arXiv Computer Vision

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.

arXiv AI
Jun 12

IterCAD: An Iterative Multimodal Agent for Visually-Grounded CAD Generation and Editing

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.

By Tao Hu, Jiaxin Ai, Licheng Wen, Xueheng Li, Shu Zou, Siqi Li, Nianchen Deng, Xinyu Cai, Hongbin Zhou, Pinlong Cai, Daocheng Fu, Yu Yang, Hairong Zhang, Botian Shi, Xuemeng Yang
Hugging Face Trending Papers
Jul 7

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

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.

arXiv AI
Jul 8

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

arXiv:2607. 05750v1 Announce Type: new 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.

By Yunhan Xu, Qifeng Wu, Xunjin Li, Yuanwei Bin, Qingsong Yao, Jianghang Gu, Guan Wang, Weihao Lv, Huiyu Yang, Wenfa Luo, Jiao Xiang, Yuntian Chen, Shiyi Chen
arXiv Computer Vision
Aug 28

Procedura: Agentic 3D Modeling with Procedural Control

Procedura is a new 3D modeling agent that treats 3D shape as code, using a large language model to generate a procedural assembly from a text prompt. It constructs an assembly graph, writes a parametric program with named parts and typed mates, and verifies each part through compile, mate, and connectivity checks before adding it. A vision critic refines the assembly step‑by‑step, and the resulting program includes per‑part materials and simulator‑validated articulation, producing sharp edges and editable, part‑structured outputs that outperform existing native 3D generators on P3D‑Bench and MechBench‑36.

By Youtian Lin, Yikang Yang, Zhanpeng Hu, Mengqi Zhou, Feihu Zhang, Xun Cao, Jiaheng Liu, Yao Yao
arXiv AI
Sep 21

Designer-RSI: Evolving Procedural Memory from User Traffic for Agentic Graphic Design

Designer‑RSI presents a continual adaptation framework that lets a frozen frontier model operate professional design software while an external procedural memory learns natural‑language design skills from user traffic. Over five rounds on 1,406 real briefs and 1,869 graded trajectories, the memory grew from 76 to 139 skills, boosting execution success from 72.7% to 99.3% and improving win rates on four design benchmarks. The study shows that widening and deepening the memory, especially together, significantly outperforms a no‑skill baseline.

By Hongyang Du, Lan Yan, Christian Flores, Asim Kadav
arXiv AI
Jun 4

Plan, Watch, Recover: A Benchmark and Architectures for Proactive Procedural Assistance

arXiv:2606. 04970v1 Announce Type: cross Abstract: We envision a proactive multi-modal assistant system which gives users real-time step-by-step guidance on a procedural task, autonomously deciding \textit{when} to interrupt, and \textit{how} to coach.

By Kaustav Kundu, Ritvik Shrivastava, Maxim Arap, Nanshu Wang, Xianhui Zhu, Quintin Fettes, Gautam Tiwari, Parth Suresh, Th\'eo Moutakanni, Alejandro Castillejo Munoz, Allen Bolourchi, Pascale Fung, Pinar Donmez, Babak Damavandi, Anuj Kumar, Seungwhan Moon