{"ID":2878270,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.19393","arxiv_id":"2508.19393","title":"GENIE-ASI: Generative Instruction and Executable Code for Analog Subcircuit Identification","abstract":"Analog subcircuit identification is a core task in analog design, essential for simulation, sizing, and layout. Traditional methods often require extensive human expertise, rule-based encoding, or large labeled datasets. To address these challenges, we propose GENIE-ASI, the first training-free, large language model (LLM)-based methodology for analog subcircuit identification. GENIE-ASI operates in two phases: it first uses in-context learning to derive natural language instructions from a few demonstration examples, then translates these into executable Python code to identify subcircuits in unseen SPICE netlists. In addition, to evaluate LLM-based approaches systematically, we introduce a new benchmark composed of operational amplifier netlists (op-amps) that cover a wide range of subcircuit variants. Experimental results on the proposed benchmark show that GENIE-ASI matches rule-based performance on simple structures (F1-score = 1.0), remains competitive on moderate abstractions (F1-score = 0.81), and shows potential even on complex subcircuits (F1-score = 0.31). These findings demonstrate that LLMs can serve as adaptable, general-purpose tools in analog design automation, opening new research directions for foundation model applications in analog design automation.","short_abstract":"Analog subcircuit identification is a core task in analog design, essential for simulation, sizing, and layout. Traditional methods often require extensive human expertise, rule-based encoding, or large labeled datasets. To address these challenges, we propose GENIE-ASI, the first training-free, large language model (L...","url_abs":"https://arxiv.org/abs/2508.19393","url_pdf":"https://arxiv.org/pdf/2508.19393v1","authors":"[\"Phuoc Pham\",\"Arun Venkitaraman\",\"Chia-Yu Hsieh\",\"Andrea Bonetti\",\"Stefan Uhlich\",\"Markus Leibl\",\"Simon Hofmann\",\"Eisaku Ohbuchi\",\"Lorenzo Servadei\",\"Ulf Schlichtmann\",\"Robert Wille\"]","published":"2025-08-26T19:39:10Z","proceeding":"cs.AR","tasks":"[\"cs.AR\",\"cs.LG\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}