发表机构
Institute for Industrial Automation and Software Engineering; University of Stuttgart; AstraZeneca（工业自动化与软件工程研究所; 斯图加特大学; 阿斯利康）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出多智能体框架，将LLM与高保真模拟模型结合，使LLM智能体可开展制药工艺设计的对照实验，生成更具体可操作的优化建议，在工业场景中表现更优。
AI 中文摘要
大语言模型（LLM）在推理、规划和工具使用方面展现出强大能力，但许多科学和工程任务所需的远不止合理的文本与代码生成，还需要理解系统对干预的响应方式，而这实际依赖于对照实验。本研究提出一种多智能体框架，使LLM智能体能够针对制药工艺设计开展基于科学模拟模型的对照实验。给定用户查询和基线配置，该系统构建结构化任务表示、设计实验、执行对比模拟、解释所得结果，并综合基于证据的工艺参数优化建议。通过在交互式智能体框架中将语言模型与高保真模拟模型耦合，所提系统支持通过干预、比较和观察进行推理，因此生成的输出比仅基于语言的推理更具体且可操作。在工业应用场景中，这一优势体现为输出特异性更高，以及用户评定的正确性和有用性得到提升。消融研究和可视化案例分析进一步证明了集成模拟的实验推理的有效性和实用价值。
英文摘要
Large language models (LLMs) have shown strong capabilities in reasoning, planning, and tool use, but many scientific and engineering tasks require more than plausible text and code generation. They require understanding how a system responds to intervention, which in practice depends on controlled experimentation. In this work, we propose a multi-agent framework that enables LLM agents to conduct controlled experiments with scientific simulation models for pharmaceutical process design. Given a user query and a baseline configuration, the system constructs a structured task representation, designs experiments, executes comparative simulation, interprets the resulting outcomes, and synthesizes evidence-based recommendations for process parameter optimization. By coupling language models with high-fidelity simulation models in an interactive agent framework, the proposed system supports reasoning through intervention, comparison, and observation. As a result, it produces more specific and actionable outputs than language-only reasoning. In an industrial application setting, this advantage is reflected in higher output specificity as well as improved user-rated correctness and helpfulness. Ablation studies and visualized case analyses further demonstrate the effectiveness and practical utility of simulation-integrated experimental reasoning.
CommentsAccepted at the 31st IEEE International Conference on Emerging Technologies and Factory Automation ETFA 2026
