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arXiv · 2610.25678

Cross-Pollination Retrieval: Finding Transferable Ideas Across Fields in Scientific Literature

Seokwon Jung·Yerang Kim·Yejin Kim·Alexander Rubinstein·Arnas Uselis·Sangwoo Park·Seong Joon Oh·Sangdoo Yun
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Abstract

Many scientific advances come from cross-pollination, carrying an idea from one field over to a problem in another. Yet retrieving papers across fields remains underexplored, with existing benchmarks confined to a narrow range of fields. We study cross-pollination retrieval, the problem of identifying transferable ideas across research fields, and introduce CPR-Dataset, a large-scale dataset constructed by mining cross-topic citations. Its evaluation split CPR-eval has 1,300 queries in 26 fields, and its training split CPR-train has 10,573 pairs. On CPR-eval, the best of eleven existing retrievers reaches a Recall@10 of only 0.26, whereas an LLM reranker reaches 0.81. Standard contrastive fine-tuning on CPR-train raises Recall@10 to as high as 0.70. The gain carries over to domains held out from training and to analogy and reasoning-intensive retrieval. On AnaloBench, it raises nDCG@10 by up to 33% over the untrained retriever, whereas training on three existing retrieval datasets lowers it. On BRIGHT, it raises nDCG@10 by up to 70%, more than twice the gain from those datasets. CPR-Dataset is available at https://huggingface.co/CPR-Dataset, and the code at https://github.com/SeokwonJung-Jay/Cross-Pollination-Retrieval.

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Seokwon Jung, Yerang Kim, Yejin Kim, Alexander Rubinstein, Arnas Uselis, Sangwoo Park, Seong Joon Oh, Sangdoo Yun. 2026-10-19. Cross-Pollination Retrieval: Finding Transferable Ideas Across Fields in Scientific Literature. https://arxiv.org/abs/2610.25678

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