{"ID":5346702,"CreatedAt":"2026-06-30T04:09:55.830587294Z","UpdatedAt":"2026-07-02T15:01:14.507804213Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.30429","arxiv_id":"2606.30429","title":"Arko-T: A Foundation Model for Text-to-Structured 3D Generation","abstract":"Text-to-3D systems can now synthesize a mechanical part from a single sentence, yet the result is a shape to render, not a design to edit. We present Arko-T, a 4B-parameter text-to-design model that maps natural-language intent directly into executable, parametric CAD programs. Rather than optimizing for code executability alone, Arko-T aligns every stage of the pipeline to a formal notion of design state, so that data curation, code normalization, and execution-grounded supervision all work to preserve the features, parameters, and construction logic that make a CAD artifact editable. Benchmarked against seven frontier LLMs across 12 metrics, Arko-T attains the best score on 8 and the second-best on 3 more, at roughly one-tenth the per-benchmark cost. The results suggest that targeted design-level training at moderate scale can match frontier general-purpose models on structured CAD generation.","short_abstract":"Text-to-3D systems can now synthesize a mechanical part from a single sentence, yet the result is a shape to render, not a design to edit. We present Arko-T, a 4B-parameter text-to-design model that maps natural-language intent directly into executable, parametric CAD programs. Rather than optimizing for code executabi...","url_abs":"https://arxiv.org/abs/2606.30429","url_pdf":"https://arxiv.org/pdf/2606.30429v1","authors":"[\"Liang Wang\",\"Zhaoyang Xi\",\"Zekai Xiang\",\"Heng Meng\",\"Qishan Zhang\",\"Pingyi Zhou\",\"Jin Liu\",\"Litao Chen\"]","published":"2026-06-29T15:09:13Z","proceeding":"cs.LG","tasks":"[\"cs.LG\"]","methods":"[\"Large Language Model\"]","has_code":false}