在科学版图中定位手稿：基于智能体AI的方法

中文摘要 AI 辅助

发表研究手稿是科学生活中常规但要求苛刻的一部分：耗时、压力大，且结果往往不确定。基于大语言模型（LLM）的智能体AI的最新进展已在多种科学任务中展现出潜力，在此我们探讨智能体AI能否通过可靠地从手稿内容及其文献背景推断其最终发表 venue，帮助研究人员应对发表过程本身。我们引入PASS（面向发表的智能体科学系统），这是一个智能体系统，能够在领域特定的文献背景下理解手稿，并预测最匹配的发表 venue。PASS通过重建手稿的局部科学邻域、追踪其主题轨迹，并在领域特定的期刊空间中推理，将每篇手稿定位在其周围的文献版图中。在跨越16个生物医学领域的超过2000篇预印本的泄漏审计基准上评估，PASS达到了50.3%的Top-1准确率和86.1%的Top-5准确率，优于最先进的LLM基线和已建立的期刊选择工具。PASS生成的质量分数，如影响潜力和新颖性，与独立的发表结果度量相一致。我们还发现，设计的文献检索模块是性能贡献最强的部分，特别是在将手稿相对于邻近工作进行定位时，并且PASS仅从摘要就能保持接近完整的性能，而LLM基线则需要完整的手稿文本。一项独立的人工评估发现，研究人员对PASS的手稿理解和推荐理由有强烈认同。PASS已作为公共平台发布（此 https URL），供广大研究人员使用。

英文摘要

Publishing a research manuscript is a routine yet demanding part of scientific life: time-consuming, stressful, and often uncertain in outcome. Recent advances in large language model (LLM)-based agentic AI have shown promise across a range of scientific tasks, and here we ask whether agentic AI can help researchers navigate the publication process itself by reliably inferring a manuscript's eventual publication venue from its content and literature context. We introduce PASS (Publication-oriented Agentic Scientific System), an agentic system that understands manuscripts within their domain-specific literature context and predicts top-matched publication venues. PASS positions each manuscript within its surrounding literature landscape by reconstructing its local scientific neighborhood, tracing its topic trajectory, and reasoning over field-specific journal spaces. Evaluated on a leakage-audited benchmark of over 2,000 preprints across 16 biomedical fields, PASS achieved Top-1 accuracy of 50.3% and Top-5 accuracy of 86.1%, outperforming state-of-the-art LLM baselines and established journal-selection tools. PASS-produced quality scores, such as impact potential and novelty, aligned with independent measures of publication outcome. We also found that the designed literature retrieval module is the strongest performance contributor, particularly for positioning manuscripts relative to nearby work, and that PASS maintained near-full performance from the abstract alone, whereas LLM baselines required the full manuscript text. An independent human evaluation found strong researcher agreement with PASS's manuscript understanding and recommendation rationale. PASS has been released as a public platform (https://ratemypaper.ai/) for broad researcher access.

发表机构

• University of Pennsylvania（宾夕法尼亚大学）
• University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
• University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）
• Stanford University（斯坦福大学）
• Yale University（耶鲁大学）

机构由 AI 辅助整理，请以论文原文为准。