Design-to-Plan is a large language model–based multi‑agent framework that automates end‑to‑end manufacturing process planning from 3D CAD models and 2D engineering drawings. The system uses an orchestrator to coordinate specialized agents for feature recognition, drawing analysis, context fusion, knowledge retrieval, process sequencing, tool selection, and report generation, integrating deterministic modules with LLM reasoning. Evaluation on 300 benchmark cases shows high success rates, strong tool selection accuracy, effective conflict detection, and reduced token usage, demonstrating the framework’s ability to produce consistent, traceable design‑to‑plan outputs.
By Muhammad Tayyab Khan, Lequn Chen, Wenhe Feng, Seung Ki Moon
arXiv:2607. 05573v1 Announce Type: new Abstract: Recent advances in Large Language Models (LLMs) and Vision-Language Models (VLMs) enable the automatic generation of parametric 3D designs from natural-language specifications.
By J de Curt\`o, Victoria Guill\'en, I. de Zarz\`a
arXiv:2606. 31252v1 Announce Type: new Abstract: Large language models can write plausible CAD scripts, but reliable industrial CAD modeling requires more than syntactically valid code: every feature, placement, and assembly relation must be accepted by an exact geometric kernel while remaining editable as parametric boundary representation geometry.
By Fumin Liu, Haoyu Zhou, Fei Hao, Lin Yang
arXiv:2607. 05750v1 Announce Type: new Abstract: Computer-aided design (CAD) for industrial components requires long-horizon procedural modeling, robust feature dependencies, editable parametric geometry, and production-grade B-Rep execution.
By Yunhan Xu, Qifeng Wu, Xunjin Li, Yuanwei Bin, Qingsong Yao, Jianghang Gu, Guan Wang, Weihao Lv, Huiyu Yang, Wenfa Luo, Jiao Xiang, Yuntian Chen, Shiyi Chen
Computer-aided design (CAD) for industrial components requires long-horizon procedural modeling, robust feature dependencies, editable parametric geometry, and production-grade B-Rep execution. Existing text-to-CAD methods have made promising progress in generating CAD programs from natural-language descriptions, but they still struggle when user prompts are ambiguous, underspecified, or only describe high-level design intent.
arXiv:2606. 30429v1 Announce Type: new 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.
By Liang Wang, Zhaoyang Xi, Zekai Xiang, Heng Meng, Qishan Zhang, Pingyi Zhou, Jin Liu, Litao Chen
arXiv:2605. 28579v2 Announce Type: replace Abstract: Large language models (LLMs) have recently advanced text-driven 3D generation, yet Text-to-CAD remains far from supporting industrial product design.
By Xiaoyu Dong, Zhi Li, Xiao-Ming Wu
arXiv:2603. 22017v2 Announce Type: replace Abstract: This work presents a collection of multi-modal domain adapted large language models built upon the instruction tuned variants of open weight models (Gemma 3, Qwen 3, Gemma 4) using a relatively small dataset of around 50 million tokens.
By Peter Pak, Amir Barati Farimani
arXiv:2608. 14615v1 Announce Type: new Abstract: Large Language Models (LLMs) perform well on established code-generation and mathematical-reasoning benchmarks, but their capabilities in mechanics and spatial geometry, here denoted as mechanical engineering awareness, has not been quantified systematically.
By Johannes Gerstmayr, Sebastian Weyrer, Tobias M\"oltner, Peter Manzl, Michael Pieber
The paper audits frozen decoder‑only large language models (LLMs) on geometric reasoning tasks using parametric CAD constraints. It probes hidden states for linear decodability, forced‑choice generation, activation‑level influence, and behavioral steerability, finding that pretraining improves decoding of local geometric relations but not sketch‑level DOF status. The study shows that decodable information is not always actionable: generation often fails to express it, and steering interventions do not reliably control outputs, revealing divergences among decodability, generation, activation influence, and steerability.
By Man Liang, Xinzhao Cheng, Faizan Wajid
arXiv:2607. 15418v1 Announce Type: new Abstract: We introduce DrawingVQA, the first benchmark designed to evaluate multimodal large language models (MLLMs) on real-world construction drawings -- a core media in architecture, civil, and many other engineering practices.
By Yoonhwa Jung, Junryu Fu, Mani Golparvar-Fard
arXiv:2608. 07547v1 Announce Type: cross Abstract: Indoor scene layout generation is a challenging task in interior design.
By Yuhao Lu, Weichen Zhang, Wenyi Xiao, Haohui Chen, Yiyun Fei