ConceptWorldModel: Can World Models Learn Dynamics in Concept Space?

abstract World models have demonstrated substantial capability in predicting future states in complex environments; however, their internal representations often remain difficult to interpret. Consequently, it remains unclear whether such models merely encode information required for prediction or develop structured semantic representations of the surrounding environment. This work investigates whether higher-order semantic information can become recoverable from hierarchical representations built from simpler grounded concepts without direct supervision of those higher-order concepts. To address this question, ConceptWorldModel introduces a hierarchical world model for autonomous driving that transforms visual observations into grounded concepts, relational representations, and higher-order scene representations. These representations define a structured concept space that is propagated through action-conditioned temporal dynamics, enabling recursive prediction of future concepts across multiple horizons. The model is evaluated on Bench2Drive using a structured concept ontology derived from driving-scene annotations and metadata. Experimental results demonstrate sustained predictive performance over extended rollouts, achieving an average precision of \(0.783\) for binary concepts and a normalized mean absolute error of \(0.435\) across scalar regression targets at a 20-step prediction horizon. Evaluation of concepts excluded from direct supervision further shows that higher-order semantic information remains recoverable across the learned representation hierarchy. Overall, the results show that future driving states can be represented and predicted through an explicit semantic concept space, providing a structured alternative to exclusively opaque latent world-model representations.