Physics-in-the-Loop: A Hybrid Agentic Architecture for Validated CAD Engineering Design
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866910236402515968 |
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| author | Berger, Elias Usama, Muhammad Mehlstäubl, Jan Saske, Bernhard Paetzold-Byhain, Kristin |
| author_facet | Berger, Elias Usama, Muhammad Mehlstäubl, Jan Saske, Bernhard Paetzold-Byhain, Kristin |
| contents | Large Language Models (LLMs) can generate Computer-Aided Design (CAD), yet lack physical comprehension required for reliable engineering design. Instead of attempting to implicitly learn physical laws from data, we propose a Hybrid Agentic-Physical Architecture that embeds validated knowledge-based engineering tools directly into the decision making loop of autonomous AI agents. In this framework, engineering design is formulated as a closed-loop, sequential decision making process guided by explicit physical verification. Based on a load case, dedicated agents iteratively plan, generate, evaluate, and revise engineering designs using knowledge-based tools as a feedback signal. We introduce a benchmark dataset and metrics for assessing functional validity in generative CAD. Our system generates more complex and physically verified designs, with a 4.2 increase in structural complexity and improving compile rate by 3.5% compared to similar agentic methods. The codebase, prompts and dataset will be made publicly available to support reproducibility and future research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_19717 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Physics-in-the-Loop: A Hybrid Agentic Architecture for Validated CAD Engineering Design Berger, Elias Usama, Muhammad Mehlstäubl, Jan Saske, Bernhard Paetzold-Byhain, Kristin Computer Vision and Pattern Recognition Large Language Models (LLMs) can generate Computer-Aided Design (CAD), yet lack physical comprehension required for reliable engineering design. Instead of attempting to implicitly learn physical laws from data, we propose a Hybrid Agentic-Physical Architecture that embeds validated knowledge-based engineering tools directly into the decision making loop of autonomous AI agents. In this framework, engineering design is formulated as a closed-loop, sequential decision making process guided by explicit physical verification. Based on a load case, dedicated agents iteratively plan, generate, evaluate, and revise engineering designs using knowledge-based tools as a feedback signal. We introduce a benchmark dataset and metrics for assessing functional validity in generative CAD. Our system generates more complex and physically verified designs, with a 4.2 increase in structural complexity and improving compile rate by 3.5% compared to similar agentic methods. The codebase, prompts and dataset will be made publicly available to support reproducibility and future research. |
| title | Physics-in-the-Loop: A Hybrid Agentic Architecture for Validated CAD Engineering Design |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2605.19717 |