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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Agent vs. Parametric World Models: Hybrid Planning for Reliable Language Agents
Jun 26, 2026Xinyuan Song, Zekun CaiWorld Models World Model-Based Planning
Emory University, Atlanta, GA, USA · The University of Tokyo, Tokyo, Japan · LocationMind, Tokyo, Japan
Understanding Rollout Error in Graph World Models
Jun 26, 2026Xinyuan Song, Zekun CaiWorld Model Learning World Models
Emory University, Atlanta, GA, USA · The University of Tokyo, Tokyo, Japan · LocationMind, Tokyo, Japan
ReWorld: Learning Better Representations for World Action Models
Jun 25, 2026Tianze Xia, Lijun Zhou, Kaixin Xiong +9Representation Learning World Model Learning
Huazhong University of Science and Technology · Xiaomi EV
Internalizing the Future: A Unified Agentic Training Paradigm for World Model Planning
Jun 25, 2026Xuan Zhang, Zhijian Zhou, Lingfeng Qiao +6World Model-Based Planning Model-Based Planning
Fudan University · Shanghai Innovation Institute · Tencent Youtu Lab
Fast LeWorldModel
Jun 24, 2026Yuntian Gao, Xiangyu XuLatent Dynamics Modeling World Model-Based Planning
Xi’an Jiaotong University
AdaReP:Adaptive Re-Planning under Model Mismatch for Neural World-Model Predictive Control
Jun 22, 2026Yutian Cheng, Xiaojian Ma, Xianhao Wang +6World Models for Robotics Sequential Decision Making
Shanghai Jiao Tong University · Beijing Institute for General Artificial Intelligence (BIGAI) · University of Science and Technology of China
Beyond the Next Step: Variable-Length Latent World Models for Long-Horizon Planning
Jun 19, 2026Tianqi Du, Qi Zhang, Yifei Wang +1Latent Dynamics Modeling World Models
State Key Lab of General Artificial Intelligence, School of Intelligence Science and Technology, Peking University · Amazon AGI SF Lab · Institute for Artificial Intelligence, Peking University
Sensorimotor World Models: Perception for Action via Inverse Dynamics
Jun 18, 2026Petr Ivashkov, Randall Balestriero, Bernhard SchölkopfRepresentation Learning World Model-Based Planning
Max Planck Institute for Intelligent Systems, Tübingen, Germany · Department of Computer Science, Brown University, Providence, RI, USA · ELLIS Institute, Tübingen, Germany +1
FlowMPC: Improving Flow Matching policies with World Models
Jun 15, 2026Chandon HamelFlow Matching World Model-Based Planning
Stanford University
Pixels to Proofs: Probabilistically-Safe Latent World Model Control via Parallel Conformal Robust MPC
Jun 14, 2026Devesh Nath, Anutam Srinivasan, Haoran Yin +3Constrained Motion Planning World Model-Based Planning
Georgia Institute of Technology
SIMMER: Benchmarking Latent Failures in LLM Executable Planning with a World Model
Jun 12, 2026Xiaoxin Lu, Ranran Haoran Zhang, Rui ZhangLarge Language Model-Based Robot Planning LLM Planning
The Pennsylvania State University, State College, PA, USA
Causal Object-Centric Models for Planning with Monte Carlo Tree Search
Jun 12, 2026Rodion Vakhitov, Leonid Ugadiarov, Alexey Skrynnik +1World Models World Model-Based Planning
MIRAI Moscow, Russia · CogAILab & MIRAI Moscow, Russia
ContactWorld: What Representations Matter for Vision-Tactile Latent World Models in Contact-Rich Manipulation
Jun 11, 2026Zhiyuan Zhang, Pokuang Zhou, Kaidi Zhang +7Representation Learning World Models for Robotics
Department of Industrial Engineering, Purdue University, West Lafayette, IN 47907, USA · Department of Mechanical Engineering, Texas A&M University, College Station, TX 77843, USA
WEAVER, Better, Faster, Longer: An Effective World Model for Robotic Manipulation
Jun 11, 2026Arnav Kumar Jain, Yilin Wu, Jesse Farebrother +2World Model Learning Long-Horizon Robotic Manipulation
Mila - Québec AI Institute · Université de Montréal · Carnegie Mellon University +1
EV-WM: Event-Verified World Models for Long-Horizon Robotic Manipulation
Jun 11, 2026Kailin Wang, Haoxiang Jie, Yaoyuan Yan +2Long-Horizon Robotic Manipulation World Models for Robotics
1AI Lab, Country Garden Services Group · 2Fudan University · 3Omni AI
A Tutorial on World Models and Physical AI
Jun 11, 2026Il-Seok OhWorld Model Learning World Models for Robotics
Department of Computer Science and Artificial Intelligence/CAIIT, Jeonju, Jeonbuk, South Korea
PLUME: Probabilistic Latent Unified World Modeling and Parameter Estimation for Multi-Finger Manipulation
Jun 9, 2026Abhinav Kumar, Soshi Iba, Rana Soltani Zarrin +1Online System Identification Dexterous Manipulation
University of Michigan · Honda Research Institute USA
$ω$ -EVA: Envision, Verify, and Act with Latent Interactive World Models
Jun 8, 2026Zhenguo Sun, Yu Sun, Hande Huang +1Robot Policy Learning World Models for Robotics
FF-JEPA: Long-Horizon Planning in World Models with Latent Planners
Jun 8, 2026Sergi Masip, Jonathan Swinnen, Yutong Hu +2Hierarchical Planning Long-Horizon Robotic Manipulation
PSI, ESAT, KU Leuven · RAM, MECH, KU Leuven
ATM: Why Latent World Models Can Fail to Plan
Jun 8, 2026Jiaheng Chen, Tinghe Zhang, Yucheng Xiao +5World Model-Based Planning Model-Based Planning
School of Software, Northeastern University
PRISM: PRior-guided Imagination Sampling in world Models
Jun 6, 2026Yuhai Wang, Jiawei Xia, Rongxuan Zhou +4World Models for Robotics World Model-Based Planning
1Northeastern University · University of California, Berkeley · 3Qiyuan Lab +1
STRIPS-WM: Learning Grounded Propositional STRIPS-style World Models from Images
Jun 5, 2026Abhiroop Ajith, Constantinos ChamzasLong-Horizon Robotic Manipulation World Models for Robotics
Department of Robotics Engineering · Worcester Polytechnic Institute · United States
PLAN-S: Bridging Planning with Latent Style Dynamics for Autonomous Driving World Models
Jun 4, 2026Xiaoyun Qiu, Jingtao He, Yijie Chen +4Roadmap-Based Motion Planning World Model-Based Planning
Intelligent Transportation Thrust, Systems Hub, and Center of Seamless Connectivity & Connected Intelligence, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou 511453, China.
Discrete-WAM: Unified Discrete Vision-Action Token Editing for World-Policy Learning
Jun 4, 2026Ziyang Yao, Haochen Liu, Yuncheng Jiang +10Hierarchical Planning World Model Learning
Xiaomi EV
AirDreamer: Generalist Drone Navigation with World Models
Jun 2, 2026Zian Liu, Andong Yang, Chunkai Yang +3RL for Robotics World Models for Robotics
Institute for AI Industry Research, Tsinghua University, Beijing, China. · Department of Electronic Engineering, Tsinghua University, Beijing, China. · School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China.
IMWM: Intuition Models Complement World Models for Latent Planning
Jun 1, 2026Baoqi Gao, Ruize Han, Miao Wang +1World Model-Based Planning AI Control
Beihang University · Shenzhen University of Advanced Technology
$τ_0$ -WM: A Unified Video-Action World Model for Robotic Manipulation
May 31, 2026Pengfei Zhou, Shengcong Chen, Di Chen +17Action-Conditioned Video Generation Long-Horizon Robotic Manipulation
1Shanghai Innovation Institute · 2AGIBOT Finch
PatchWorld: Gradient-Free Optimization of Executable World Models
May 29, 2026Jiaxin Bai, Yue Guo, Yifei Dong +13Belief-Space Planning World Model Learning
1Hong Kong Baptist University · 2Independent Researcher · 3HKUST +4
World Models: A Comprehensive Survey of Architectures, Methodologies, Reasoning Paradigms, and Applications
May 28, 2026Arif Hassan Zidan, Yi Pan, Hanqi Jiang +23World Model Learning World Model-Based Planning
School of Computer and Cyber Sciences, Augusta University, Augusta, GA, USA · School of Computing, University of Georgia, Athens, GA, USA · Tandon School of Engineering, New York University, Brooklyn, NY, USA +9
Physically Viable World Models: A Case for Query-Conditioned Embodied AI
May 28, 2026Adam J. Thorpe, Stepan Tretiakov, Cheng-Hsi Hsiao +6World Models for Robotics Causal World Models
The University of Texas at Austin

Jun 26, 2026, cs.AIJ/K move · Enter open · S save

Agent vs. Parametric World Models: Hybrid Planning for Reliable Language Agents

Xinyuan Song, Zekun Cai

Emory University, Atlanta, GA, USA · The University of Tokyo, Tokyo, Japan · LocationMind, Tokyo, Japan

World Models World Model-Based Planning Hallucination in Language Models Model-Based Planning LLM Agent Planning

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Language agents plan by generating not only actions but also implicit predictions of how the world will change. These imagined state updates make agents flexible, but they also create a distinct failure mode: hallucinated state claims can be written into context and propagated across subsequent decisions. In contrast, parametric world models provide measurable transition errors but are often weaker semantic planners. We study this tradeoff in graph-structured planning environments and introduce metrics for agent-world-model error, including hallucinated-state rate, propagation depth, and long-horizon error growth. We then propose Hybrid World-Model Planning (Hybrid-WM), which keeps the language model as the planner while using a small parametric transition model to predict action validity, state deltas, risk, and value. A consistency gate compares the agent's imagined delta with the parametric prediction and triggers targeted revision only under disagreement. Across four graph-structured planning benchmarks, Hybrid-WM improves success while reducing hallucinated state propagation. In live GPT-4o-mini evaluations, it reduces hallucinated-state rate from 0.176 to 0.035; in calibrated simulator ablations, it improves success from 0.668 to 0.838 with modest additional inference. These results suggest that lightweight parametric transition models can serve as effective grounding mechanisms for language-agent planning without replacing semantic reasoning.

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