[2603.04756] MOOSEnger -- a Domain-Specific AI Agent for the MOOSE Ecosystem

Computer Science > Artificial Intelligence arXiv:2603.04756 (cs) [Submitted on 5 Mar 2026] Title:MOOSEnger -- a Domain-Specific AI Agent for the MOOSE Ecosystem Authors:Mengnan Li, Jason Miller, Zachary Prince, Alexander Lindsay, Cody Permann View a PDF of the paper titled MOOSEnger -- a Domain-Specific AI Agent for the MOOSE Ecosystem, by Mengnan Li and 4 other authors View PDF HTML (experimental) Abstract:MOOSEnger is a tool-enabled AI agent tailored to the Multiphysics Object-Oriented Simulation Environment (MOOSE). MOOSE cases are specified in HIT ".i" input files; the large object catalog and strict syntax make initial setup and debugging slow. MOOSEnger offers a conversational workflow that turns natural-language intent into runnable inputs by combining retrieval-augmented generation over curated docs/examples with deterministic, MOOSE-aware parsing, validation, and execution tools. A core-plus-domain architecture separates reusable agent infrastructure (configuration, registries, tool dispatch, retrieval services, persistence, and evaluation) from a MOOSE plugin that adds HIT-based parsing, syntax-preserving ingestion of input files, and domain-specific utilities for input repair and checking. An input precheck pipeline removes hidden formatting artifacts, fixes malformed HIT structure with a bounded grammar-constrained loop, and resolves invalid object types via similarity search over an application syntax registry. Inputs are then validated and optionally smoke-te...