Bold projections that artificial intelligence will accelerate scientific discovery have raced ahead of evidence from working scientists, and the field still lacks large-scale, scientist-in-the-loop tests of these claims. Here we mount the largest such evaluation to date and map what AI cannot yet do for science. We invited authors of 121,640 recent preprints across biology, medicine, chemistry, and the social sciences to judge ideas that large language models (LLMs) generated from the context and puzzles of their own papers. 6,749 scientists returned 25,139 sets of ratings on novelty, empirical feasibility, probability of being true, and favorability of adoption. Three patterns emerge. First, non-reasoning LLMs collapse into a narrow "hivemind" of similar ideas; reasoning models roam a wider hypothesis space, yet no model class spontaneously proposes null hypotheses -- a move humans make more freely. Second, scientists reward ideas that resemble their own and prize probability over novelty, though social scientists tolerate risk more readily than life scientists. Senior social scientists are the harshest critics, and their skepticism is well-earned: LLMs falter most in pluralistic fields like the social sciences that demand context-aware interpretation and evolving theories. Third, automated evaluators on which the community currently relies -- LLM-as-a-judge, artificial metrics, and even state-of-the-art (SOTA) models -- agree only weakly with expert judgment, and retrieval augmentation and scientist persona prompting yield only marginal gains. A Qwen3-14B reward model we post-trained on human ratings captures field taste nuances, beats SOTA models by up to 27%, and closes the gap to the inter-rater consistency of independent peer reviewers. For all the hype, today's scientific AI still represents a collaborator whose imagination, outputs and judgment benefit from human grounding.

翻译：大胆预测认为人工智能将加速科学发现，但这些预测已远超来自在职科学家的实际证据支持，该领域仍缺乏大规模、有科学家参与的此类主张测试。在此我们开展了迄今为止最大规模的评估，并描绘了人工智能尚无法为科学做什么。我们邀请121640篇近期预印本（涵盖生物学、医学、化学和社会科学领域）的作者，对大型语言模型根据其论文背景和未解难题生成的设想进行评判。6749名科学家返回了25139组关于新颖性、实证可行性、真实概率及采纳倾向性的评分。三个模式浮现：第一，非推理型大型语言模型陷入类似想法的狭窄"蜂巢思维"；推理模型探索更广泛的假设空间，但尚无模型类别能自发提出零假设——这是人类更自由做出的行动。第二，科学家偏爱与自己研究相似的设想，更看重概率而非新颖性，尽管社会科学家比生命科学家更愿意承担风险。资深社会科学家是最严苛的批评者，他们的怀疑实至名归：大型语言模型在社会科学等要求情境感知解读和理论演变的多元领域表现最差。第三，当前学界依赖的自动评估系统（如LLM作为评判者、人工指标甚至最先进的模型）仅与专家判断弱一致，而检索增强和科学家角色提示仅带来边际收益。我们基于人类评分后训练的Qwen3-14B奖励模型捕捉了领域品味细微差异，比最先进模型性能提升27%，并缩小了与独立同行评审者间评分一致性的差距。尽管炒作不断，当今科学人工智能仍是一个需要人类基础来支撑想象力、输出和判断的合作者。