Morning Edition · Thursday, July 30, 2026Published at 1:37 AM EDT · New York
Sakana AI and NYU Train a Diffusion Transformer to Generate Editable Minecraft Worlds
Dream-Cubed learns from billions of blocks with a 280-million-parameter 3D diffusion model, treating each cube as a token for controllable, inpaintable terrain.
Sakana AI and New York University have released Dream-Cubed, a system that generates playable, controllable Minecraft worlds by learning directly at the native block resolution. Each 32-by-32-by-32 chunk is fed into a roughly 280-million-pa…
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Part of a tracked trend
Interactive, Controllable World Models
Generative models increasingly produce editable, structured 3D environments rather than passive media, pushing world models toward simulation, games, and robotics training.
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