines the limits of VLM and VLA — processing visual information without truly understanding the world — and explains why world models are emerging as the next step.