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; they can cause equipment damage or experimental failure. We propose 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 Design-Verify-Rectify workflow, ensuring hardware compliance before execution. Furthermore, we address the context bottleneck inherent in complex device schemas by Semantic Symbol Grounding, reducing token consumption by ~6* 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. Code: https://github.com/YuyangSunshine/bioproagent | Website: https://yuyangsunshine.github.io/BioPro-Project.

翻译：大语言模型（LLM）在科学发现中展现了显著的推理能力，但在湿实验的物理执行环节仍存在难以跨越的鸿沟。在这些不可逆环境中，概率性幻觉不仅意味着错误，更可能导致设备损坏或实验失败。我们提出BioProAgent，一种将概率规划锚定于确定性有限状态机（FSM）的神经符号框架。我们引入状态增强规划机制，强制执行严谨的设计-验证-纠正工作流，确保在执行前满足硬件合规性。此外，我们通过语义符号基础解决了复杂设备模式固有的上下文瓶颈问题，通过符号抽象将令牌消耗降低约6倍。在扩展的BioProBench基准测试中，BioProAgent达到95.6%的物理合规性（相比之下ReAct为21.0%），表明神经符号约束对于不可逆物理环境中的可靠自主性至关重要。代码：https://github.com/YuyangSunshine/bioproagent | 网站：https://yuyangsunshine.github.io/BioPro-Project。