arXiv:2608. 09296v1 Announce Type: new Abstract: A CAD model is not engineering-grade merely because it looks correct.
Computer-use agents are increasingly evaluated in realistic desktop environments, but existing benchmarks provide limited coverage of professional engineering workflows whose outputs are persistent, s...
arXiv:2609.16251v1 Announce Type: new Abstract: Computer-use agents are increasingly evaluated in realistic desktop environments, but existing benchmarks provide limited coverage of professional engi...
VisCAD is a foundation model suite that tackles AI-assisted computer-aided design for industrial products, covering both part-level and assembly-level generation. Its core component, VisCAD‑M1, is a 27B model trained for part-level design generation and outperforms existing models on PubCADBench and RealCADBench, achieving a part-level score of 0.5540 and reaching 0.5797 when used as a test-time verifier. VisCAD also offers a domain-specific harness that improves complex assembly generation compared to general-purpose harnesses, showing quantitative and qualitative advantages.
CADReasoner is a model that iteratively refines its CAD reconstruction by comparing the input shape with the predicted mesh, outputting a runnable CadQuery Python program. It fuses multi‑view renders and point clouds and uses a scan‑simulation protocol to bridge realism gaps during training and evaluation. The approach achieves state‑of‑the‑art performance on DeepCAD, Fusion 360, and MCB benchmarks for both clean and scan‑sim tracks.
Recent advances in large language models and programmatic CAD have significantly improved Text-to-CAD generation for individual parts. However, production-ready mechanical assembly generation remains largely unsolved.
arXiv:2607. 05123v1 Announce Type: new Abstract: Recent advances in large language models and programmatic CAD have significantly improved Text-to-CAD generation for individual parts.
arXiv:2605. 28579v2 Announce Type: replace Abstract: Large language models (LLMs) have recently advanced text-driven 3D generation, yet Text-to-CAD remains far from supporting industrial product design.
RealCADBench is a new benchmark for evaluating intent‑to‑program parametric CAD modeling, featuring 12,632 tasks drawn from 19 factory‑automation categories and covering text, 2D drawings, product photos, and rendered images for both Part and Assembly modeling. The study reports results on a 1,770‑task evaluation slice, using metrics such as executability, Solid IoU, Surface IoU, and a rubric‑based visual‑semantic identity Judge. Across nine standalone and six frontier‑scale large models, no single model dominates all four metrics, highlighting diverse strengths and failure modes like missing fine structures and incorrect assembly placement.
arXiv:2606. 13368v1 Announce Type: new Abstract: Computer-Aided Design is pivotal in modern manufacturing, yet existing automated methods predominantly rely on open-loop, one-shot generation, creating a mismatch with iterative real-world practices.
Reconstructing an editable CAD model from a 3D shape remains a challenging engineering task. Existing methods can propose CAD operations, but no single source of proposals works equally well across di...
arXiv:2606. 31252v1 Announce Type: new Abstract: Large language models can write plausible CAD scripts, but reliable industrial CAD modeling requires more than syntactically valid code: every feature, placement, and assembly relation must be accepted by an exact geometric kernel while remaining editable as parametric boundary representation geometry.