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 that coordinates performance sizing, subsystem optimization, geometry generation, and multiphysics assessment, while a provenance‑aware knowledge graph and a typed Design Intermediate Representation maintain consistent design information and enforce revision‑aware checks. In a representative simulation‑based design, RocketAgent successfully navigated infeasible cooling searches, incorporated engineer‑approved operating‑point revisions, and coordinated subsequent geometry generation and multiphysics assessment to support final configuration selection, with controlled tests confirming the proper invalidation of superseded inputs.
By Junxiang He, Runze Mao, Kun He, Teng Zhang, Liming Zheng, Ke Xiao, Zhi X. Chen
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:2606. 01385v1 Announce Type: cross Abstract: Software architecture design is a critical yet inherently complex and knowledge-intensive phase that requires balancing competing quality attributes and adapting to evolving requirements.
By Ruiyin Li, Yiran Zhang, Xiyu Zhou, Yangxiao Cai, Peng Liang, Weisong Sun, Jifeng Xuan, Zhi Jin, Yang Liu
TO-Agents is a multi‑agent AI framework that translates natural‑language design intent into iterative topology optimization. It converts a human problem description into solver inputs, runs the optimizer, renders 3D topologies, and employs a judge agent to critique and revise results using multiview vision‑language reasoning. Evaluated on a cantilever beam and a phone‑stand design, the system achieved preference‑aligned designs in 60% of trials, outperforming an ablated pipeline by up to six times and enabling end‑to‑end intent‑to‑prototype design with additive manufacturing.
By Isabella A. Stewart, Hongrui Chen, Faez Ahmed
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
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
AstroAgentBench is a seven-family benchmark designed to evaluate agentic planning for space mission tasks such as scheduling, observation planning, constellation design, and relay support. The benchmark requires agents to submit planning artifacts that are verified for schema, timing, geometry, resources, and mission value, with results reported as validity and normalized scores compared to task-specific solver references. Across five LLM agent systems and 35 held-out cases, the strongest systems approach or exceed solver-reference scores on several families, while weaker systems often fail to produce high-value valid plans, and even strong systems lose quality on geometric, product-level, or design-heavy tasks. Failure analyses identify task-contract misformulation and weak solution construction as key issues, and successful runs demonstrate that agent calibration against verifier feedback and adaptive search can improve performance; ablation studies show that procedure injection and memory accumulation help when they provide the missing formulation, calibration, or search support.
By Weiyi Wang, Xinchi Chen, Jingjing Gong, Xuanjing Huang, Xipeng Qiu
Design-to-Plan is a large language model–based multi‑agent framework that automates end‑to‑end manufacturing process planning from 3D CAD models and 2D engineering drawings. The system uses an orchestrator to coordinate specialized agents for feature recognition, drawing analysis, context fusion, knowledge retrieval, process sequencing, tool selection, and report generation, integrating deterministic modules with LLM reasoning. Evaluation on 300 benchmark cases shows high success rates, strong tool selection accuracy, effective conflict detection, and reduced token usage, demonstrating the framework’s ability to produce consistent, traceable design‑to‑plan outputs.
By Muhammad Tayyab Khan, Lequn Chen, Wenhe Feng, Seung Ki Moon
Computer-use agents are increasingly evaluated in realistic desktop environments, but existing benchmarks provide limited coverage of professional engineering workflows whose outputs are persistent, s...
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