← Back to all news
arXiv AI By Xintong Dong, Chuanyang Li, Peng Zheng, Chuqi Han, Jiaxin Jing, Hailong Shen, Yanzhi Song, Zhouwang Yang

HistCAD: Constraint-Aware Parametric CAD Histories for Evaluating Editability

Read the original on arXiv AI →

HistCAD is an executable representation and dataset comprising 180,495 parametric construction histories with sketch constraints and feature operations, split into Academic and Industrial collections. The benchmark evaluates whether predicted constraints can enable a history to reproduce an initial model and perform a specified dimensional edit, measuring success when the model matches the required geometry, reaches the target value, preserves relations, and rebuilds through the entire history. Predictors trained on both collections achieve 52.4% edit success on Academic data and 29.0% on Industrial data, with models retaining only endpoint-connectivity constraints sometimes reaching the target but failing to preserve all relations.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

Related stories

arXiv Machine Learning
Jun 3

CADFit: Precise Mesh-to-CAD Program Generation with Hybrid Optimization

arXiv:2605. 01171v2 Announce Type: replace-cross Abstract: Despite recent progress, recovering parametric CAD construction sequences from geometric input, such as meshes or point clouds, is a key challenge for design and manufacturing, as existing CAD reconstruction and generation methods are largely restricted to difficult-to-edit formats like meshes or Breps or editable simple sketch-and-extrude pipelines and low-complexity datasets.

By Ghadi Nehme, Eamon Whalen, Faez Ahmed
multimodalbenchmarks
More like this →
arXiv Computer Vision
Sep 25

AgenticCADedit: A Stateful, Tool-Mediated Agentic Approach to Multimodal 3D CAD Editing

AgenticCADedit introduces a stateful, tool‑mediated approach to multimodal 3D CAD editing, transforming the process from generating a single complete program to executing a sequence of incremental, verifiable actions on a persistent CAD state. By committing each step, inspecting geometry, and selectively reverting faulty operations, the method preserves partial progress and builds upon earlier edits. Experiments across three large language models show substantial gains in validity and acceptance, with the weakest baseline model’s validity rising from 51.0% to 94.8% and a token‑cost reduction of 66.7% compared to neuralCAD‑Edit.

By Saptarshi Neil Sinha, Mika Silvan Goschke, Paul Julius K\"uhn, Arjan Kuijper, Michael Weinmann
llmsagentsmultimodal
More like this →
arXiv Machine Learning
Oct 8

CADFather: Autonomous CAD Reconstruction through Coordinated Tool Use

CADFather is an autonomous system that reconstructs editable CAD models from 3D meshes by coordinating vision‑language assistants, learned and algorithmic tools, and numerical optimization. It evaluates and refines candidate CAD programs, maintaining alternatives for each part and preserving the best valid result throughout reconstruction. The system uses pretrained models without additional training and is evaluated on multiple benchmark datasets for reconstruction quality and execution validity.

By Gennadiy Savrasov, Maksim Elistratov, Nikita Gavrilov, Albert Garifullin, Oleg Pavlov, Soslan Kabisov, Vladimir Frolov, Anton Konushin, Andrey Kuznetsov, Dmitrii Zhemchuzhnikov
agentsmultimodal
More like this →