Nianchen Deng, Jiaxin Ai
Automating industrial CAD design and manufacturing places distinctive demands on multimodal foundation models: the model must see engineering drawings and 3D geometry screenshots, write correct parametric-modelling scripts and Windows COM API code, and cover the full range from single parts to assemblies. General-purpose multimodal models fall short on these tasks, while single-task fine-tuning is too narrow to support the diverse calls that upper-layer agents issue. We build IndustryForge-27B on top of Qwen3.5-VL-27B by curating and integrating six industrial-CAD sub-corpora totaling ∼52k multimodal samples—CAD Visual QA (CAD-VQA), parametric CAD code (text2cadquery), assembly-level CAD code (text2cadquery-assembly), and three COM sub-corpora for Inventor / SolidWorks (com_2d / com_3d / com_assembly)—and training with a unified multi-task SFT recipe. Across four CAD-domain benchmarks IndustryForge-27B lifts the base model by +33.65 pp on average and outperforms the strong closed-source model gpt-5.4 on all four; across eleven general-capability benchmarks it retains, and slightly improves upon, the base model (+1.56 pp mean, no catastrophic forgetting). IndustryForge-27B will serve as the common substrate for downstream industrial-agent projects, providing a unified starting point for a full-stack industrial agent that spans from CAD design to industrial-software operation, from parts to assemblies, and from single-shot generation to closed-loop self-improvement.
@article{e6bd5cf7-0d7e-423f-a8e6-65bdf0facb94,
title={IndustryForge-27B: A Domain-Enhanced Multimodal Foundation Model for IndustrialCAD},
author={Nianchen Deng and Jiaxin Ai},
year={2026},
language={en}
}TY - JOUR TI - IndustryForge-27B: A Domain-Enhanced Multimodal Foundation Model for IndustrialCAD AU - Nianchen Deng AU - Jiaxin Ai PY - 2026 LA - en ER -
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