Author

Jiangtong Zhu

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Preprint Aug 2026

4D-WAM: 4D Consistent World Modeling for Autonomous Driving

4D-WAM is presented, a model that leverages geometric foundation models for training-time supervision to enable 4D consistent world modeling and effectively models 4D consistent scene evolution and achieves state-of-the-art performance on challenging NAVSIM-v1 and NAVSIM-v2 benchmarks.

Jiacheng Fu, Yibo Yuan, Meng Tian et al. · 1 citation · ⚡1

#artificial intelligence Mar 2026

AtomicVLA: Unlocking the Potential of Atomic Skill Learning in Robots

This work proposes AtomicVLA, a unified planning-and-execution framework that jointly generates task-level plans, atomic skill abstractions, and fine-grained actions, and introduces a flexible routing encoder that automatically assigns dedicated atomic experts to new skills, enabling continual learning.

Likui Zhang, Tao Tang, Zhihao Zhan et al. · arXiv.org · 19 citations · ⚡3

Preprint Aug 2026

SUV: Future Scene Understanding as Video Generation for End-to-End Driving

This work introduces SUV, a unified end-to-end driving framework that casts future Scene Understanding as Video generation using a pretrained video foundation model, and shows that structured future supervision and direct future-stream access yield higher trajectory planning scores.

Yibo Yuan, Jiacheng Fu, Jiangtong Zhu et al. · 1 citation