This paper presents JARVIS, a novel multi-agent framework that leverages Large Language Models (LLMs) and domain expertise to generate high-quality scripts for specialized Electronic Design Automation (EDA) tasks. By combining a domain-specific LLM trained with synthetically generated data, a custom compiler for structural verification, rule enforcement, code fixing capabilities, and advanced retrieval mechanisms, our approach achieves significant improvements over state-of-the-art domain-specific models. Our framework addresses the challenges of data scarcity and hallucination errors in LLMs, demonstrating the potential of LLMs in specialized engineering domains. We evaluate our framework on multiple benchmarks and show that it outperforms existing models in terms of accuracy and reliability. Our work sets a new precedent for the application of LLMs in EDA and paves the way for future innovations in this field.

翻译：本文提出JARVIS——一种创新的多智能体框架，该框架利用大语言模型（LLMs）与领域专业知识，为专业化的电子设计自动化（EDA）任务生成高质量脚本。通过整合基于合成数据训练的领域专用LLM、用于结构验证的自定义编译器、规则强制执行机制、代码修复功能以及先进的检索机制，我们的方法相较于当前最先进的领域专用模型取得了显著提升。该框架有效应对了LLMs在数据稀缺和幻觉错误方面的挑战，展现了LLMs在专业工程领域的应用潜力。我们在多个基准测试上对该框架进行评估，结果表明其在准确性与可靠性方面均优于现有模型。本工作为LLMs在EDA领域的应用树立了新标杆，并为该领域的未来创新开辟了道路。