Which Predictions Suffice for a World-Model Planner's Decision? Certifying Reward and Value Evidence by Re-Planning

World-model planners allocate search or optimization among actions using predicted rewards and values. We study the minimum prediction evidence sufficient to preserve a decision's action and preference statistics under native re-execution. Two challenges arise: (1) Frozen computation. Scoring recorded computation omits the planner's adaptation to replaced predictions. (2) Single-set explanation. One passing set leaves minimum size, alternatives, shared evidence, and reliability across planning streams unresolved. We introduce , a sufficiency-certification framework whose interventions govern recorded and newly visited prediction read sites. Exact auditing recovers the minimum size, all minimum certificates, and their core; budgeted search and constrained repair return verified sets. Certificates are execution-conditioned, while independent calibration certifies cross-stream reliability with a simultaneous finite-sample guarantee. We prove that the original transcript alone cannot generally decide sufficiency and that execution-conditioned and stream-reliable minimum sizes can differ by \Theta(H). Across four planners and six tasks, native re-execution rejects 78.9% of the tested frozen-candidate explanations on TD-MPC2 and 16.5% of frozen-tree explanations. At a 384-query cap, default FutureCert recovers the exact minimum size on 93.0% of TD-MPC2 decisions, compared with 86.5% for its Beam-4 variant. Exact audits expose alternative support and repair opportunities; cross-stream analysis measures the additional evidence required for reliability.