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.

翻译：科学发现必须清晰传达才能充分发挥其潜力。若缺乏有效沟通，即便是最具突破性的成果也可能被忽视或误解。科学家主要通过同行评审来交流工作并获取学界反馈。然而，当前评审体系常因评审者间反馈不一致，最终阻碍稿件的完善并限制其潜在影响力。本文提出一种由大语言模型驱动的新方法APRES，该方法基于评估准则更新科学论文文本。我们的自动化方法发现了能高度预测未来引用次数的评估准则，并将其与APRES集成于自动化系统中，通过修订论文提升其质量与影响力。关键在于实现该目标时需保持核心科学内容不变。我们验证了APRES的成功应用：其未来引用预测的平均绝对误差较次优基线模型降低19.6%，且修订后的论文在人类专家评估中获得79%的优先选择率。本研究为使用大语言模型作为作者投稿前压力测试工具提供了有力实证支持。最终，我们的工作旨在增强而非取代人类专家评审的核心作用——应当由人类来甄别真正重要的发现，引导科学推动知识进步并丰富人类生活。