大模型
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普林斯顿王梦迪：AI尚未真正发现新科学，答案可能不在模型里
在2026外滩大会主论坛上，普林斯顿大学AI创新中心主任王梦迪提出，当前大模型虽掌握大量人类知识，但在真正科学发现上仍有明显局限：模型更擅长找到‘最可能’的答案，而真正的新发现常位于概率分布的长尾。其团队与普林斯顿社会学系谢宇合作，用ChatGPT、Claude等做‘模拟人生’实验，让模型模拟1930年出生的英国人并自主生成人生轨迹，结果发现模型在婚龄、职业、性别等问题上高估常见情形、显著低估少数与长尾分布。她认为这解释了为何大模型在数学、编程等有明确验证器（编译器、Unit Test、Lean形式化证明）的领域进展快，而在物理、化学、生物等基础科学尚未产生同等规模原创发现；真实科研验证困难，涉及复杂实验设备、人类判断与跨团队协作，且信息更多未必提升有效新信息，反而可能降低信噪比。她提出AI走向自主科学发现的关键不是让模型知道更多，而是让真实世界越来越可验证，其团队正尝试在量子材料研究中搭建自动化实验平台，将原本耗时数月的石墨烯实验流程自动化，并把实验设备与能力封装为API。
AI推荐理由 普林斯顿王梦迪在2026外滩大会主论坛提出：大模型擅长找概率分布的最大似然点，却低估长尾，这解释了其在数学、编程（有编译器、Unit Test、Lean等验证器）进展快，而在物理、化学、生物等基础科学尚未产生同等规模原创发现；其团队与谢宇合作以ChatGPT、Claude做‘模拟人生’实验，发现模型在婚龄、职业、性别等问题上高估常见情形、低估少数与长尾分布；并尝试搭建量子材料自动化实验平台，把石墨烯实验流程与设备能力封装为API。该观点提供了对AI自主科学发现瓶颈的具体机制解释与可验证实验线索，但属会议发言转述，实验细节与数据未在片段中给出，需保留其判断与实验结论的不确定性。
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Why hasn't AI truly discovered new science yet? Wang Mengdi from Princeton: The answer may not be in the model
AIbase基地
Published inAI News · 6 min read · Sep 10, 2026
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On September 10, during the main forum of the 2026 Inclusion · Bund Conference, Professor Wang Mengdi, Director of the Center for AI Innovation at Princeton University, raised a question: How far is AI from autonomous discovery of new science?
Her judgment is that today's large models have mastered a vast amount of human knowledge, but they still face significant limitations in true scientific discovery: large models are better at finding the "most likely" answer, while truly new discoveries often lie in the long tail of probability distributions.
Wang Mengdi's team recently collaborated with Professor Xie Yu from Princeton University's Sociology Department to conduct a "Simulated Life" experiment using large models such as ChatGPT and Claude. For example, they asked the model to simulate a British person born in 1930 and autonomously generate their life trajectory afterward.
The experiment found that in multiple issues such as marriage age, occupation, and gender, the large model tends to overestimate the most common situations, but significantly underestimate minority situations and long-tail distributions.
"The model learns the point of maximum likelihood in a probability distribution, but may underestimate or even ignore knowledge in the long tail," said Wang Mengdi.
She believes this also explains why large models have made rapid progress in areas like mathematics and coding, but have not yet produced original discoveries of the same scale in fundamental scientific fields such as physics, chemistry, and biology.
The difference lies in the fact that mathematics and coding have relatively clear "validators." Coding can be verified through compilers and Unit Tests, while mathematics can use formal proof systems like Lean. The model can keep trying, get feedback, correct answers, and then proceed to the next training round. However, in real scientific research, verification is much more difficult.
"Ideas are cheap. You need to show me code or an experiment," said Wang Mengdi. Scientific research often involves complex experimental equipment, human judgment, and cross-team collaboration, and many experiments cannot be stably replicated. More information does not mean that truly effective new information is increasing; instead, it may reduce the "signal-to-noise ratio" in the research process.
In her view, the key to AI moving towards autonomous scientific discovery is not making the model know more, but making the real world increasingly verifiable.
Wang Mengdi's team is trying to build such an infrastructure. In quantum material research, they have established an automated experimental platform, automating the graphene experimental process that originally took researchers months to complete, and encapsulating the experimental equipment and capabilities into APIs so other research institutions can call and replicate the experiments. At the same time, they are also exploring an intelligent operating system for research laboratories called LabOS, allowing multimodal AI to perceive the experimental environment through smart glasses, assist researchers in operations, and further connect software, AI models, robots, and real experimental equipment.
Wang Mengdi believes that what truly determines whether AI can become a "discoverer" is not how large the model is, but whether there exists an infrastructure that connects hypotheses, validation, traceability, and replication. Only when the experimental process in the physical world can be systematically recorded, continuously validated, and repeatedly replicated can AI possibly transform the "likelihood" learned in training into exploration of "possibility."
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