CADIR, an agent-friendly executable intermediate representation for CAD generation and cross-backend editing, and a construction-graph retrieval method for text and image queries that supports both full-graph and subgraph retrieval, enabling agents to leverage complete models and modeling substructures.
Abstract
Large language models have made it possible to generate executable computer-aided design (CAD) programs from natural-language descriptions or images. However, existing methods represent modeling processes as backend-specific sequential scripts with implicit dependencies or as static geometry, making it difficult to simultaneously preserve construction history, stable topological references, and feature-level editability across different CAD systems. We present CADIR, an agent-friendly executable intermediate representation for CAD generation and cross-backend editing. Built on the OCCT geometry kernel via OCP, CADIR provides explicit, compositional modeling operations and fine-grained execution diagnostics. During program execution, CADIR records modeling operations, parameter dependencies, constraints, and topology selections in a construction graph. To enable reliable cross-backend reconstruction, we introduce Geometric Signature Matching, which identifies corresponding edges and faces despite parameter changes and backend differences, allowing adapters to reconstruct native editable feature histories in FreeCAD, SolidWorks, and Fusion 360. Building on this representation, we further propose a construction-graph retrieval method for text and image queries that supports both full-graph and subgraph retrieval, enabling agents to leverage complete models and modeling substructures. Extensive experiments demonstrate that CADIR achieves higher geometric fidelity and execution reliability than existing CAD representations, that construction-graph retrieval further improves model generation quality, and that cross-backend editing enables reliable model reconstruction and post-reconstruction editing across multiple CAD environments.
Advances in large language models (LLMs) have sparked interest in automating parametric CAD modeling through natural language. Existing LLM-based approaches often treat CAD modeling as flat text generation, overlooking the hierarchical structure and geometric constraints inherent in CAD programs. We present CAD-Factory, a Text-to-CAD generation system for language-driven CAD modeling that explicitly models the structural and parametric semantics of CAD programs. Our core contribution is a new formulation of CAD generation as structured program synthesis, coupled with a learnable hierarchical CAD program representation that disentangles structural topology from parameter instantiation. Building on this formulation, CAD-Factory adopts a manager–programmer–reviewer architecture: a planning agent infers program hierarchy, a coding agent instantiates symbolic and numerical parameters, and an evaluation module enforces structural validity and geometric feasibility, which supports structure-aware reasoning, constraint-consistent generation, and interpretable program synthesis. An editor–viewer loop further enables part-aware code refinement through visual feedback, supporting iterative and controllable design workflows. We also contribute a process-oriented annotation pipeline and a Text–CAD dataset with parameter-free or partially specified descriptions that reflect real-world design expression. Extensive experiments demonstrate that CAD-Factory significantly improves structural correctness and geometric consistency across diverse CAD code generation and editing tasks, establishing a structured, interpretable, and robust framework for AI-assisted design.
Yang Liu, Daxuan Ren, Yijie Ding et al.· International Conference on...· 0 citations
An Agent-centric Domain-Specific Language (aDSL) and a role-specialized multi-agent system to close the gap between programmatic interfaces and reasoning strengths of LLMs, which favor semantic structure and spatial relations over fragile numeric choices.
Rui-Huan Wang, Si-Tong Wei, Jia-Qi He et al.· 0 citations
ArtisanCAD is a skill-guided industrial CAD agent with expert-grounded knowledge distillation that enables expert CATIA recordings to be distilled into reusable skills, allowing ArtisanCAD to generate editable CATIA-native B-Rep models for new variant requests.
Yunhan Xu, Qifeng Wu, Xunjin Li et al.· 3 citations· ⚡1
LLMForge is introduced, a multi-model text-to-CAD framework integrating JSON-schema validation, analytic feature scoring, mesh synthesis, and multi-round iterative refinement, studied under two critique regimes.
Recovering an editable design file from a raster image is a common and costly bottleneck in modern design workflows, yet remains challenging since editability depends on recovering multi-modal attributes, such as typography, vector geometry, colors, grouping, and layer ordering. We present ReDesign, an agentic framework that grows an editable layer hierarchy by selecting and composing specialized tools across modalities. To keep this long decision process reliable despite imperfect tool outputs, we introduce graceful verification at each expansion, which provides local accept, prune, or retry feedback that prevents error accumulation and avoids large scale reruns. To evaluate editability at scale, we introduce the Figma Edit Replay Benchmark, consisting of 909 raw Figma files and 14,796 controlled edit instructions that replay edits on reconstructed outputs. Across this benchmark and standard reconstruction metrics, ReDesign achieves strong visual fidelity while delivering the highest editability across layout, color, and text edits, outperforming layered decomposition baselines and serial tool use pipelines.
Jooyeol Yun, Jintae Park, Hyesu Lim et al.· 2 citations· ⚡1
A CAD model is not engineering-grade merely because it looks correct. It must satisfy design requirements, respond predictably to parameter changes, support controlled edits, match a reference structural response under a declared analysis, and connect to other parts through valid joints. We present CADEngBench, a two-track benchmark for these capabilities. CADEngBench-P evaluates 300 parametric parts, each used for one zero-to-CAD task and one functional-editing task (600 tasks in total), through boundary-representation (B-Rep) validity, engineering and DFM checks, parameter-family perturbations, functional editing, and matched linear-static FEA in CalculiX. CADEngBench-A evaluates 150 body pairs through ranked joint retrieval, exact face-and-edge grounding, joint-frame prediction, and kinematic verification. Across eight multimodal, code-capable models, editing supplied CAD is substantially easier than generating it, while complex edits and matched FEA remain difficult. Assembly predictions often locate the relevant region but fail to recover the recorded joint or mating entities. These results show that CAD evaluation must test engineering behavior rather than appearance alone.