A 'Finding Inspiring Papers' Benchmark Created by 184 Researchers
- What
- We created ScholarCatalyst, a dataset where first authors of 207 recent computer science papers indicated which prior papers actually helped their research, and presented a retrieval task to find them.
- Key result
- Agentic search (AI that iteratively performs tasks on its own) did not show better performance than basic search methods (recall of 0.42 versus 0.48 based on the top 20 results). Agents based on Claude Fable 5.1 only reached 0.51.
- Why it matters
- This indicates that current AI still lacks the expert intuition needed to identify necessary prior ideas from vast research literature, pointing to the need for new training methods. While this can be viewed as a step forward for AI assisting scientific research, actual commercialization or industrial effects cannot be confirmed from the abstract.
Background
While AI is making progress in solving open problems, scientists still lead in the ability to identify prior ideas needed for new problems from a constantly growing body of research literature. To measure this capability, the researchers chose an approach where researchers who actually know which papers helped their research confirm it directly.
Method
Using an automated pipeline to scale author annotation, 184 first authors indicated candidate papers for 207 recent computer science papers that advanced or could have advanced their research, along with detailed justifications. The task is to find these papers within only the literature that existed at the start of the project, given an initial research question.
Main results
Agentic search showed similar or lower performance compared to basic embedding search, despite using the same search tools (0.42 versus 0.48). Agents based on Claude Fable 5.1, which might have seen completed papers during training, stayed at 0.51.
Limitations & open points
From the abstract alone, the detailed composition of the dataset, the full definition of evaluation metrics, and the scalability to fields outside of computer science cannot be confirmed. Since the presented figures are results from specific settings, performance with other models or methods is unknown.
Glossary
- Embedding search — A method that converts texts and questions into numerical bundles (vectors) to find documents with close meanings.
- Agent (AI agent) — An AI that plans on its own and performs a given task by using tools such as search multiple times.
- Recall@20 — The ratio indicating how many of the correct papers are included within the top 20 results produced by the AI.
- Benchmark — A set of common test questions created to compare multiple AIs under the same criteria.
- Training data — Materials that an AI model has previously seen and learned from during its training process.
This summary was written by AI from the paper's abstract and reviewed by the editor. Summaries on this page were translated by AI from the editor-reviewed Korean version. Check the original for exact figures and context.
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