arXiv cs.AI· El Mustapha Mansouri, Eikichi Kimura, Masato Koga, Keigo Arai·· 14 小时前AI 评分58
协作 AI 智能体实现固态量子自旋自主实验研究
Collaborative AI agents for autonomous experimental research with solid-state quantum spins
AI 导读
研究团队搭建了一个自主研究实验室,其中基于大语言模型的智能体互相评估彼此工作,并能中止或调整研究方向。在金刚石量子自旋实验中,智能体推导出预测微波脉冲序列响应所需的最少校准设置数,并通过计算验证和设计实验区分竞争性预测。初次实验虽与理论一致,但重复参考测量显示信号在采集过程中漂移,智能体据此判定结果不确定并修订测量协议,修订后的实验满足验收标准并支持理论预测。
正文 · 原文
Abstract:An autonomous research laboratory could conduct an entire study, from formulating questions to preparing manuscripts. Artificial intelligence (AI) offers one route towards this goal. Recent AI systems take on several research roles, but may draw conclusions that the evidence does not support. Here, we present an autonomous research laboratory in which agents based on large language models assess one another's work and can stop or redirect investigations. They can generate and reject research questions, require separate theoretical checks and conduct experiments designed to challenge their claims. We demonstrate these capabilities by investigating ensembles of quantum spins in diamond. The agents derived the minimum number of calibration settings needed to predict responses to a defined family of microwave pulse sequences. They checked the result computationally and designed experiments to distinguish competing predictions. An initial experiment agreed with the theoretical predictions, but repeated reference measurements showed that the signal was drifting during data collection. The agents therefore treated the result as inconclusive and revised the measurement protocol without relaxing the acceptance criteria. The revised experiment met those criteria and supported the theoretical predictions. This study demonstrates that collaborating AI agents can select research questions, derive theoretical results and test their predictions experimentally.
| Comments: | 17 pages, 5 figures. Code available at this https URL |
| Subjects: | Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA); Quantum Physics (quant-ph) |
| Cite as: | arXiv:2610.40589 [cs.AI] |
| (or arXiv:2610.40589v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.40589 arXiv-issued DOI via DataCite |
Submission history
From: El Mustapha Mansouri [view email]
[v1]
Thu, 29 Oct 2026 17:47:50 UTC (214 KB)
来源:arXiv cs.AI · arxiv.org