TechRxiv survey sorts AI world models into four paradigms; long-horizon consistency and generalization flagged as key challenges

[TechRxiv] Qi Lyu, Jiahua Dong and co-authors have released a survey on world models in artificial intelligence on TechRxiv. According to the survey, world models offer a unified approach to modeling how environments evolve over time by learning predictive representations of states and observations; advances in large-scale generative modeling and multimodal foundation models have broadened their applicability across interactive and multimodal domains, yet existing research remains fragmented across modeling paradigms, application domains and evaluation protocols. The authors categorize world models into four branches with formal mathematical formulations: observation-level generative, latent space, reinforcement learning-based and object-centric world models. The survey reviews applications spanning robotics, autonomous driving, scientific discovery, game simulation, GUI-based agents, and interpretability and trustworthiness, and analyzes benchmarks, new evaluation metrics, simulation platforms and comparative results across world models. The authors said long-horizon consistency and generalization remain key challenges and outline directions for future research; the survey maintains an actively updated GitHub repository to track developments and aims to offer a unified reference for understanding, comparing and advancing world models.