AutoBioLab: From Scientific Intent to Machine Executable Laboratory Automation

While large language models (LLMs) can increasingly translate scientific requests into instrument commands, generating a sequence of executable commands does not guarantee the intended experimental state. We study this gap in automated liquid handling, where intermediate mixtures may be transferred, diluted, and reused across multiple steps, making later operations to depend on the material changes produced earlier. To bridge LLM reasoning with precise physical realities, we introduce AutoBioLab, an agentic system that fundamentally decouples the verification of scientific intent from robotic execution. By pairing LLM-driven planning with deterministic solvers, material state replay, separate scientific and robotic contracts, this agentic architecture convert abstract experimental designs into a verifiable physical state before any action is taken. Empirically, this decoupled approach more than doubles the end-to-end planning success rate across multiple state-of-the-art LLMs, raising the number of fully correct plans from 21.3 to 44.0 out of 48, which enhance the open-source model to approximate the SOTA performance of current commercial model. Furthermore, our runtime agent improves the successful recovery of human injected errors from 66.3% to 82.3%. Ultimately, AutoBioLab demonstrates that separating what an experiment should achieve from how a robot must move is essential for reliable, machine-executable laboratory automation.