{"total":64,"rows":[{"id":"2103.00020","title":"Learning Transferable Visual Models From Natural Language Supervision","created":"2021-02-26","cited_by_count":33744,"area":"wm"},{"id":"2301.08243","title":"Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture","created":"2023-01-19","cited_by_count":1039,"area":"wm"},{"id":"2404.08471","title":"Revisiting Feature Prediction for Learning Visual Representations from Video","created":"2024-02-15","cited_by_count":531,"area":"wm"},{"id":"2206.08853","title":"MineDojo: Building Open-Ended Embodied Agents with Internet-Scale Knowledge","created":"2022-06-17","cited_by_count":478,"area":"wm"},{"id":"2111.00210","title":"Mastering Atari Games with Limited Data","created":"2021-10-30","cited_by_count":237,"area":"wm"},{"id":"2406.08481","title":"Enhancing End-to-End Autonomous Driving with Latent World Model","created":"2024-06-12","cited_by_count":166,"area":"wm"},{"id":"2110.14565","title":"DreamerPro: Reconstruction-Free Model-Based Reinforcement Learning with Prototypical Representations","created":"2021-10-27","cited_by_count":74,"area":"wm"},{"id":"2403.00504","title":"Learning and Leveraging World Models in Visual Representation Learning","created":"2024-03-01","cited_by_count":72,"area":"wm"},{"id":"2204.11371","title":"Learning Symmetric Embeddings for Equivariant World Models","created":"2022-04-24","cited_by_count":54,"area":"wm"},{"id":"2603.12231","title":"Temporal Straightening for Latent Planning","created":"2026-03-12","cited_by_count":38,"area":"wm"},{"id":"2111.01587","title":"Procedural Generalization by Planning with Self-Supervised World Models","created":"2021-11-02","cited_by_count":31,"area":"wm"},{"id":"2211.10831","title":"Joint Embedding Predictive Architectures Focus on Slow Features","created":"2022-11-20","cited_by_count":30,"area":"wm"},{"id":"2605.26379","title":"When Does LeJEPA Learn a World Model?","created":"2026-05-25","cited_by_count":30,"area":"wm"},{"id":"2203.00494","title":"DreamingV2: Reinforcement Learning with Discrete World Models without Reconstruction","created":"2022-03-01","cited_by_count":29,"area":"wm"},{"id":"2503.04416","title":"Learning Transformer-based World Models with Contrastive Predictive Coding","created":"2025-03-06","cited_by_count":20,"area":"wm"},{"id":"2606.20104","title":"Sensorimotor World Models: Perception for Action via Inverse Dynamics","created":"2026-06-18","cited_by_count":20,"area":"wm"},{"id":"2306.01828","title":"Unifying (Machine) Vision via Counterfactual World Modeling","created":"2023-06-02","cited_by_count":19,"area":"wm"},{"id":"2307.02427","title":"FOCUS: Object-Centric World Models for Robotics Manipulation","created":"2023-07-05","cited_by_count":17,"area":"wm"},{"id":"2601.14354","title":"VJEPA: Variational Joint Embedding Predictive Architectures as Probabilistic World Models","created":"2026-01-20","cited_by_count":17,"area":"wm"},{"id":"2605.09241","title":"Sub-JEPA: Subspace Gaussian Regularization for Stable End-to-End World Models","created":"2026-05-10","cited_by_count":16,"area":"wm"},{"id":"2504.13065","title":"EchoWorld: Learning Motion-Aware World Models for Echocardiography Probe Guidance","created":"2025-04-17","cited_by_count":15,"area":"wm"},{"id":"2405.15083","title":"MuDreamer: Learning Predictive World Models without Reconstruction","created":"2024-05-23","cited_by_count":13,"area":"wm"},{"id":"2412.17226","title":"OLiDM: Object-aware LiDAR Diffusion Models for Autonomous Driving","created":"2024-12-23","cited_by_count":13,"area":"wm"},{"id":"2206.04384","title":"Value Memory Graph: A Graph-Structured World Model for Offline Reinforcement Learning","created":"2022-06-09","cited_by_count":12,"area":"wm"},{"id":"2312.06721","title":"Understanding Physical Dynamics with Counterfactual World Modeling","created":"2023-12-11","cited_by_count":11,"area":"wm"},{"id":"2312.09056","title":"ReCoRe: Regularized Contrastive Representation Learning of World Model","created":"2023-12-14","cited_by_count":11,"area":"wm"},{"id":"2503.02143","title":"Four Principles for Physically Interpretable World Models","created":"2025-03-04","cited_by_count":10,"area":"wm"},{"id":"2603.18202","title":"R2-Dreamer: Redundancy-Reduced World Models without Decoders or Augmentation","created":"2026-03-18","cited_by_count":10,"area":"wm"},{"id":"2311.17593","title":"LanGWM: Language Grounded World Model","created":"2023-11-29","cited_by_count":8,"area":"wm"},{"id":"2603.10422","title":"World2Act: Latent Action Post-Training from World Model Dynamics","created":"2026-03-11","cited_by_count":8,"area":"wm"},{"id":"2608.22764","title":"LpWM: A Case for Sparse Representations in World Models","created":"2026-08-24","cited_by_count":8,"area":"wm"},{"id":"2502.05857","title":"EgoAgent: A Joint Predictive Agent Model in Egocentric Worlds","created":"2025-02-09","cited_by_count":7,"area":"wm"},{"id":"2505.03176","title":"seq-JEPA: Autoregressive Predictive Learning of Invariant-Equivariant World Models","created":"2025-05-06","cited_by_count":7,"area":"wm"},{"id":"2509.09737","title":"World Modeling with Probabilistic Structure Integration","created":"2025-09-10","cited_by_count":7,"area":"wm"},{"id":"2203.11024","title":"Multi-View Dreaming: Multi-View World Model with Contrastive Learning","created":"2022-03-15","cited_by_count":6,"area":"wm"},{"id":"2608.05720","title":"PhyLatent: Learning Dynamics-Relevant Representations for JEPA World Models","created":"2026-08-06","cited_by_count":6,"area":"wm"},{"id":"2209.14932","title":"Contrastive Unsupervised Learning of World Model with Invariant Causal Features","created":"2022-09-29","cited_by_count":5,"area":"wm"},{"id":"2405.18193","title":"In-Context Symmetries: Self-Supervised Learning through Contextual World Models","created":"2024-05-28","cited_by_count":5,"area":"wm"},{"id":"2503.21449","title":"Towards Generating Realistic 3D Semantic Training Data for Autonomous Driving","created":"2025-03-27","cited_by_count":5,"area":"wm"},{"id":"2506.05419","title":"Dream to Generalize: Zero-Shot Model-Based Reinforcement Learning for Unseen Visual Distractions","created":"2025-06-05","cited_by_count":4,"area":"wm"},{"id":"2211.12774","title":"Prototypical context-aware dynamics generalization for high-dimensional model-based reinforcement learning","created":"2022-11-23","cited_by_count":3,"area":"wm"},{"id":"2506.02612","title":"Simple, Good, Fast: Self-Supervised World Models Free of Baggage","created":"2025-06-03","cited_by_count":3,"area":"wm"},{"id":"2510.26782","title":"Cloning Deterministic Worlds: The Critical Role of Latent Geometry in Long-Horizon World Models","created":"2025-10-30","cited_by_count":3,"area":"wm"},{"id":"2607.18715","title":"DWM: Separating World Effects from Actions in Latent World Models","created":"2026-07-21","cited_by_count":3,"area":"wm"},{"id":"2107.11676","title":"The Impact of Negative Sampling on Contrastive Structured World Models","created":"2021-07-24","cited_by_count":2,"area":"wm"},{"id":"2508.10489","title":"Learning State-Space Models of Dynamic Systems from Arbitrary Data using Joint Embedding Predictive Architectures","created":"2025-08-14","cited_by_count":2,"area":"wm"},{"id":"2604.10333","title":"Zero-shot World Models Are Developmentally Efficient Learners","created":"2026-04-11","cited_by_count":2,"area":"wm"},{"id":"2406.08862","title":"Cognitively Inspired Energy-Based World Models","created":"2024-06-13","cited_by_count":1,"area":"wm"},{"id":"2602.07277","title":"Cross-View World Models","created":"2026-02-07","cited_by_count":1,"area":"wm"},{"id":"2603.07545","title":"DreamSAC: Learning Hamiltonian World Models via Symmetry Exploration","created":"2026-03-08","cited_by_count":1,"area":"wm"},{"id":"2606.19889","title":"SurgVista: Long-Horizon Surgical World Modeling with Plausible Instrument-Tissue Dynamics","created":"2026-06-18","cited_by_count":1,"area":"wm"},{"id":"2609.29092","title":"DAWN: Noise-Robust Quadruped Parkour via Depth-Denoising World Models","created":"2026-09-24","cited_by_count":1,"area":"wm"},{"id":"2403.10875","title":"Probabilistic World Modeling with Asymmetric Distance Measure","created":"2024-03-16","cited_by_count":0,"area":"wm"},{"id":"2408.05781","title":"CURLing the Dream: Contrastive Representations for World Modeling in Reinforcement Learning","created":"2024-08-11","cited_by_count":0,"area":"wm"},{"id":"2604.16585","title":"The Global Neural World Model: Spatially Grounded Discrete Topologies for Action-Conditioned Planning","created":"2026-04-17","cited_by_count":0,"area":"wm"},{"id":"2605.15466","title":"Entity-Centric World Models: Interaction-Aware Masking for Causal Video Prediction","created":"2026-05-14","cited_by_count":0,"area":"wm"},{"id":"2605.25620","title":"Back to Parsimonious Latents: Learning Task-Centric World Models from Visual Foundations","created":"2026-05-25","cited_by_count":0,"area":"wm"},{"id":"2606.29465","title":"Prototype Latent World Model Replay for Class-Incremental Learning","created":"2026-06-28","cited_by_count":0,"area":"wm"},{"id":"2608.07409","title":"UniJEPA: A Unified Joint-Embedding Predictive Architecture for Task-Agnostic Visual World Modeling","created":"2026-08-07","cited_by_count":0,"area":"wm"},{"id":"2609.01215","title":"REFACTOR-VLA: Unsupervised Library Learning of Typed Motor Programs","created":"2026-09-01","cited_by_count":0,"area":"wm"},{"id":"2609.22175","title":"Contrastive World Models","created":"2026-08-27","cited_by_count":0,"area":"wm"},{"id":"2609.29171","title":"Representation World Model: Learning States, Transition and Executable Plans in Representation","created":"2026-09-24","cited_by_count":0,"area":"wm"},{"id":"2610.06805","title":"H-JEPA: End-to-End Learning of Hierarchical World Models for Visual Planning","created":"2026-10-05","cited_by_count":0,"area":"wm"},{"id":"2610.07028","title":"Identifiable World Models from Pretrained Diffusion Representations","created":"2026-10-04","cited_by_count":0,"area":"wm"}],"next_cursor":null}