SURVEYOR: Training-Free Decomposition for Long-Range Latent World-Model Planning

Latent world-model planners succeed at short goal distances but fail at long ones, and hierarchical variants repair this failure by decomposing a distant goal into a sequence of latent subgoals. However, existing hierarchical planners make two decisions by fixed schedule rather than by evidence: they regenerate the subgoal sequence at every replanning step, even when the previous draft remains consistent with execution, and they decompose every goal, even when the executor could reach it directly. We introduce Surveyor, a training-free, plug-and-play decomposition layer that sits between an executor and a frozen world model and makes both decisions from the world model's own latents and predictions. A drafter proposes latent subgoals a fixed number of steps ahead; an accept rule reuses the remaining draft while the achieved latent state agrees with the subgoal just pursued; and an arbiter bypasses or retires drafting once the goal is predicted to be within the executor's reach. Both decisions use a tolerance calibrated offline from observation pairs that the benchmark's success criterion treats as equivalent. Extensive experiments over five executors, spanning sampling-based, gradient-based and amortized planners, and four environments show that Surveyor raises success at the longest PushT goal distance from below 10% to above 92% for every search-based executor while issuing up to two fifths fewer drafter calls than every-step drafting, and improves both the average and the worst-case success of all five executors over the seventeen combinations of environment and goal distance.