LabCompiler: A Framework for Verifiable Human-Machine Laboratory Execution from Imperfect Protocols
Closing the loop of AI-driven science requires reliably translating natural-language protocols into executable laboratory workflows despite protocol defects and heterogeneous hardware. Existing approaches often couple scientific interpretation with device-specific code generation, making errors difficult to localize and limiting reuse across laboratory platforms. We introduce LabCompiler, a robust protocol-to-execution framework that transforms imperfect natural-language protocols into verifiable human-machine workflows. Its protocol firewall diagnoses and repairs defects before compilation while preserving unaffected content, guided by troubleshooting knowledge accumulated through pertur...
| Authors | Jingyuan Li, Dongzhan Zhou, Minting Pan et al. |
|---|---|
| Primary category | cs.AI |
| Published | 14 October 2026 |
| Last updated at source | 14 October 2026 |
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