Generated GPU implementations are commonly accepted after a few differential tests, although replacement obligations may remain unchecked. We present KernelMutate, an empirical study of validation reliability using controlled faults, released implementations, and replacement tests. A five-draw anchor accepts 548 of 838 CUDA candidates. The full suite finds native FP32 witnesses for 136 accepted candidates and dtype-only findings for another 362, mostly low-precision execution limitations without established publisher guarantees. Source-guided testing identifies the full suite's 692 candidates at 26.1% lower charged cost, but yields only dtype evidence for 16 accepted candidates with native witnesses in the full suite. Independently observed context and boundary findings cover the same candidate set. Task-held-out template matching reveals differences in ranking and observation coverage: global top-eight recommendations match more probes than class-conditioned lists (51 versus 48 of 75), while conditional analyses restrict the apparent advantage at smaller limits. These template-matching results are offline measurements, not deployed-suite gains. Independent controls and replacement cases expose numerical-policy, binding, and custom-execution boundaries. Validation therefore needs to report witness domains and observation coverage alongside candidate yield and cost.

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