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This paper investigates whether AI research agents broaden or narrow scientific exploration by generating 37,802 scientific ideas using four agent frameworks and six LLMs based on shared seed literature. The study finds that AI-generated ideas are more concentrated, remain closer to the seed literature, and receive fewer citations than human-authored papers, suggesting a tendency towards local elaboration rather than broad exploration. Further analysis reveals that differences from prior work mainly involve recombining existing methods, not introducing novel research questions.
AI research agents, despite their potential, currently excel at incremental improvements within established research areas, but struggle to generate truly novel scientific directions.
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.
