{"total":38,"rows":[{"id":"2510.12796","title":"DriveVLA-W0: World Models Amplify Data Scaling Law in Autonomous Driving","created":"2025-10-14","cited_by_count":134,"area":"wm"},{"id":"2602.06521","title":"DriveWorld-VLA: Unified Latent-Space World Modeling with Vision-Language-Action for Autonomous Driving","created":"2026-02-06","cited_by_count":37,"area":"wm"},{"id":"2510.18313","title":"OmniNWM: Omniscient Driving Navigation World Models","created":"2025-10-21","cited_by_count":33,"area":"wm"},{"id":"2604.04198","title":"DriveVA: Video Action Models are Zero-Shot Drivers","created":"2026-04-05","cited_by_count":23,"area":"wm"},{"id":"2605.09701","title":"DriveFuture: Future-Aware Latent World Models for Autonomous Driving","created":"2026-05-10","cited_by_count":17,"area":"wm"},{"id":"2512.10226","title":"Latent Chain-of-Thought World Modeling for End-to-End Driving","created":"2025-12-11","cited_by_count":15,"area":"wm"},{"id":"2603.24587","title":"DreamerAD: Efficient Reinforcement Learning via Latent World Model for Autonomous Driving","created":"2026-03-25","cited_by_count":15,"area":"wm"},{"id":"2605.14696","title":"EponaV2: Driving World Model with Comprehensive Future Reasoning","created":"2026-05-14","cited_by_count":15,"area":"wm"},{"id":"2605.28544","title":"DriveWAM: Video Generative Priors Enable Scalable World-Action Modeling for Autonomous Driving","created":"2026-05-27","cited_by_count":14,"area":"wm"},{"id":"2512.04441","title":"MindDrive: An All-in-One Framework Bridging World Models and Vision-Language Model for End-to-End Autonomous Driving","created":"2025-12-04","cited_by_count":12,"area":"wm"},{"id":"2604.02714","title":"ExploreVLA: Dense World Modeling and Exploration for End-to-End Autonomous Driving","created":"2026-04-03","cited_by_count":12,"area":"wm"},{"id":"2605.10426","title":"CoWorld-VLA: Thinking in a Multi-Expert World Model for Autonomous Driving","created":"2026-05-11","cited_by_count":12,"area":"wm"},{"id":"2603.27287","title":"Uni-World VLA: Interleaved World Modeling and Planning for Autonomous Driving","created":"2026-03-28","cited_by_count":11,"area":"wm"},{"id":"2604.09059","title":"Learning Vision-Language-Action World Models for Autonomous Driving","created":"2026-04-10","cited_by_count":11,"area":"wm"},{"id":"2606.15869","title":"Metis: A Generalizable and Efficient World-Action Model for Autonomous Driving and Urban Navigation","created":"2026-06-14","cited_by_count":11,"area":"wm"},{"id":"2608.07468","title":"SimWAM: A Simple World Action Model for End-to-End Autonomous Driving","created":"2026-08-07","cited_by_count":9,"area":"wm"},{"id":"2603.25741","title":"Vega: Learning to Drive with Natural Language Instructions","created":"2026-03-26","cited_by_count":6,"area":"wm"},{"id":"2606.16274","title":"GraphWorld: Long-Horizon Planning with World Models for End-to-End Autonomous Driving","created":"2026-06-15","cited_by_count":6,"area":"wm"},{"id":"2607.29031","title":"Auto-JEPA: A Latent World Model of Continuous Intent for End-to-End Autonomous Driving","created":"2026-07-31","cited_by_count":5,"area":"wm"},{"id":"2608.19085","title":"DA-WAM: Decision-Aligned Future Latents for Driving World Models","created":"2026-08-19","cited_by_count":5,"area":"wm"},{"id":"2606.05645","title":"Discrete-WAM: Unified Discrete Vision-Action Token Editing for World-Policy Learning","created":"2026-06-04","cited_by_count":4,"area":"wm"},{"id":"2507.23325","title":"FASTopoWM: Fast-Slow Lane Segment Topology Reasoning with Latent World Models","created":"2025-07-31","cited_by_count":3,"area":"wm"},{"id":"2607.09655","title":"OpenLongTail: Generative Scaling of Long-Tail Driving Data","created":"2026-07-10","cited_by_count":2,"area":"wm"},{"id":"2607.17521","title":"GeoWorldAD: Geometry World Action Model for Autonomous Driving","created":"2026-07-20","cited_by_count":2,"area":"wm"},{"id":"2605.10564","title":"DeepSight: Long-Horizon World Modeling via Latent States Prediction for End-to-End Autonomous Driving","created":"2026-05-11","cited_by_count":1,"area":"wm"},{"id":"2607.20988","title":"HyWorldVLA: A Vision-Language-Action Model with Hybrid World Modeling for Autonomous Driving","created":"2026-07-23","cited_by_count":1,"area":"wm"},{"id":"2610.13781","title":"SimVTA: Simulation-Grounded Video-Trajectory Alignment for Driving World Action Models","created":"2026-10-09","cited_by_count":1,"area":"wm"},{"id":"2510.12560","title":"CoIRL-AD: Collaborative-Competitive Imitation-Reinforcement Learning in Latent World Models for Autonomous Driving","created":"2025-10-14","cited_by_count":0,"area":"wm"},{"id":"2603.19219","title":"DriveTok: 3D Driving Scene Tokenization for Unified Multi-View Reconstruction and Understanding","created":"2026-03-19","cited_by_count":0,"area":"wm"},{"id":"2605.22089","title":"LVDrive: Latent Visual Representation Enhanced Vision-Language-Action Autonomous Driving Model","created":"2026-05-21","cited_by_count":0,"area":"wm"},{"id":"2605.24004","title":"Reason--Imagine--Act: Closed-Loop LLM Decision Making with World Models for Autonomous Driving","created":"2026-05-19","cited_by_count":0,"area":"wm"},{"id":"2606.28758","title":"X-Mind: Efficient Visual Chain-of-Thought via Predictive World Model for End-to-End Driving","created":"2026-06-27","cited_by_count":0,"area":"wm"},{"id":"2609.03572","title":"Drive-HWM: Hierarchical World Models for Dynamic-Latent Guided Autonomous Driving","created":"2026-09-03","cited_by_count":0,"area":"wm"},{"id":"2609.28064","title":"SlackDrive: Reclaiming Runtime Slack for Adaptive Driving Inference","created":"2026-09-23","cited_by_count":0,"area":"wm"},{"id":"2609.33737","title":"MomWorld: Momentum-Aware Latent World Model for Long-Horizon Autonomous Driving","created":"2026-09-27","cited_by_count":0,"area":"wm"},{"id":"2609.37970","title":"PhysWAM: Physically Consistent World Action Model for Autonomous Driving","created":"2026-09-29","cited_by_count":0,"area":"wm"},{"id":"2610.14871","title":"MORE: Learning Motion-Oriented Registers for Driving World Models and Planning","created":"2026-10-10","cited_by_count":0,"area":"wm"},{"id":"2610.17903","title":"DriveWLA: Unifying Future Dynamics and Scene Semantics through World-Language-Action Modeling for Autonomous Driving","created":"2026-10-13","cited_by_count":0,"area":"wm"}],"next_cursor":null}