AI造了个能一直玩的虚拟世界，你说话它就变
你见过那种游戏里一进去就固定死的场景吧？这篇论文搞了个AI，你给它一张图或一句话，它就能生成一个你可以自由探索、而且会持续演变的虚拟世界。关键不是生成一段视频就完事，而是你操作时它实时响应，画面不跳帧、不穿帮，还能一直稳定生成下去——比如你让角色往前走，AI就持续生成新的场景，而且前后逻辑连贯。它用了一个150亿参数的模型，把生成拆成短片段自动拼接，还用了特殊记忆机制防止画面漂移。最狠的是，它把原本需要30步的推理压缩到4步，速度够快才能让你玩起来不卡。目前它还不是你明天就能用的产品，但方向很明确：未来游戏可能不再需要程序员一帧帧做，你说句话，AI就给你造一个活的世界。
📄 原文摘要(英文)
Unlike conventional video game development, which relies on labor-intensive pipelines for asset production, animation, physics, and programming, video world models generate interactive environments from user inputs instantly. It enable us to create customized, explorable, and continuously evolving virtual world from text, an image, or video. Realizing this vision requires four tightly coupled capabilities: interaction, persistent spatiotemporal consistency, stable long-horizon generation, and efficient response. We present AlayaWorld, an interactive long-horizon video world model that generates 24-fps video at 540p and 720p. Built on a 15B video diffusion transformer, AlayaWorld generates short latent chunks autoregressively under camera trajectories and switchable text prompts. Its 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 roll-outs. We further 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. On iWorld-Bench, AlayaWorld achieves the best performance over long-horizon generation. Conceived as a full-stack, open-source, and long-term project, AlayaWorld is intended to provide an extensible foundation for future research on interactive video world models.