{"total":21,"rows":[{"id":"2104.05632","title":"Augmented World Models Facilitate Zero-Shot Dynamics Generalization From a Single Offline Environment","created":"2021-04-12","cited_by_count":42,"area":"wm"},{"id":"2305.14879","title":"ByteSized32: A Corpus and Challenge Task for Generating Task-Specific World Models Expressed as Text Games","created":"2023-05-24","cited_by_count":23,"area":"wm"},{"id":"2305.02749","title":"Explainable Reinforcement Learning via a Causal World Model","created":"2023-05-04","cited_by_count":22,"area":"wm"},{"id":"2403.09976","title":"AD3: Implicit Action is the Key for World Models to Distinguish the Diverse Visual Distractors","created":"2024-03-15","cited_by_count":10,"area":"wm"},{"id":"2510.19788","title":"Benchmarking World-Model Learning with Environment-Level Queries","created":"2025-10-22","cited_by_count":10,"area":"wm"},{"id":"2302.08669","title":"Learning to Forecast Aleatoric and Epistemic Uncertainties over Long Horizon Trajectories","created":"2023-02-17","cited_by_count":6,"area":"wm"},{"id":"2404.00462","title":"Zero-shot Safety Prediction for Autonomous Robots with Foundation World Models","created":"2024-03-30","cited_by_count":6,"area":"wm"},{"id":"2502.01366","title":"Trajectory World Models for Heterogeneous Environments","created":"2025-02-03","cited_by_count":4,"area":"wm"},{"id":"2109.08342","title":"Dropout's Dream Land: Generalization from Learned Simulators to Reality","created":"2021-09-17","cited_by_count":3,"area":"wm"},{"id":"2412.12870","title":"Physically Interpretable World Models via Weakly Supervised Representation Learning","created":"2024-12-17","cited_by_count":3,"area":"wm"},{"id":"2508.09561","title":"Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges","created":"2025-08-13","cited_by_count":3,"area":"wm"},{"id":"2407.13518","title":"Model-based Policy Optimization using Symbolic World Model","created":"2024-07-18","cited_by_count":2,"area":"wm"},{"id":"2511.15407","title":"IPR-1: Interactive Physical Reasoner","created":"2025-11-19","cited_by_count":2,"area":"wm"},{"id":"2211.16315","title":"Learning and Understanding a Disentangled Feature Representation for Hidden Parameters in Reinforcement Learning","created":"2022-11-29","cited_by_count":1,"area":"wm"},{"id":"2110.00808","title":"Cycle-Consistent World Models for Domain Independent Latent Imagination","created":"2021-10-02","cited_by_count":0,"area":"wm"},{"id":"2112.02817","title":"ED2: Environment Dynamics Decomposition World Models for Continuous Control","created":"2021-12-06","cited_by_count":0,"area":"wm"},{"id":"2205.01435","title":"RLFlow: Optimising Neural Network Subgraph Transformation with World Models","created":"2022-05-03","cited_by_count":0,"area":"wm"},{"id":"2403.15908","title":"Deep Gaussian Covariance Network with Trajectory Sampling for Data-Efficient Policy Search","created":"2024-03-23","cited_by_count":0,"area":"wm"},{"id":"2601.01577","title":"HanoiWorld : A Joint Embedding Predictive Architecture BasedWorld Model for Autonomous Vehicle Controller","created":"2026-01-04","cited_by_count":0,"area":"wm"},{"id":"2607.17560","title":"Reinforcement Learning: From Algorithms To Foundation Models","created":"2026-07-20","cited_by_count":0,"area":"wm"},{"id":"2609.34159","title":"WorldGraph: Graph-Native World Modeling","created":"2026-09-28","cited_by_count":0,"area":"wm"}],"next_cursor":null}