Executable checks can reject incorrect model-generated programs, but rejection alone does not determine whether fresh sampling or diagnostic repair is preferable. We present a controlled, resource-bounded conceptual replication and extension of established recovery policies: a single answer (N), one verification-triggered redraw (R), and one diagnostic repair (D), for Ornith 1.5 9B and Gemma 4 E4B served locally in nominal 4-bit MLX format. The frozen evaluation contains 72 tasks: 40 HumanEval+ problems, 16 SQL tasks and 16 shell tasks. Policies share initial responses and use only public checks for online decisions; every candidate is model-generated. Each task has one stochastic trajectory per model, so the study does not estimate variation across independent sampling blocks. Redraw increases observed final-test success from 51.4% to 59.7% for Ornith and from 55.6% to 62.5% for Gemma. Diagnostic repair reaches 52.8% and 63.9%, respectively. None of four internally prespecified paired contrasts is significant after Holm correction (minimum adjusted p = 0.125). Public checks falsely accept 25 Ornith and 11 Gemma candidates, demonstrating that acceptance is not a correctness guarantee. A post-review audit of initial answers gives perfect-retry upper bounds of 76.4% and 87.5%, respectively: accepted initial errors cannot trigger recovery under the fixed gate. This one-block, resource-bounded study provides a finite-cohort experimental record, not policy equivalence, a general model ranking or universal reliability

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