CAD-Assistant: Tool-Augmented VLLMs as Generic CAD Task Solvers
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Abstract (arXiv)
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.
Key facts. Cited by 40 papers in the map (data through 14 Oct 2026). Builds on 58 of its 78 references that are in the map. First posted on arXiv 18 Dec 2024.
Builds on
58 of 78 references.
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50 most cited of 58 All 58 (JSON)
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