RF-Agent: A Practical Framework for Building Language Agents for RFIC Design
Researchers present RF-Agent, a framework that generates a domain-specific reasoning dataset for radio-frequency integrated circuit (RFIC) design using knowledge distilled from seven canonical RF textbooks. The resulting dataset contains over 11,000 samples and includes a dedicated multiple-choice benchmark. Experiments demonstrate that supervised fine-tuning notably enhances RF reasoning performance, particularly for small and medium-sized language models, and that semantic retrieval is the most effective retrieval-augmented generation (RAG) strategy tested.
Why it matters: This work introduces the first large-scale, reusable dataset and benchmark for RF circuit design, addressing a key bottleneck in applying language models to this specialized engineering domain.
Full story at: arXiv Computation and Language ↗