# Autonomous Laboratory

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arXiv Nov 9, 2026
A commercial robot closes the lab loop: agentic optimization needs up to 52% fewer iterations than Bayesian optimization
A self-driving-laboratory testbed built from a commercial lab robot, an absorbance plate reader and a swappable optimizer completed six independent campaigns of 48 closed-loop iterations each, over 24 cumulative hours of unattended operation. Five of the six campaigns matched their target absorbance spectra within measurement error, at an average RMSE of OD. In the same physical loop, an agentic optimizer driven by Claude Opus 5 matched all three targets in fewer iterations than Bayesian optimization, cutting iterations by 52% on target 1 and 32% on target 2, and converging in 11 iterations on target 3, which Bayesian optimization failed to match within 48.
A self-driving-laboratory testbed built from a commercial lab robot, an absorbance plate reader and a swappable optimizer completed six independent campaigns of 48 closed-loop iterations each, over 24 cumulative hours of unattended operation. Five of the six campaigns matched their target absorbance spectra within measurement error, at an average RMSE of OD. In the same physical loop, an agentic optimizer driven by Claude Opus 5 matched all three targets in fewer iterations than Bayesian optimization, cutting iterations by 52% on target 1 and 32% on target 2, and converging in 11 iterations on target 3, which Bayesian optimization failed to match within 48.
Tony Qin（First author）
Autonomous Laboratory Bayesian Optimization LLM agent Color Formulation
arXiv Oct 30, 2026
AI agents vote down their own run: a diamond-spin lab rejects a passing experiment, then earns the evidence
A laboratory of large-language-model agents, each holding a separate research role, asked how many calibration measurements are needed to predict a diamond nitrogen-vacancy spin ensemble's response to arbitrary phase-controlled microwave pulse sequences. The principal-investigator agent tightened a proposed bound of 91 calibrations to an exact minimum of 10; the first acquisition met all ten precommitted accuracy criteria but failed phase and reference controls and was ruled inconclusive, and after the agents revised the protocol without relaxing the criteria, the final measurement supported the prediction while two interpolation methods predicted zero.
A laboratory of large-language-model agents, each holding a separate research role, asked how many calibration measurements are needed to predict a diamond nitrogen-vacancy spin ensemble's response to arbitrary phase-controlled microwave pulse sequences. The principal-investigator agent tightened a proposed bound of 91 calibrations to an exact minimum of 10; the first acquisition met all ten precommitted accuracy criteria but failed phase and reference controls and was ruled inconclusive, and after the agents revised the protocol without relaxing the criteria, the final measurement supported the prediction while two interpolation methods predicted zero.
Institute of Science Tokyo
El Mustapha Mansouri（First author）
Autonomous Laboratory Multi-agent system Auctopus Nitrogen-vacancy centres Sample Complexity
arXiv Sep 17, 2026
Evidence-Grounded Agentic Formulation Development in an Autonomous Laboratory
The study presents Andromeda 2, an agentic system that reasons over structured in-house experimental evidence and invokes computational and experimental tools, achieving a 50% high-AUC hit rate for paclitaxel self-emulsifying drug delivery system (SEDDS) development at a matched budget of 96 formulations (versus 17% for Andromeda 1 and 2% for wet-lab DoE) and identifying 12 formulations meeting all four target product profile (TPP) objectives (versus 6 and 0), while an ablation showed that access to structured in-house evidence increased mean AUC by 34%.
The study presents Andromeda 2, an agentic system that reasons over structured in-house experimental evidence and invokes computational and experimental tools, achieving a 50% high-AUC hit rate for paclitaxel self-emulsifying drug delivery system (SEDDS) development at a matched budget of 96 formulations (versus 17% for Andromeda 1 and 2% for wet-lab DoE) and identifying 12 formulations meeting all four target product profile (TPP) objectives (versus 6 and 0), while an ablation showed that access to structured in-house evidence increased mean AUC by 34%.
Michael Craig（First author）
Andromeda 2 Agentic System Autonomous Laboratory Oral Paclitaxel Formulation

You’re all caught up

A commercial robot closes the lab loop: agentic optimization needs up to 52% fewer iterations than Bayesian optimization

AI agents vote down their own run: a diamond-spin lab rejects a passing experiment, then earns the evidence

Evidence-Grounded Agentic Formulation Development in an Autonomous Laboratory