AlayaWorld v1.1报告出来了，空间记忆换成了3D点缓存渲染，条件编码统一到因果VAE，做世界模型的值得瞄一眼。
AlayaWorld v1.1技术报告发布，提出改进版交互式长时程世界建模。新版本将空间记忆由深度扭曲法改为流式3D点缓存渲染器。视觉条件被编码到与生成视频一致的因果VAE潜空间中。具体包括六处修改：采用运动感知潜条件、因果编码重渲染空间记忆等。报告未改变主干架构与分块自回归生成方案。
AlayaWorld: Interactive Long-Horizon World Modeling - Full Technical Report (v1.1)
This report presents an improved version of AlayaWorld. While the backbone architecture, chunk-wise autoregressive generation scheme, and training data remain unchanged from the previous release, we substantially revise how conditioning signals are represented and integrated into the model. The new design is guided by a simple principle: conditioning signals should match the generated content as closely as possible in both latent representation and temporal structure. To this end, we make two major changes. First, we replace the previous depth-warping-based spatial memory with a streaming 3D point-cache renderer. Second, we redesign the conditioning pipeline so that visual conditions are encoded in the same causal-VAE latent space, with temporal statistics consistent with those of the generated video. Concretely, the new version introduces six modifications: (1) replacing static-frame image conditioning with motion-aware latent conditioning; (2) causally encoding re-rendered spatial memory as a continuous sequence; (3) aligning the temporal-memory window in pixel space; (4) adopting hard memory dropout that removes memory tokens rather than zeroing them; (5) unifying the VAE encoding and decoding protocol across training and inference; and (6) removing the camera AdaLN branch, such that viewpoint control is provided entirely through the re-rendered spatial condition.