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