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To this end, we introduce LaMAGIC, a language model-based topology generation model that leverages supervised finetuning for automated analog circuit design. LaMAGIC can efficiently generate an optimized circuit design from the custom specification in a single pass. The generated circuit is validated by the simulator to meet the performance requirement with high precision. Our approach involves a meticulous development and analysis of various input and output formulations for circuit. These formulations can ensure canonical representations and align with the autoregressive nature of LMs for representing analog circuits as graphs. In addition, our novel transformer model supports float-input to effectively learn the mapping between numerical performance and circuits. The experimental results show that LaMAGIC achieves a success rate of up to 96% under a strict tolerance of 0.01. Also, we examine the scalability and adaptability of LaMAGIC under scarce data scenario on more complex circuits. Our findings reveal the enhanced effectiveness of our succinct float-input canonical formulation with identifier, suggesting its suitability for handling intricate circuits. Our ablation study evaluates various design choices of LM training and inference, providing insights for future domain-specific generation tasks. This research not only demonstrates the potential of language models in graph generation, but also builds a foundational framework for future explorations in automated analog circuit design.<\/jats:p>","DOI":"10.1145\/3799428","type":"journal-article","created":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T21:44:31Z","timestamp":1772747071000},"page":"1-21","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["LaMAGIC: Advanced Circuit Formulations for Language-Model-based Topology Generation for Analog Integrated Circuits"],"prefix":"10.1145","volume":"31","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3115-0733","authenticated-orcid":false,"given":"Chen-Chia","family":"Chang","sequence":"first","affiliation":[{"name":"Electrical and Computer Engineering, Duke University","place":["Durham, United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7486-2143","authenticated-orcid":false,"given":"Wan-Hsuan","family":"Lin","sequence":"additional","affiliation":[{"name":"University of California Los Angeles","place":["Los Angeles, United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-5263-4837","authenticated-orcid":false,"given":"Yikang","family":"Shen","sequence":"additional","affiliation":[{"name":"MIT-IBM Watson AI Lab","place":["Cambridge, United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6840-7160","authenticated-orcid":false,"given":"Guanglei","family":"Zhou","sequence":"additional","affiliation":[{"name":"Electrical and Computer Engineering, Duke University","place":["Durham, United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1486-8412","authenticated-orcid":false,"given":"Yiran","family":"Chen","sequence":"additional","affiliation":[{"name":"Duke University","place":["Durham, United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0579-2268","authenticated-orcid":false,"given":"Xin","family":"Zhang","sequence":"additional","affiliation":[{"name":"IBM Research","place":["Yorktown Heights, United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2026,4,11]]},"reference":[{"key":"e_1_3_1_2_2","unstructured":"Jitendra Bhandari Vineet Bhat Yuheng He Hamed Rahmani Siddharth Garg and Ramesh Karri. 2024. 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