How should we evaluate generation systems that combine autoregressive (AR) and diffusion decoding?We study this question through *Speculative Refinement* (SpecRef), a training-free hybrid method that warm-starts a masked diffusion language model from an AR draft using entropy-guided selective masking.Evaluating SpecRef across six benchmarks (HumanEval, MBPP, GSM8K, BBH, ARC-Challenge, HellaSwag) with three distinct evaluation protocols (execution-based pass@1, exact-match, log-likelihood scoring), we surface several findings relevant beyond our specific system:(1) code benchmarks conflate structural discovery with logical correctness: providing a syntactic scaffold lifts accuracy from near zero to over 20% without changing the model, indicating that much of the baseline failure is structural;(2) a *refinement tension* phenomenon where multi-stage correction degrades already-correct tokens, exposing benchmark saturation ceilings invisible to single-model evaluation;(3) log-likelihood and generative evaluation produce different model rankings for the same model pair, suggesting they measure different capabilities;(4) standard Python post-processing silently breaks code evaluation for non-AR generators.These observations apply to any multi-stage or non-autoregressive generation pipeline and point toward more diagnostic evaluation practices.