ArtisanCAD: An Industrial-Level CAD Agent with Expert-Grounded Knowledge Distillation

• URL: http://arxiv.org/abs/2607.05750v2
• Date: Wed, 08 Jul 2026 02:05:52 GMT
• Title: ArtisanCAD: An Industrial-Level CAD Agent with Expert-Grounded Knowledge Distillation
• Abstract summary: We present ArtisanCAD, a skill-guided industrial CAD agent with expert-grounded knowledge distillation.<n>The core of ArtisanCAD is CAD intermediate representation (CAD-IR), an executable procedural representation.<n>On the Text2CAD benchmark, CAD-IR improves generation from intermediate prompts by reducing mean Chamfer Distance from $14.83$ to $9.88$.
• Score: 17.07760884362884
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Computer-aided design (CAD) for industrial components requires long-horizon procedural modeling, robust feature dependencies, editable parametric geometry, and production-grade B-Rep execution. Existing text-to-CAD methods have made promising progress in generating CAD programs from natural-language descriptions, but they still struggle when user prompts are ambiguous, underspecified, or only describe high-level design intent. They also rarely exploit expert procedural knowledge naturally available in industrial workflows, such as CATIA operation recordings, macro logs, drawing notes, and engineering descriptions. We present ArtisanCAD, a skill-guided industrial CAD agent with expert-grounded knowledge distillation. The core of ArtisanCAD is CAD intermediate representation (CAD-IR), an executable procedural representation that encodes parameters, ordered operations, MCP tool bindings, dependencies, generated entities, and verification rules. CAD-IR plays two key roles: it first serves as the carrier for distilling expert CAD procedures into reusable parameterized skills; then it provides a procedural scaffold that turns vague or intermediate-level prompts into complete executable CAD operations. ArtisanCAD retrieves expert-derived skills, instantiates and revises CAD-IR, executes the resulting procedure through a dedicated CATIA-MCP backend, and uses multi-view visual feedback for iterative refinement, and finally generates production-ready B-Rep models. On the Text2CAD benchmark, CAD-IR improves generation from intermediate prompts by reducing mean Chamfer Distance from $14.83$ to $9.88$, showing its ability to bridge ambiguous textual intent and executable CAD construction. On four complex automotive components, CAD-IR enables expert CATIA recordings to be distilled into reusable skills, allowing ArtisanCAD to generate editable CATIA-native B-Rep models for new variant requests.

Related papers

• CADWorld: Computer-Use Benchmark for Long-Horizon Computer-Aided Design [10.481042166600417]
  CADWorld is a benchmark for long-horizon computer use in FreeCAD.<n>It contains 200 tasks spanning 11 mechanical-CAD workflow categories, including sketching, part modeling, assembly, CAM, FEM, measurement, mesh processing, and technical drawing.<n>We find that weaker agents often fail before producing a valid artifact, whereas stronger agents increasingly fail on structural, geometric, and construction-process requirements.
  arXiv  Detail & Related papers  (2026-09-14T19:19:05Z)
• Embodied CAD: Solver-Grounded LLM Agents for Parametric B-Rep Assembly Modeling [7.918861077775763]
  We present Embodied CAD, solver-grounded LLM agents for parametric B-Rep assembly modeling.<n>The framework combines action grammar constraints, deterministic parameter resolution, and solver-derived rewards for supervised warm-up and GRPO-style refinement.<n>We evaluate Embodied CAD on multi-step mechanical, industrial equipment, and mold-oriented assembly tasks using solver-aligned metrics.
  arXiv  Detail & Related papers  (2026-06-30T07:31:01Z)
• BenchCAD: A Comprehensive, Industry-Standard Benchmark for Programmatic CAD [14.284307846349774]
  We present BenchCAD, a unified benchmark for industrial CAD reasoning.<n> BenchCAD contains 17,900 execution-verified CadQuery programs across 106 industrial part families.<n>It evaluates models through visual question answering, code question answering, image-to-code generation, and instruction-guided code editing.
  arXiv  Detail & Related papers  (2026-05-11T17:13:36Z)
• CADFS: A Big CAD Program Dataset and Framework for Computer-Aided Design with Large Language Models [80.47087401394882]
  We introduce CADFS, a data-centric framework that enables large vision-language models to generate complex CAD design histories.<n>We construct a dataset of 450k real-world CAD models spanning 15 modeling operations.<n>Fine-tuning a VLM on this representation yields state-of-the-art results in text-conditioned CAD generation and image-based reconstruction.
  arXiv  Detail & Related papers  (2026-05-03T15:12:36Z)
• Towards High-Fidelity CAD Generation via LLM-Driven Program Generation and Text-Based B-Rep Primitive Grounding [18.983809090879348]
  We present FutureCAD, a novel text-to-CAD framework for high-fidelity CAD generation.<n>Our method generates executable CadQuery scripts, and introduces a text-based query mechanism.<n>Experiments show that FutureCADGround achieves state-of-the-art CAD generation performance.
  arXiv  Detail & Related papers  (2026-03-12T11:54:29Z)
• DreamCAD: Scaling Multi-modal CAD Generation using Differentiable Parametric Surfaces [67.45867367326105]
  We propose DreamCAD, a multi-modal generative framework that directly produces editable BReps from point-level supervision.<n>DreamCAD represents each BRep as a set of parametric patches and uses a differentiable tessellation method to generate meshes.<n>DreamCAD achieves state-of-the-art performance on ABC and averse benchmarks across text, image, and point modalities.
  arXiv  Detail & Related papers  (2026-03-05T19:04:07Z)
• From Intent to Execution: Multimodal Chain-of-Thought Reinforcement Learning for Precise CAD Code Generation [47.67703214044401]
  We propose CAD-RL, a multimodal Chain-of-Thought guided reinforcement learning framework for CAD modeling code generation.<n>Our method combines Cold Start with goal-driven reinforcement learning post training using three task-specific rewards.<n>Experiments demonstrate that CAD-RL achieves significant improvements in reasoning quality, output precision, and code executability.
  arXiv  Detail & Related papers  (2025-08-13T18:30:49Z)
• CReFT-CAD: Boosting Orthographic Projection Reasoning for CAD via Reinforcement Fine-Tuning [31.342222156939403]
  We introduce CReFT-CAD, a two-stage fine-tuning paradigm that first employs a curriculum-driven reinforcement learning stage with difficulty-aware rewards to build reasoning ability steadily.<n>We release TriView2CAD, the first large-scale, open-source benchmark for orthographic projection reasoning.
  arXiv  Detail & Related papers  (2025-05-31T13:52:56Z)
• CAD-Coder: An Open-Source Vision-Language Model for Computer-Aided Design Code Generation [4.092348452904736]
  This paper introduces CAD-Coder, an open-source Vision-Language Model (VLM) explicitly fine-tuned to generate editable CAD code (CadQuery Python) directly from visual input.<n>Leveraging a novel dataset that we created--GenCAD-Code, consisting of over 163k CAD-model image and code pairs--CAD-Coder outperforms state-of-the-art VLM baselines.
  arXiv  Detail & Related papers  (2025-05-20T17:34:44Z)
• CADCrafter: Generating Computer-Aided Design Models from Unconstrained Images [69.7768227804928]
  CADCrafter is an image-to-parametric CAD model generation framework that trains solely on synthetic textureless CAD data.<n>We introduce a geometry encoder to accurately capture diverse geometric features.<n>Our approach can robustly handle real unconstrained CAD images, and even generalize to unseen general objects.
  arXiv  Detail & Related papers  (2025-04-07T06:01:35Z)
• CAD-Assistant: Tool-Augmented VLLMs as Generic CAD Task Solvers [20.440350517523314]
  CAD-Assistant addresses user queries by generating actions that are iteratively executed on a Python interpreter equipped with the FreeCAD software.<n>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.
  arXiv  Detail & Related papers  (2024-12-18T12:57:56Z)
• PS-CAD: Local Geometry Guidance via Prompting and Selection for CAD Reconstruction [86.726941702182]
  We introduce geometric guidance into the reconstruction network PS-CAD. We provide the geometry of surfaces where the current reconstruction differs from the complete model as a point cloud. Second, we use geometric analysis to extract a set of planar prompts, that correspond to candidate surfaces.
  arXiv  Detail & Related papers  (2024-05-24T03:43:55Z)
• AutoCAD: Automatically Generating Counterfactuals for Mitigating Shortcut Learning [70.70393006697383]
  We present AutoCAD, a fully automatic and task-agnostic CAD generation framework. In this paper, we present AutoCAD, a fully automatic and task-agnostic CAD generation framework.
  arXiv  Detail & Related papers  (2022-11-29T13:39:53Z)
• CADOps-Net: Jointly Learning CAD Operation Types and Steps from Boundary-Representations [17.051792180335354]
  This paper proposes a new deep neural network, CADOps-Net, that jointly learns the CAD operation types and the decomposition into different CAD operation steps. Compared to existing datasets, the complexity and variety of CC3D-Ops models are closer to those used for industrial purposes.
  arXiv  Detail & Related papers  (2022-08-22T19:12:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.