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CAD-Assistant: Tool-Augmented VLLMs as Generic CAD Task Solvers
Created by
Haebom
Author
Dimitrios Mallis, Ahmet Serdar Karadeniz, Sebastian Cavada, Danila Rukhovich, Niki Foteinopoulou, Kseniya Cherenkova, Anis Kacem, Djamila Aouada
Outline
CAD-Assistant is a general-purpose CAD agent for AI-driven design. It is based on a tool augmentation paradigm that utilizes vision and large-scale language models (VLLMs) as a planner and leverages CAD-specific tools. It responds to multi-modal user queries by generating tasks that are repeatedly executed in a Python interpreter accessible through FreeCAD's Python API. It evaluates the impact of generated CAD commands on geometry and adjusts subsequent tasks based on the evolving CAD design state. It considers various CAD-specific tools, including a sketch image parameterizer, a rendering module, and a 2D section generator. It has been evaluated on several CAD benchmarks, outperforming both the VLLM baseline and supervised learning-based task-specific approaches. Beyond existing benchmarks, it qualitatively demonstrates the potential of the tool-augmented VLLM as a general-purpose CAD solver across a variety of workflows.
Takeaways, Limitations
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Takeaways:
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Suggesting the possibility of building a general-purpose AI-based CAD system through a tool augmentation approach based on VLLM.
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Complex design tasks can be performed by integrating various CAD-specific tools.
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Proven performance through existing benchmarks and qualitative evaluations
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Demonstrating the potential of a general-purpose CAD solver applicable to a variety of workflows
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Limitations:
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Lack of specific information on the type and scale of benchmarks currently presented.
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Additional validation is needed for compatibility with various CAD software and tools.
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The need for generalized performance verification for complex and unpredictable design processes.
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Lack of specific explanations for qualitative evaluations in addition to quantitative evaluations