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Machine-translated benchmarks can be unreliable, but this automated pipeline offers a practical, scalable way to flag low-quality translations and prioritize human review.
Surprisingly, even with carefully designed retrieval pipelines, smaller language models still fall short of larger models on complex scientific reasoning tasks, suggesting that retrieval and model scale are complementary, not interchangeable.