APRES：一种代理论文修订与评估系统
APRES: An Agentic Paper Revision and Evaluation System
AI总结：
APRES利用大型语言模型改进论文质量，通过评估标准提升引用预测，79%的专家更喜欢修订后的论文。
摘要：
科学发现必须清晰地传达出来，以充分发挥其潜力。没有有效的沟通，即使是最具有突破性的发现也可能会被忽视或误解。科学家通过同行评审来交流他们的工作并获得社区的反馈，然而当前的系统往往在评审员之间提供不一致的反馈，最终阻碍了论文的改进并限制了其潜在影响。在本文中，我们介绍了一种新的方法APRES，该方法由大型语言模型（LLMs）驱动，用于根据评估标准更新科学论文的文本。我们的自动化方法发现了一个高度预测未来引用次数的评估标准，并将其整合到一个自动系统中，该系统修订论文以提高其质量和影响。至关重要的是，这一目标应在不改变核心科学内容的情况下实现。我们证明了APRES的成功，其在平均平均误差方面将未来引用预测提高了19.6%，并显示我们的论文修订过程使论文在79%的情况下被人类专家评估者认为优于原稿。我们的发现为使用LLMs作为帮助作者在提交前压力测试手稿的工具提供了强有力的实证支持。最终，我们的工作旨在补充而不是取代人类专家评审员的必不可少的作用，因为应该是人类来辨别哪些发现真正重要，引导科学向推进知识和丰富生活迈进。
英文摘要：
Scientific discoveries must be communicated clearly to realize their full potential. Without effective communication, even the most groundbreaking findings risk being overlooked or misunderstood. The primary way scientists communicate their work and receive feedback from the community is through peer review. However, the current system often provides inconsistent feedback between reviewers, ultimately hindering the improvement of a manuscript and limiting its potential impact. In this paper, we introduce a novel method APRES powered by Large Language Models (LLMs) to update a scientific papers text based on an evaluation rubric. Our automated method discovers a rubric that is highly predictive of future citation counts, and integrate it with APRES in an automated system that revises papers to enhance their quality and impact. Crucially, this objective should be met without altering the core scientific content. We demonstrate the success of APRES, which improves future citation prediction by 19.6% in mean averaged error over the next best baseline, and show that our paper revision process yields papers that are preferred over the originals by human expert evaluators 79% of the time. Our findings provide strong empirical support for using LLMs as a tool to help authors stress-test their manuscripts before submission. Ultimately, our work seeks to augment, not replace, the essential role of human expert reviewers, for it should be humans who discern which discoveries truly matter, guiding science toward advancing knowledge and enriching lives.
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。
