WorldTraces: A Data Attribution Framework for Predictive World Models

Data attribution measures how much each training sample shapes what a model predicts. However, it has not been explored in predictive world models that learn representations from large-scale sequential data such as trajectories and rollouts. We remark that there is a disconnection between existing training-data attribution methods and distinct behaviors learned by a world model such as JEPA. Specifically, the majority of existing data attribution methods are built around a differentiable loss against a ground-truth label, whereas JEPA's exponential moving average target and multi-step latent rollouts leave no single scalar output for a sample's influence to be traced back to. To fill this gap, we propose WorldTraces to measure training data's attribution in future-latent prediction, representation consistency, and temporal latent structure. It combines these responses into a query-conditioned ranking of training examples. While the architectures of existing world models varied differently from each other, we focus on JEPA because it has become a leading architecture for learning general-purpose world representations, making it a practically important test case for tracing which training data drives its learned dynamics and downstream behaviors. Experiments show that WorldTraces rankings predict the behavioral effects of grouped checkpoint-level forgetting-directed interventions across several subset sizes and maintain a consistent ordering across all three behaviors. WorldTraces aligns more closely with the effects observed under subset interventions and shows a sharper response when top-ranked examples are corrupted. It also avoids the need for subset retraining or stored training trajectories, with runtime scaling roughly linearly in the candidate pool size. Together, these results position WorldTraces as a practical foundation for training-data attribution of world models.