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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Latest papers 180

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Latent Goal Prediction from Language for Model-Based Planning
May 26, 2026Samuel Barbeau, Simon Roy, Giovanni Beltrame +2World Model-Based Planning Long-Horizon Planning
1École de Technologie Supérieure, Montréal · 2International Laboratory on Learning Systems (ILLS) · 3Polytechnique Montréal +4
Back to Parsimonious Latents: Learning Task-Centric World Models from Visual Foundations
May 25, 2026Minghao Fu, Fan Feng, Nicklas Hansen +1World Model Learning World Models
University of California, San Diego
World Model Control by Trajectory Reachability Metrics
May 21, 2026Liangyu Li, Shengzhi Wang, Libin Qiu +2Reachability Analysis World Models for Robotics
Tongji University
Efficient Agentic Reasoning Through Self-Regulated Simulative Planning
May 21, 2026Mingkai Deng, Jinyu Hou, Lara Sá Neves +4LLM Planning World Model-Based Planning
Carnegie Mellon University · Institute of Foundation Models (IFM)
Robo-Cortex: A Self-Evolving Embodied Agent via Dual-Grain Cognitive Memory and Autonomous Knowledge Induction
May 18, 2026Nga Teng Chan, Yi Zhang, Yechi Liu +9Embodied Navigation World Model-Based Planning
1The Hong Kong University of Science and Technology · 2X-Humanoid · Institute of Automation, CAS +1
Learning Multi-Timescale Abstractions for Hierarchical Combinatorial Planning
May 16, 2026Vivienne Huiling Wang, Tinghuai Wang, Joni PajarinenWorld Model-Based Planning Hierarchical RL
Department of Electrical Engineering and Automation, Aalto University, Finland · Qutwo, Finland
Imperfect World Models are Exploitable
May 15, 2026Logan Mondal Bhamidipaty, Esmeralda S. Whitammer, David Abel +2Reward Hacking World Model-Based Planning
University of Edinburgh · Stanford University
Slot-MPC: Goal-Conditioned Model Predictive Control with Object-Centric Representations
May 14, 2026Jonathan Spieler, Angel Villar-Corrales, Sven BehnkeWorld Models for Robotics World Model-Based Planning
Autonomous Intelligent Systems, Computer Science Institute VI - Intelligent Systems and Robotics, Center for Robotics and the Lamarr Institute for Machine Learning and Artificial Intelligence, University of Bonn, Germany
EponaV2: Driving World Model with Comprehensive Future Reasoning
May 14, 2026Jiawei Xu, Zhizhou Zhong, Zhijian Shu +8World Model Learning World Model-Based Planning
1PCA Lab, VCIP, College of Computer Science, Nankai University · 2Horizon Robotics · 3HKUST +4
Do Enterprise Systems Need Learned World Models? The Importance of Context to Infer Dynamics
May 12, 2026Jishnu Sethumadhavan Nair, Patrice Bechard, Rishabh Maheshwary +14Distribution Shift World Model-Based Planning
1ServiceNow · 2Mila
The DAWN of World-Action Interactive Models
May 12, 2026Hongbo Lu, Liang Yao, Chenghao He +6Diffusion-Based Planning World Model-Based Planning
1COWARobot Co. Ltd · 2Shanghai Jiao Tong University · 3Hohai University
Latent Geometry Beyond Search: Amortizing Planning in World Models
May 9, 2026Hoang Nguyen, Xiaohao Xu, Xiaonan HuangWorld Model-Based Planning Model-Based Planning
Department of Robotics, University of Michigan, Ann Arbor
Is the Future Compatible? Diagnosing Dynamic Consistency in World Action Models
May 8, 2026Bo-Kai Ruan, Teng-Fang Hsiao, Ling Lo +1World Models for Robotics World Model-Based Planning
National Yang Ming Chiao Tung University
Predictive but Not Plannable: RC-aux for Latent World Models
May 8, 2026Wenyuan Li, Guang Li, Keisuke Maeda +2Reachability Analysis World Model-Based Planning
Hokkaido University
See Tomorrow, Act Today: Foresight-Driven Autonomous Driving
May 8, 2026Bozhou Zhang, Nan Song, Yuang Wang +3End-to-End Autonomous Driving World Models
School of Data Science, Fudan University · Shanghai Innovation Institute · Imperial College London +1
Reconstruction or Semantics? What Makes a Latent Space Useful for Robotic World Models
May 7, 2026Nilaksh, Saurav Jha, Artem Zholus +1World Model Learning World Models for Robotics
Chandar Research Lab · Mila – Quebec AI Institute · Polytechnique Montréal +1
HaM-World: Soft-Hamiltonian World Models with Selective Memory for Planning
May 7, 2026Haoyun Tang, Haodong Cui, Keyao Xu +2World Model Learning Latent Dynamics Modeling
1Xi’an Jiaotong University · 2Huazhong University of Science and Technology · 3Nankai University +1
TRAP: Tail-aware Ranking Attack for World-Model Planning
May 3, 2026Siyuan Duan, Ke Zhang, Xizhao LuoBackdoor Attacks World Model-Based Planning
Department of Computer Science and Technology, Soochow University Suzhou, China
Physically Native World Models: A Hamiltonian Perspective on Generative World Modeling
May 1, 2026Sen Cui, Jingheng MaLatent Dynamics Modeling World Models
Tsinghua University
Lifting Embodied World Models for Planning and Control
Apr 28, 2026Alex N. Wang, Trevor Darrell, Pavel Izmailov +2Robotic Control World Model-Based Planning
Computer Science, New York University · BAIR, UC Berkeley
The Global Neural World Model: Spatially Grounded Discrete Topologies for Action-Conditioned Planning
Apr 17, 2026Noureddine KermicheWorld Model Learning World Model-Based Planning
Western Digital Corporation, Irvine, CA, USA
Grounded World Model: Latent Planning with Language Goals
Apr 13, 2026Quanyi Li, Lan Feng, Haonan Zhang +4Zero-Shot Robotic Manipulation World Models for Robotics
EPFL · NUS
JEPA-Bisim: Learning Robust Visual Representations for Planning with Joint-Embedding Predictive World Models
Feb 20, 2026Leonardo F. Toso, Davit Shadunts, Yunyang Lu +5Visual Representation Learning World Models for Robotics
Department of Electrical Engineering, Columbia University, New York, USA. · Capital One, USA.
H-WM: Robotic Task and Motion Planning Guided by Hierarchical World Model
Feb 11, 2026Jinbang Huang, Wenyuan Chen, Zhiyuan Li +9Long-Horizon Robotic Manipulation World Models for Robotics
Huawei Noah’s Ark Lab · University of Toronto · University of British Columbia +1
Imagine-then-Plan: Agent Learning from Adaptive Lookahead with World Models
Jan 13, 2026Youwei Liu, Jian Wang, Hanlin Wang +2World Model-Based Planning Model-Based Planning
Central South University · Department of Computing, The Hong Kong Polytechnic University · College of Computer Science, Sichuan University
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
What Drives Success in Physical Planning with Joint-Embedding Predictive World Models?
Dec 30, 2025Basile Terver, Tsung-Yen Yang, Jean Ponce +2World Model-Based Planning Joint-Embedding Predictive Architecture
Meta FAIR · Inria Paris · Ecole normale supérieure / PSL +1
From Discrete Plans to Real-World Execution: A World-Model-Driven Framework for Execution-Aware Multi-Agent Path Finding
Nov 26, 2025Jingtian Yan, Shuai Zhou, He Jiang +2Multi-Robot Motion Planning World Models for Robotics
This paper was produced by the IEEE Publication Technology Group. They are in Piscataway, NJ.
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
Diagnosing and Dynamically Filtering Occupancy World Models for Active Mapping
Date pendingJiahui Zhang, Gongbo Liang, Yu ZhangNext-Best-View Planning World Models for Robotics
Boise State University · Texas A&M University–San Antonio

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

Latent Goal Prediction from Language for Model-Based Planning

Samuel Barbeau, Simon Roy, Giovanni Beltrame, Christian Desrosiers+1

1École de Technologie Supérieure, Montréal · 2International Laboratory on Learning Systems (ILLS) · 3Polytechnique Montréal+4

World Model-Based Planning Long-Horizon Planning Model-Based Planning Latent World Models

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Planning with world models is bottlenecked by compounding prediction errors and the difficulty of defining optimizable goals. Visual targets provide precise local gradients but poor distant guidance, while language is flexible yet limited by noisy cross-modal alignment or dependence on large generative models unsuited for the high-sampling nature of model-based planning. To address these challenges, we introduce Latent Goal Prediction from Language (LAGO), a framework that predicts both sequences of intermediate goal states from language instructions and action-conditioned rollouts, all within the same latent space. Rather than optimizing toward a single global objective, LAGO dynamically decomposes instructions into explicitly predicted, locally tractable latent subgoals. By updating these subgoals online and using a soft minimum trajectory cost during planning, LAGO enables an agent to follow coherent latent trajectories over long horizons. Evaluation across multiple environments planning horizons shows that LAGO avoids the sharp degradation of prior methods. By achieving robust and precise long-horizon planning purely from language, LAGO bridges the precision of visual goals with the flexibility of text-guided control.

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