arXiv AI
Sep 30

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

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.

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
arXiv AI
Aug 26

Beyond Executable Models: The Pufibara Agent Harness and the Modelica Agent Workflow Benchmark for Physical System Modeling

The paper introduces Pufibara, an agent harness designed to maintain engineering state and evidence across revisions in Modelica-based physical system modeling. It also presents a 232-task Modelica Agent Workflow Benchmark covering model repair, generation, and tuning, evaluated by an external benchmark-owned evaluator. Experiments show Pufibara outperforms Claude Code in task success and resource efficiency across two LLM backends.

By Zizhe Wang
arXiv AI
Jun 12

From Digital to Physical: Digital Agents as Autonomous Coaches for Physical Intelligence

arXiv:2601. 21570v2 Announce Type: replace Abstract: The field of Embodied AI is witnessing a rapid evolution toward general-purpose robotic systems, fueled by high-fidelity simulation and large-scale data collection.

By Zixing Lei, Genjia Liu, Yuanshuo Zhang, Qipeng Liu, Yuzhu Cai, Sixiang Chen, Jixian Wu, Yunhong Wang, Weixin Li, Chuan Wen, Bo Zhao, Shanghang Zhang, Wenzhao Lian, Siheng Chen
arXiv AI
Jun 17

EngTrace: A Symbolic Benchmark for Verifiable Process Supervision of Engineering Reasoning

arXiv:2511. 01650v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are increasingly entering specialized, safety-critical engineering workflows governed by strict quantitative standards and immutable physical laws, making rigorous evaluation of their reasoning capabilities imperative.

By Ayesha Gull, Muhammad Usman Safder, Rania Elbadry, Fan Zhang, Veselin Stoyanov, Preslav Nakov, Zhuohan Xie
arXiv AI
Jun 17

A Framework for Evaluating Agentic Skills at Scale

arXiv:2606. 17819v1 Announce Type: cross Abstract: Agent skills -- structured, reusable knowledge artifacts that augment LLM agent capabilities -- have been rapidly adopted in industry, yet their cross-domain impact and use across commercial and open-source models remain under-studied, and no reusable methodology exists for evaluating an individual skill.

By Maksim Shaposhnikov, Nicolas Fortuin, Simon Stipcich, Maria I. Gorinova, Amy Heineike, Rob Willoughby