Graph-Guided Long-Horizon MPC with LeWorldModel

Terminal latent distance can poorly reflect progress in long-horizon world-model planning. We add a temporal distance representation, a graph of offline experience, and a budget-conditioned reachability critic to a frozen LeWorldModel (LeWM). Graph search supplies an intermediate target. The critic scores predicted endpoints by the expected time to reach the goal, and the controller switches to latent L2 distance near the goal. We keep the encoder, predictor, and cross-entropy method (CEM) search settings fixed. We bound the number of replanning calls needed to reach the goal under explicit execution, model, search, and critic error conditions. We also bound critic error across horizons and identify when the critic's score equals a truncated expected time to reach the goal. On fixed held-out tasks, success on goals from a different episode increases from 14.4 to 42.4% on Push-T, 29.2 to 65.6% on Reacher, and 16.4 to 27.6% on Cube. Our method shows greatest improvement on long-horizon tasks, while remaining competitive with LeWM on goals 25 steps ahead in Push-T and Reacher. Ablations show that graph guidance and the critic are complementary on Push-T, whereas Cube benefits primarily from the learned temporal representation.