MOOSEnger is a simulation‑aware AI agent framework designed for the MOOSE ecosystem, integrating an interchangeable reasoning model with domain knowledge, revised simulation artifacts, MOOSE‑specific validation, and executable solver feedback. Its generate‑check‑repair‑run workflow uses MOOSE knowledge retrieval, HIT‑aware parsing, syntax metadata, diagnostics, and revision‑controlled authoring to bind evidence to each input revision and guide bounded repair before acceptance. Across 200 prompts, MOOSEnger raises executable success from 5% to 89.5% with GPT‑5.2 and from 0% to 76.5% with Gemma 4 31B, and a ten‑case benchmark shows all generated inputs meet semantic alignment, with eight also meeting numerical‑accuracy criteria.
By Mengnan Li, Jason Miller, Zaid Abulawi, Zachary Prince, Matt Kohl, Jack M. Cavaluzzi, Guillaume Giudicelli, Casey T. Icenhour, Alexander Lindsay, Cody Permann
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
arXiv:2606. 09774v2 Announce Type: replace Abstract: Configuring an advanced scientific simulator, translating a modeling goal into a valid, runnable input deck, is a persistent bottleneck that costs domain scientists hours to days.
By Matthew Ho, Brian Liu, Jixuan Chen, Audrey Wang, Lianhui Qin
arXiv:2603. 20253v3 Announce Type: replace-cross Abstract: Evaluating LLM agents for scientific tasks has focused on token costs while ignoring tool-use costs like simulation time and experimental resources.
By Yadi Cao, Sicheng Lai, Jiahe Huang, Yang Zhang, Zach Lawrence, Rohan Bhakta, Izzy F. Thomas, Mingyun Cao, Chung-Hao Tsai, Zihao Zhou, Yidong Zhao, Hao Liu, Alessandro Marinoni, Alexey Arefiev, Rose Yu
arXiv:2606. 09774v1 Announce Type: new Abstract: Advanced scientific simulators expose specialized input languages that turn simulation goals into executable configurations, but learning them can cost domain scientists hours to days.
By Matthew Ho, Brian Liu, Jixuan Chen, Audrey Wang, Lianhui Qin
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