BioProSuite

The automation of scientific experimentation is hindered by the inability of LLMs to reliably handle accuracy-critical biological protocols. We introduce BioProBench (550k task instances) to expose the reasoning gap, and BioProAgent, a neuro-symbolic framework. By anchoring probabilistic planning in a deterministic Finite State Machine (FSM), our agent ensures hardware compliance and significantly outperforms GPT-4 baselines.

We present BioProBench, the first large-scale resource dedicated to procedural reasoning in biological experimental protocols, containing a BioProCorpus of nearly 27,000 protocols and over 550,000 structured instances, covering diverse subfields of biology.

BioProAgent: Neuro-Symbolic Grounding for Constrained Scientific Planning

• State-Augmented Adaptive Planning (FSM-Constrained Script-Free Planner): Abandons rigid linear workflows and adopts a neuro-symbolic framework that constrains probabilistic planning via a deterministic Finite State Machine (FSM). The Agent leverages State-Augmented Planning to flexibly select retrieval, draft generation, or code production based on current states, addressing LLMs' limitations in handling the rigorous constraints of physical actuation in wet-lab scenarios.
• Scientific Review: Incorporates a strict scientific reflection mechanism (Validator) to automatically check for missing control groups, logical flaws, parameter rationality and machine code validity. This enforces a rigorous Draft-Verify-Rectify (DVR) workflow, ensuring the scientific rigor of experimental protocols.
• Automation Hardware Alignment: Reads laboratory device and consumable inventories (CSV), mapping natural language steps to specific machine operations via Semantic Symbol Grounding, reducing token consumption by ~6×.
• Hybrid Memory System:
  • Short-Term Memory: Combines both episodic memory and working memory to maintain long-horizon protocol consistency.
  • Long-Term Memory: Integrates Mem0 to recall past experimental experiences.
• Human-in-the-Loop: Proactively requests user confirmation at critical decision points to ensure safety in high-risk wet-lab operations.

BioProAgent eliminates the trade-off between scientific reasoning and physical safety. Compared to state-of-the-art baselines, it excels in hardware compliance, long-horizon stability, and cost-efficiency:

• Unmatched Physical Compliance: Achieves a 95.6% physical compliance rate, acting as a crucial safety interlock against hallucinations that typically cause ReAct agents to fail catastrophically (21.0%).
• Autonomous Self-Correction: While all standard baseline agents exhibit a 0% correction rate against injected errors, BioProAgent's FSM dynamically overwrites unsafe trajectories, restoring physical safety to 88.7%.
• Cost Efficiency: By decoupling high-dimensional data payloads via Semantic Symbol Grounding, it reduces token consumption by ~82% compared to AutoGPT, while maintaining a 100% success rate in 60-step long-horizon workflows.

Standard LLM agents operate in an open-loop manner: generating a dangerous parameter (e.g., exceeding centrifuge limits) leads to immediate execution and physical failure. BioProAgent proactively intercepts these hallucinations.