OpenAI Researcher Noam Brown: Math Is Just an Appetizer for the Multi-Agent Era
In a podcast interview, OpenAI researcher Noam Brown discussed multi-agent systems, recursive self-improvement, and superalignment, stating that the 10,000-agent system that solved a Millennium Prize problem contributed at most 10% of the effort. He emphasized that math is just a precursor to the multi-agent era, where scaling test-time compute and agent swarms will drive further advances.
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OpenAI推理之父最新访谈！数学只是多智能体时代的开胃菜 衡宇 2026-09-30 22:03:43 来源： 量子位 千禧年难题的突破，10000个Agent最多占了10%的功劳。 程浅 发自 凹非寺 量子位 | 公众号QbitAI “Navier–Stokes千禧年难题的突破，10000个Agent最多占了10%的功劳。” 此判断出自OpenAI研究员，o1核心作者NoamBrown之口。 NoamBrown， 人称“OpenAI推理教父”。 在几乎所有人还在卷模型参数的2023年，他就已经开始押注test-time compute scaling（推理时计算） 。 一年后，o1-preview横空出世、震动行业。如今，TTS早已成为行业共识，成了人手一个的“深度思考”按钮。 △Dwarkesh Patel播客《OpenAI researcher on agent swarms & recursive self-improvement》 现在，NoamBrown又带来了他目前的研究方向—— 多智能体（multi-Agent）系统。 此前，OpenAI发动了10000个Agent，耗时88小时，输出1300亿token，拿下千禧年数学难题。 关于此事，NoamBrown在最新播客中从多智能体系统聊起，深入到了公司经营、RSI发展、超级对齐等领域…… 一个个问题也接连浮现： 当难题越来越少，模型强到一秒解题，将如何继续提升能力？ 如果把这批Agent投入到递归自我改进研发中，会发生什么？ 前沿模型如果一个长任务可以做三个月，而发布周期只有两个月，怎么办？ AI越来越会隐藏自己的思维链，人应该如何监测？ 当AGI已经超越人类智慧，在那时，我们如何知道对齐问题已经解决了？ …… 关于这些问题，对话全文如下。 多智能体系统如何拿下千禧年难题？ 主持人： 今天我们对话的是OpenAI研究员 Noam Brown 。他是o1及此后一系列推理模型的奠基性贡献者之一，目前的研究方向是多智能体系统。 你们上周宣布，一个由1万个不同AI Agent组成的系统，历时88小时、消耗1300亿token，解出了一道千禧年难题。 实际上大约两三年前，你就指出，只要把推理时的算力投入放大，就能预见几年之后模型的基础能力会到什么水平。 我觉得你现在又站到了类似的位置：Agent的规模已大幅扩展
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