AI论文爆发式增长，但产学合作仍滞后于预期。
Structural shifts in institutional participation and collaboration within the AI arXiv preprint research ecosystem
- 用LLM分析arXiv数据，追踪2021-2025年学术机构与产业合作变化。
- ChatGPT发布后论文量激增，但跨机构合作指数仍远低于随机水平。
- 揭示生成式AI高投入特性正阻碍产学深度协同，适合关注科研生态者阅读。
大语言模型（LLMs）的出现标志着科学生态系统中的一次重大技术变革，尤其在人工智能（AI）领域。本文基于2021至2025年arXiv预印本平台（cs.AI）的数据，研究了AI研究格局的结构性变化。由于AI发展迅速，预印本生态已成为实时反映科学动态的关键指标，常比正式同行评审发表早数月甚至数年。通过多阶段数据收集与增强流程，并结合基于LLM的机构分类方法，我们分析了出版量、作者团队规模及学术—产业合作模式的演变。结果显示，自ChatGPT发布后，论文产出量出现前所未有的激增，学术机构仍是研究输出的主要来源。然而，学术—产业合作仍受抑制，以归一化合作指数（NCI）衡量，其值在所有主要子领域均显著低于随机混合基准。这些发现凸显了持续存在的机构分化现象，表明生成式AI研究的资本密集特性正在重塑科学协作的边界。
原文摘要 · Abstract (English)
The emergence of large language models (LLMs) represents a significant technological shift within the scientific ecosystem, particularly within the field of artificial intelligence (AI). This paper examines structural changes in the AI research landscape using a dataset of arXiv preprints (cs.AI) from 2021 through 2025. Given the rapid pace of AI development, the preprint ecosystem has become a critical barometer for real-time scientific shifts, often preceding formal peer-reviewed publication by months or years. By employing a multi-stage data collection and enrichment pipeline in conjunction with LLM-based institution classification, we analyze the evolution of publication volumes, author team sizes, and academic--industry collaboration patterns. Our results reveal an unprecedented surge in publication output following the introduction of ChatGPT, with academic institutions continuing to provide the largest volume of research. However, we observe that academic--industry collaboration is still suppressed, as measured by a Normalized Collaboration Index (NCI) that remains significantly below the random-mixing baseline across all major subfields. These findings highlight a continuing institutional divide and suggest that the capital-intensive nature of generative AI research may be reshaping the boundaries of scientific collaboration.
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