用符号规则约束大模型，让实验规划不再出错。
BioProAgent: Neuro-Symbolic Grounding for Constrained Scientific Planning
- 将大模型规划锚定在确定性状态机中，避免错误执行。
- 在生物实验基准上实现95.6%的物理执行合规率。
- 适合需要高可靠性、不可逆操作的实验室自动化场景。
大型语言模型在科学发现中展现出强大推理能力，但在湿实验环境中难以实现可靠执行。在不可逆的实验场景中，概率性幻觉不仅导致结果错误，还可能造成设备损坏或实验失败。我们提出BioProAgent，一种神经符号框架，将概率性规划锚定于确定性的有限状态机（FSM）。引入状态增强型规划机制，强制执行设计-验证-修正的工作流，确保硬件兼容性后再执行。此外，通过语义符号接地技术，解决复杂设备架构带来的上下文瓶颈，使令牌消耗减少约6倍。在扩展的BioProBench基准测试中，BioProAgent实现95.6%的物理合规率（对比ReAct的21.0%），表明神经符号约束对不可逆物理环境中的可靠自主至关重要。代码：https://github.com/YuyangSunshine/bioproagent | 网站：https://yuyangsunshine.github.io/BioPro-Project。
原文摘要 · Abstract (English)
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.
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