Vector-Symbolic Object Representations for Causal World Models

World models forecast latent states by learning an internal representation of an environment's dynamics. Object-centric variants factor that state into per-object slots, but a slot is an opaque learned vector that requires a trained probe to read, and only supports some edits. We replace the learned slot with a vector-symbolic expression that pairs an object's identity with an encoding of its position, and predict with a graph network over these newly defined slots. Reading a slot, editing a scene and forming the relative position of two objects all become fixed algebraic operations with no trained parameters, and the interaction between any two objects can be toggled inside the predictor. We compare this model with current state-of-the-art, including a coordinate GNN (ref) and C-JEPA on NRI spring-coupled particles and CLEVRER. Beyond single step accuracy, we evaluate the models on closed-loop rollout, recovery of the true coupling graph, and the consequence of removing an object in a setting where ground truth is available. Our model provides superior performance, scoring $0.49\times$ on rollout compared to ballistic constant velocity prediction versus $0.68\times$ (coord-GNN) and $0.58\times$ (C-JEPA), and recovers the coupling graph at $0.919\pm .005$ ROC-AUC against $0.866\pm .003$ (coord-GNN) and $0.810\pm .005$ (C-JEPA).