Vision-language-action (VLA) policies are often treated as checkpoint-defined objects: if the weights, prompt, and benchmark suite match, the deployment is assumed to be the same policy. Robot execution breaks this assumption because the same normalized model output can become a different physical action after action unnormalization and controller conventions are applied. This creates a deployment-safety gap: safety review can certify the checkpoint while missing the executable robot policy that reaches the controller. We formalize this gap as an executable policy specification problem: a VLA policy includes the learned model, action representation, metadata-selected unnormalizer, and controller-facing conventions. Under this view, identical checkpoints can be executable-inequivalent. For quantile-style action normalization, we derive a closed-form metadata mismatch transform and an ExecSpec certificate that measures action-space semantic drift without model inference or rollout. On LIBERO-Goal replay, substituting a plausible sibling metadata key yields mean drift 0.199 over six non-gripper action dimensions and reduces success from 28/28 to 2/28 under full substitution. On LIBERO-Spatial replay, the same substituted key reduces success from 26/26 to 0/26. The same full-substitution protocol gives 0/28 success for all four Object substitutions and 0/23 or 1/23 success on Long. Identity-key, replay-validity, no-op filtering, raw-vs-correct replay, mask/gripper, synthetic upper-bound, and OpenVLA-style unnormalizer interface checks rule out several simpler explanations. These results do not certify closed-loop or hardware safety. They support a narrower deployment-safety view: action-space metadata is part of the executable policy and should be checked before rollout.

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