{"slug": "rf-agent-a-practical-framework-for-building-language-agents-for-rfic-design", "title": "RF-Agent: A Practical Framework for Building Language Agents for RFIC Design", "summary": "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.", "body_md": "arXiv:2607.18772v1 Announce Type: new\nAbstract: 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.", "url": "https://wpnews.pro/news/rf-agent-a-practical-framework-for-building-language-agents-for-rfic-design", "canonical_source": "https://www.machinebrief.com/news/rf-agent-a-practical-framework-for-building-language-agents-2skb", "published_at": "2026-07-22 04:00:00+00:00", "updated_at": "2026-07-22 04:09:20.371205+00:00", "lang": "en", "topics": ["artificial-intelligence", "large-language-models", "ai-research", "ai-tools"], "entities": ["RF-Agent", "arXiv"], "alternates": {"html": "https://wpnews.pro/news/rf-agent-a-practical-framework-for-building-language-agents-for-rfic-design", "markdown": "https://wpnews.pro/news/rf-agent-a-practical-framework-for-building-language-agents-for-rfic-design.md", "text": "https://wpnews.pro/news/rf-agent-a-practical-framework-for-building-language-agents-for-rfic-design.txt", "jsonld": "https://wpnews.pro/news/rf-agent-a-practical-framework-for-building-language-agents-for-rfic-design.jsonld"}}