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English(EN) MUSES: A Benchmark for Prospective Intellectual-Roots Retrieval

新的MUSES基准旨在检索生成性科学论文

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员推出MUSES，这是一个旨在改进科学发现中影响力文献检索的新基准。与专注于相关性和流行度的现有系统不同，MUSES旨在识别对未来工作具有生成性的论文，即使它们不太为人所知。该基准包含一百万个实例，并按论文熟悉度和功能轴（如修辞溯源和作者认可溯源）进行构建。初步实验表明，随着任务从一般引文转向更具体的作者认可，检索性能显著下降，凸显了识别真正学术渊源的挑战。 AI

影响 该基准可能带来更有效的AI驱动的科学文献发现和研究综合工具。

排序理由 该集群描述了一个新的学术基准和相关的科学文献信息检索方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.IR (Information Retrieval) 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新的MUSES基准旨在检索生成性科学论文

本文如何被排名

Signal score
0 / 100
Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket
Tool
该集群描述了一个新的学术基准和相关的科学文献信息检索方法。[lever_c_demoted from research: ic=1 ai=1.0]
Source corroboration
Single-source cluster
Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics
paper, other
Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance
High
Clearly on-topic for AI-industry coverage.
Story freshness
44 days old
Aged out of breaking-news scoring windows; ranking reflects the durable signal from the full source set.

完整方法见我们的编辑标准。

报道来源 [1]

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

    MUSES：面向未来知识根源检索的基准测试

    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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