RocketAgent is a long‑horizon engineering agent designed to streamline the multidisciplinary preliminary design of liquid‑rocket thrust chambers. It employs a single plan‑owning Coding Agent to coordinate tasks such as performance sizing, subsystem optimization, geometry generation, and multiphysics assessment, while a provenance‑aware knowledge graph and a typed Design Intermediate Representation manage method selection and shared design data. Revision‑aware checks invalidate affected results and block superseded inputs, ensuring that changes are properly approved and that subsequent design steps are coordinated, as demonstrated in a representative simulation‑based design and controlled scenario tests.
The paper introduces MADA, a Large Language Model–powered multi‑agent framework that coordinates specialized agents—Job Management, Geometry, and Inverse Design—to automate complex design workflows on high‑performance computing systems. In the context of Richtmyer–Meshkov Instability suppression for Inertial Confinement Fusion, MADA iteratively refines designs by launching ensemble simulations, generating meshes, and proposing new designs based on simulation outcomes, achieving improved suppression with minimal manual effort. The framework demonstrates how coordinated reasoning, simulation, and specialized tools can be scaled for rapid, automated design exploration.
By Harshitha Menon, Charles F. Jekel, Kevin Korner, M. Giselle Fernandez-Godino, Brian Gunnarson, Nathan K. Brown, Michael Stees, Walter Nissen, Meir H. Shachar, Dane M. Sterbentz, William J. Schill, Yue Hao, Robert Rieben, William Quadros, Steve Owen, Scott Mitchell, Ismael D. Boureima, Jonathan L. Belof
AI for Research (AI4Research) leverages AI to automate and improve scientific workflows. While experimental design is a critical stage of the research process, prior work has focused primarily on code implementation and execution, overlooking the importance of this stage, and no benchmark exists to evaluate AI's ability to conduct systematic experiment design.
arXiv:2608. 03501v1 Announce Type: new Abstract: AI for Research (AI4Research) leverages AI to automate and improve scientific workflows.
By Zejun Liu, Jian Wu, Ru Peng, Yuliang Ji, Dongyuan Li, Renhe Jiang, Yue Zhang
arXiv:2608. 16733v1 Announce Type: cross Abstract: Physical design algorithms operate within tightly coupled, multi-stage optimization flows, where stage-local gains may vanish or induce downstream degradation.
By Haixu Liu, Lei Zhou, Yuhao Ren, Yumao Wu, Zhiang Wang
SimuVerity is a new benchmark that evaluates text-to-executable Simulink model-generation tasks across ten engineering domains, using 101 tasks that include executable-system profiles and native simulation scenarios. The benchmark employs a hierarchical evaluator that first checks artifact delivery, native executability, and engineering qualification, then scores qualified models on six dimensions such as accuracy, output quality, mechanistic fidelity, control and causal integrity, operating-domain robustness, and dynamic response. In tests of six agent systems, the best achieved only a 42.86 overall score, revealing that structural similarity is a poor proxy for engineering performance and that high-scoring models can still have severe visual-layout disorder.
By Ruiqi Zhang, Jiahao Wang, Mingxuan Li, Haichen Luo, Chaoting Wang, Guoyu Mou, Keyu Lai, Hanchao Lv, Jiaxu Wang, Yibo Zheng, Aijun Yang, Xiaohua Wang