Collaboration is the defining mode of modern science, yet its core mechanism -- feedback -- remains hard to observe, difficult to scale, and unequally distributed. Here we test whether large language models (LLMs) can contribute to this hidden but vital practice and reallocate scientific feedback, an essential yet scarce resource for knowledge production. In a global large-scale randomized field experiment, we delivered customized LLM-generated feedback for over 31,000 arXiv preprints across 150 fields and more than 45,000 researchers from 133 geographic regions. Relative to controls, authors who received feedback had a significantly higher likelihood of revising their manuscripts, corresponding to a 12.55% relative increase over the baseline revision rate. Exposure to AI feedback also increased authors' subsequent use of LLM tools in their future papers, suggesting longer-run shifts in scientific practice. These effects were strongest among authors from non-English-dominant research regions, manuscripts less embedded in the scholarly literature, and teams with lower h-indexes and earlier career stages, consistent with the idea that AI feedback may provide the greatest benefit where access to timely critique is otherwise limited. Together, these findings provide causal evidence that structured AI-based interventions can transform access to scientific feedback from a largely private advantage into a more widely distributed resource, with broader implications for productivity, equity, and capacity across the global research system.

翻译：协作是现代科学的基本模式，但其核心机制——反馈——却难以观察、难以规模化且分布不均。本研究检验大语言模型（LLM）能否参与这一隐性但至关重要的实践，并重新分配科学反馈——这一知识生产中不可或缺却稀缺的资源。通过一项全球大规模随机实地实验，我们向来自133个地理区域的45000多名研究者发布了涉及150个领域的31000余篇arXiv预印本的自定义LLM生成反馈。相较于对照组，接受反馈的作者修改手稿的概率显著提高，相当于在基准修改率基础上提升了12.55%。接触AI反馈还增加了作者在未来论文中使用LLM工具的频率，这表明科学实践可能发生了长期性转变。这些效应在来自非英语主导研究区域的作者、学术文献嵌入度较低的手稿、以及h指数较低或处于早期职业阶段的团队中最为显著，印证了"AI反馈可能在那些难以获取及时评审资源的领域产生最大效益"的假设。综合来看，这些发现提供了因果证据，表明基于AI的结构化干预能够将科学反馈从一种私人优势转化为更广泛分布的资源配置，这对全球研究体系的生产力、公平性和创新能力具有深远意义。