A recent proposal introduces a new framework for world modeling that seeks to unify the currently fragmented field by identifying fundamental building blocks grounded in stochastic processes. This framework highlights the critical role of both discrete and continuous processes, aiming to create a more cohesive understanding of how world models can be constructed. By emphasizing these natural building blocks, the approach addresses limitations in existing models and suggests pathways toward scalability. The proposed framework is supported by theoretical insights and evidence, positioning it as a foundational step for developing structured world models that can operate effectively at larger scales. This unification effort could streamline research efforts and improve the design of AI systems that rely on accurate and scalable representations of complex environments. The framework’s focus on stochastic processes provides a common language for integrating diverse modeling techniques. Overall, this development marks a significant advancement in the pursuit of more robust and scalable world models.