RF-Agent: A Practical Framework for Building Language Agents for RFIC Design

Researchers introduce RF-Agent, a framework using textbook-driven knowledge distillation to build language agents for radio-frequency integrated circuit (RFIC) design, creating the first RF-domain reasoning dataset with over 11,000 samples and a multiple-choice benchmark. Domain-specific supervised fine-tuning significantly improves RF reasoning in large language models, especially for small and medium-sized models, while semantic retrieval outperforms other retrieval-augmented generation configurations.

arXiv:2607.18772v1 Announce Type: new Abstract: 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.