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Collaborative AI agents for autonomous experimental research with solid-state quantum spins

A team led by El Mustapha Mansouri posted an arXiv paper on 29 October 2026 describing an autonomous research laboratory in which large-language-model agents review one another's work and can halt or redirect investigations. Testing the system on 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, then designed experiments to distinguish competing predictions; when repeated reference measurements showed the signal drifting during data collection, the agents treated the initial result as inconclusive and revised the measurement protocol without relaxing acceptance criteria, and the revised experiment met those criteria and supported the theoretical predictions. The authors present the study as evidence that collaborating AI agents can select research questions, derive theoretical results and test their predictions experimentally.

read2 min views3 publishedOct 30, 2026
Collaborative AI agents for autonomous experimental research with solid-state quantum spins
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  [Submitted on 29 Oct 2026]


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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.

Submission history #

From: El Mustapha Mansouri [
[view email](http://arxiv.org/show-email/30b29f58/2610.40589)]

**[v1]** Thu, 29 Oct 2026 17:47:50 UTC (214 KB)

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