awesome-agentic-world-modeling

Awesome Agentic World Modeling

This repository is a collection of world modeling progress based on Agentic World Modeling: Foundations, Capabilities, Laws, and Beyond, covering 1,000+ papers and benchmarks grouped by section in reverse chronological order and available in a public Notion database. Released under the MIT License. Check out our poster.

@article{chu2026agenticworldmodelingfoundations,
  title         = {Agentic World Modeling: Foundations, Capabilities, Laws, and Beyond},
  author        = {Meng Chu and Xuan Billy Zhang and Kevin Qinghong Lin and Lingdong Kong and Jize Zhang and Teng Tu and Weijian Ma and Ziqi Huang and Senqiao Yang and Wei Huang and Yeying Jin and Zhefan Rao and Jinhui Ye and Xinyu Lin and Xichen Zhang and Qisheng Hu and Shuai Yang and Leyang Shen and Wei Chow and Yifei Dong and Fengyi Wu and Quanyu Long and Bin Xia and Shaozuo Yu and Mingkang Zhu and Wenhu Zhang and Jiehui Huang and Haokun Gui and Runyi Li and Chenyu Tang and Dong Huang and Xuhang Chen and Rui Liu and Chengzu Li and Shiyi Du and Xu Huang and Haoxuan Che and Long Chen and Qifeng Chen and Wenya Wang and Wenxuan Zhang and Xiaojuan Qi and Yang Deng and Yanwei Li and Mike Zheng Shou and Zhi-Qi Cheng and See-Kiong Ng and Ziwei Liu and Philip Torr and Jiaya Jia},
  year          = {2026},
  eprint        = {2604.22748},
  archivePrefix = {arXiv},
  primaryClass  = {cs.AI},
  url           = {https://arxiv.org/abs/2604.22748}
}

News

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Table of Contents

• Taxonomy Overview
• L1: Predictor
• L2: Simulator
• L3: Evolver
• Benchmarks & Evaluation
• Related Surveys
• Welcome to Contribute

Overview

L1: Predictor

Methods learning local one-step operators: state inference, forward dynamics, observation decoding, and inverse dynamics.

Physical World

• Keeping JEPA World Models Plannable When Little of the Frame Moves (arXiv, 2026)
• ω-0: A Latent Predictive World Action Model for Concurrent Humanoid Loco-Manipulation (arXiv, 2026)
• 3D Point World Models: Point Completion Enables More Accurate Dynamics Learning (arXiv, 2026)
• A Compositional Object-Based Approach to Learning Physical Dynamics (ICLR, 2016)
• A Game Theoretic Framework for Model Based Reinforcement Learning (ICML, 2020)
• A Generalist Agent (Trans. Mach. Learn. Res., 2022)
• A Generalist Dynamics Model for Control (arXiv, 2023)
• A Lightweight Library for Energy-Based Joint-Embedding Predictive Architectures (arXiv, 2026)
• A Path Towards Autonomous Machine Intelligence (OpenReview preprint, 2022)
• A-JEPA: Joint-Embedding Predictive Architecture Can Listen (arXiv, 2023)
• Accelerating Model-Based Reinforcement Learning with State-Space World Models (arXiv, 2025)
• AcrossVAM1.0: Particle World Modeling for Text-Assisted Robot Video Prediction (arXiv, 2026)
• AcrossWAM1.0: A Modular Latent World-Action Stack for Compact Robot Policies (arXiv, 2026)
• ACT-JEPA: Novel Joint-Embedding Predictive Architecture for Efficient Policy Representation Learning (arXiv, 2025)
• Action Shapley: A Training Data Selection Metric for World Model in Reinforcement Learning (arXiv, 2026)
• AD3: Implicit Action is the Key for World Models to Distinguish the Diverse Visual Distractors (ICML, 2024)
• AdaJEPA: An Adaptive Latent World Model (arXiv, 2026)
• Adaptive World Models: Learning Behaviors by Latent Imagination Under Non-Stationarity (arXiv, 2024)
• Advancing Humanoid Locomotion: Mastering Challenging Terrains with Denoising World Model Learning (RSS, 2024)
• Advancing Semantic Future Prediction through Multimodal Visual Sequence Transformers (CVPR, 2025)
• Affordances Enable Partial World Modeling with LLMs (arXiv, 2026)
• AnyPos: Automated Task-Agnostic Actions for Bimanual Manipulation (arXiv, 2025)
• AquaJEPA: An Action-Conditioned Multimodal JEPA Family for Underwater Robot Dynamics (arXiv, 2026)
• Arti-JEPA: Adapting Video World Model to Real-Time MRI of the Vocal Tract for Speech-Production Analysis (arXiv, 2026)
• AtomVLA: Scalable Post-Training for Robotic Manipulation via Predictive Latent World Models (arXiv, 2026)
• Auto-JEPA: A Latent World Model of Continuous Intent for End-to-End Autonomous Driving (arXiv, 2026)
• Back to Parsimonious Latents: Learning Task-Centric World Models from Visual Foundations (arXiv, 2026)
• Beyond Euclidean Proximity: Repairing Latent World Models with Horizon-Matched Trajectory Reachability Metrics (arXiv, 2026)
• Beyond Instance Slots: Semantically Rich World Models for Physical Interaction Planning (arXiv, 2026)
• Beyond Myopic World Models: Long-Horizon End-to-End Training for Direct Future Prediction (arXiv, 2026)
• BiTAgent: A Task-Aware Modular Framework for Bidirectional Coupling between Multimodal Large Language Models and World Models (arXiv, 2025)
• Bootstrap Off-policy with World Model (arXiv, 2025)
• Bootstrapping Action-Grounded Visual Dynamics in Unified Vision-Language Models (arXiv, 2025)
• Bounded Exploration with World Model Uncertainty in Soft Actor-Critic Reinforcement Learning Algorithm (arXiv, 2024)
• Bounding Distributional Shifts in World Modeling through Novelty Detection (IEEE/RJS International Conference on Intelligent RObots and Systems, 2025)
• BRICKS-WM: Building Reusability via Interface Composition Kinetics for Structured World Models (arXiv, 2026)
• Building spatial world models from sparse transitional episodic memories (arXiv, 2025)
• CAER: Causal Action Effect Reweighting for World Model Training (arXiv, 2026)
• Can World Models Benefit VLMs for World Dynamics? (arXiv, 2025)
• Cardiac Copilot: Automatic Probe Guidance for Echocardiography with World Model (arXiv, 2024)
• CarFormer: Self-Driving with Learned Object-Centric Representations (ECCV, 2024)
• Causal-JEPA: Learning World Models through Object-Level Latent Interventions (arXiv, 2026)
• CausalVAE as a Plug-in for World Models: Towards Reliable Counterfactual Dynamics (arXiv, 2026)
• Chain of World: World Model Thinking in Latent Motion (arXiv, 2026)
• CLAW: Learning Continuous Latent Action World Models via Adversarial Latent Regularization (arXiv, 2026)
• Cloning Deterministic Worlds: The Critical Role of Latent Geometry in Long-Horizon World Models (arXiv, 2025)
• Closing the Train-Test Gap in World Models for Gradient-Based Planning (arXiv, 2025)
• Code as Worlds: Agentic Discovery of Executable World Representations for Physical Reasoning (arXiv, 2026)
• Cognitively Inspired Energy-Based World Models (arXiv, 2024)
• CoIRL-AD: Collaborative-Competitive Imitation-Reinforcement Learning in Latent World Models for Autonomous Driving (arXiv, 2025)
• Compact Visuotactile World Models for Lifting: Prediction, Reward Alignment, and Force Constraints (arXiv, 2026)
• ConfAL-WM: Confidence-Guided Active Learning for Action-Conditioned World Models (arXiv, 2026)
• ContactGuard: Pre-Contact Execution Monitoring with Action-Conditioned Latent World Models (arXiv, 2026)
• Context and Diversity Matter: The Emergence of In-Context Learning in World Models (arXiv, 2025)
• Contextual Latent World Models for Offline Meta Reinforcement Learning (arXiv, 2026)
• Correcting a learned physical invariant improves world-model rollouts (arXiv, 2026)
• CoT-VLA: Visual Chain-of-Thought Reasoning for Vision-Language-Action Models (CVPR, 2025)
• Coupled Control and Wireless World Models for Resilient Remote Robotic Control (arXiv, 2026)
• CRISP: A Spatiotemporal Camera-Radar Backbone for Driving via Forecasting-Based World-Model Pretraining (arXiv, 2026)
• CWM: Contrastive World Models for Action Feasibility Learning in Embodied Agent Pipelines (arXiv, 2026)
• Cycle-World: Mitigating Error Accumulation in Long-term Video World Models via Reverse-Prediction Cycle Consistency (ECCV, 2026)
• DAWM: Diffusion Action World Models for Offline Reinforcement Learning via Action-Inferred Transitions (arXiv, 2025)
• DayDreamer: World Models for Physical Robot Learning (CoRL, 2022)
• Decoupling Intention from Trajectory: A Representational Deduction Framework for World Action Models (arXiv, 2026)
• Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models (NeurIPS, 2018)
• DELE-w0.5: Inferring Action from Future Latent State for Robotic Manipulation (arXiv, 2026)
• Delta-JEPA: Learning Action-Sensitive World Models via Latent Difference Decoding (arXiv, 2026)
• Demo-JEPA: Joint-Embedding Predictive Architecture for One-shot Cross-Embodiment Imitation (arXiv, 2026)
• Describe-Then-Act: Proactive Agent Steering via Distilled Language-Action World Models (arXiv, 2026)
• Deterministic World Models for Verification of Closed-loop Vision-based Systems (arXiv, 2025)
• Diagnosing and Dynamically Filtering Occupancy World Models for Active Mapping (arXiv, 2026)
• DIAL: Decoupling Intent and Action via Latent World Modeling for End-to-End VLA (arXiv, 2026)
• Differentiable Raycasting for Self-supervised Occupancy Forecasting (ECCV, 2022)
• Diffusion Transformer World-Action Model for AV Scene Prediction (arXiv, 2026)
• DiLA: Disentangled Latent Action World Models (arXiv, 2026)
• DINO-Foresight: Looking into the Future with DINO (arXiv, 2024)
• DINO-WM: World Models on Pre-trained Visual Features enable Zero-shot Planning (ICML, 2024)
• Discrete Codebook World Models for Continuous Control (ICLR, 2025)
• Disentangled World Models: Learning to Transfer Semantic Knowledge from Distracting Videos for Reinforcement Learning (ICCV, 2025)
• DiT4DiT: Jointly Modeling Video Dynamics and Actions for Generalizable Robot Control (arXiv, 2026)
• DiWA: Diffusion Policy Adaptation with World Models (arXiv, 2025)
• DLWM: Dual Latent World Models enable Holistic Gaussian-centric Pre-training in Autonomous Driving (arXiv, 2026)
• Do Transformer World Models Give Better Policy Gradients? (ICML, 2024)
• Dream to Control: Learning Behaviors by Latent Imagination (ICLR, 2019)
• Dream to Drive With Predictive Individual World Model (IEEE Transactions on Intelligent Vehicles, 2024)
• DREAM-Chunk: Reactive Action Chunking with Latent World Model (arXiv, 2026)
• DREAMer-VXS: A Latent World Model for Sample-Efficient AGV Exploration in Stochastic, Unobserved Environments (arXiv, 2025)
• DreamerV3-XP: Optimizing exploration through uncertainty estimation (arXiv, 2025)
• Dreaming of Many Worlds: Learning Contextual World Models Aids Zero-Shot Generalization (RLJ, 2024)
• Dreaming the Unseen: World Model-regularized Diffusion Policy for Out-of-Distribution Robustness (arXiv, 2026)
• Dreaming when Necessary: Advancing World Action Models with Adaptive Multi-Modal Reasoning (arXiv, 2026)
• Dreaming: Model-based Reinforcement Learning by Latent Imagination without Reconstruction (ICRA, 2020)
• DreamLedger: Where to Refuse World-Model Imagination Using Execution-Settled Credit (arXiv, 2026)
• DreamMimic: Learning Visuomotor Whole-Body Loco-Manipulation via World Model (arXiv, 2026)
• DreamSAC: Learning Hamiltonian World Models via Symmetry Exploration (arXiv, 2026)
• DreamVLA: A Vision-Language-Action Model Dreamed with Comprehensive World Knowledge (arXiv, 2025)
• DreamWAM: Beyond RGB Future Prediction for World Action Models (arXiv, 2026)
• Drive-HWM: Hierarchical World Models for Dynamic-Latent Guided Autonomous Driving (arXiv, 2026)
• Drive-JEPA: Video JEPA Meets Multimodal Trajectory Distillation for End-to-End Driving (arXiv, 2026)
• Driving Beyond Privilege: Distilling Dense-Reward Knowledge into Sparse-Reward Policies (arXiv, 2025)
• Dueling World Models: Advantage-Style Action Channels for Common-Mode Distractor Rejection (arXiv, 2026)
• DUET-DINO: Simultaneous Cross-View World Modeling for Latent Planning in Robot Manipulation (arXiv, 2026)
• DWM: Separating World Effects from Actions in Latent World Models (arXiv, 2026)
• Dynamic Sparsity: Challenging Common Sparsity Assumptions for Learning World Models in Robotic Reinforcement Learning Benchmarks (AAAI, 2025)
• Dynamics-Aligned Latent Imagination in Contextual World Models for Zero-Shot Generalization (arXiv, 2025)
• DynaMo: In-Domain Dynamics Pretraining for Visuo-Motor Control (NeurIPS, 2024)
• DynaWM: Dynamics-Aware Distillation with World Model and Momentum Targets for Smooth Locomotion over Continuous Stairs (arXiv, 2026)
• DynFlowDrive: Flow-Based Dynamic World Modeling for Autonomous Driving (arXiv, 2026)
• DynVLA: Learning World Dynamics for Action Reasoning in Autonomous Driving (arXiv, 2026)
• Efficient Image-Goal Navigation with Representative Latent World Model (arXiv, 2025)
• Ego-Dynamics-Augmented World Model for Autonomous Driving with Zero-Shot Cross-Chassis Adaptation (arXiv, 2026)
• EgoAgent: A Joint Predictive Agent Model in Egocentric Worlds (ICCV, 2025)
• EgoWAM: World Action Models Beyond Pixels with In-the-Wild Egocentric Human Data (arXiv, 2026)
• Embed to Control: A Locally Linear Latent Dynamics Model for Control from Raw Images (NeurIPS, 2015)
• Embodied World Models Emerge from Navigational Task in Open-Ended Environments (arXiv, 2025)
• Enfold: Folding World Model Imagination into Predictive Representations for Ultra-Efficient Embodied Control (arXiv, 2026)
• Enhance Sample Efficiency and Robustness of End-to-End Urban Autonomous Driving via Semantic Masked World Model (IEEE transactions on intelligent transportation systems (Print), 2022) — End-to-end autonomous driving provides a feasible way to automatically maximize overall driving system performance by directly mapping the raw pixels from a front-facing camera to control signals.
• Enhancing End-to-End Autonomous Driving with Latent World Model (ICLR, 2024)
• Enhancing Policy Learning with World-Action Model (arXiv, 2026)
• Entity-Centric World Models: Interaction-Aware Masking for Causal Video Prediction (arXiv, 2026)
• EWAM: An Enhanced World Action Model for Closed-Loop Online Adaptation in Embodied Intelligence (arXiv, 2026)
• F1: A Vision-Language-Action Model Bridging Understanding and Generation to Actions (arXiv, 2025)
• Factored Latent Action World Models (arXiv, 2026)
• Factored World Models for Zero-Shot Generalization in Robotic Manipulation (arXiv, 2022)
• Factorized Latent Dynamics for Video JEPA: An Empirical Study of Auxiliary Objectives (arXiv, 2026)
• Failure Detection for Surgical Robot Imitation Policies via Flow-Matching World Modeling (arXiv, 2026)
• FARM: Reading Failure Signals from the Internal Predictive States of a Frozen Robotic World Model (arXiv, 2026)
• FASTopoWM: Fast-Slow Lane Segment Topology Reasoning with Latent World Models (arXiv, 2025)
• FAWAM: Force-Aware World Action Models for Closed-Loop Contact-Rich Manipulation (arXiv, 2026)
• FF-JEPA: Long-Horizon Planning in World Models with Latent Planners (arXiv, 2026)
• Finetuning Offline World Models in the Real World (CoRL, 2023)
• FLARE: Learning Future-Aware Latent Representations from Vision-Language Models for Autonomous Driving (arXiv, 2026)
• FLARE: Robot Learning with Implicit World Modeling (arXiv, 2025)
• Flash-WAM: Modality-Aware Distillation for World Action Models (arXiv, 2026)
• Flow Equivariant World Models: Memory for Partially Observed Dynamic Environments (arXiv, 2026)
• Flow Matching in Feature Space for Stochastic World Modeling (arXiv, 2026)
• FlowVLA: Visual Chain of Thought-based Motion Reasoning for Vision-Language-Action Models (arXiv, 2025)
• FOCUS: Object-Centric World Models for Robotics Manipulation (arXiv, 2023)
• Foresight Diffusion: Improving Sampling Consistency in Predictive Diffusion Models (arXiv, 2025)
• Foresight Without Seeing: Latent Futures for World Action Models (arXiv, 2026)
• ForeTime-VLA: Causal Future-Token Distillation from a World Action Model for Conveyor-Belt Manipulation (arXiv, 2026)
• Foundational World Models Accurately Detect Bimanual Manipulator Failures (arXiv, 2026)
• FRAPPE: Infusing World Modeling into Generalist Policies via Multiple Future Representation Alignment (arXiv, 2026)
• FSF-Net: Enhance 4D Occupancy Forecasting with Coarse BEV Scene Flow for Autonomous Driving (arXiv, 2024)
• GaussianDream++: Efficient 3D Gaussian World Modeling for Robotic Manipulation (arXiv, 2026)
• GaussianWAM: Distilling Geometry and Semantics from 3D Gaussian Fields into World-Action Models (arXiv, 2026)
• GaussianWorld: Gaussian World Model for Streaming 3D Occupancy Prediction (CVPR, 2024)
• GenAD: Generative End-to-End Autonomous Driving (ECCV, 2024)
• General agents contain world models (arXiv, 2025)
• Geographic Diversity Beats Data Volume for Cross-Domain Generalization in Zero-Label JEPA Driving World Models (arXiv, 2026)
• GeoSem-WAM: Geometry- and Semantic-Aware World Action Models (arXiv, 2026)
• GeoWAM: Visual Geometry World Action Models for Autonomous Driving (arXiv, 2026)
• GeoWorld-VLM: Geometry from World Models for Vision-Language Models (arXiv, 2026)
• GeoWorldAD: Geometry World Action Model for Autonomous Driving (arXiv, 2026)
• GlanceWAM: Sparse Test-Time Imagination for World-Action Models (arXiv, 2026)
• Goal-VLA: Image-Generative VLMs as Object-Centric World Models Empowering Zero-shot Robot Manipulation (arXiv, 2025)
• Gradient-based Planning with World Models (arXiv, 2023)
• GraphWorld: Long-Horizon Planning with World Models for End-to-End Autonomous Driving (arXiv, 2026)
• Grounding Large Language Models In Embodied Environment With Imperfect World Models (arXiv, 2024)
• Grounding Video Models to Actions through Goal Conditioned Exploration (ICLR, 2024)
• HarmonyDream: Task Harmonization Inside World Models (ICML, 2023)
• HEAT: Heterogeneous End-to-End Autonomous Driving via Trajectory-Guided World Models (arXiv, 2026)
• HERA: Historical Evidence Routing Adapter for Physical Prediction in Latent World Models (arXiv, 2026)
• Hierarchical JEPA Meets Predictive Remote Control in Beyond 5G Networks (arXiv, 2026)
• Hindsight for Foresight: Unsupervised Structured Dynamics Models from Physical Interaction (IEEE/RJS International Conference on Intelligent RObots and Systems, 2020)
• hint2: Hierarchical World Models for Inference-Time Temporal Logic Guidance (arXiv, 2026)
• How Hard is it to Confuse a World Model? (arXiv, 2025)
• HyWorldVLA: A Vision-Language-Action Model with Hybrid World Modeling for Autonomous Driving (arXiv, 2026)
• IGOR: Image-GOal Representations are the Atomic Control Units for Foundation Models in Embodied AI (arXiv, 2024)
• IMPACT: Attention Is the Interaction Map for Scalable Interaction-Aware World Model Training (arXiv, 2026)
• In-Context Reinforcement Learning via Communicative World Models (arXiv, 2025)
• In-Context World Modeling for Robotic Control (arXiv, 2026)
• InDRiVE: Intrinsic Disagreement based Reinforcement for Vehicle Exploration through Curiosity Driven Generalized World Model (arXiv, 2025)
• InDRiVE: Reward-Free World-Model Pretraining for Autonomous Driving via Latent Disagreement (arXiv, 2025)
• Inference-time Physics Alignment of Video Generative Models with Latent World Models (arXiv, 2026)
• INTACT: Isomorphic Intent-to-Action Learning for Search-Free World Models (arXiv, 2026)
• Inter-environmental world modeling for continuous and compositional dynamics (arXiv, 2025)
• InternVLA-A1.5: Unifying Understanding, Latent Foresight, and Action for Compositional Generalization (arXiv, 2026)
• Intuitive physics understanding emerges from self-supervised pretraining on natural videos (arXiv, 2025)
• IRL-VLA: Training an Vision-Language-Action Policy via Reward World Model (arXiv, 2025)
• Is Conditional Generative Modeling all you need for Decision-Making? (ICLR, 2022)
• Is Forward Prediction Enough? Physical State Grounding for JEPA World Models (arXiv, 2026)
• Iso-Dream: Isolating and Leveraging Noncontrollable Visual Dynamics in World Models (NeurIPS, 2022)
• JEDI: Joint Embedding Diffusion World Model for Online Model-Based Reinforcement Learning (arXiv, 2026)
• JEPA for RL: Investigating Joint-Embedding Predictive Architectures for Reinforcement Learning (arXiv, 2025)
• JEPA-Anything: Learning Predictive Models across Different Worlds (arXiv, 2026)
• JEPA-VLA: Video Predictive Embedding is Needed for VLA Models (arXiv, 2026)
• KAN-Dreamer: Benchmarking Kolmogorov-Arnold Networks as Function Approximators in World Models (arXiv, 2025)
• Keep the Future, Drop the Rollout: RIFT for World Action Models (arXiv, 2026)
• Kinodynamic Motion Planning for Mobile Robot Navigation across Inconsistent World Models (arXiv, 2025)
• Knowledge Graphs as World Models for Semantic Material-Aware Obstacle Handling in Autonomous Vehicles (arXiv, 2025)
• Koopman Dreamer: Spectrally Constrained Latent Dynamics for Stable World-Model Imagination (arXiv, 2026)
• Label-Efficient Grasp Joint Prediction with Point-JEPA (arXiv, 2025)
• Language-conditioned world model improves policy generalization by reading environmental descriptions (arXiv, 2025)
• LaST-VLA: Thinking in Latent Spatio-Temporal Space for Vision-Language-Action in Autonomous Driving (arXiv, 2026)
• Latent Action Pretraining from Videos (arXiv, 2024)
• Latent Action Pretraining Through World Modeling (arXiv, 2025)
• Latent Action World Models for Control with Unlabeled Trajectories (arXiv, 2025)
• LeWAM: Latent evolving World Action Model (arXiv, 2026)
• Latent Policy Steering with Embodiment-Agnostic Pretrained World Models (arXiv, 2025)
• Latent Video Prediction Learns Better World Models (arXiv, 2026)
• LaWAM: Latent World Action Models for Efficient Dynamics-Aware Robot Policies (arXiv, 2026)
• LAWM-3D: Learning 3D-Aware Latent Actions from Human Videos for Generalizable Robot World Models (arXiv, 2026)
• LD4WAM: Learning Latent Dynamics from Human Videos for World Action Models (arXiv, 2026)
• LDA-1B: Scaling Latent Dynamics Action Model via Universal Embodied Data Ingestion (arXiv, 2026)
• LeapBot-WA: World-Anchor Action Models via Predictive Latent Alignments (arXiv, 2026)
• Learned Perceptive Forward Dynamics Model for Safe and Platform-aware Robotic Navigation (Robotics, 2025)
• Learning 4D Geometric Priors for Inference-Efficient World Action Models (arXiv, 2026)
• Learning Abstract World Models with a Group-Structured Latent Space (arXiv, 2025)
• Learning and Leveraging World Models in Visual Representation Learning (arXiv, 2024)
• Learning Humanoid Locomotion with World Model Reconstruction (arXiv, 2025)
• Learning Implicit Causal World Models from Multi-Agent Demonstrations (arXiv, 2026)
• Learning Invariant Visual Representations for Planning with Joint-Embedding Predictive World Models (arXiv, 2026)
• Learning Latent Action World Models In The Wild (arXiv, 2026)
• Learning Latent Dynamic Robust Representations for World Models (ICML, 2024)
• Learning Latent Dynamics for Planning from Pixels (ICML, 2018)
• Learning Robot Manipulation from Audio World Models (arXiv, 2025)
• Learning to Act from Actionless Videos through Dense Correspondences (ICLR, 2023)
• Learning to Poke by Poking: Experiential Learning of Intuitive Physics (Advances in Neural Information Processing Systems, 2016)
• Learning to Reach Goals via Iterated Supervised Learning (ICLR, 2021)
• Learning to Sample: Reinforcement Learning-Guided Sampling for Autonomous Vehicle Motion Planning (arXiv, 2025)
• Learning to unfold cloth: Scaling up world models to deformable object manipulation (arXiv, 2026)
• Learning to Use Imagination: Progress-Conditioned Future Utilization for World Action Models (arXiv, 2026)
• Learning World Models for Unconstrained Goal Navigation (NeurIPS, 2024)
• LeFlow: Generative Latent Flow Planning for World Models (arXiv, 2026)
• LeWorldModel: Stable End-to-End Joint-Embedding Predictive Architecture from Pixels (arXiv, 2026)
• Linear Spatial World Models Emerge in Large Language Models (arXiv, 2025)
• LpWM: A Case for Sparse Representations in World Models (arXiv, 2026)
• LSRE: Latent Semantic Rule Encoding for Real-Time Semantic Risk Detection in Autonomous Driving (arXiv, 2025)
• LVDrive: Latent Visual Representation Enhanced Vision-Language-Action Autonomous Driving Model (arXiv, 2026)
• LWDrive: Layer-Wise World-Model-Guided Vision-Language Model Planning for Autonomous Driving (arXiv, 2026)
• Making Foresight Actionable: Repurposing Representation Alignment in World Action Models (arXiv, 2026)
• Making Latent Evolution Explicit: Operator-Structured Transitions for World Action Models (arXiv, 2026)
• MAMBA: an Effective World Model Approach for Meta-Reinforcement Learning (ICLR, 2024)
• ManiGaussian++: General Robotic Bimanual Manipulation with Hierarchical Gaussian World Model (IEEE/RJS International Conference on Intelligent RObots and Systems, 2025)
• ManiGaussian: Dynamic Gaussian Splatting for Multi-task Robotic Manipulation (ECCV, 2024)
• Mask World Model: Predicting What Matters for Robust Robot Policy Learning (arXiv, 2026)
• Masked Visual Actions for Unified World Modeling (arXiv, 2026)
• Masked World Models for Visual Control (CoRL, 2022)
• Mastering diverse control tasks through world models (Nature, 2025)
• Mastering Diverse Domains through World Models (arXiv, 2023)
• Mastering Visual Continuous Control: Improved Data-Augmented Reinforcement Learning (ICLR, 2022)
• MC-JEPA: A Joint-Embedding Predictive Architecture for Self-Supervised Learning of Motion and Content Features (arXiv, 2023)
• Memory as Plans: World-Action Modeling with Memory-Grounded Planning (arXiv, 2026)
• Metis: A Generalizable and Efficient World-Action Model for Autonomous Driving and Urban Navigation (arXiv, 2026)
• mimic-video: Video-Action Models for Generalizable Robot Control Beyond VLAs (arXiv, 2025)
• MMaDA-VLA: Large Diffusion Vision-Language-Action Model with Unified Multi-Modal Instruction and Generation (arXiv, 2026)
• Mobile Manipulation with Active Inference for Long-Horizon Rearrangement Tasks (arXiv, 2025)
• Modality-Autoregressive World-Action Models (arXiv, 2026)
• MoDem-V2: Visuo-Motor World Models for Real-World Robot Manipulation (ICRA, 2023)
• MOSH-WM: Mask-Grounded Soft-Hamiltonian Dynamics for Object-Centric World Models (arXiv, 2026)
• MrCoM: A Meta-Regularized World-Model Generalizing Across Multi-Scenarios (AAAI, 2025)
• Multi-Camera Unified Pre-Training via 3D Scene Reconstruction (IEEE Robotics and Automation Letters, 2023)
• Multi-Stage Manipulation with Demonstration-Augmented Reward, Policy, and World Model Learning (ICML, 2025)
• Multi-View Video Diffusion Policy: A 3D Spatio-Temporal-Aware Video Action Model (arXiv, 2026)
• Multimodal Dreaming: A Global Workspace Approach to World Model-Based Reinforcement Learning (arXiv, 2025)
• NavCoT: Boosting LLM-Based Vision-and-Language Navigation via Learning Disentangled Reasoning (IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024)
• NavigateDiff: Visual Predictors are Zero-Shot Navigation Assistants (arXiv, 2025)
• Neural Fields as World Models (arXiv, 2026)
• Neural Motion Simulator Pushing the Limit of World Models in Reinforcement Learning (CVPR, 2025)
• Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuning (ICRA, 2017)
• Next Embedding Prediction Makes World Models Stronger (arXiv, 2026)
• Next Forcing: Causal World Modeling with Multi-Chunk Prediction (arXiv, 2026)
• No Gaussian Required: Contrastive Inverse Dynamics for JEPA World Models (arXiv, 2026)
• NORA-1.5: A Vision-Language-Action Model Trained using World Model- and Action-based Preference Rewards (arXiv, 2025)
• NRSeg: Noise-Resilient Learning for BEV Semantic Segmentation via Driving World Models (IEEE Transactions on Image Processing, 2025)
• Object-Centric World Model for Language-Guided Manipulation (arXiv, 2025)
• Object-Centric World Models for Causality-Aware Reinforcement Learning (AAAI, 2025)
• OccVLA: Vision-Language-Action Model with Implicit 3D Occupancy Supervision (arXiv, 2025)
• ODEWorld: A Continuous Predictive Architecture via Physical-Time Flow (arXiv, 2026)
• Off-Manifold Refinement: Guiding Video Generators with a Frozen World Model (arXiv, 2026)
• On Memory: A comparison of memory mechanisms in world models (arXiv, 2025)
• On the Identifiability of Controlled World Models (arXiv, 2026)
• One Model for All Tasks: Leveraging Efficient World Models in Multi-Task Planning (arXiv, 2025)
• One-shot World Models Using a Transformer Trained on a Synthetic Prior (arXiv, 2024)
• Orbit-Planner: Towards Latent World Models for On-Orbit Obstacle Avoidance of Satellite Agents (arXiv, 2026)
• Orthogonal JEPA: Factorized Predictive States for Latent World Models (arXiv, 2026)
• Overcoming Statistical Bias in Action-Controllable World Models (arXiv, 2026)
• OWMDrive: Causality-Aware End-to-End Autonomous Driving via 4D Occupancy World Model (IROS, 2026)
• PACT: Perception-Action Causal Transformer for Autoregressive Robotics Pre-Training (IEEE/RJS International Conference on Intelligent RObots and Systems, 2022)
• PBD-AG: Persistent Baseline-Delta Active Graphs with Uncertainty-Aware Inspection for Long-Horizon Service Robots (arXiv, 2026)
• PerceptDrive: Perception Prior World-Action Modeling with Adaptive Expert Routing for End-to-End Autonomous Driving (arXiv, 2026)
• PH-Dreamer: A Physics-Driven World Model via Port-Hamiltonian Generative Dynamics (arXiv, 2026)
• PhyLatent: Learning Dynamics-Relevant Representations for JEPA World Models (arXiv, 2026)
• PhysCoRe: Physics-Corrected Residual World Models for Material-Aware Deformable Dynamics (arXiv, 2026)
• Physical Autoregressive Model for Robotic Manipulation without Action Pretraining (arXiv, 2025)
• Physically Interpretable World Models via Weakly Supervised Representation Learning (arXiv, 2024)
• PIGDreamer: Privileged Information Guided World Models for Safe Partially Observable Reinforcement Learning (ICML, 2025)
• PILCO: A Model-Based and Data-Efficient Approach to Policy Search (ICML, 2011)
• PIVOT-R: Primitive-Driven Waypoint-Aware World Model for Robotic Manipulation (NeurIPS, 2024)
• PLAN-S: Bridging Planning with Latent Style Dynamics for Autonomous Driving World Models (arXiv, 2026)
• Planning from Pixels using Inverse Dynamics Models (ICLR, 2020)
• Planning with the Views (arXiv, 2026)
• PLUME: Probabilistic Latent Unified World Modeling and Parameter Estimation for Multi-Finger Manipulation (arXiv, 2026)
• Point Cloud Forecasting as a Proxy for 4D Occupancy Forecasting (CVPR, 2023)
• Point Tracking Improves World Action Models (arXiv, 2026)
• Pre-training Contextualized World Models with In-the-wild Videos for Reinforcement Learning (NeurIPS, 2023)
• Pre-VLA: Preemptive Runtime Verification for Reliable Vision-Language-Action and World-Model Rollouts (arXiv, 2026)
• Predictive Inverse Dynamics Models are Scalable Learners for Robotic Manipulation (arXiv, 2024)
• Prismatic World Model: Learning Compositional Dynamics for Planning in Hybrid Systems (arXiv, 2025)
• Privileged Foresight Distillation: Zero-Cost Future Correction for World Action Models (arXiv, 2026)
• Probing the effectiveness of World Models for Spatial Reasoning through Test-time Scaling (arXiv, 2025)
• ProTerrain: Probabilistic Physics-Informed Rough Terrain World Modeling (arXiv, 2025)
• PV-WM: A Heterogeneous Micro-Macro World Model for Articulated Pedestrian-Vehicle Co-Rollout (arXiv, 2026)
• QQWorld: Quantile-Quantile Matching for World Model Regularization (arXiv, 2026)
• R2-Dreamer: Redundancy-Reduced World Models without Decoders or Augmentation (arXiv, 2026)
• Real-World Robot Control by Deep Active Inference With a Temporally Hierarchical World Model (IEEE Robotics and Automation Letters, 2025)
• RECON: Rapid Exploration for Open-World Navigation with Latent Goal Models (CoRL, 2021)
• Reconstruction or Semantics? What Makes a Latent Space Useful for Robotic World Models (arXiv, 2026)
• Reinforcement Learning with Inverse Rewards for World Model Post-training (arXiv, 2025)
• Reinformed Dreamer: An Asymmetric World Model Efficiently Trained through Latent Guidance (arXiv, 2026)
• Render, Don’t Decode: Weight-Space World Models with Latent Structural Disentanglement (arXiv, 2026)
• RENEW: Towards Learning World Models and Repairing Model Exploitation from Preferences (arXiv, 2026)
• Representing Positional Information in Generative World Models for Object Manipulation (European Conference on Artificial Intelligence, 2024)
• RESBev: Making BEV Perception More Robust (arXiv, 2026)
• Reset-free Reinforcement Learning with World Models (Trans. Mach. Learn. Res., 2024)
• ResWM: Residual-Action World Model for Visual RL (arXiv, 2026)
• RetailSMV: Exocentric vs. Egocentric Adaptation of Foundation Video World Models in Retail (arXiv, 2026)
• Revisiting Feature Prediction for Learning Visual Representations from Video (Trans. Mach. Learn. Res., 2024)
• Reward-free World Models for Online Imitation Learning (ICML, 2024)
• ReWorld: Learning Better Representations for World Action Models (arXiv, 2026)
• RoboFlow4D: A Lightweight Flow World Model Toward Real-Time Flow-Guided Robotic Manipulation (arXiv, 2026)
• Rollout-Decoded Reconstruction for Long-Horizon Prediction in Latent World Models (arXiv, 2026)
• RynnWorld-4D: 4D Embodied World Models for Robotic Manipulation (arXiv, 2026)
• Safety Certification in the Latent space using Control Barrier Functions and World Models (International Conference on Intelligent Cloud Computing, 2025)
• SANTS: A State-Adaptive Scheduler for World Action Models (arXiv, 2026)
• SCALE: State-Calibrated Latent Embeddings for JEPA Planning in the Right Geometry (arXiv, 2026)
• Seeing through Imagination: Learning Scene Geometry via Implicit Spatial World Modeling (arXiv, 2025)
• Self-Aware Active Learning Enables Continual Improvement in Autonomous Driving (arXiv, 2026)
• Self-Consistent Model-based Adaptation for Visual Reinforcement Learning (IJCAI, 2025)
• Self-Supervised JEPA-based World Models for LiDAR Occupancy Completion and Forecasting (arXiv, 2026)
• Self-supervised Multi-future Occupancy Forecasting for Autonomous Driving (Robotics, 2024)
• Self-Supervised Representation Learning with Joint Embedding Predictive Architecture for Automotive LiDAR Object Detection (AAAI, 2025)
• Semantic Belief-State World Model for 3D Human Motion Prediction (arXiv, 2026)
• SENSEI: Semantic Exploration Guided by Foundation Models to Learn Versatile World Models (ICML, 2025)
• seq-JEPA: Autoregressive Predictive Learning of Invariant-Equivariant World Models (arXiv, 2025)
• SimuDICE: Offline Policy Optimization Through World Model Updates and DICE Estimation (arXiv, 2024)
• SkyJEPA: Learning Long-Horizon World Models for Zero-Shot Sim-to-Real Control of Quadrotors (arXiv, 2026)
• SPARTAN: A Sparse Transformer World Model Attending to What Matters (arXiv, 2024)
• Spatial-Interactor: Learning Spatial Reasoning through Interaction with the Observable Physical World (arXiv, 2026)
• Spatiotemporal Decoupling for Efficient Vision-Based Occupancy Forecasting (CVPR, 2024)
• Spectral-Target Physical Latent Structuring for JEPA-Style World Models (arXiv, 2026)
• Spiking World Model with Multi-Compartment Neurons for Model-based Reinforcement Learning (Proceedings of the National Academy of Sciences of the United States of America, 2025)
• SR-AIF: Solving Sparse-Reward Robotic Tasks From Pixels with Active Inference and World Models (ICRA, 2024)
• [**ST-WAM: Semantic-Temporal World Action Model for Robust Manipulation under Visual Di

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