{"total":16,"rows":[{"id":"2512.13030","title":"Motus: A Unified Latent Action World Model","created":"2025-12-15","cited_by_count":251,"area":"wm"},{"id":"2510.10125","title":"Ctrl-World: A Controllable Generative World Model for Robot Manipulation","created":"2025-10-11","cited_by_count":167,"area":"wm"},{"id":"2509.22642","title":"WoW: Towards a World omniscient World model Through Embodied Interaction","created":"2025-09-26","cited_by_count":67,"area":"wm"},{"id":"2511.19861","title":"GigaWorld-0: World Models as Data Engine to Empower Embodied AI","created":"2025-11-25","cited_by_count":62,"area":"wm"},{"id":"2604.27792","title":"Motubrain: An Advanced World Action Model for Robot Control","created":"2026-04-30","cited_by_count":57,"area":"wm"},{"id":"2606.19531","title":"ImageWAM: Do World Action Models Really Need Video Generation, or Just Image Editing?","created":"2026-06-17","cited_by_count":38,"area":"wm"},{"id":"2603.17808","title":"EVA: Aligning Video World Models with Executable Robot Actions via Inverse Dynamics Rewards","created":"2026-03-18","cited_by_count":14,"area":"wm"},{"id":"2509.23958","title":"Reinforcement Learning with Inverse Rewards for World Model Post-training","created":"2025-09-28","cited_by_count":13,"area":"wm"},{"id":"2607.13960","title":"GigaWorld-Policy-0.5: A Faster and Stronger WAM Empowered by AutoResearch","created":"2026-07-15","cited_by_count":11,"area":"wm"},{"id":"2605.10942","title":"HarmoWAM: Harmonizing Generalizable and Precise Manipulation via Adaptive World Action Models","created":"2026-05-11","cited_by_count":10,"area":"wm"},{"id":"2601.18323","title":"TC-IDM: Grounding Video Generation for Executable Zero-shot Robot Motion","created":"2026-01-26","cited_by_count":8,"area":"wm"},{"id":"2604.09330","title":"VAG: Dual-Stream Video-Action Generation for Embodied Data Synthesis","created":"2026-04-10","cited_by_count":6,"area":"wm"},{"id":"2608.24885","title":"Do Robotic World Models Really Follow Actions? Diagnosing and Aligning Action-Conditioned Generation for Policy Learning","created":"2026-08-25","cited_by_count":2,"area":"wm"},{"id":"2607.02840","title":"TACO: TActile World Model as a Self-COrrector for Scalable Robot Policy Post-Training","created":"2026-07-03","cited_by_count":1,"area":"wm"},{"id":"2607.16602","title":"PAVXploreRL: Physical-Action-Visual World Model Reinforcement Learning with Action Exploration","created":"2026-07-18","cited_by_count":1,"area":"wm"},{"id":"2609.09155","title":"SyncWorld: Visual Calibration Enables World Models as Zero-Shot Simulators","created":"2026-09-08","cited_by_count":1,"area":"wm"}],"next_cursor":null}