# MOOSEnger: A Simulation-Aware AI Agent Framework for the MOOSE Ecosystem

arXiv: https://arxiv.org/abs/2603.04756 · HTML: https://mlsanity.com/paper/2603.04756/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Reasoning for agents, planning and code, one of 17 directions in LLM reasoning.
- Builds on 4 of its 23 references that are in the map.
- First posted on arXiv 5 Mar 2026.

## Placed in

- LLM reasoning / [Reasoning for agents, planning and code](https://mlsanity.com/lr/reasoning-for-agents-planning-and-code/)
- LLM agents and tool use / [Web, search, and research agents](https://mlsanity.com/agents/web-search-and-research-agents/)

## Builds on (most cited first, 4 of 4)

- [MetaOpenFOAM: an LLM-based multi-agent framework for CFD](https://mlsanity.com/paper/2407.21320/) (2024)
- [MooseAgent: A LLM Based Multi-agent Framework for Automating Moose Simulation](https://mlsanity.com/paper/2504.08621/) (2025)
- [Foam-Agent: A Large Language Model-Based Multi-Agent Framework for Automating Computational Fluid Dynamics Workflows](https://mlsanity.com/paper/2505.04997/) (2025)
- [ChatCFD: An LLM-Driven Agent for End-to-End CFD Automation with Structured Knowledge and Reasoning](https://mlsanity.com/paper/2506.02019/) (2025)

## Built on by (most cited first, 2 of 2)

- [Your Simulation Runs but Solves the Wrong Physics: PDE-Grounded Intent Verification for LLM-Generated Multiphysics Simulation Code](https://mlsanity.com/paper/2605.09360/) (2026)
- [Deploying Frontier Agentic Technology in MOOSEnger, a Multiphysics-Capable AI Assistant](https://mlsanity.com/paper/2608.15881/) (2026)

## Abstract (arXiv)

MOOSEnger is a modeling and simulation AI agent framework for the Multiphysics Object-Oriented Simulation Environment (MOOSE) ecosystem, built around a simulation-aware harness that combines an interchangeable reasoning model with grounded domain knowledge, revised simulation artifacts, MOOSE-specific validation, and executable solver feedback. This surrounding system addresses a central limitation of one-shot large language model generation: small syntax, schema, reference, or solver-configuration errors can prevent a plausible input from executing, while successful execution alone does not establish scientific correctness. MOOSEnger's simulation-aware harness integrates MOOSE knowledge retrieval, Hierarchical Input Text (HIT)-aware parsing, syntax metadata, language-server diagnostics, revision-controlled authoring, and local or MCP-backed validation and execution in a generate-check-repair-run workflow that binds evidence to each input revision and guides bounded repair before acceptance. Across 200 prompts spanning eight simulation families, the MOOSEnger harness increases executable success from 10/200 (5%) to 179/200 (89.5%) with GPT 5.2 API and from 0/200 to 153/200 (76.5%) with Gemma 4 31B. A complementary ten-case Method of Manufactured Solutions benchmark moves beyond executability: all ten generated inputs satisfy the semantic-alignment criterion, and eight execute successfully while meeting the prescribed single-mesh numerical-accuracy criterion. These results show that executable reliability depends on the complete agent system rather than on the reasoning model alone, and that simulation-aware harnessing provides a path toward physics-informed verification and future full application-level and engineering verification and validation implementation.