arXiv AI By Hongcheng Gao, Hailong Qu, Yu Lei, Henghui Sun, Haoyang Li, Yipeng Wei, Naihao Xue, Xiaohan Yu, Zhuo Tao, Yihe Zang, Yajiao Wang, Jingyi Tang, Yi Li, Jingjing Zhou, Jie Luo, Bohan Zeng, Chengyu Shen, Hao Jiang, Chong Chen, Bowen Qu, Olive Huang, Zeqiang Wang

EngiWorld: What Can Frontier Agents Deliver in Professional Engineering Environments?

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EngiWorld is a new benchmark that tests autonomous agents across the full engineering design loop, covering 1,301 expert‑curated tasks in six domains (CAD, CAE, CAM, BIM, EDA, and 3D visualization) and 26 professional software platforms with both GUI and CLI interfaces. It introduces an artifact‑centric evaluation method that programmatically verifies geometric validity, physical feasibility, and rule compliance of both final and intermediate artifacts, scoring tasks continuously rather than with binary success. Initial tests of seven frontier models show a large capability gap, with the best model scoring only 44.3 on the EngiScore and just 3.6% of multi‑software attempts succeeding.

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.

arXiv AI
Aug 26

EngiAI: Capability-Based Evaluation of Tool-Connected LLM Agents for Engineering Design

EngiAI introduces a capability-based evaluation framework for tool-connected engineering agents, assessing workflow execution, retrieval-assisted parameter selection, HPC orchestration, and training-code authoring using execution traces and engineering artifacts. The framework was applied to four LLM backends on EngiBench Beams2D and Photonics2D, revealing that proprietary models outperform open-source ones in workflow completion and HPC orchestration, while indexed retrieval improves parameter selection. The study demonstrates that evaluating distinct skills separately provides clearer insight into failure mechanisms than end-to-end success rates alone.

By Gioele Molinari, Florian Felten, Soheyl Massoudi, Mark Fuge
arXiv AI
2d ago

MatToolBench: Benchmarking Multimodal Agents in Real-World Materials Science Workflows

MatToolBench is a new benchmark that evaluates multimodal GUI agents on professional materials science software. It contains 204 tasks across 10 tools in three modalities—GUI operation, OriginPro scripting, and code-based database queries—executed inside a Windows 11 VM. The benchmark offers fine-grained, expert-decomposed scoring and a high-performing multimodal judge for aesthetic assessment, revealing that strong general benchmark performance does not transfer to scientific workflows.

By Mei Wu, Rui Xie, Runyu Zhang, Yuqiang Li, Tianfan Fu, Bo Chen, Kai Yu, Xin Chen, Lu Chen
arXiv AI
Jul 17

StructureClaw: Traceable LLM Agents and an Executable Benchmark for Structural Engineering Workflows

arXiv:2607. 14896v1 Announce Type: cross Abstract: Addressing a structural-engineering request requires more than a single answer; it requires a chain of interdependent artifacts: interpreted requirements, a computable model, validation records, solver outputs, code-check records, and a final report.

By Sizhong Qin, Yi Gu, Yao Jiang, Ao Cai, Changjian Zhou, Shaoxuan Shuai, Jiachang Wang, Tianhao Shen, Yueqiang Li, Xinhao Li, Li Zeng, Yueshi Chen, Dachen Gao, Genrong Xu, Wenjie Liao, Xinzheng Lu
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