AI 中文总结
研究提出Sketch2DES工作流，利用开放权重LLMs将排队网络图像转换为可验证仿真模型，经多阶段处理，在多图表评估中各阶段可靠性高，相比直接代码生成提升多项性能，证明结构化工作流模型生成对LLM辅助仿真建模的可行性。
AI 中文摘要
近期工作探索使用大语言模型（LLMs）自动化仿真模型构建，通常直接从自然语言描述生成可执行代码，但这给验证和可重复性带来挑战，尤其是对无编程专业知识的用户。我们提出Sketch2DES，一种将排队网络的图表表示转换为使用开放权重LLMs的可验证离散事件仿真模型的草图到仿真工作流。该工作流有三个阶段：使用多模态LLM将图表翻译成半结构化文本描述；通过带有基于反射的验证循环的LLM转换为模式验证的结构化数据（JSON）；使用软件适配器确定性转换为可执行仿真模型。中间工件可在执行前检查和自动验证。我们在八个不同复杂程度的排队网络图表上评估该方法。工作流在所有阶段都实现了高可靠性，结果与人工编码和分析基准在统计上无差异。与直接代码生成相比，该工作流提高了可重复性、透明度和可验证性，同时减少了对编程专业知识的需求。局限性包括模型范围受限和依赖准确的视觉解释。结果证明了基于工作流的结构化模型生成作为LLM辅助仿真建模的稳健基础的可行性。
英文摘要
Recent work has explored the use of Large Language Models (LLMs) to automate simulation model building, typically by generating executable code directly from natural language descriptions. However, this raises challenges for verification and reproducibility particularly for users without programming expertise. We propose Sketch2DES, a sketch-to-simulation workflow that converts diagrammatic representations of queuing networks into verifiable discrete-event simulation models using open-weight LLMs. The workflow has three stages: (1) translation of a diagram into a semi-structured textual description using a multimodal LLM; (2) conversion into schema-validated structured data (JSON) via an LLM with a reflection-based verification loop; and (3) deterministic transformation into an executable simulation model using a software adapter. Intermediate artefacts can therefore be inspected and automatically validated before execution. We evaluate the approach on eight queuing-network diagrams of varying complexity. The workflow achieved high reliability for all stages, and results were statistically indistinguishable from human-coded and analytical benchmarks. Compared to direct code generation, the workflow improves reproducibility, transparency, and verifiability, while reducing the need for programming expertise. Limitations include restricted model scope and dependence on accurate visual interpretation. The results demonstrate the feasibility of structured, workflow-based model generation as a robust foundation for LLM-assisted simulation modelling.
