用教材构建首个射频电路推理数据集，提升大模型设计能力
RF-Agent: A Practical Framework for Building Language Agents for RFIC Design
- 基于7本射频教材构建多智能体问答链，生成超1.1万样本数据集
- 微调比检索增强更有效，语义检索优于关键词和混合方式
- 为射频电路大模型研究提供可复用的数据与评测基准
大型语言模型在电子设计自动化领域进展迅速，但在射频电路设计中的应用仍受限于领域专用数据集稀缺和标准评测基准缺失。本文提出RF-Agent，通过教材驱动的知识蒸馏方法，构建首个射频领域推理数据集。采用多智能体问题-思考-求解-回答（QTSA）流程，将七本经典射频教材的章节级内容转化为超过11,000个样本，并建立专用多选题评测基准。在该基准上评估两种适配策略：监督微调（SFT）与三种检索增强生成（RAG）配置（语义、关键词、混合）。实验表明，领域微调显著提升各类模型的射频推理能力，尤其对中小型模型效果更佳；在RAG中，语义检索表现最优，说明嵌入对齐比简单融合更适合射频逻辑推理。该数据集与基准为未来射频电路大模型研究提供可复用基础。
原文摘要 · Abstract (English)
Large language models (LLMs) have driven rapid progress in electronic design automation (EDA), yet their application to radio-frequency (RF) circuit design remains limited by the scarcity of domain-specific datasets and standardized benchmarks. We present RF-Agent, which addresses this gap through textbook-driven knowledge distillation. A multi-agent Question-Thinking-Solution-Answer (QTSA) pipeline converts a subsection-level corpus from seven canonical RF textbooks into the first-of-its-kind RF-domain reasoning dataset (over 11,000 samples) with a dedicated multiple-choice benchmark. On this benchmark we study two adaptation strategies: supervised fine-tuning (SFT) and three retrieval-augmented generation (RAG) configurations (semantic, keyword, hybrid). Across multiple LLM families, domain-specific SFT significantly improves RF reasoning, especially for small and medium-sized models; among RAG configurations, semantic retrieval performs best, indicating embedding-based context alignment suits RF reasoning better than naive fusion. The dataset and benchmark provide a reusable foundation for future work on LLM-aided RF circuit design.
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