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This paper proposes LabCompiler, a system that uses LLM-based protocol repair and a device-agnostic graph representation to turn imperfect lab protocols into executable human-machine workflows. The reported gain over frontier LLMs in error correction comes from the authors' own benchmark, which includes perturbations they designed and iGEM-sourced errors, so independent validation on real labs will matter. For AI readers, the main takeaway is the architectural idea of separating scientific semantics from hardware-specific code, which is a useful pattern for agentic lab automation.
LabCompiler: A Framework for Verifiable Human-Machine Laboratory Execution from Imperfect Protocols
arXiv:2610.19550v1 Announce Type: new Abstract: 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 perturbation-guided self-evolution. A two-level, device-agnostic graph representation separates experimental semantics from hardware-specific implementation and supports capability-based device assignment with coordinated human execution. We further introduce a protocol recovery benchmark covering four controlled perturbation types and naturally occurring errors collected from protocols written by iGEM participants. Experiments show that LabCompiler outperforms frontier LLMs in protocol error correction while enabling coordinated human-machine execution across heterogeneous, partially automated laboratory settings.
Abstract: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 perturbation-guided self-evolution. A two-level, device-agnostic graph representation separates experimental semantics from hardware-specific implementation and supports capability-based device assignment with coordinated human execution. We further introduce a protocol recovery benchmark covering four controlled perturbation types and naturally occurring errors collected from protocols written by iGEM participants. Experiments show that LabCompiler outperforms frontier LLMs in protocol error correction while enabling coordinated human-machine execution across heterogeneous, partially automated laboratory settings.
Artificial Intelligence (cs.AI)
arXiv:2610.19550 [cs.AI]
(or arXiv:2610.19550v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2610.19550 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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