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CAD-Assistant: Tool-Augmented VLLMs as Generic CAD Task Solvers

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arxiv 2412.13810 v3 pith:IC36BRC4 submitted 2024-12-18 cs.CV cs.AIcs.LGcs.RO

Dimitrios Mallis , Ahmet Serdar Karadeniz , Sebastian Cavada , Danila Rukhovich , Niki Foteinopoulou , Kseniya Cherenkova , Anis Kacem , Djamila Aouada This is my paper · ORCID
classification cs.CVcs.AIcs.LGcs.RO
keywords cad-assistantactionsbenchmarkscad-specificdesigngeneral-purposepythonsolvers
verification ladder T0 review T1 audit T2 compute T3 formal
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We propose CAD-Assistant, a general-purpose CAD agent for AI-assisted design. Our approach is based on a powerful Vision and Large Language Model (VLLM) as a planner and a tool-augmentation paradigm using CAD-specific tools. CAD-Assistant addresses multimodal user queries by generating actions that are iteratively executed on a Python interpreter equipped with the FreeCAD software, accessed via its Python API. Our framework is able to assess the impact of generated CAD commands on geometry and adapts subsequent actions based on the evolving state of the CAD design. We consider a wide range of CAD-specific tools including a sketch image parameterizer, rendering modules, a 2D cross-section generator, and other specialized routines. CAD-Assistant is evaluated on multiple CAD benchmarks, where it outperforms VLLM baselines and supervised task-specific methods. Beyond existing benchmarks, we qualitatively demonstrate the potential of tool-augmented VLLMs as general-purpose CAD solvers across diverse workflows.

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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. BrepLLM: Enabling Large Language Models to Understand Boundary Representations

    cs.CV 2025-12 conditional novelty 6.0 of 10

    BrepLLM feeds boundary-representation CAD graphs directly into an LLM and reports state-of-the-art CAD captioning and generative classification over point-cloud 3D-LLM baselines.

  2. 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.

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