Autoregressive

Figure from InSpatio-World: Real-Time 4D World Simulation via Spatiotemporal Autoregressive Modeling The ability to simulate a 4D world — one that evolves in time and can be viewed from arbitrary perspectives — is a foundational capability for autonomous driving, robotics, and embodied AI. Existing video generation models produce visually compelling sequences but lack spatial consistency when the camera moves. 3D reconstruction methods achieve geometric fidelity but struggle with dynamic scenes and real-time performance. InSpatio-World bridges this gap through a spatiotemporal autoregressive (STAR) architecture that combines the strengths of both paradigms. ...

中文版本：阅读中文版 Figure from DiffusionDrive: Truncated Diffusion Model for End-to-End Autonomous Driving Autoregressive (AR) trajectory generation — predicting driving trajectories as sequences of discrete tokens, much like language models predict text — has emerged as a powerful paradigm for end-to-end autonomous driving. But how do we turn continuous trajectories into discrete tokens? How do we ensure the tokenized representation preserves enough fidelity for planning? And how does the AR paradigm combine with diffusion and reinforcement learning to produce state-of-the-art results? This article walks through the complete pipeline, from tokenization theory to RL post-training. ...