Experiment-as-Code Labs: A Declarative Stack for AI-Driven Scientific Discovery
To unleash the full potential of AI for Science, we must untether the agents from a purely digital environment. The agent's ability to control and explore in real-world labs is essential because the physical lab remains foundational to scientific discovery. While some tasks can be performed on a computer (e.g., data analysis, running simulated experiments), Eureka moments could occur at any time while operating lab instruments (e.g., when a scientist notices unexpected clues, intuition may prompt a real-time course change). Although autonomous labs are on the rise, which expose programmable APIs to control scientific instruments via software, bridging the gap between increasingly powerful...
| Authors | Zhenning Yang, Yuhan Chen, Patrick Tser Jern Kon et al. |
|---|---|
| Primary category | eess.SY |
| Published | 6 May 2026 |
| Last updated at source | 16 May 2026 |
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