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New MUSES benchmark targets retrieval of generative scientific papers

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced MUSES, a new benchmark designed to improve the retrieval of influential prior literature in scientific discovery. Unlike existing systems that focus on relevance and popularity, MUSES aims to identify papers that were generative for future work, even if less familiar. The benchmark includes a million instances and is structured by paper familiarity and functional axes, such as rhetorical roots and author-endorsed roots. Initial experiments show a significant drop in retrieval performance as the task moves from general citations to more specific author endorsements, highlighting the challenge of identifying true intellectual lineage. AI

IMPACT This benchmark could lead to more effective AI-powered tools for scientific literature discovery and research synthesis.

RANK_REASON The cluster describes a new academic benchmark and associated methods for information retrieval in scientific literature. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.IR (Information Retrieval) →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New MUSES benchmark targets retrieval of generative scientific papers

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The cluster describes a new academic benchmark and associated methods for information retrieval in scientific literature. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Hong Yu ·

    MUSES: A Benchmark for Prospective Intellectual-Roots Retrieval

    Scientific discovery depends on finding prior literature that shapes what comes next. Existing retrieval systems optimize for relevance and popularity, often favoring central papers over less familiar works that later prove generative. We introduce \textbf{MUSES}, a million-insta…

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