Time-Aware World Model for Adaptive Prediction and Control

In this work, we introduce the Time-Aware World Model (TAWM), a model-based approach that explicitly incorporates temporal dynamics. By conditioning on the time-step size, {\Delta}t, and training over a diverse range of {\Delta}t values -- rather than sampling at a fixed time-step -- TAWM learns both high- and low-frequency task dynamics across diverse control problems. Grounded in the information-theoretic insight that the optimal sampling rate depends on a system's underlying dynamics, this time-aware formulation improves both performance and data efficiency. Empirical evaluations show that TAWM consistently outperforms conventional models across varying observation rates in a variety of control tasks, using the same number of training samples and iterations. Our code can be found online at:this http URL.

@article{nhu2025_2506.08441,
  title={ Time-Aware World Model for Adaptive Prediction and Control },
  author={ Anh N. Nhu and Sanghyun Son and Ming Lin },
  journal={arXiv preprint arXiv:2506.08441},
  year={ 2025 }
}