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ScholarCatalyst finds agentic search trails embedding retrieval on expert judgments

huggingface.co by Sohyeon Kim et al. Editor score 7/10

The benchmark uses judgments from 184 lead authors across 207 papers; agentic search reaches 0.42 Recall@20 versus 0.48 for embedding retrieval, while a stronger agent reaches 0.51. It gives engineers a grounded test of whether research agents add value over retrieval tools.

What

- ScholarCatalyst is a benchmark for retrieving papers that could have advanced a research project, based on author-provided judgments.

How

- It uses labels from 184 lead authors across 207 papers; agentic search scored 0.42 Recall@20 versus 0.48 for embedding retrieval.

Why it matters

- The results point to a need for training that gives scientific agents expert intuition for searching broad research corpora.

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