World Model-Based Planning

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49 papers in the last four weeks, up 145% on the four weeks before. 0.5% of all new papers.

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WISE: World-model-guided Imagination Scheduling for Efficient Post-training of Vision-Language-Action Models
Sep 3, 2026Chenhao Zhang, Hanyu Zhao, Hang Cheng +2Robot Skill Learning World Models for Robotics
1Tsinghua University · 2Beijing Academy of Artificial Intelligence (BAAI)
Toward Physically Grounded JEPA World Models for Goal-Conditioned Robotic Planning
Sep 3, 2026Muyuan Liu, Yue Huang, Zheng Liang +1World Models for Robotics World Model-Based Planning
GENISOM AI, Beijing, China
Latent Energy Action Planning with World Models
Sep 3, 2026Phu Pham, Aniket BeraTrajectory Optimization World Model-Based Planning
Department of Computer Science, Purdue University, USA
Spectral-Target Physical Latent Structuring for JEPA-Style World Models
Sep 2, 2026Penghao Zhu, Salvatore Penachio, Kaustav Mukherjee +1World Model Learning World Model-Based Planning
Kaliber Labs San Francisco, CA 94103, USA
Towards a Belief-Based World Model for LLM Agents
Aug 31, 2026Shubham Kumar, Harshit Kumar, Narendra Ahuja +1Belief-Space Planning Partially Observable RL
UIUC · IBM
The Intervention Gap in Latent World Models
Aug 30, 2026Donna VakalisWorld Model-Based Planning Model-Based Planning
Mila – Quebec AI Institute · University of Montreal
Does Latent Planning Survive Point Clouds? Action-Conditioned JEPA World Models for Geometric Observations and Goals
Aug 29, 2026Fabio F. Oberweger, Michael Schwingshackl, Markus MurschitzWorld Models for Robotics World Model-Based Planning
AIT Austrian Institute of Technology Assistive & Autonomous Systems
Hydra: A Navigation World Action Model with Discrete Latent Planning and Continuous Flow-Matching Execution
Aug 29, 2026Mohammad Nazeri, Alexandyr Card, Samira Huber +6Kinodynamic Motion Planning World Models for Robotics
George Mason University · Kiel University · Ludwig Maximilian University Munich +1
Reinforced Planning with Latent World Models
Aug 19, 2026Armin Sommer, Jannik SchillingWorld Model-Based Planning Robotic RL
Pantheon Industries
Reperesentation Geometry Matters for Planning with JEPA World Models
Aug 17, 2026Jiaming Hu, Yan Zheng, Shi Bo +9Representation Learning Neural Representation Geometry
Boston University · Unity Technologies
The Objective Is the Bottleneck: Latent World Models Encode What Their Planners Cannot Use
Aug 13, 2026Joyjeet SinghWorld Model-Based Planning Model-Based Planning
Independent researcher
Better Slots, Better Worlds: Representation Quality & Robustness in Object-Centric World Models
Aug 12, 2026Shukrullo Nazirjonov, Sai Prasanna, Anna Manasyan +1World Models World Model-Based Planning
University of Tuebingen · Max Planck Institute for Intelligent Systems
VIScore: Diagnosing Planning-Relevant Quality in Latent World Models
Aug 11, 2026Haiyu Wu, Randall Balestriero, Morgan LevineRepresentation Learning World Models
Altos Labs · Brown University
Flex- $π$ : A Multi-Stream World-Action Model with Compute Flexibility
Aug 11, 2026Ge Yan, Jinghao Liu, Yuzhi Fan +4World Models World Model-Based Planning
University of Washington · Allen Institute for AI
The Evaluation Protocol Determines the Result: An Independent Reproduction of LeWorldModel on TwoRoom
Aug 10, 2026Joyjeet SinghWorld Model-Based Planning Long-Horizon Agent Evaluation
Independent researcher
Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning
Aug 10, 2026Yapeng Liu, Yuanzhao Zhai, Bo Ding +2World Model Learning World Models
PDL Lab, College of Computer Science and Technology, National University of Defense Technology
HarnessWAM: Bridging Prediction and Deliberation in World Action Models
Aug 10, 2026Zhaopeng Gu, Bingke Zhu, Tianxi Lin +8World Models for Robotics Large Language Model-Based Robot Planning
Institute of Automation, Chinese Academy of Sciences, Beijing, China · School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China · Yinwang Intelligent Technology Co., Ltd., Shenzhen, China +2
Rethink Before You Execute: Adaptive Execution for World Action Models
Aug 10, 2026Feng Ye, Yiming Zhao, Yong Yu +5World Models for Robotics LLM Planning
Wangxuan Institute of Computer Technology, Peking University · Simplexity Robotics
Latent World Models with Monotone Planning Costs for Image-Goal Navigation
Aug 10, 2026Amirhosein Chahe, Siwei Cai, Lifeng ZhouVisual Navigation World Model Learning
Drexel University
SurgWMBench: A Vision-Based Benchmark for World-Modeling Surgical Instrument Motion Planning
Aug 8, 2026Huanrong Liu, Weiliang Huang, Bob Zhang +3World Models for Robotics World Model-Based Planning
University of Macau · Xiamen University · National University of Singapore
MemWM: Memory-Augmented Text-Based World Model
Aug 7, 2026Yujun Wang, Tao Zhang, Jinhe Bi +9LLM World Models World Model Learning
LMU Munich · Munich Center for Machine Learning (MCML) · Huawei Heisenberg Research Center +4
Transformers Struggle to Use Their Emergent World Models: Revisiting the Tower of Hanoi, and the Illusion of Thinking
Aug 7, 2026Devin Pereira, Willem ZuidemaLLM World Models LLM Planning
1Artificial Intelligence Program, University of Amsterdam · ELLIS Unit Amsterdam · Institute for Logic Language and Computation, University of Amsterdam
Is Forward Prediction Enough? Physical State Grounding for JEPA World Models
Aug 7, 2026Haodong Yan, Jiaguan Zhu, Mingyuan Jia +12Physical Reasoning World Models for Robotics
1The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China · 2COCO Matrix
PhyLatent: Learning Dynamics-Relevant Representations for JEPA World Models
Aug 6, 2026Xi Zeng, Haojie Ren, Ziying Song +2Representation Learning World Model Learning
School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore · The University of Sheﬃeld, Sheﬃeld, United Kingdom · School of Artiﬁcial Intelligence (School of Software), Yanshan University, Qinhuangdao, China
Uncertainty-Aware World Model for Aerial Image-Goal Navigation
Aug 6, 2026Deyi Zhu, Haoyu Fan, Yinan Zhu +4Uncertainty Quantification World Models for Robotics
Tsinghua Shenzhen International Graduate School, Tsinghua University
WorldCycle: Self-Verifiable Reinforcement Learning for Long-Horizon Video World Models
Aug 5, 2026Bohai Gu, Yueyang Yuan, Taiyi Wu +9World Model-Based Planning Action-Conditioned World Models
1The Hong Kong University of Science and Technology · 2Wuhan University · 3AI Technology Center, Tencent Video, Tencent
ProWorld: Progress-Aware Hyperbolic World Models for Long-Horizon Visual Goal Reaching
Aug 3, 2026Zihan Liu, Yuzhe Zhuang, Yuanzu Li +4World Model-Based Planning Long-Horizon Planning
The Hong Kong University of Science and Technology (Guangzhou) · Independent Researcher · The Chinese University of Hong Kong +1
QQWorld: Quantile-Quantile Matching for World Model Regularization
Jul 30, 2026Zhoushun Yu, Xiaoyu Hu, Xiangyu XuWorld Model Learning World Model-Based Planning
Xi’an Jiaotong University
World Action Planner: Generalizable Decision-Making with Action-Conditioned World Models
Jul 30, 2026Xiangcheng Zhang, Yilun DuWorld Models for Robotics World Model-Based Planning
Harvard University
ActSWM: Action-Sensitive World Models for Long-Horizon Planning in Open-World Games
Jul 29, 2026Zhenfeng Gan, ZiTong Zeng, Jiajun Cheng +3Latent Action Learning World Model-Based Planning

Sep 3, 2026, cs.ROJ/K move · Enter open · S save

WISE: World-model-guided Imagination Scheduling for Efficient Post-training of Vision-Language-Action Models

Chenhao Zhang, Hanyu Zhao, Hang Cheng, Tengfei Pan+1

1Tsinghua University · 2Beijing Academy of Artificial Intelligence (BAAI)

Robot Skill Learning World Models for Robotics World Model-Based Planning Efficient VLA Models Robotic Manipulation Vision-Language-Action Models+1

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Post-training VLA policies typically rely on supervised fine-tuning with costly expert demonstrations or reinforcement learning with expensive and potentially unstable real-world exploration. World models offer a promising alternative by evaluating candidate behaviors through imagined futures, yet effective post-training requires more than accurate prediction: imagination must be scheduled where it is useful, bounded within reliable horizons, and translated into trustworthy policy supervision. In robotic manipulation, the value of imagination varies substantially across execution stages, while extended rollouts can accumulate prediction errors and introduce unreliable learning signals. We introduce WISE (World-model-guided Imagination Scheduling for Efficient Post-training of Vision-Language-Action Models), a unified framework that coordinates when and how world-model imagination is used during policy refinement. WISE selectively invokes imagination at interaction-relevant states, performs bounded multi-view rollouts, evaluates candidate futures using progress and completion signals, and uses their relative outcomes to refine actions generated from real interaction contexts. Extensive experiments with both

π_0

and

π_{0.5}

demonstrate consistent improvements across diverse manipulation tasks while reducing GPU computation time by approximately 80% compared with full imagination. Real-world evaluations further show substantial gains in robustness and generalization under diverse real-world distribution shifts.

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