Stress-testing large language model agents in a robotic chemistry laboratory
AI is evaluated through knowledge, reasoning and plan generation, yet scientific agency requires reliable physical action and adaptation to evidence. Here, we use a robotic chemistry laboratory as a physical-world testbed to make scientific agency measurable. Its 45 modular workstations exposed as machine-readable skills enabled 4,608 trials. Only 3.3% of trials produced expert-assessed executable workflows under laboratory constraints; even the best system achieved 28.1%. Long-horizon planning remained a challenge: only three executable workflows exceeded 30 operations, although the longest contained 44. Across five rounds, experimental feedback prompted local adjustments but no workflow...
| Authors | Lulu Guo, Yingkai Sun, Xiaobo Li et al. |
|---|---|
| Primary category | cs.AI |
| Published | 25 July 2026 |
| Last updated at source | 25 July 2026 |
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