{"total":20,"rows":[{"id":"2111.12594","title":"Conditional Object-Centric Learning from Video","created":"2021-11-24","cited_by_count":248,"area":"wm"},{"id":"2204.13661","title":"Toward Compositional Generalization in Object-Oriented World Modeling","created":"2022-04-28","cited_by_count":22,"area":"wm"},{"id":"2107.08577","title":"Structured World Belief for Reinforcement Learning in POMDP","created":"2021-07-19","cited_by_count":21,"area":"wm"},{"id":"2307.09205","title":"Learning Dynamic Attribute-factored World Models for Efficient Multi-object Reinforcement Learning","created":"2023-07-18","cited_by_count":15,"area":"wm"},{"id":"2603.04553","title":"Latent Particle World Models: Self-supervised Object-centric Stochastic Dynamics Modeling","created":"2026-03-04","cited_by_count":15,"area":"wm"},{"id":"2310.12690","title":"Neurosymbolic Grounding for Compositional World Models","created":"2023-10-19","cited_by_count":14,"area":"wm"},{"id":"2204.13022","title":"Binding Actions to Objects in World Models","created":"2022-04-27","cited_by_count":10,"area":"wm"},{"id":"2411.06890","title":"SPARTAN: A Sparse Transformer World Model Attending to What Matters","created":"2024-11-11","cited_by_count":10,"area":"wm"},{"id":"2501.16443","title":"Object-Centric World Models from Few-Shot Annotations for Sample-Efficient Reinforcement Learning","created":"2025-01-27","cited_by_count":10,"area":"wm"},{"id":"2202.05333","title":"Factored World Models for Zero-Shot Generalization in Robotic Manipulation","created":"2022-02-10","cited_by_count":6,"area":"wm"},{"id":"2404.15109","title":"Compete and Compose: Learning Independent Mechanisms for Modular World Models","created":"2024-04-23","cited_by_count":4,"area":"wm"},{"id":"2501.14174","title":"Dreamweaver: Learning Compositional World Models from Pixels","created":"2025-01-24","cited_by_count":4,"area":"wm"},{"id":"2511.14262","title":"Object-Centric World Models for Causality-Aware Reinforcement Learning","created":"2025-11-18","cited_by_count":4,"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":"2408.11816","title":"Efficient Exploration and Discriminative World Model Learning with an Object-Centric Abstraction","created":"2024-08-21","cited_by_count":2,"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":"2401.00057","title":"Generalization properties of contrastive world models","created":"2023-12-29","cited_by_count":0,"area":"wm"},{"id":"2511.06136","title":"When Object-Centric World Models Meet Policy Learning: From Pixels to Policies, and Where It Breaks","created":"2025-11-08","cited_by_count":0,"area":"wm"},{"id":"2606.16489","title":"BRICKS-WM: Building Reusability via Interface Composition Kinetics for Structured World Models","created":"2026-06-15","cited_by_count":0,"area":"wm"},{"id":"2608.22750","title":"MOSH-WM: Mask-Grounded Soft-Hamiltonian Dynamics for Object-Centric World Models","created":"2026-08-24","cited_by_count":0,"area":"wm"}],"next_cursor":null}