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Scholar催化剂:检索有助于新研究的论文的基准

原文标题 · ScholarCatalyst: A Benchmark for Retrieving Papers That Inspire New Research

作者:Sohyeon Kim、Yoonho Lee、Bo Liu|是什么让伟大的科学家变得伟大?即使人工智能系统开始在开放问题上取得进展,科学家们在感知埋藏在不断增长的研究档案中的哪一个先前想法方面仍然远远领先于他们。为了研究这项技能,我们聘请了第一手了解哪些早期工作推进了他们已完成的项目的研究人员,论文可以作为其中想法的指针。美国

来源:arXiv cs.AI/CL/LG · arxiv.org

正文为英文,可一键机器翻译(仅首次需要等待)

Computer Science > Artificial Intelligence

Title: ScholarCatalyst: A Benchmark for Retrieving Papers That Inspire New Research

Abstract: What makes great scientists great? Even as AI systems start to make progress on open problems, scientists remain far ahead of them at sensing which prior idea, buried in an ever-growing archive of research, a new problem needs. To study this skill, we draw on researchers who know firsthand which earlier work advanced their completed projects, with papers serving as pointers to the ideas within. Using our automated pipeline that makes author annotation scalable, we build ScholarCatalyst by having 184 lead authors of 207 recent computer science papers label which candidates did or could have advanced their project, each with a detailed rationale. We introduce a retrieval task with author-provided judgments: given an initial research question, retrieve these papers from only the literature available when the project began. Agentic search does no better than embedding retrieval (0.42 vs. 0.48 Recall@20) despite calling that same retriever as a tool. Even an agent built on Claude Fable 5.1, which may have seen the completed papers during training, reaches only 0.51 R@20. These results highlight the need for new training recipes that equip models with expert intuition for searching broad corpora. We envision ScholarCatalyst as a step toward scientific agents that can take a half-formed idea and point to the prior research it needs.

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