What Rollout Error Magnitude Misses in Latent World-Model Planning

Rollout error is often summarized by its magnitude, but a planner's outcome depends on which action that error causes it to select. We ask whether error direction provides decision-relevant information beyond error magnitude in latent world-model planning. For squared latent-goal cost, an exact decomposition separates residual energy from a signed projection onto the goal displacement. We isolate the projection's value by comparing two oracle scores that use the same candidate actions and each candidate's actual error energy: $M+2E$ and $N=M+2E-p$. On 256-action PushT pools from 48 new sources, using the projection raises first-plan physical success from 69/96 to 80/96 for the original predictor and from 63/96 to 79/96 for an independently trained predictor on its own pools; true latent-cost regret also decreases. Additional interventions examine how prediction history and error direction affect action rankings. In Cube-Single, energy-aware selection already recovers 29 of the 30 pools containing a successful action, leaving little additional success to recover; a learned calibrator does not reliably reproduce the PushT diagnostic gain. These results show that goal-related error direction matters beyond the tested energy-aware scores, while leaving its use in a deployable planner unresolved.