Latent World Models

Latest papers 196

50 papers in the last four weeks, up 194% on the four weeks before. 0.3% of all new papers.

World Machine: Towards Generative World Modeling for Time-Series
May 21, 2026Elton Cardoso do Nascimento, Alexandre da Silva Simões, Esther Luna Colombini +2World Model Learning Latent World Models
Universidade Estadual de Campinas (UNICAMP), {FEEC, IC} · Universidade Estadual Paulista (UNESP), Instituto de Ciˆencia e Tecnologia, Sorocaba
World Model Control by Trajectory Reachability Metrics
May 21, 2026Liangyu Li, Shengzhi Wang, Libin Qiu +2Reachability Analysis World Models for Robotics
Tongji University
ChronoMedicalWorld: A Medical World Model for Learning Patient Trajectories from Longitudinal Care Data
May 21, 2026Jiangyuan Wang, Xuyong Chen, Junwei He +3Latent Dynamics Modeling Healthcare
Beijing KidneyTec Medical Technology Co., Ltd., Beijing, China
PH-Dreamer: A Physics-Driven World Model via Port-Hamiltonian Generative Dynamics
May 18, 2026Xueyu Luan, Chenwei ShiDynamical Systems Reinforcement Learning
School of Electronic and Information Engineering Tongji University Shanghai 200092, China · Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai 200092, China
Why We Need World Models for AGI: Where LLMs Fail and How World Models May Outperform
May 13, 2026Feisal Alaswad, Batoul Aljaddouh, Maher Alrahhal +2Latent Dynamics Modeling Long-Horizon Agent Evaluation
Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur 603203, Tamil Nadu, India · Bio-Sensing and Bio-Sensors Group Smart Automation and Communication Technologies Research Institute of Sciences and Engineering University of Sharjah, UAE · Department of Computer Engineering College of Computing and Informatics University of Sharjah, UAE
JEDI: Joint Embedding Diffusion World Model for Online Model-Based Reinforcement Learning
May 13, 2026Jing Yu Lim, Rushi Shah, Zarif Ikram +4World Model Learning Joint-Embedding Predictive Architecture
National University of Singapore · usc
PriorZero: Bridging Language Priors and World Models for Decision Making
May 12, 2026Junyu Xiong, Yuan Pu, Jia Tang +1LLM-Guided RL Latent World Models
University of Science and Technology of China · Shanghai Artificial Intelligence Laboratory · Nanjing University of Aeronautics and Astronautics +1
Why Conclusions Diverge from the Same Observations: Formalizing World-Model Non-Identifiability via an Inference
May 12, 2026Toru TakahashiWorld Model Learning Cognitive Modeling
Human Informatics and Systems Laboratory, Doshisha University, Kyoto, Japan · Linked Open Data Initiative, NPO, Tokyo, Japan · Keio Research Institute at SFC, Fujisawa, Japan +1
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
DriveFuture: Future-Aware Latent World Models for Autonomous Driving
May 10, 2026Yufeng Hong, Xiaotian Zhou, Yingyan Li +6World Models for Autonomous Driving Latent World Models
Beijing Institute of Technology · Institute of Automation, Chinese Academy of Sciences · Beihang University +5
MCP-Cosmos: World Model-Augmented Agents for Complex Task Execution in MCP Environments
May 9, 2026Giridhar Ganapavarapu, Dhaval PatelLLM World Models World Models
IBM, New York
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
One Token Per Frame: Reconsidering Visual Bandwidth in World Models for VLA Policy
May 8, 2026Zuojin Tang, Shengchao Yuan, Xiaoxin Bai +4Action-Conditioned World Models Efficient VLA Models
Zhejiang University · Central South University · Harbin Institute of Technology +2
LaWM: Least Action World Models for Long-Horizon Physical Consistency from Visual Observations
May 8, 2026Qixin Xiao, Maani GhaffariPhysical Consistency in Video Generation World Models for Robotics
University of Michigan
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
Driver-WM: A Driver-Centric Traffic-Conditioned Latent World Model for In-Cabin Dynamics Rollout
May 6, 2026Haozhuang Chi, Daosheng Qiu, Hao Su +4World Model Learning Causal World Models
Nanyang Technological University, Singapore · Hubei University, Wuhan, China · Osaka University, Osaka, Japan
Latent State Design for World Models under Sufficiency Constraints
May 3, 2026Keon Woo KimRepresentation Learning Predictive Representation Learning
Om Labs
Being-H0.7: A Latent World-Action Model from Egocentric Videos
Apr 30, 2026Hao Luo, Wanpeng Zhang, Yicheng Feng +6Visuomotor Policy Learning Efficient VLA Models
BeingBeyond Team
Biased Dreams: Limitations to Epistemic Uncertainty Quantification in Latent Space Models
Apr 28, 2026Julia Berger, Bernd Frauenknecht, Sebastian Trimpe +1Uncertainty Quantification Latent Dynamics Modeling
1RWTH Aachen University
Xiaomi OneVL: One-Step Latent Reasoning and Planning with Vision-Language Explanation
Apr 20, 2026Jinghui Lu, Jiayi Guan, Zhijian Huang +47Trajectory Generation VLMs for Autonomous Driving
Sonata: A Hybrid World Model for Inertial Kinematics under Clinical Data Scarcity
Apr 20, 2026Blaise Delaney, Salil Patel, Yuji Xing +3Medical Diagnosis Predictive Representation Learning
1TimeTrace Labs · 2NVIDIA · University of Oxford
World-Value-Action Model: Implicit Planning for Vision-Language-Action Systems
Apr 16, 2026Runze Li, Hongyin Zhang, Junxi Jin +5Vision-Language-Action Models Model-Based Planning
Westlake University Hangzhou, China · Nanjing University Suzhou Campus, 1520 Taihu Avenue, Suzhou, China
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
From Topology to Trajectory: LLM-Driven World Models For Supply Chain Resilience
Apr 13, 2026Jia LuoLLM Agent Planning Supply Chain Management
Huazhong University and Science and Technology Wuhan, China
DexWorldModel: Causal Latent World Modeling towards Automated Learning of Embodied Tasks
Apr 13, 2026Yueci Deng, Guiliang Liu, Kui JiaWorld Models for Robotics Latent World Models
DexForce AI
CausalVAE as a Plug-in for World Models: Towards Reliable Counterfactual Dynamics
Apr 9, 2026Ziyi Ding, Xianxin Lai, Weiyu Chen +2Causal World Models Counterfactual Evaluation
Tsinghua Shenzhen International Graduate School, Tsinghua University · The University of Hong Kong · INFIFORCE Intelligent Technology
Toward Consistent World Models with Multi-Token Prediction and Latent Semantic Enhancement
Apr 7, 2026Qimin Zhong, Hao Liao, Haiming Qin +4Multi-Token Prediction Latent World Models
Shenzhen University · Microsoft Research Asia
Task Ecologies and the Evolution of World-Tracking Representations in Large Language Models
Apr 7, 2026Giulio Valentino Dalla RivaAutoregressive Language Modeling Latent World Models
Baffelan OÜ
Hierarchical Planning with Latent World Models
Apr 3, 2026Wancong Zhang, Basile Terver, Artem Zholus +7Hierarchical Planning Long-Horizon Robotic Manipulation
FAIR at Meta · New York University · Mila - Québec AI Institute +1
DLWM: Dual Latent World Models enable Holistic Gaussian-centric Pre-training in Autonomous Driving
Apr 1, 2026Yiyao Zhu, Ying Xue, Haiming Zhang +8World Models for Autonomous Driving 3D Occupancy Prediction
HKUST · CUHK-SZ · USTC +1

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World Machine: Towards Generative World Modeling for Time-Series

Elton Cardoso do Nascimento, Alexandre da Silva Simões, Esther Luna Colombini, Ricardo Ribeiro Gudwin+1

Universidade Estadual de Campinas (UNICAMP), {FEEC, IC} · Universidade Estadual Paulista (UNESP), Instituto de Ciˆencia e Tecnologia, Sorocaba

World Model Learning Latent World Models Time Series Transformers

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World models represent a paradigm shift in generative AI, pursuing predictive understanding and controllable simulation of environments in a structured and generalizable way. We present World Machine, a generative world-modeling architecture for time series. It is a transformer-based architecture with latent states that enables adaptation to different amounts of observed data and contexts. This shows an improvement over traditional transformers, which have a computational and memory cost that scales quadratically with the context. Experiments on a proposed synthetic dataset, Toy1D, validate the approach's feasibility, demonstrate capabilities not found in conventional transformers, and highlight the contributions of each component of the training protocol.