Researchers have developed a new framework for understanding and building world models in artificial intelligence. World models are essential components of model-based reinforcement learning, which enables agents to learn from their environment by predicting future outcomes. The authors argue that there are three distinct types of world models: those that focus on the environment, those that focus on the agent, and those that consider the joint interaction between the two. They use computational mechanics to define canonical predictive models for these cases, demonstrating how coupling and support restriction can simplify complex systems while preserving their essential properties. --- Why it matters: This work matters because it provides a deeper understanding of world models in AI, which is crucial for developing more efficient and effective reinforcement learning algorithms. The framework developed by the authors has implications for researchers working on model-based reinforcement learning, as it clarifies how different types of world models can be used to simplify complex systems. Source: https://arxiv.org/abs/2608.20401