LeWM++: Repairing the Planning Interface of Latent World Models

Latent world models enable visual control without reconstructing pixels, yet remain unreliable over long horizons. We identify three planning mismatches that worsen with goal offset: target mismatch between a distant goal and a short reliable rollout; search mismatch between sparse effective action sequences and uninformed sampling; and cost mismatch between goal-reaching semantics and restrictive terminal-only cost. Addressing these failures requires a planning interface that effectively translates latent representations into actions. We introduce LeWorldModel++ (LeWM++), a planning-interface framework built on a frozen latent world model. LeWM++ comprises three complementary components guided by a central principle: preserving global intent through the final goal while using a local target to drive short-horizon planning. LatentPathFlow generates a temporally aligned latent path and uses its endpoint as the local target; a final-goal-conditioned Action Chunk Prior guides CEM search with temporally coherent action-chunk proposals; and Min-over-Horizon scores each rollout by its closest approach to the local target. On the LeWM Control Suite, LeWM++ achieves the highest average success at all four evaluated goal offsets; as goal distance increases, its lead over LeWM grows from 12.0 to 32.3 percentage points. Across eight visual OGBench datasets, LeWM++ achieves 50.62% average success, exceeding the model-based planning method Latent Diffusion Planning (LDP) by 34.2 percentage points. On two real-robot tasks, LeWM++ outperforms goal-conditioned Diffusion Policy by 18.9 percentage points, demonstrating its control effectiveness under the hardware protocol. These results show that effective latent planning depends not only on predictive dynamics, but also on aligning the planner's target, proposal, and cost.