When Best Prediction Fails: Adjacent Set Action Reconstruction for Latent World Model Planning

Controllers based on sampling and latent world models rank candidate action sequences by predicted terminal cost, execute the first action block, and replan. Residual prediction error can give an infeasible sequence an anomalously low cost, even when the terminal objective reflects physical task progress. A larger proposal pool creates more opportunities for such errors to outrank feasible alternatives. We call this conditional failure proposal overgeneration. In Cube candidate execution audits, increasing the proposal budget from 72 to 288 reduces minimum latent cost selection feasibility from .375 to .062 for position targets and from .344 to .031 for position and yaw targets, although every larger pool contains a feasible sequence. We introduce Adjacent Set Action Reconstruction (ASAR). Among low-cost proposals, ASAR identifies an adjacent set using standardized early action prefixes, reconstructs a locally weighted action sequence with a light minimum-cost anchor, and executes its first block. On 75 Carry and Release queries, Kernel ASAR improves event completion success over minimum cost selection by 28.0, 24.0, and 18.7 percentage points under latent cost and by 18.7, 20.0, and 17.3 points under a trajectory reachability cost at 72, 144, and 288 proposals. Finite-pool analysis characterizes lower-tail selection risk, radius support under an explicit probability gap, and containment of the executed action block under a local physical condition.