World Model Learning

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World model learning studies how agents can represent a changing environment and predict what may happen next, especially after an action. Common approaches include latent-state models such as RSSMs and JEPAs, video diffusion models, and structured representations based on objects, graphs, geometry, physics, or executable rules. These models are used for imagined rollouts and planning in settings such as robot manipulation, autonomous driving, games, and web or GUI agents, as well as for scientific forecasting and simulation. A central challenge is that strong one-step or visual predictions do not guarantee useful decisions: long-horizon consistency, action accuracy, uncertainty, and transfer to unfamiliar settings remain difficult to balance with computational cost.

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419 papers88 papers in the last four weeks, up 83% on the four weeks before. 0.5% of all new papers.

Probabilistic Dreaming for World Models
Mar 5, 2026Gavin WongWorld Model Learning World Models
Yale University, New Haven, CT
Latent Particle World Models: Self-supervised Object-centric Stochastic Dynamics Modeling
Mar 4, 2026Tal Daniel, Carl Qi, Dan Haramati +5World Model Learning Object-Centric World Models
Carnegie Mellon University · UT Austin · Brown University +2
MultiGen: Level-Design for Editable Multiplayer Worlds in Diffusion Game Engines
Mar 3, 2026Ryan Po, David Junhao Zhang, Amir Hertz +3Interactive World Models Video World Models
Stanford University · Google
Chain of World: World Model Thinking in Latent Motion
Mar 3, 2026Fuxiang Yang, Donglin Di, Lulu Tang +6Vision-Language-Action Models Reasoning for VLA Models
Harbin Institute of Technology · Li Auto · Beijing Academy of Artificial Intelligence (BAAI) +3
Discrete World Models via Regularization
Mar 2, 2026Davide Bizzaro, Luciano SerafiniWorld Model Learning World Models
University of Padua, Padua, Italy · Fonadazione Bruno Kessler, Trento, Italy
COMBAT: Conditional World Models for Behavioral Agent Training
Feb 28, 2026Anmol Agarwal, Pranay Meshram, Sumer Singh +5Action-Conditioned World Models World Model Learning
Overworld AI · Indian Institute of Science Education and Research Bhopal
Social-JEPA: Emergent Geometric Isomorphism
Feb 28, 2026Youjin WangRepresentation Learning Visual Representation Learning
Renmin University of China
NeuroHex: A Brain-Inspired Hex Coordinate System to Enable Highly Computationally-Efficient World Models for Continuous Online-Adaptive Learning
Feb 27, 2026Quinn Jacobson, Joe Luo, Jingfei Xu +4Spatial Reasoning Neural Representation Geometry
NeuroAI Computer Architecture Lab (NCAL), Carnegie Mellon University
Foundation World Models for Agents that Learn, Verify, and Adapt Reliably Beyond Static Environments
Feb 27, 2026Florent DelgrangeFormal Methods for RL World Models
AI Lab, Vrije Universiteit Brussel & Flanders Make Brussels, Belgium
MetaOthello: A Controlled Study of Multiple World Models in Transformers
Feb 26, 2026Aviral Chawla, Galen Hall, Juniper LovatoTransformer Interpretability World Model Learning
Vermont Complex Systems Institute, University of Vermont, Burlington, VT · University of Michigan, Ann Arbor, MI
The Trinity of Consistency as a Defining Principle for General World Models
Feb 26, 2026Jingxuan Wei, Siyuan Li, Yuhang Xu +21World Models World Model Learning
GeoWorld: Geometric World Models
Feb 26, 2026Zeyu Zhang, Danning Li, Ian Reid +1World Model Learning World Models
ANU · MBZUAI
Beyond Attention: True Adaptive World Models via Spherical Kernel Operator
Feb 25, 2026Vladimer KhasiaWorld Model Learning Attention Mechanisms
Independent Researcher
World Guidance: World Modeling in Condition Space for Action Generation
Feb 25, 2026Yue Su, Sijin Chen, Haixin Shi +7Vision-Language-Action Models World Model Learning
ByteDance Seed · The University of Hong Kong
Geometric Priors for Generalizable World Models via Vector Symbolic Architecture
Feb 25, 2026William Youngwoo Chung, Calvin Yeung, Hansen Jin Lillemark +3World Model Learning World Models
University of California, Irvine, United States · University of California, San Diego, United States
MRI Contrast Enhancement Kinetics World Model
Feb 22, 2026Jindi Kong, Yuting He, Cong Xia +2Magnetic Resonance Imaging Medical Image Synthesis
Case Western Reserve University, Cleveland, OH, USA · Jiangsu Cancer Hospital, Nanjing, Jiangsu, China · Southeast University, Nanjing, Jiangsu, China
Neural Fields as World Models
Feb 21, 2026Joshua NunleyWorld Model Learning Action-Conditioned World Models
Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington · Cognitive Science Program, Indiana University, Bloomington
WebWorld: A Large-Scale World Model for Web Agent Training
Feb 16, 2026Zikai Xiao, Jianhong Tu, Chuhang Zou +7LLM World Models World Model Learning
Qwen Team, Alibaba Group · Zhejiang University
Neuro-Symbolic Synergy for World Modeling
Feb 11, 2026Hongyu Zhao, Siyu Zhou, Haolin Yang +2LLM World Models World Model Learning
Department of Computer Science University of Maryland College Park, MD 20742, USA · Faculty of Engineering and IT Australian AI Institute University of Technology Sydney Sydney, Australia · Department of Statistics University of Chicago Chicago, IL 60637, USA +2
Horizon Imagination: Efficient On-Policy Rollout in Diffusion World Models
Feb 8, 2026Lior Cohen, Ofir Nabati, Kaixin Wang +2Reinforcement Learning World Model Learning
Technion · Microsoft Research
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
College of Computer Science and the Academy for Advanced Interdisciplinary Studies, Nankai University, Tianjin 300350, China
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

Mar 5, 2026J/K move · Enter open · S save

Probabilistic Dreaming for World Models

Gavin Wong

Yale University, New Haven, CT

World Model Learning World Models Latent World Models Model-Based RL

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"Dreaming" enables agents to learn from imagined experiences, enabling more robust and sample-efficient learning of world models. In this work, we consider innovations to the state-of-the-art Dreamer model using probabilistic methods that enable: (1) the parallel exploration of many latent states; and (2) maintaining distinct hypotheses for mutually exclusive futures while retaining the desirable gradient properties of continuous latents. Evaluating on the MPE SimpleTag domain, our method outperforms standard Dreamer with a 4.5% score improvement and 28% lower variance in episode returns. We also discuss limitations and directions for future work, including how optimal hyperparameters (e.g. particle count K) scale with environmental complexity, and methods to capture epistemic uncertainty in world models.