At its core, DIVINER builds on the concept that self-supervised models are simulators - systems trained with predictive loss that can simulate probabilistic rollouts that obey learned distributions across an implicit world model. But DIVINER goes further by embedding optimization directly into its architecture, transforming from passive prediction to active pursuit.