ScholarCatalyst: 研究灵感检索基准
ScholarCatalyst: A Benchmark for Retrieving Papers That Inspire New Research
ScholarCatalyst 基准揭示当前 AI 在科研文献检索上的局限，Claude Fable 5.1 也未能超越传统嵌入方法。
ScholarCatalyst 基准由 207 篇计算机科学论文的 184 位通讯作者标注构建。该基准测试模型在给定初始研究问题时，从项目开始时可用的文献中检索相关论文的能力。结果显示，智能体搜索在 Recall@20 指标上仅达到 0.42，而嵌入检索达到 0.48，即使是基于 Claude Fable 5.1 构建的智能体也仅达到 0.51 R@20。
ScholarCatalyst: A Benchmark for Retrieving Papers That Inspire New Research
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.
