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

arXiv cs.CL LLM Agents Yueqi Xing, Houbo He, Jolie Wang, Erin Ni, Shikai Wang, Qiufeng Li, Weidong Cao, Taiyun Chi 2026-07-21

TL;DR - RF-Agent uses a multi-agent pipeline to distill seven RF textbooks into an 11,000-sample reasoning dataset and benchmark for radio-frequency circuit design. It establishes practical data and adaptation methods for applying LLMs to a specialized EDA domain.

The QTSA pipeline generates question, reasoning, solution, and answer data from textbook subsections.
Domain-specific supervised fine-tuning substantially improves RF reasoning, particularly for small and medium models.
Semantic retrieval outperforms keyword and hybrid RAG configurations.
The released dataset and multiple-choice benchmark provide reusable infrastructure for evaluating RF-design language agents.

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