arXiv:2610.40589v1 Announce Type: new
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.
arxiv
Collaborating LLM Agents Run Autonomous Quantum Spin Experiments and Reject Their Own Results
Researchers present an autonomous research laboratory in which LLM-based agents review each other's work and can halt or redirect investigations, including generating and rejecting research questions and demanding independent theoretical checks. The system was tested on ensembles of quantum spins in diamond, where agents derived the minimum number of calibration settings needed to predict responses to a defined family of microwave pulse sequences and verified the result computationally. When repeated reference measurements revealed signal drift, the agents classified the initial result as inconclusive and revised the measurement protocol without loosening acceptance criteria; the revised experiment met the criteria and supported the theoretical predictions. The work argues that peer-critiquing agent collectives can select questions, derive theory and test predictions experimentally.
It offers a concrete demonstration that multi-agent adversarial self-review can catch flawed experimental evidence in a physical-science setting, a key question for anyone building autonomous research systems.
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