BioProAgent: Neuro-Symbolic Grounding for Constrained Scientific Planning
arXiv:2603.00876v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated significant reasoning capabilities in scientific discovery but struggle to bridge the gap to physical execution in wet-labs. In these irreversible environments, probabilistic hallucinations are not merely incorrect, but also cause equipment damage or experimental failure. To address this, we propose \textbf{BioProAgent}, a neuro-symbolic framework that anchors probabilistic planning in a deterministic Finite State Machine (FSM). We introduce a State-Augmented Planning mechanism that enforces a rigorous \textit{Design-Verify-Rectify} workflow, ensuring hardware compliance before execution. Furthermore, we address the context bottleneck inherent in complex device schemas by \textit{Semantic Symbol Grounding}, reducing token consumption by $\sim$6$\times$ through symbolic abstraction. In the extended BioProBench benchmark, BioProAgent achieves 95.6\% physical compliance (compared to 21.0\% for
arXiv:2603.00876v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated significant reasoning capabilities in scientific discovery but struggle to bridge the gap to physical execution in wet-labs. In these irreversible environments, probabilistic hallucinations are not merely incorrect, but also cause equipment damage or experimental failure. To address this, we propose \textbf{BioProAgent}, a neuro-symbolic framework that anchors probabilistic planning in a deterministic Finite State Machine (FSM). We introduce a State-Augmented Planning mechanism that enforces a rigorous \textit{Design-Verify-Rectify} workflow, ensuring hardware compliance before execution. Furthermore, we address the context bottleneck inherent in complex device schemas by \textit{Semantic Symbol Grounding}, reducing token consumption by $\sim$6$\times$ through symbolic abstraction. In the extended BioProBench benchmark, BioProAgent achieves 95.6\% physical compliance (compared to 21.0\% for ReAct), demonstrating that neuro-symbolic constraints are essential for reliable autonomy in irreversible physical environments. \footnote{Code at https://github.com/YuyangSunshine/bioproagent and project at https://yuyangsunshine.github.io/BioPro-Project/}
Executive Summary
The article introduces BioProAgent, a neuro-symbolic framework designed to bridge the gap between large language models and physical execution in wet-labs. It achieves 95.6% physical compliance by anchoring probabilistic planning in a deterministic Finite State Machine and introducing a State-Augmented Planning mechanism. This ensures hardware compliance before execution and reduces token consumption through symbolic abstraction.
Key Points
- ▸ Introduction of BioProAgent, a neuro-symbolic framework for constrained scientific planning
- ▸ Use of a deterministic Finite State Machine to anchor probabilistic planning
- ▸ State-Augmented Planning mechanism for rigorous Design-Verify-Rectify workflow
Merits
Improved Physical Compliance
BioProAgent achieves 95.6% physical compliance, significantly outperforming ReAct
Efficient Token Consumption
Semantic Symbol Grounding reduces token consumption by approximately 6 times
Demerits
Complexity of Framework
The neuro-symbolic framework may be challenging to implement and require significant expertise
Limited Generalizability
The framework may not be directly applicable to other domains or environments
Expert Commentary
The introduction of BioProAgent represents a significant advancement in the field of scientific planning, particularly in addressing the challenges of probabilistic hallucinations in irreversible environments. The use of a deterministic Finite State Machine and State-Augmented Planning mechanism provides a rigorous framework for ensuring hardware compliance before execution. However, further research is needed to fully explore the potential of this framework and its applications in other domains.
Recommendations
- ✓ Further testing and validation of BioProAgent in various wet-lab environments
- ✓ Exploration of the potential applications of BioProAgent in other domains, such as robotics or manufacturing