# Scalable Multi-Agent Lab Framework for Lab Optimization

arXiv: https://arxiv.org/abs/2208.09099 · HTML: https://mlsanity.com/paper/2208.09099/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 30 references that are in the map.
- First posted on arXiv 19 Aug 2022.

## Builds on (most cited first, 3 of 3)

- [Pyro: Deep Universal Probabilistic Programming](https://mlsanity.com/paper/1810.09538/) (2018)
- [GPflow: A Gaussian process library using TensorFlow](https://mlsanity.com/paper/1610.08733/) (2016)
- [Autonomous synthesis of metastable materials](https://mlsanity.com/paper/2101.07385/) (2021)

## Built on by (most cited first, 4 of 4)

- [A Comprehensive Review of AI Agents: Transforming Possibilities in Technology and Beyond](https://mlsanity.com/paper/2508.11957/) (2025)
- [Bayesian Co-navigation: Dynamic Designing of the Materials Digital Twins via Active Learning](https://mlsanity.com/paper/2404.12899/) (2024)
- [Co-orchestration of Multiple Instruments to Uncover Structure-Property Relationships in Combinatorial Libraries](https://mlsanity.com/paper/2402.02198/) (2024)
- [Learning material synthesis-process-structure-property relationship by data fusion: Bayesian Coregionalization N-Dimensional Piecewise Function Learning](https://mlsanity.com/paper/2311.06228/) (2023)

## Abstract (arXiv)

Autonomous materials research systems allow scientists to fail smarter, learn faster, and spend less resources in their studies. As these systems grow in number, capability, and complexity, a new challenge arises - how will they work together across large facilities? We explore one solution to this question - a multi-agent laboratory control frame-work. We demonstrate this framework with an autonomous material science lab in mind - where information from diverse research campaigns can be combined to ad-dress the scientific question at hand. This framework can 1) account for realistic resource limits such as equipment use, 2) allow for machine learning agents with diverse learning capabilities and goals capable of running re-search campaigns, and 3) facilitate multi-agent collaborations and teams. The framework is dubbed the MULTI-agent auTonomous fAcilities - a Scalable frameworK aka MULTITASK. MULTITASK makes possible facility-wide simulations, including agent-instrument and agent-agent interactions. Through MULTITASK's modularity, real-world facilities can come on-line in phases, with simulated instruments gradually replaced by real-world instruments. We hope MULTITASK opens new areas of study in large-scale autonomous and semi-autonomous research campaigns and facilities.