Latent World Models

Latest papers 196

50 papers in the last four weeks, up 194% on the four weeks before. 0.3% of all new papers.

DreamerAD: Efficient Reinforcement Learning via Latent World Model for Autonomous Driving
Mar 25, 2026Pengxuan Yang, Yupeng Zheng, Deheng Qian +11RL for Autonomous Driving Latent World Models
Institute of Automation, CAS · Chongqing Chang’an Technology Co., Ltd · School of Advanced Interdisciplinary Sciences, UCAS +1
Latent-WAM: Latent World Action Modeling for End-to-End Autonomous Driving
Mar 25, 2026Linbo Wang, Yupeng Zheng, Qiang Chen +13End-to-End Autonomous Driving World Models for Autonomous Driving
Institute of Automation, Chinese Academy of Sciences · Chongqing Chang’an Technology Co., Ltd · Zhongguancun Academy +2
ThinkJEPA: Empowering Latent World Models with Large Vision-Language Reasoning Model
Mar 23, 2026Haichao Zhang, Yijiang Li, Shwai He +5Latent World Models Trajectory Prediction
Northeastern University · University of California San Diego · University of Maryland +2
What Do World Models Learn in RL? Probing Latent Representations in Learned Environment Simulators
Mar 23, 2026Xinyu ZhangWorld Models World Model Learning
Anyscale
DynFlowDrive: Flow-Based Dynamic World Modeling for Autonomous Driving
Mar 20, 2026Xiaolu Liu, Yicong Li, Song Wang +3World Models for Autonomous Driving Latent World Models
Zhejiang University · National University of Singapore
EyeWorld: A Generative World Model of Ocular State and Dynamics
Mar 14, 2026Ziyu Gao, Xinyuan Wu, Xiaolan Chen +10Medical Image Analysis Medical Image Representation Learning
School of Optometry, The Hong Kong Polytechnic University, Hong Kong. · Eye Center of Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310009, Zhejiang, China · Zhejiang Provincial Key Laboratory of Ophthalmology, Zhejiang Provincial Clinical Research Center for Eye Diseases, Zhejiang Provincial Engineering Institute On Eye Diseases, Hangzhou, 310009, Zhejiang, China +5
LeWorldModel: Stable End-to-End Joint-Embedding Predictive Architecture from Pixels
Mar 13, 2026Lucas Maes, Quentin Le Lidec, Damien Scieur +2World Model Learning Joint-Embedding Predictive Architecture
Mila & Université de Montréal · New York University · Samsung SAIL +1
World Model for Battery Degradation Prediction Under Non-Stationary Aging
Mar 11, 2026Kai Chin Lim, Khay Wai SeeLithium-Ion Batteries RUL Estimation
Independent Researcher, Australia
Probabilistic Dreaming for World Models
Mar 5, 2026Gavin WongWorld Model Learning World Models
Yale University, New Haven, CT
Beyond Pixel Histories: World Models with Persistent 3D State
Mar 3, 2026Samuel Garcin, Thomas Walker, Steven McDonagh +5World Models Interactive World Models
University of Edinburgh · Microsoft Research
FRAPPE: Infusing World Modeling into Generalist Policies via Multiple Future Representation Alignment
Feb 19, 2026Han Zhao, Jingbo Wang, Wenxuan Song +5World Models for Robotics Latent World Models
Zhejiang University · Westlake University · HKUST (GZ) +3
Factored Latent Action World Models
Feb 18, 2026Zizhao Wang, Chang Shi, Jiaheng Hu +4Latent World Models Latent Action Learning
University of Texas at Austin · Sony AI
CLARITY: Medical World Model for Guiding Treatment Decisions by Modeling Context-Aware Disease Trajectories in Latent Space
Dec 8, 2025Tianxingjian Ding, Yuanhao Zou, Chen Chen +2Healthcare Clinical Decision Support
Institute of Artificial Intelligence, University of Central Florida
How Well Do Latent World Models Understand Partially Observable Safety Constraints?
Oct 7, 2025Matthew Kim, Kensuke Nakamura, Andrea BajcsyLatent World Models Robot Safety
UC San Diego · Carnegie Mellon University
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
Mitigating Covariate Shift in Imitation Learning for Autonomous Vehicles Using Latent Space Generative World Models
Sep 25, 2024Alexander Popov, Alperen Degirmenci, David Wehr +9End-to-End Autonomous Driving Imitation Learning
NVIDIA

Mar 25, 2026J/K move · Enter open · S save

DreamerAD: Efficient Reinforcement Learning via Latent World Model for Autonomous Driving

Pengxuan Yang, Yupeng Zheng, Deheng Qian, Zebin Xing+10

Institute of Automation, CAS · Chongqing Chang’an Technology Co., Ltd · School of Advanced Interdisciplinary Sciences, UCAS+1

RL for Autonomous Driving Latent World Models Model-Based RL Diffusion Model Acceleration

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We introduce DreamerAD, the first latent world model framework that enables efficient reinforcement learning for autonomous driving by compressing diffusion sampling from 100 steps to 1 - achieving 80x speedup while maintaining visual interpretability. Training RL policies on real-world driving data incurs prohibitive costs and safety risks. While existing pixel-level diffusion world models enable safe imagination-based training, they suffer from multi-step diffusion inference latency (2s/frame) that prevents high-frequency RL interaction. Our approach leverages denoised latent features from video generation models through three key mechanisms: (1) shortcut forcing that reduces sampling complexity via recursive multi-resolution step compression, (2) an autoregressive dense reward model operating directly on latent representations for fine-grained credit assignment, and (3) Gaussian vocabulary sampling for GRPO that constrains exploration to physically plausible trajectories. DreamerAD achieves 87.7 EPDMS on NavSim v2, establishing state-of-the-art performance and demonstrating that latent-space RL is effective for autonomous driving.