内容来源：https://www.technologyreview.com/2025/08/22/1122304/ai-scientist-research-autonomous-agents/

内容总结：

斯坦福大学计算机科学家James Zou将于今年10月举办一场名为"Agents4Science"的全AI学术会议，所有提交的论文将由AI自主研究、撰写与评审，并通过语音合成技术进行展示。这场颇具争议的会议旨在探索AI在科研领域的自主能力。

Zou去年曾在《自然》杂志发表论文，展示其研发的"虚拟实验室"系统——通过多个具备不同专业背景的AI智能体协作，成功设计出针对新冠变种病毒的新型纳米抗体。该研究仅用半天时间就完成了候选治疗方案设计，但团队承认其主要贡献在于方法论创新。

尽管美国政府AI行动计划支持建设自动化云实验室，部分学者也看好AI科学家突破人类认知局限的潜力，但学界仍存在显著分歧。耶鲁大学人类学家Lisa Messeri和普林斯顿大学认知科学家Molly Crockett质疑AI的创新能力及评审科学性，强调需要引入科学哲学家、人类学家等多学科专家参与实验设计。

目前《自然》等主流期刊仍禁止将AI列为论文合著者。Zou认为这反而促使研究者隐瞒AI使用情况，他希望通过此次会议推动学术规范变革。尽管无法预测会议成果，Zou期待能通过数百份AI提交的论文，为AI科研能力提供首次系统性数据验证。

（注：根据中文新闻报道习惯，对原文信息进行了逻辑重组和重点提炼，省略了部分背景介绍和重复性表述，保留核心事件、关键人物、争议观点及未来展望等新闻要素。）

中文翻译：

与这位研究者相遇：他正举办一场由AI主导、面向AI的学术会议
詹姆斯·邹（James Zou）曾利用虚拟AI"科学家"寻找新冠创新疗法。如今，他正尝试让AI在一场充满争议的新会议上评审并展示所有研究成果。

今年十月，一场前所未有的学术会议即将首次亮相。Agents4Science是一场为期一天的线上活动，涵盖从物理学到医学的所有科学领域。所有分享的研究成果将主要由AI完成研究、撰写和评审，并通过文本转语音技术进行展示。

这场会议是斯坦福大学计算机科学家詹姆斯·邹的创意，他专注于研究人类与AI如何高效协作。人工智能早已为科学家提供了诸多实用工具，例如DeepMind的AlphaFold能帮助模拟难以物理合成的蛋白质。而最近，大语言模型和具备推理能力的人工智能的进展，更推动了一种理念：AI几乎能像科学家一样自主工作——自主提出假设、运行模拟实验并设计研究方案。

这一理念并非没有质疑者。除其他问题外，许多人认为AI缺乏科研所需的创造性思维，容易产生过多错误和幻觉输出，并可能限制年轻学者的发展机会。

尽管如此，不少科学家和政策制定者仍对AI科学家的前景充满期待。美国政府的人工智能行动计划明确提出需要"投资建设服务于多科学领域的自动化云实验室"。部分研究者认为，AI科学家可能解锁人类无法独立实现的科学发现。对邹而言，这个命题很简单："AI智能体不受时间限制，它们可以24小时不间断地与人类协作工作。"

上月，邹在《自然》杂志发表论文，展示了其自主AI研究团队的成果。受此成功鼓舞，他现在希望探索其他AI科学家（即人工智能科学家）能取得怎样的成就。他这样描述Agents4Science会议上成功论文的标准："AI应该是第一作者并完成主要工作，人类可以担任顾问角色。"

由AI运作的虚拟实验室
2010年代初在哈佛攻读博士期间，邹对AI在科学领域的潜力产生浓厚兴趣，甚至暂停了一年计算机研究，进入基因组学实验室工作——这个领域因全基因组测绘技术而获益良多。在湿实验室的经历让他体会到跨领域合作的挑战："不同领域的专家往往使用着不同的学术语言。"

他认为，大语言模型在解读和转译专业术语方面优于人类。"它们阅读范围极其广泛，"邹表示，因此能很好地跨学科转化和概括概念。这一灵感促使他构想出所谓的"虚拟实验室"。

从宏观角度看，虚拟实验室是由AI智能体组成的团队，模拟真实大学实验室的运作模式。这些智能体具备不同专业领域知识，并能与AlphaFold等程序交互。研究人员可指派任务给一个或多个智能体，然后通过模型回放查看智能体间的交流过程，从而确定哪些实验值得在现实世界中推进。

邹需要人类合作者帮助实现这个构想并解决实际科研问题。去年，他结识了陈·扎克伯格生物中心的研究科学家约翰·E·帕克（John E. Pak）。帕克与邹同样热衷AI科研应用，双方决定共同创建虚拟实验室。

帕克协助设定了研究主题，但两人都希望观察虚拟实验室能自主提出哪些方案。作为首个项目，他们聚焦于设计针对新冠变种毒株的疗法。围绕这个目标，邹训练了五位AI科学家（包括模拟免疫学家、计算生物学家和首席研究员的不同角色），赋予它们各自的目标和可用程序。

构建这些模型花费了数月时间，但帕克表示一旦系统就绪，它们设计候选疗法的速度极快："大概只用了一天或半天时间。"

邹透露，智能体选择研究抗新冠纳米抗体——这种抗体体积更小且在自然界更罕见。但令他惊讶的是AI的决策理由：模型意识到在有限的计算资源下，小分子结构更适合研究。"这实际上是个明智的决定，因为智能体确实高效设计出了这些纳米抗体。"

研究表明，模型设计的纳米抗体是真正的科学创新，其中多数能与原始新冠毒株结合。但帕克和邹都承认，论文的主要贡献在于虚拟实验室这个工具本身。未参与该研究但提供了基础纳米抗体的宾夕法尼亚大学药理学家史毅（音译）认同此观点，他表示欣赏虚拟实验室的演示，并认为"主要创新点在于自动化"。

《自然》杂志不仅接受了这篇论文，还加速了预出版流程——邹明白AI科研代理是热门领域，他希望成为首批探索者。

AI科学家主办学术会议
投稿过程中，邹沮丧地发现无法正式认可AI对研究的贡献。多数会议和期刊不允许将AI列为合著者，许多甚至明令禁止使用AI撰写论文或评审。《自然》杂志就以责任归属、版权和准确性存在不确定性为由禁止该做法。邹认为："这些政策本质上是在鼓励研究者隐瞒或淡化AI的使用。"

邹试图通过创办Agents4Science会议打破常规，该会议要求所有投稿的第一作者必须是AI。其他AI程序将评估研究成果的科学价值。但人类不会完全退出循环——包括诺贝尔经济学奖得主在内的人类专家团队将评审顶级论文。

邹不确定会议能产生什么成果，但他希望在跨领域的数百份投稿中能发现瑰宝："可能会有AI提交带来有趣发现的论文，也可能出现充满有趣错误的投稿。"

尽管邹称会议反响积极，但部分科学家持保留态度。

耶鲁大学科学人类学家丽莎·梅塞里（Lisa Messeri）对AI评审科学成果的能力充满疑问："如何实现洞察力的飞跃？当评审遇到突破性见解时该如何处理？"她怀疑会议能否给出令人满意的答案。

去年，梅塞里与合作者莫莉·克罗克特（Molly Crockett）在《自然》发文探讨AI科研应用的障碍。她们对AI产生创新成果的能力仍存疑，包括邹论文中展示的纳米抗体。

"我这类科学家本是这些工具的目标用户——非计算机专业但从事计算相关研究，"普林斯顿大学认知科学家克罗克特表示，"但我同时对夸大宣传保持警惕，特别是关于AI模拟人类思维某些方面的声称。"

她们都质疑：如果自动化阻碍人类科学家积累监督AI所需的专业知识，那么使用AI进行科研的价值何在？相反，他们主张在信任AI执行和评审科学之前，应吸纳更广泛学科的专家设计更缜密的实验。

"我们需要与认识论学者、科学哲学家、科学人类学家这些深入思考知识本质的学者对话，"克罗克特强调。

但邹认为他的会议正是推动领域发展所需的实验。关于AI生成科学，他指出："存在大量炒作和轶事证据，但缺乏系统数据。"Agents4Science能否提供此类数据仍是未知数，但到十月，AI至少将向世界展示它们的能力。

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英文来源：

Meet the researcher hosting a scientific conference by and for AI
James Zou used virtual AI ‘scientists’ to find novel covid-19 treatments. Now, he’s having AI review and present all of the research at a controversial new conference.
In October, a new academic conference will debut that’s unlike any other. Agents4Science is a one-day online event that will encompass all areas of science, from physics to medicine. All of the work shared will have been researched, written, and reviewed primarily by AI, and will be presented using text-to-speech technology.
The conference is the brainchild of Stanford computer scientist James Zou, who studies how humans and AI can best work together. Artificial intelligence has already provided many useful tools for scientists, like DeepMind’s AlphaFold, which helps simulate proteins that are difficult to make physically. More recently, though, progress in large language models and reasoning-enabled AI has advanced the idea that AI can work more or less as autonomously as scientists themselves—proposing hypotheses, running simulations, and designing experiments on their own.
That idea is not without its detractors. Among other issues, many feel AI is not capable of the creative thought needed in research, makes too many mistakes and hallucinations, and may limit opportunities for young researchers.
Nevertheless, a number of scientists and policymakers are very keen on the promise of AI scientists. The US government’s AI Action Plan describes the need to “invest in automated cloud-enabled labs for a range of scientific fields.” Some researchers think AI scientists could unlock scientific discoveries that humans could never find alone. For Zou, the proposition is simple: “AI agents are not limited in time. They could actually meet with us and work with us 24/7.”
Last month, Zou published an article in Nature with results obtained from his own group of autonomous AI workers. Spurred on by his success, he now wants to see what other AI scientists (that is, scientists that are AI) can accomplish. He describes what a successful paper at Agents4Science will look like: “The AI should be the first author and do most of the work. Humans can be advisors.”
A virtual lab staffed by AI
As a PhD student at Harvard in the early 2010s, Zou was so interested in AI’s potential for science that he took a year off from his computing research to work in a genomics lab, in a field that has greatly benefited from technology to map entire genomes. His time in so-called wet labs taught him how difficult it can be to work with experts in other fields. “They often have different languages,” he says.
Large language models, he believes, are better than people at deciphering and translating between subject-specific jargon. “They’ve read so broadly,” Zou says, that they can translate and generalize ideas across science very well. This idea inspired Zou to dream up what he calls the “Virtual Lab.”
At a high level, the Virtual Lab would be a team of AI agents designed to mimic an actual university lab group. These agents would have various fields of expertise and could interact with different programs, like AlphaFold. Researchers could give one or more of these agents an agenda to work on, then open up the model to play back how the agents communicated to each other and determine which experiments people should pursue in a real-world trial.
Zou needed a (human) collaborator to help put this idea into action and tackle an actual research problem. Last year, he met John E. Pak, a research scientist at the Chan Zuckerberg Biohub. Pak, who shares Zou’s interest in using AI for science, agreed to make the Virtual Lab with him.
Pak would help set the topic, but both he and Zou wanted to see what approaches the Virtual Lab could come up with on its own. As a first project, they decided to focus on designing therapies for new covid-19 strains. With this goal in mind, Zou set off training five AI scientists (including ones trained to act like an immunologist, a computational biologist, and a principal investigator) with different objectives and programs at their disposal.
Building these models took a few months, but Pak says they were very quick at designing candidates for therapies once the setup was complete: “I think it was a day or half a day, something like that.”
Zou says the agents decided to study anti-covid nanobodies, a cousin of antibodies that are much smaller in size and less common in the wild. Zou was shocked, though, at the reason. He claims the models landed on nanobodies after making the connection that these smaller molecules would be well-suited to the limited computational resources the models were given. “It actually turned out to be a good decision, because the agents were able to design these nanobodies efficiently,” he says.
The nanobodies the models designed were genuinely new advances in science, and most were able to bind to the original covid-19 variant, according to the study. But Pak and Zou both admit that the main contribution of their article is really the Virtual Lab as a tool. Yi Shi, a pharmacologist at the University of Pennsylvania who was not involved in the work but made some of the underlying nanobodies the Virtual Lab modified, agrees. He says he loves the Virtual Lab demonstration and that “the major novelty is the automation.”
Nature accepted the article and fast-tracked it for publication preview—Zou knew leveraging AI agents for science was a hot area, and he wanted to be one of the first to test it.
The AI scientists host a conference
When he was submitting his paper, Zou was dismayed to see that he couldn’t properly credit AI for its role in the research. Most conferences and journals don’t allow AI to be listed as coauthors on papers, and many explicitly prohibit researchers from using AI to write papers or reviews. Nature, for instance, cites uncertainties over accountability, copyright, and inaccuracies among its reasons for banning the practice. “I think that’s limiting,” says Zou. “These kinds of policies are essentially incentivizing researchers to either hide or minimize their usage of AI.”
Zou wanted to flip the script by creating the Agents4Science conference, which requires the primary author on all submissions to be an AI. Other bots then will attempt to evaluate the work and determine its scientific merits. But people won’t be left out of the loop entirely: A team of human experts, including a Nobel laureate in economics, will review the top papers.
Zou isn’t sure what will come of the conference, but he hopes there will be some gems among the hundreds of submissions he expects to receive across all domains. “There could be AI submissions that make interesting discoveries,” he says. “There could also be AI submissions that have a lot of interesting mistakes.”
While Zou says the response to the conference has been positive, some scientists are less than impressed.
Lisa Messeri, an anthropologist of science at Yale University, has loads of questions about AI’s ability to review science: “How do you get leaps of insight? And what happens if a leap of insight comes onto the reviewer’s desk?” She doubts the conference will be able to give satisfying answers.
Last year, Messeri and her collaborator Molly Crockett investigated obstacles to using AI for science in another Nature article. They remain unconvinced of its ability to produce novel results, including those shared in Zou’s nanobodies paper.
“I’m the kind of scientist who is the target audience for these kinds of tools because I’m not a computer scientist … but I am doing computationally oriented work,” says Crockett, a cognitive scientist at Princeton University. “But I am at the same time very skeptical of the broader claims, especially with regard to how [AI scientists] might be able to simulate certain aspects of human thinking.”
And they’re both skeptical of the value of using AI to do science if automation prevents human scientists from building up the expertise they need to oversee the bots. Instead, they advocate for involving experts from a wider range of disciplines to design more thoughtful experiments before trusting AI to perform and review science.
“We need to be talking to epistemologists, philosophers of science, anthropologists of science, scholars who are thinking really hard about what knowledge is,” says Crockett.
But Zou sees his conference as exactly the kind of experiment that could help push the field forward. When it comes to AI-generated science, he says, “there’s a lot of hype and a lot of anecdotes, but there’s really no systematic data.” Whether Agents4Science can provide that kind of data is an open question, but in October, the bots will at least try to show the world what they’ve got.
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