{"total":13,"rows":[{"id":"2512.13030","title":"Motus: A Unified Latent Action World Model","created":"2025-12-15","cited_by_count":251,"area":"wm"},{"id":"2602.06949","title":"DreamDojo: A Generalist Robot World Model from Large-Scale Human Videos","created":"2026-02-06","cited_by_count":135,"area":"wm"},{"id":"2602.10098","title":"VLA-JEPA: Enhancing Vision-Language-Action Model with Latent World Model","created":"2026-02-10","cited_by_count":118,"area":"wm"},{"id":"2507.23682","title":"villa-X: Enhancing Latent Action Modeling in Vision-Language-Action Models","created":"2025-07-31","cited_by_count":63,"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":"2605.07079","title":"Learning Visual Feature-Based World Models via Residual Latent Action","created":"2026-05-08","cited_by_count":4,"area":"wm"},{"id":"2602.01960","title":"Grounding Generated Videos in Feasible Plans via World Models","created":"2026-02-02","cited_by_count":3,"area":"wm"},{"id":"2607.09185","title":"Causally Debiased Latent Action Model for Embodied Action Conditioned World Models","created":"2026-07-10","cited_by_count":3,"area":"wm"},{"id":"2605.16412","title":"SCAR: Self-Supervised Continuous Action Representation Learning","created":"2026-05-13","cited_by_count":1,"area":"wm"},{"id":"2605.06298","title":"Render, Don't Decode: Weight-Space World Models with Latent Structural Disentanglement","created":"2026-05-07","cited_by_count":0,"area":"wm"},{"id":"2609.32193","title":"Devol-ONE: One Autoregressive Mixture of Transformers to Unify Vision-Language-Action and Latent World Modeling","created":"2026-09-26","cited_by_count":0,"area":"wm"},{"id":"2610.20160","title":"OneLA: Learning Many Worlds from Latent Actions","created":"2026-10-14","cited_by_count":0,"area":"wm"},{"id":"2610.20604","title":"VITAL: From Visual Motion to Precise Action","created":"2026-10-14","cited_by_count":0,"area":"wm"}],"next_cursor":null}