Machine-learning engineer Zhou argues that larger translation models improve fluency and average accuracy across widely used language pairs. Computational linguist Pereira finds that the same systems normalize rare grammatical constructions and perform poorly for sparsely represented dialects. Pereira argues that both findings are accurate: aggregate benchmarks reward common patterns, whereas the losses appear in low-frequency varieties that averages obscure. The relationship between the two positions is best described by which choice? Consider whether the second view depends on, limits, rejects, reconciles, or merely converges with the first view about machine-translation quality.