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Structured metadata, not just titles, is key to accurately mapping publications to diverse mathematical software communities.
A surprising representation mismatch between syntax and semantics in scientific formulae can be effectively bridged through contrastive learning, enhancing retrieval performance.
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.
