World Model Learning

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Bilinear World Models: Learning Representations with Structured Dynamics for Efficient Control
Sep 28, 2026Antonio Pariente, Ignacio Boero, Nikolai Matni +1Dynamical Systems World Model Learning
University of Pennsylvania
One-Step Next-Latent Prediction Is Not a World Model
Sep 28, 2026Shitong Wang, Zhongang Cai, Yuzhou HongWorld Model Learning Latent Dynamics Modeling
FlexiWorld: Learning and Planning via Flexible Action Chunks Across Multiple Time Scales
Sep 28, 2026Shidu Ren, Qilin Gu, Zhenghao Ni +4World Model Learning World Model-Based Planning
University of Toronto · Zhejiang University · Tencent Jarvis Lab +3
WM-VLM: Probing Internal World Models for Interleaved Visual-Textual Reasoning
Sep 28, 2026Yuheng Zha, Yilei Wang, Qiyue Gao +5World Model Learning 3D Spatial Reasoning
UC San Diego · Institute of Foundation Models, MBZUAI · Carnegie Mellon University
Learning What to Recall: Adaptive Multi-Cue Episodic Memory for World Models
Sep 28, 2026Beomsu Kim, Chieh-Hsin Lai, Bac Nguyen +3Multimodal Memory World Model Learning
KAIST · Sony Group Corporation · Imperial College London
Dexterous Tactile World Model
Sep 28, 2026Ziyao Zeng, Xiatao Sun, Hao Wang +6Video Diffusion Models World Model Learning
Yale University · Texas A&M University · University of California, Los Angeles +1
WorldGraph: Graph-Native World Modeling
Sep 28, 2026Zezhong Ding, Yipeng Li, Xike XieWorld Model Learning World Models
School of Artificial Intelligence and Data Science, University of Science and Technology of China (USTC) · Data Darkness Lab, Suzhou Institute for Advanced Research, USTC · School of Biomedical Engineering, USTC
Do World Models Learn Global Understanding?
Sep 28, 2026Alexander Detkov, Matt ThomsonLLM World Models World Model Learning
California Institute of Technology Pasadena, CA 91125, USA
ReDrive: Shaping Representations with World Modeling for End-to-End Driving
Sep 27, 2026Yueting Zhu, Shaoyu Chen, Yuehao Song +4World Model Learning End-to-End Autonomous Driving
Huazhong University of Science & Technology · Horizon Robotics
ViBR-WM: Visual Bayesian Regression for World Modeling
Sep 27, 2026Jifan Li, Ning NingWorld Model Learning Time Series Forecasting
Department of Statistics, Texas A&M University College Station, TX 77843
SLIP-VLA: Single-Step Latent Imagination for Policy Learning in Vision-Language-Action Models
Sep 27, 2026Tianfu Li, Haoxuan Xu, Wenbo Chen +7World Model Learning Visuomotor Policy Learning
The Hong Kong University of Science and Technology (Guangzhou). · The Hong Kong University of Science and Technology. · Nanyang Technological University. +1
MA-JEPA: Joint-Embedding World Models for Multi-Agent Reinforcement Learning
Sep 27, 2026Brandon Gary Kaplowitz, Osaze James Obahor, Christian Schroeder de WittGame-Playing Agents World Model Learning
Department of Engineering Science, University of Oxford
VIDEAS: Distilling Explicit Action Semantics from Demonstration Videos for World Models via Prior-Guided Simulation
Sep 27, 2026Jianan Wang, Haoquan Zhai, Siyang Zhang +7World Model Learning World Models for Robotics
College of Computer Science and Technology, National University of Defense Technology · School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences · Intelligent Game and Decision Lab (IGDL) +1
Does Learning to Predict the World Help Agents Act? Auditing World-Model Post-Training
Sep 27, 2026Xinyu Che, Hang Yan, Yanchen Liu +5World Model Learning RL Post-Training
Xi’an Jiaotong University · University of Southern California · University of the Chinese Academy of Sciences +1
Adaptive Latent Capacity for World Models
Sep 26, 2026Idan Achituve, Lior Dikstein, Idit Diamant +2World Model Learning Latent Dynamics Modeling
Arm Research Israel
Frozen Flows Forget: Diagnosing and Restoring Lost Motion in a Latent-flow World Model
Sep 23, 2026Xiwen Chen, Rigaudiere Z. Li, Zhiruo Zhou +2World Model Learning Latent Dynamics Modeling
Shenzhen International Graduate School, Tsinghua University · Shanghai Jiaotong University · Wuhan University of Technology
TriWorldBench: A Tri-View Consistency Perspective on Embodied World Models
Sep 22, 2026Xuanyi Liu, Haofeng Wang, Ruiqi Li +9World Model Learning Robot Manipulation Benchmarks
Peking University · Tsinghua University · Beihang University +3
Relationally Grounded Latent World Models for Autonomous Driving
Sep 21, 2026Fabian Schmidt, Markus Enzweiler, Abhinav ValadaWorld Model Learning Autonomous Driving
Institute for Intelligent Systems, Esslingen University of Applied Sciences, Germany. · Department of Computer Science, University of Freiburg, Germany.
What Matters in Designing World Action Models: An Empirical Study
Sep 21, 2026Chao Tang, Haoqing Wang, Zilang Cen +9World Model Learning Causal World Models
Samsung Robotics eXperience · Samsung R&D Institute China–Beijing · Zhongguancun Academy +2
OnlineWM: Causality-Aware Active Online Learning for Effective World Modeling
Sep 20, 2026Yikun Miao, Fangqi Zhu, Quanxin Shou +6World Model Learning Causal World Models
Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong SAR, China
WOLF: World Model Guided LiDAR Exploration with Predictive Frontiers
Sep 20, 2026Yuyang Tian, Penghui Yang, Pengyuan Wu +7World Model Learning UAV Navigation
University of Science and Technology of China, Hefei, China. · Shanghai Artificial Intelligence Laboratory, Shanghai, China. · East China University of Science and Technology, Shanghai, China. +3
JEPA-Anything: Learning Predictive Models across Different Worlds
Sep 17, 2026Taoyong Cui, Zhongyao Wang, Xinyue Xu +10World Model Learning Joint-Embedding Predictive Architecture
Continual Enterprise World Model Discovery in Dynamic Systems
Sep 17, 2026Shambhavi Mishra, David Vazquez, Perouz Taslakian +3World Model Learning World Model Evaluation
ServiceNow Research · LIVIA, ILLS, ÉTS Montréal · Université Polytechnique Montréal +3
Modality-Autoregressive World-Action Models
Sep 15, 2026Adam Hung, Bardienus P. Duisterhof, Deva Ramanan +1World Model Learning Robotic Manipulation
Carnegie Mellon University
One Model, Two Physical Stories: Auditing Misalignment in Multi-Modal World Modeling
Sep 13, 2026Geigh Zollicoffer, Minh Vu, Rajiv Ranasinghe +1Physical Consistency in Video Generation World Model Learning
1Theoretical Division, Los Alamos National Laboratory · 2Computing and Artificial Intelligence Division, Los Alamos National Laboratory · 3Earth and Environmental Sciences Division, Los Alamos National Laboratory
World Models for Cross-Machine CNC Transfer under Partial Sensor Overlap
Sep 13, 2026Ayoub Louaye Bouaziz, Matthieu Ostertag, Anton DemaslesManufacturing World Model Learning
Université de Bretagne Occidentale, Brest, France · Mines Nancy, Université de Lorraine, Nancy, France
Measuring the Value of World-Model Updates: A Counterfactual Utility Protocol for Continual Adaptation
Sep 11, 2026Anqi Peter Li, Kaden KimCounterfactual Evaluation World Model Learning
Substrate Labs · UC Berkeley
Amortized Low-Rank Adaptation for Model-Based Reinforcement Learning
Sep 10, 2026Fernando Palafox, David Fridovich-KeilTest-Time Adaptation World Model Learning
University of Texas at Austin Austin, TX, USA
SyncWorld: Visual Calibration Enables World Models as Zero-Shot Simulators
Sep 8, 2026Yuncong Yang, Zhengtao Han, Furkan Ozyurt +6World Model Learning World Models for Robotics
UMass Amherst · UC Berkeley · NYU +1
Earth System World Model for What-If Simulations: A Case Study for Terrestrial Ecosystems
Sep 8, 2026Zhihao Wang, Ruichen Wang, Ruohan Li +6Surrogate Modeling World Model Learning
University of Maryland · Rutgers University · University of Texas at Austin +1

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Bilinear World Models: Learning Representations with Structured Dynamics for Efficient Control

Antonio Pariente, Ignacio Boero, Nikolai Matni, Alejandro Ribeiro

University of Pennsylvania

Dynamical Systems World Model Learning World Model-Based Planning Action-Conditioned World Models Representation Learning for RL Model-Based Planning

Open paper AskPDF arXiv

World models jointly learn latent representations and dynamics that predict how high-dimensional observations evolve under actions. In this work, we propose a JEPA-style world model in which, rather than learning arbitrary latent dynamics, we restrict them to follow a bilinear parameterization. This structure enables efficient planning and control while shifting the modeling burden onto the encoder, encouraging richer representations that expose the controllable geometry of the system. In particular, this structured parameterization allows us to structurally enforce action recoverability, thereby preventing representation collapse by construction. Although prescribing a bilinear parametrization may appear restrictive, we show that a broad class of nonlinear dynamical systems admits a transformation under which the dynamics become bilinear. Empirically, we show across standard 2D and 3D control tasks that representations with bilinear-parameterized dynamics can be learned directly from high-dimensional observations, reducing planning time by nearly three orders of magnitude while retaining or even improving control accuracy. We also propose more demanding regimes of longer-horizon planning and real-time control, and demonstrate that our method succeeds in both, moving JEPA-style world models beyond short-horizon offline planning.

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