AI研究代理缩小科学探索范围

摘要

AI研究代理如今能够生成研究思路、设计实验、运行代码以及撰写论文，这为大规模AI辅助科学发现带来了可能性。当前许多代理框架明确鼓励生成新颖且具有高影响力的想法。然而，AI辅助构思究竟是拓展了科学探索的广度，还是主要集中于现有工作领域，仍不明确。我们将AI研究代理视为科学搜索系统进行研究。利用四个AI研究代理框架和六个大型语言模型，我们从AI及机器学习领域中按引用定义的研究方向共享的种子文献出发，生成了37,802个科学思路。随后，我们将生成的AI思路与同一研究领域的人类作者论文、基于相同种子文献衍生的人类后续研究以及种子文献本身进行对比。实验结果显示出一致的四个特征。其一，AI生成的思路比同一研究领域的人类作者论文更为集中。其二，AI生成的思路相较于人类后续工作，更贴近其起始文献。其三，与AI生成思路最相似的论文往往后续引用率较低。其四，当AI生成思路与先前工作存在差异时，这种差异主要源于对现有技术方法的重新组合，而非引入根本性的新研究问题。总体而言，当前的AI研究代理似乎更擅长局部细化，而非拓展科学探索的广度。

English

AI research agents can now generate research ideas, design experiments, run code, and draft papers, raising the possibility of large-scale AI-assisted scientific discovery. Many current agent frameworks explicitly encourage the generation of novel and high-impact ideas. Yet it remains unclear whether AI-assisted ideation broadens scientific exploration or mainly concentrates around existing work. We study AI research agents as scientific search systems. Using four AI research-agent frameworks and six large language models, we generate 37,802 scientific ideas from shared seed literature across citation-defined research areas in AI and machine learning. We then compare the resulting AI ideas against human-authored papers from the same research areas, follow-on human research emerging from the same seed literature, and the seed literature itself. Across experiments, four consistent patterns emerge. First, AI-generated ideas are substantially more concentrated than human-authored papers from the same research areas. Second, AI-generated ideas remain much closer to their starting literature than later human follow-on work does. Third, papers most similar to AI-generated ideas tend to receive lower subsequent citations. Fourth, when AI-generated ideas differ from prior work, the differences arise primarily from recombining existing technical methods rather than introducing fundamentally new research questions. Overall, current AI research agents appear better suited to local elaboration than to broadening scientific exploration.