FAME：通过连续时间流形演变进行学术影响预测
FAME: Forecasting Academic Impact via Continuous-Time Manifold Evolution
AI总结：
本文提出FAME框架，通过动态流形演变模型预测科研论文的学术影响，实验显示其在多维影响预测上优于现有LLM评估器。
摘要：
大型语言模型（LLMs）越来越多地用于构思和评估研究想法，但评估这些判断极具挑战性，因为新想法的真实影响可能需要数年才能显现。本文通过人类撰写的论文影响预测作为可验证的代理任务来解决这一挑战。在前瞻性预测研究中，我们发现前沿LLMs无法可靠地区分高影响论文与普通出版物，表明基于静态文本的判断不足以进行科学评估。为了解决这一限制，我们提出了FAME（通过连续时间流形演变预测学术影响），一种时空框架，用于建模科学主题的动态轨迹。FAME将论文投影到一个由文本特征和验证知识流图信息的动态潜在空间中，学习几何约束，使具有影响的论文与领域前进动量对齐。在3,200篇arXiv论文上进行的实验表明，FAME在前瞻性多维影响预测上始终优于最先进的LLM评估器。此外，将FAME的动态几何信号整合到LLMs中显著提高了其预测性能。这些结果支持论文影响预测作为有用的、可测量的代理基准，并将FAME定位为自动化科学评估的强大、轨迹意识的基础。
英文摘要：
Large Language Models (LLMs) are increasingly used to brainstorm and evaluate research ideas, yet assessing such judgments is fundamentally difficult because the true impact of a new idea may take years to emerge. We address this challenge by using the impact forecasting of human-authored manuscripts as a verifiable proxy task. In a prospective forecasting study, we find that frontier LLMs fail to reliably distinguish high-impact papers from ordinary publications, suggesting that static text-based judging is insufficient for scientific evaluation. To address this limitation, we propose $\textbf{FAME}$ ($\underline{\text{F}}$orecasting $\underline{\text{A}}$cademic Impact via Continuous-Time $\underline{\text{M}}$anifold $\underline{\text{E}}$volution), a spatiotemporal framework for modeling the dynamic trajectories of scientific topics. FAME projects papers into a dynamic latent space informed by textual features and a verified knowledge-flow graph, learning geometric constraints that align impactful manuscripts with the forward momentum of their fields. Experiments on 3,200 arXiv papers across three fast-evolving subfields show that FAME consistently and substantially outperforms state-of-the-art LLM evaluators in prospective multidimensional impact forecasting. Furthermore, integrating FAME's dynamic geometric signals into LLMs significantly improves their forecasting performance. These results support manuscript impact forecasting as a useful, measurable proxy benchmark and position FAME as a strong, trajectory-aware foundation for automated scientific evaluation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
