💡 The paper presents AlayaWorld, an interactive long-horizon video world model that generates interactive environments from user inputs instantly. Unlike conventional video game development, which relies on labor-intensive pipelines for asset production, animation, physics, and programming, video world models like AlayaWorld enable the creation of customized, explorable, and continuously evolving virtual worlds from text, image, or video.

To achieve this, AlayaWorld requires four tightly coupled capabilities: interaction, persistent spatiotemporal consistency, stable long-horizon generation, and efficient response. The model is built on a 15B video diffusion transformer and generates 24-fps video at 540p and 720p. It produces short latent chunks autoregressively under camera trajectories and switchable text prompts.

The model's bounded visual context combines a persistent sink frame, compressed temporal history, geometry-aligned spatial memory, and recent-frame conditioning. To reduce long-term drift, the model is trained with corrupted histories and prediction residuals collected from its own rollouts. The authors also introduce a discrete autoregressive distillation formulation that combines distribution-matching distillation, self-forcing++, and consistency distillation, reducing inference from approximately 30 sampling steps to four steps per chunk.

The results show that AlayaWorld achieves the best performance over long-horizon generation on the iWorld-Bench dataset. The model is conceived as a full-stack, open-source, and long-term project, intended to provide an extensible foundation for future research on interactive video world models. Overall, AlayaWorld's contributions include its ability to generate interactive and stable long-horizon video, its efficient response to user inputs, and its potential to enable the creation of complex and evolving virtual worlds.