这篇论文用一套严谨的方法测出了LLM和人类科研人员在脑洞上的真实差距，发现LLM的想法集中但套路化，和人比还是有明显偏差。
这篇论文构建了一个大规模评估框架，从高质量人类研究论文中逆向工程出可能激发核心想法的少量相关先前工作。通过提示LLM从论文标题和摘要生成新想法，作者引入双轴研究品味分类法（机会模式和研究范式）来量化人类与LLM想法之间的差异。在多个LLM生成的想法集中，观察到一致的分布差距：LLM想法过度集中在桥接式机会和综合方法上，而人类论文参考分布更广泛地分布在构建差距和贡献的方式上。结果表明，即使强大的LLM能产生合理想法，其范围仍比人类研究品味窄且系统性偏移。
LLMs are increasingly used to brainstorm research ideas, but existing evaluations mostly judge individual ideas by novelty, feasibility, or expert preference. We instead ask: how far are current LLM-generated ideas from human researchers? To characterize this gap, we build a large-scale evaluation framework for ideation from high-quality human research papers. For each paper, we reverse-engineer a small set of closely related prior works that likely inspired its core idea. LLMs are then prompted to generate a new idea from the set of paper titles and summaries. We introduce a two-axis research-taste taxonomy to profile each idea by its opportunity pattern and research paradigm, and use it to quantify the divergence between human and LLM ideas. Across idea sets generated by different LLMs, we observe a consistent distributional gap: LLM ideas are disproportionately concentrated around bridge-like opportunities and synthesis methods, whereas the human paper reference distribution spreads more broadly across ways of framing gaps and constructing contributions. This result suggests that strong LLMs can produce a range of reasonable ideas, but that range remains narrower than, and systematically shifted relative to, human research taste.