用多模态智能体生成精准Simulink仿真模型
SimuGen: Multi-modal Agentic Framework for Constructing Block Diagram-Based Simulation Models
- 引入视觉与知识协同的多智能体框架，提升模型生成可靠性
- 支持自动调试与测试，实现可复现的仿真代码生成
- 适合工程仿真与自动化建模需求的科研人员使用
大型语言模型在数学推理和代码生成方面表现优异，但在仿真领域仍面临挑战，尤其在生成Simulink模型时效果不佳，这可能源于其预训练数据中缺乏Simulink相关知识。为此，我们提出SimuGen——一个基于多模态智能体的框架，通过结合可视化Simulink图与领域知识，自动生成准确的Simulink仿真代码。该框架由调查员、单元测试评审员、代码生成器、执行器、调试定位器和报告撰写器等专业化智能体组成，并依托领域专用知识库协同工作。这种模块化、可解释的设计实现了高效、鲁棒且可复现的仿真建模。项目源码已公开于 https://github.com/renxinxing123/SimuGen_beta。
原文摘要 · Abstract (English)
Recent advances in large language models (LLMs) have shown impressive performance in mathematical reasoning and code generation. However, LLMs still struggle in the simulation domain, particularly in generating Simulink models, which are essential tools in engineering and scientific research. Our preliminary experiments indicate that LLM agents often fail to produce reliable and complete Simulink simulation code from text-only inputs, likely due to the lack of Simulink-specific data in their pretraining. To address this challenge, we propose SimuGen, a multimodal agent-based framework that automatically generates accurate Simulink simulation code by leveraging both the visual Simulink diagram and domain knowledge. SimuGen coordinates several specialized agents, including an investigator, unit test reviewer, code generator, executor, debug locator, and report writer, supported by a domain-specific knowledge base. This collaborative and modular design enables interpretable, robust, and reproducible Simulink simulation generation. Our source code is publicly available at https://github.com/renxinxing123/SimuGen_beta.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。