{"ID":2832374,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.05371","arxiv_id":"2512.05371","title":"ChipMind: Retrieval-Augmented Reasoning for Long-Context Circuit Design Specifications","abstract":"While Large Language Models (LLMs) demonstrate immense potential for automating integrated circuit (IC) development, their practical deployment is fundamentally limited by restricted context windows. Existing context-extension methods struggle to achieve effective semantic modeling and thorough multi-hop reasoning over extensive, intricate circuit specifications. To address this, we introduce ChipMind, a novel knowledge graph-augmented reasoning framework specifically designed for lengthy IC specifications. ChipMind first transforms circuit specifications into a domain-specific knowledge graph ChipKG through the Circuit Semantic-Aware Knowledge Graph Construction methodology. It then leverages the ChipKG-Augmented Reasoning mechanism, combining information-theoretic adaptive retrieval to dynamically trace logical dependencies with intent-aware semantic filtering to prune irrelevant noise, effectively balancing retrieval completeness and precision. Evaluated on an industrial-scale specification reasoning benchmark, ChipMind significantly outperforms state-of-the-art baselines, achieving an average improvement of 34.59% (up to 72.73%). Our framework bridges a critical gap between academic research and practical industrial deployment of LLM-aided Hardware Design (LAD).","short_abstract":"While Large Language Models (LLMs) demonstrate immense potential for automating integrated circuit (IC) development, their practical deployment is fundamentally limited by restricted context windows. Existing context-extension methods struggle to achieve effective semantic modeling and thorough multi-hop reasoning over...","url_abs":"https://arxiv.org/abs/2512.05371","url_pdf":"https://arxiv.org/pdf/2512.05371v1","authors":"[\"Changwen Xing\",\"SamZaak Wong\",\"Xinlai Wan\",\"Yanfeng Lu\",\"Mengli Zhang\",\"Zebin Ma\",\"Lei Qi\",\"Zhengxiong Li\",\"Nan Guan\",\"Zhe Jiang\",\"Xi Wang\",\"Jun Yang\"]","published":"2025-12-05T02:09:49Z","proceeding":"cs.AI","tasks":"[\"cs.AI\",\"cs.AR\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}