Generative AI systems such as ChatGPT are increasingly used in scientific writing, yet their broader implications for the organization of scientific knowledge remain unclear. We examine whether AI-assisted writing intensity, measured as the share of text in a paper that is predicted to exhibit features consistent with LLM-generated text, is associated with scientific disruption and knowledge recombination. Using approximately two million full-text research articles published between 2021 and 2024 and linked to citation networks, we document a sharp temporal pattern beginning in 2023. Before 2023, higher AI-assisted writing intensity is weakly or negatively associated with disruption; after 2023, the association becomes positive in within-author, within-field analyses. Over the same period, the positive association between AI-assisted writing intensity and cross-field citation breadth weakens substantially, and the negative association with citation concentration attenuates. Thus, the post-2023 increase in disruption is not accompanied by broader knowledge sourcing. These patterns suggest that generative AI is associated with more disruptive citation structures without a corresponding expansion in cross-field recombination. Rather than simply broadening the search space of science, AI-assisted writing may be associated with new forms of recombination built from relatively narrower knowledge inputs.

翻译：生成式人工智能系统（如ChatGPT）正日益广泛地应用于科学写作领域，然而，它们对科学知识组织结构的深远影响仍不明确。本研究探讨了人工智能辅助写作强度（以论文中预测具有大语言模型生成文本特征的段落占比衡量）是否与科学颠覆性及知识重组相关联。利用2021年至2024年间发表的约两百万篇全文学术论文及其引文网络数据，我们揭示了一个始于2023年的显著时间模式。2023年之前，较高的人工智能辅助写作强度与颠覆性呈弱负相关或负相关；2023年之后，在作者内和领域内分析中，这种关联转变为正相关。同期，人工智能辅助写作强度与跨领域引文广度之间的正相关关系显著减弱，而与引文集中度的负相关关系也有所缓和。因此，2023年后出现的颠覆性增强并未伴随更广泛的知识来源拓展。这些模式表明，生成式人工智能与更具颠覆性的引文结构相关，但并未相应促进跨领域重组。人工智能辅助写作可能并非简单地拓展科学的搜索空间，而是与建立在相对狭窄知识输入基础上的新型重组形式相关联。