AlayaWorld is a long-horizon interactive video world model open-sourced by Alaya Lab on July 8, under the Apache-2.0 license. The project page and technical report are already online (inference code, pretrained weights, training code, and dataset will be released in batches). It unifies the three hard problems of "real-time camera control + prompt switching + long-horizon memory consistency" into an autoregressive framework, maintaining scene recognizability and trajectory stability in minute-level rollout scenarios. Technically, AlayaWorld is built around four properties: interactivity — 3D render cache + lightweight AdaLN camera modulation handle navigation, with chunk-level prompt switching injecting new events mid-generation; consistency — explicit 3D cache re-projects spatially by viewpoint, compressed frame-history embedding maintains temporal continuity, and the model can still recognize old locations when returning; stability — drifted history is introduced during training, paired with an error bank that feeds accumulated error back into memory and targets, preventing the minute-level rollout error snowball; real-time — few-step DMD distillation + short temporal block generation, with prompt switching at chunk boundaries, lowering visual and semantic latency. What's worth noting is its engineering route: the Roadmap puts weights, training code, and training data on the open-source plan in one go, rather than only releasing a demo. The core members belong to Shanda Group (contact email on shanda.com), a world-model open-source project led by a Chinese team, together with Amap's ABot-World and Alibaba's Wan series, forming another pole of the "open-source wave" of world models in July. Commentary: today's open-source world models are mostly stuck at "short clips + constrained camera"; AlayaWorld takes the path that's more product-like — playable, interactive, minute-level runnable — and the combination of 3D cache and DMD distillation is a pragmatic fork of Sora's end-to-end paradigm.