Planning with Generated Worlds: Calibration-Aware Cross-World Verification

Generative world models increasingly expose 3D environments and action-conditioned rollouts to downstream agents for planning. Yet visual realism, geometric fidelity, and rollout consistency do not establish that generated worlds retain task-relevant structure for reliable action. We formulate this gap as generated-world actionability: whether generated worlds support reliable decisions, when their hypotheses and observations provide decision-relevant evidence, and how that evidence should be combined for decision-making. To investigate and address these questions, we operationalize actionability in route-level planning over competing world hypotheses, with a limited verification budget and potentially unreliable observations. On canonical episodes, single-hypothesis commitment and cross-world voting collide in 20% and 24% of cases, respectively, while random and information-gain verification become less safe as they assimilate additional evidence. These results reveal a Verification Paradox: assimilating additional miscalibrated evidence can increase collision risk when correlated world errors and misaligned observations are fused overconfidently. We therefore introduce Calibration-Aware Cross-World Verification (CWV), a training-free decision layer that forms cross-world route beliefs, targets decision-critical verification, down-weights inconsistent observations through innovation-gated inverse-variance fusion, and commits using lower confidence bounds. Base CWV is collision-free across canonical camera budgets; Gated CWV removes the matched-seed high-budget negative-control failure and reduces regret by 66%. Broader evaluations identify three empirical boundaries of actionability: candidate informativeness, task sensitivity, and observation calibration. Together, these results show that generated worlds can support reliable action when decision-relevant variation and external evidence are used in a calibrated way.