The breakthrough lies in radical compression. While typical world models require hundreds of tokens per frame, Lucid v1 compresses each Minecraft frame to just 15 tokens. This 600x reduction in computational complexity enables real-time inference on consumer hardware without sacrificing visual fidelity or temporal consistency.

Our VAE architecture achieves this through aggressive latent space compression combined with GAN-based perceptual loss, preserving essential game mechanics while discarding redundant visual information.

The model employs a causally-trained diffusion transformer that learns not just visual patterns but the underlying physics and logic of the game world. By conditioning each frame on past observations and actions, it maintains coherent cause-and-effect relationships across extended gameplay sessions.

We perform rollouts using a diffusion forcing autoregressive technique, allowing the model to perform endless generation of gameplay.

Lucid v1 demonstrates that extreme compression and causal modeling can coexist. The model successfully captures physics-based interactions, player actions and their consequences, and environmental persistence—all while operating at a fraction of the computational cost of previous approaches.

This represents a fundamental shift in world modeling: from maximizing token count to maximizing information density. The result is the first neural game engine capable of true real-time interaction on consumer hardware.

This breakthrough opens entirely new possibilities for interactive AI systems. When game worlds can be generated in real-time by neural networks, the boundaries between creation and play dissolve completely.

Beyond gaming, this technology enables interactive simulations, training environments, and new forms of digital experience that exist only in the learned representations of neural networks—accessible to anyone with consumer hardware.