ZYT-World: A Real-Time Controllable World Model for Closed-Loop Autonomous-Driving Simulation

Boni Hu; Xiong Wei; Haoming Huang; Yong Huang; Chenbo Wang; Yi Yang; Jiancheng Wang; Ruicheng Zhu; Zhimin Yang; Guanglai Liu; Qiaowan Jin; Dongzhuo Wang; Haiwei Kuang; Jiajun Fan; Yue Wu; Jiaxin Wei; Hao Sun; Feihong Yan; Wei Bi; Kaixuan Wang; Zichao Guo; Xiaozhi Chen

arXiv · 2609.21712

ZYT-World: A Real-Time Controllable World Model for Closed-Loop Autonomous-Driving Simulation

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Abstract

Generative world models offer controllable and repeatable closed-loop simulation for end-to-end and vision-language-action driving policies, but production deployment exposes three unresolved requirements: faithfully reproducing a mixed fisheye-pinhole rig at native resolutions; reconciling causal, per-timestep interaction with long-horizon stability and low latency; and preserving scene identity when a location is revisited. We present ZYT-World, a single architecture that natively generates four fisheye views with field of view > 180° and three pinhole views. Projection-specific Plucker adapters encode camera geometry, ego-motion adaptive layer normalization provides global motion control, and a lightweight pixel-aligned layout conditions traffic participants and signals through instance-level boxes, headings and colors. Heterogeneous training combines full-rig geometric coverage with high-resolution detail. Teacher forcing, causal consistency distillation, self-rollout distribution matching distillation, and RigCritic transform a 40-step bidirectional teacher into a one-step, per-latent streaming generator, with RigCritic evaluating the seven-view rig jointly. A 19M-parameter variational autoencoder decoder (TinyVAE), W8A8 quantization, and our inference engine reduce decoding, backbone, and incremental-execution costs, respectively. Finally, cross-trajectory pairs derived from real captures train a plug-in implicit-memory module that preserves place-specific evidence. On the internal multi-view test set, the one-step model retains more than 90% of the teacher's PSNR and SSIM, while FID, FVD, and LPIPS stay within 11% of the teacher. Under the generator-only timing in Figure 2, it is 107.7 times faster than the 40-step bidirectional teacher. TinyVAE decodes 59.8 times faster than Wan. 30s rollouts and cross-trajectory revisits show the intended long-horizon and memory behavior.

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Boni Hu, Xiong Wei, Haoming Huang, Yong Huang, Chenbo Wang, Yi Yang, Jiancheng Wang, Ruicheng Zhu, Zhimin Yang, Guanglai Liu, Qiaowan Jin, Dongzhuo Wang, Haiwei Kuang, Jiajun Fan, Yue Wu, Jiaxin Wei, Hao Sun, Feihong Yan, Wei Bi, Kaixuan Wang, Zichao Guo, Xiaozhi Chen. 2026-09-18. ZYT-World: A Real-Time Controllable World Model for Closed-Loop Autonomous-Driving Simulation. https://arxiv.org/abs/2609.21712

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