BioProAgent: Neuro-Symbolic Grounding for Constrained Scientific Planning

In three linesBioProAgent combines LLMs with finite state machines to plan biological experiments in wet-labs. The system enforces a Design-Verify-Rectify workflow and reduces token consumption by ~6× through symbolic abstraction. On BioProBench, it achieves 95.6% physical compliance versus 21.0% for ReAct.

Summary generated by Claude — human-verified