World Model Learning

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UniDrive-WM: Unified Understanding, Planning and Generation World Model for Autonomous Driving
Jan 7, 2026Zhexiao Xiong, Xin Ye, Burhan Yaman +5Vision-Language Models World Model Learning
Bosch Research North America · Bosch Center for Artificial Intelligence (BCAI) · Arizona State University +2
MobileDreamer: Generative Sketch World Model for GUI Agent
Jan 7, 2026Yilin Cao, Yufeng Zhong, Zhixiong Zeng +6Mobile GUI Automation World Model Learning
State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences · Meituan · School of Artificial Intelligence, University of Chinese Academy of Sciences
Soft Geometric Inductive Bias for Object Centric Dynamics
Dec 17, 2025Hampus Linander, Conor Heins, Alexander Tschantz +2World Model Learning World Models
VERSES AI · University of Sussex, Department of Informatics
Higher Embedding Dimension Creates a Stronger World Model for a Simple Sorting Task
Oct 21, 2025Brady Bhalla, Honglu Fan, Nancy Chen +1Transformer Interpretability Representation Learning
California Institute of Technology · Pasadena, CA 91125 · Google DeepMind +3
OmniNWM: Omniscient Driving Navigation World Models
Oct 21, 2025Bohan Li, Zhuang Ma, Dalong Du +10Action-Conditioned Video Generation World Model Learning
Shanghai Jiao Tong University · Ningbo Institute of Digital Twin, Eastern Institute of Technology, Ningbo, China · PhiGent +3
A Comprehensive Survey on World Models for Embodied AI
Oct 19, 2025Xinqing Li, Xin He, Le Zhang +3World Model Learning World Models for Robotics
Imaginative World Modeling with Scene Graphs for Embodied Agent Navigation
Aug 9, 2025Yue Hu, Junzhe Wu, Ruihan Xu +5World Model Learning Embodied Navigation
University of Michigan, Ann Arbor, US
Geometry-Centered 3D Latent World Models for Growing Surfaces
May 29, 2025Xiaoyi Liu, Hao TangWorld Model Learning Latent World Models
Department of Computer Science Brown University · School of Computer Science Peking University
ACT-JEPA: Novel Joint-Embedding Predictive Architecture for Efficient Policy Representation Learning
Jan 24, 2025Aleksandar Vujinovic, Aleksandar KovacevicRepresentation Learning World Model Learning
Faculty of Technical Sciences, University of Novi Sad
Ego-Dynamics-Augmented World Model for Autonomous Driving with Zero-Shot Cross-Embodiment Adaptation
Date pendingZhidong Wang, Jingsong Liang, Zirui Li +3World Model Learning End-to-End Autonomous Driving
School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore · Collaborative Initiative, Interdisciplinary Graduate Programme, Nanyang Technological University, Singapore · College of Computing and Data Science, Nanyang Technological University, Singapore
PAVXploreRL: Physical-Action-Visual World Model Reinforcement Learning with Action Exploration
Date pendingHan Wang, Zijun Wang, Shuoshuo Xue +4World Model Learning World Models
Singapore University of Technology and Design · Sun Yat-sen University

Jan 7, 2026, cs.CVJ/K move · Enter open · S save

UniDrive-WM: Unified Understanding, Planning and Generation World Model for Autonomous Driving

Zhexiao Xiong, Xin Ye, Burhan Yaman, Sheng Cheng+4

Bosch Research North America · Bosch Center for Artificial Intelligence (BCAI) · Arizona State University+2

Vision-Language Models World Model Learning VLMs for Autonomous Driving Video Prediction World Models for Autonomous Driving Autonomous Driving Planning

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World models have become central to autonomous driving, where accurate scene understanding and future prediction are crucial for safe control. Recent work has explored using vision-language models (VLMs) for planning, yet existing approaches typically treat perception, prediction, and planning as separate modules. We propose UniDrive-WM, a unified VLM-based world model that jointly performs driving-scene understanding, trajectory planning, and trajectory-conditioned future image generation within a single architecture. UniDrive-WM's trajectory planner predicts a future trajectory, which conditions a VLM-based image generator to produce plausible future frames. These predictions provide additional supervisory signals that enhance scene understanding and iteratively refine trajectory generation. We further compare discrete and continuous output representations for future image prediction, analyzing their influence on downstream driving performance. Experiments on the challenging Bench2Drive benchmark show that UniDrive-WM produces high-fidelity future images and improves planning performance by 7.3% in L2 trajectory error and 10.4% in collision rate over the previous best method. These results demonstrate the advantages of tightly integrating VLM-driven reasoning, planning, and generative world modeling for autonomous driving. The project page is available at https://unidrive-wm.github.io/UniDrive-WM.

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