arXiv 论文提出协作式 AI 智能体自主开展固态量子自旋实验研究
Collaborative AI agents for autonomous experimental research with solid-state quantum spins
AI 导读
arXiv:2610.40589 提出一个由大语言模型智能体构成的自主研究实验室,智能体互相评估工作、可停止或改变研究方向,并演示于金刚石量子自旋研究。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量,经计算验证后设计实验区分竞争预测;发现信号漂移后将结果判为不确定,在不放宽验收标准的前提下修订测量协议并再次实验支持理论预测。
推荐理由
论文展示了基于大语言模型的协作智能体如何互相审查、否定不确定结果并改写实验方案,对自主实验室方向有参考价值。
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