Generalizing LLM-agent control of lab automation beyond chemistry

Develop a generalizable integration framework that enables large language model–based agents to interface with and control robotic platforms and laboratory automation systems across experimental domains beyond wet‑lab chemistry, extending the Coscientist demonstration of LLM-directed automated chemical reactions to other scientific fields such as robotics and field sciences.

Background

The paper argues that vibe researching is presently constrained in domains requiring embodied interaction, such as wet-lab experiments and robotics. While multimodal models can process images, reliably conducting embodied research requires connecting language agents to physical systems.

Coscientist is cited as a proof-of-concept where LLM agents directed automated chemistry equipment to execute reactions. However, the authors note that making such agent–instrument integrations work across other experimental domains remains unresolved and constitutes an open engineering challenge.