[2603.24736] AutoSAM: an Agentic Framework for Automating Input File Generation for the SAM Code with Multi-Modal Retrieval-Augmented Generation

Computer Science > Artificial Intelligence arXiv:2603.24736 (cs) [Submitted on 25 Mar 2026] Title:AutoSAM: an Agentic Framework for Automating Input File Generation for the SAM Code with Multi-Modal Retrieval-Augmented Generation Authors:Zaid Abulawi (1 and 2), Zavier Ndum Ndum (1 and 2), Eric Cervi (2), Rui Hu (2), Yang Liu (1) ((1) Department of Nuclear Engineering, Texas A&M University, (2) Nuclear Science and Engineering Division, Argonne National Laboratory) View a PDF of the paper titled AutoSAM: an Agentic Framework for Automating Input File Generation for the SAM Code with Multi-Modal Retrieval-Augmented Generation, by Zaid Abulawi (1 and 2) and 7 other authors View PDF HTML (experimental) Abstract:In the design and safety analysis of advanced reactor systems, constructing input files for system-level thermal-hydraulics codes such as the System Analysis Module (SAM) remains a labor-intensive task. Analysts must extract and reconcile design data from heterogeneous engineering documents and manually translate it into solver-specific syntax. In this paper, we present AutoSAM, an agentic framework that automates SAM input file generation. The framework combines a large language model agent with retrieval-augmented generation over the solver's user guide and theory manual, together with specialized tools for analyzing PDFs, images, spreadsheets, and text files. AutoSAM ingests unstructured engineering documents, including system diagrams, design reports, and data tables...