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From Idea to CAD: A Language Model-Driven Multi-Agent System for Collaborative Design

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arxiv 2503.04417 v1 pith:UCDLZE2F submitted 2025-03-06 cs.AI cs.MA

classification cs.AIcs.MA
keywords designengineeringmodelapproacharchitectureassurancelanguagemodels
verification ladder T0 review T1 audit T2 compute T3 formal
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Creating digital models using Computer Aided Design (CAD) is a process that requires in-depth expertise. In industrial product development, this process typically involves entire teams of engineers, spanning requirements engineering, CAD itself, and quality assurance. We present an approach that mirrors this team structure with a Vision Language Model (VLM)-based Multi Agent System, with access to parametric CAD tooling and tool documentation. Combining agents for requirements engineering, CAD engineering, and vision-based quality assurance, a model is generated automatically from sketches and/ or textual descriptions. The resulting model can be refined collaboratively in an iterative validation loop with the user. Our approach has the potential to increase the effectiveness of design processes, both for industry experts and for hobbyists who create models for 3D printing. We demonstrate the potential of the architecture at the example of various design tasks and provide several ablations that show the benefits of the architecture's individual components.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. CAD-Coder: An Open-Source Vision-Language Model for Computer-Aided Design Code Generation

    cs.CV 2025-05 conditional novelty 6.0 of 10

    Fine-tuning a LLaVA-style vision-language model on 163k synthetic image-CadQuery pairs yields a model that compiles every test script and matches CAD solids better than general VLMs.

  2. Human-in-the-Loop: Quantitative Evaluation of 3D Models Generation by Large Language Models

    cs.CV 2025-09 reject novelty 4.0 of 10

    Quantitative geometry scores across four input types show semantic richness improves LLM-generated CAD fidelity, with code-based prompts reaching perfect scores only after human code edits.

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