arXiv Computer Vision By Jiahao Cui, Jiaye Li, Wenhao Zhang, Yaoyi Li, Shan Luan, Quanhui Tang, Jianhua Han, Lin Cheng, Hang Xu, Siyu Zhu

WAM-Sim0.1: World Anchored Memory Meets Online RL for Consistent and Realistic Driving Simulation

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WAM‑Sim0.1 is a driving world model that uses a persistent hierarchical memory to anchor context across pose, scene, and instance tokens, addressing structural, identity, and quality drift in autoregressive simulations. It incorporates online flow‑matching reinforcement learning to refine self‑generated rollouts, guided by visual rewards and a vision‑language physical judge, thereby improving visual sharpness and physical plausibility. On the nuScenes benchmark, WAM‑Sim achieves high generation fidelity (FID = 3.47, FVD = 25.72) and strong downstream perception metrics (24.16 mAP, 71.23 road mIoU), while maintaining scene similarity and identity coherence over minute‑scale rollouts.

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