用LLM+视觉编程帮航空航天设计提速，支持专家级几何建模。
LLM-based Visual Code Completion for Aerospace Geometric Design
- 基于ReAct框架和GPT 5.4，实现视觉代码自动补全。
- 用户测试显示工程师认可建议质量，但推理延迟影响复杂任务体验。
- 配套发布翼型设计专用插件与18项专家任务数据集，适合航空设计者使用。
大型语言模型（LLMs）和视觉语言模型（VLMs）在视觉代码补全方面取得显著进展，但航空航天行业因注重安全与可解释性，目前尚无主流厂商公开部署基于LLM的几何设计助手。本文提出一种面向航空航天工程设计的LLM驱动视觉编程助手，采用可视化ReAct方法与GPT 5.4实现。同时开发了Wingbuilder——一个专用于航空几何抽象的Grasshopper插件库，并构建了包含18个不同难度层级、由专家设计的航空视觉编程数据集（AVPD），附带真实解。通过两名大型飞机制造商资深工程师的用户测试发现，该助手生成的建议被评价为有用，但因ReAct推理速度慢，仅适用于耗时较长的复杂任务；参与者普遍表示认可工具价值，愿未来继续使用。
原文摘要 · Abstract (English)
Recent advances in both Large Language Models (LLMs) and Vision Language Models (VLMs) have seen a step change in their ability to perform visual code completion, but the aerospace industry, which prioritizes safety and explainabilty over rapid LLM adoption, currently has no publicly announced LLM-based geometric design copilot systems in commercial use by aerospace Original Equipment Manufacturers (OEMs). This paper presents a LLM-based visual programming copilot application for aerospace engineering design tasks, using a visual programming variant of the ReAct methodology and GPT 5.4. In addition to the copilot, we describe Wingbuilder, a new Grasshopper plugin library with custom components for aerospace-specific geometry abstraction, and an associated Aerospace Visual Programming Dataset (AVPD) with 18 aerospace expert designed tasks at different levels of difficulty alongside ground truth solutions. We evaluate our copilot application with a user trial involving two experienced aerospace engineers from a large aircraft manufacturing company. We find our copilot visual programming ReAct methodology was successful in generating suggestions that participants found helpful, but slow ReAct inference times limit its usefulness to more complex time-consuming tasks where waiting for good copilot solution suggestion was worthwhile. Participants reported they liked the tool and would be willing to use it in the future.
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。