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

• URL: http://arxiv.org/abs/2603.24736v1
• Date: Wed, 25 Mar 2026 18:59:13 GMT
• Title: AutoSAM: an Agentic Framework for Automating Input File Generation for the SAM Code with Multi-Modal Retrieval-Augmented Generation
• Abstract summary: AutoSAM ingests unstructured engineering documents, including system diagrams, design reports, and data tables.<n>It extracts simulation-relevant parameters into a human-auditable intermediate representation.<n>The framework achieves 100% utilization of structured inputs, about 88% extraction from PDF text, and 100% completeness in vision-based geometric extraction.
• Score: 5.938383019072487
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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, extracts simulation-relevant parameters into a human-auditable intermediate representation, and synthesizes validated, solver-compatible input decks. Its multimodal retrieval pipeline integrates scientific text extraction, vision-based figure interpretation, semantic embedding, and query answering. We evaluate AutoSAM on four case studies of increasing complexity: a single-pipe steady-state model, a solid-fuel channel with temperature reactivity feedback, the Advanced Burner Test Reactor core, and the Molten Salt Reactor Experiment primary loop. Across all cases, the agent produces runnable SAM models consistent with expected thermal-hydraulic behavior while explicitly identifying missing data and labeling assumed values. The framework achieves 100% utilization of structured inputs, about 88% extraction from PDF text, and 100% completeness in vision-based geometric extraction. These results demonstrate a practical path toward prompt-driven reactor modeling, in which analysts provide system descriptions and supporting documentation while the agent translates them into transparent, and executable, SAM simulations.

Related papers

• AgentX: Towards Agent-Driven Self-Iteration of Industrial Recommender Systems [82.01232437066487]
  AgentX is a production-deployed multi-agent system that restructures this production function.<n>It autonomously generates, implements, evaluates, and learns from recommendation experiments.<n>AgentX orchestrates four tightly coupled stages in a closed loop.
  arXiv  Detail & Related papers  (2026-06-25T10:42:28Z)
• Agentic Design of Compositional Descriptors via Autoresearch for Materials Science Applications [0.0]
  We use composition-based materials-property prediction to test whether autoresearch agents can perform a task beyond model selection.<n>We introduce Automat, an autoresearch framework where a coding agent based on a large language model generates composition-only descriptors for chemical compounds.
  arXiv  Detail & Related papers  (2026-05-14T10:30:17Z)
• ChemGraph-XANES: An Agentic Framework for XANES Simulation and Analysis [4.554542395453299]
  ChemGraph-XANES is an agentic framework for automated XANES simulation and analysis.<n>It unifies natural-language task specification, structure acquisition, FDMNES input generation, spectral normalization, and data curation.
  arXiv  Detail & Related papers  (2026-04-17T16:15:19Z)
• Sketch2Simulation: Automating Flowsheet Generation via Multi Agent Large Language Models [0.0]
  Converting process sketches into executable simulation models remains a major bottleneck in process systems engineering.<n>Recent advances in generative AI have improved both engineering-diagram interpretation and flowsheet generation, but these remain largely disconnected.<n>We present an end-to-end multi-agent large language model system that converts process diagrams directly into executable Aspen HYSYS flowsheets.
  arXiv  Detail & Related papers  (2026-03-25T03:36:46Z)
• FABRIC: Framework for Agent-Based Realistic Intelligence Creation [3.940391073007047]
  Large language models (LLMs) are increasingly deployed as agents, expected to decompose goals, invoke tools, and verify results in dynamic environments.<n>We present a unified framework for synthesizing agentic data using only LLMs, without any human-in-the-loop supervision.
  arXiv  Detail & Related papers  (2025-10-20T18:20:22Z)
• CoDA: Agentic Systems for Collaborative Data Visualization [57.270599188947294]
  Deep research has revolutionized data analysis, yet data scientists still devote substantial time to manually crafting visualizations.<n>Existing approaches, including simple single- or multi-agent systems, often oversimplify the task.<n>We introduce CoDA, a multi-agent system that employs specialized LLM agents for metadata analysis, task planning, code generation, and self-reflection.
  arXiv  Detail & Related papers  (2025-10-03T17:30:16Z)
• ChemActor: Enhancing Automated Extraction of Chemical Synthesis Actions with LLM-Generated Data [53.78763789036172]
  We present ChemActor, a fully fine-tuned large language model (LLM) as a chemical executor to convert between unstructured experimental procedures and structured action sequences.<n>This framework integrates a data selection module that selects data based on distribution divergence, with a general-purpose LLM, to generate machine-executable actions from a single molecule input.<n>Experiments on reaction-to-description (R2D) and description-to-action (D2A) tasks demonstrate that ChemActor achieves state-of-the-art performance, outperforming the baseline model by 10%.
  arXiv  Detail & Related papers  (2025-06-30T05:11:19Z)
• MooseAgent: A LLM Based Multi-agent Framework for Automating Moose Simulation [1.951051209076837]
  This paper proposes an automated solution framework, MooseAgent, for the multi-physics simulation framework MOOSE.<n>MooseAgent combines large-scale pre-trained language models (LLMs) with a multi-agent system.<n>Results show that MooseAgent can automate the MOOSE simulation process to a certain extent.
  arXiv  Detail & Related papers  (2025-04-11T15:25:50Z)
• LatteReview: A Multi-Agent Framework for Systematic Review Automation Using Large Language Models [0.2999888908665658]
  LatteReview is a Python-based framework that leverages large language models (LLMs) and multi-agent systems to automate key elements of the systematic review process.<n>The framework supports features such as Retrieval-Augmented Generation (RAG) for incorporating external context, multimodal reviews, Pydantic-based validation for structured inputs and outputs, and asynchronous programming for handling large-scale datasets.
  arXiv  Detail & Related papers  (2025-01-05T17:53:00Z)
• Multi-Agent Sampling: Scaling Inference Compute for Data Synthesis with Tree Search-Based Agentic Collaboration [81.45763823762682]
  This work aims to bridge the gap by investigating the problem of data synthesis through multi-agent sampling.<n>We introduce Tree Search-based Orchestrated Agents(TOA), where the workflow evolves iteratively during the sequential sampling process.<n>Our experiments on alignment, machine translation, and mathematical reasoning demonstrate that multi-agent sampling significantly outperforms single-agent sampling as inference compute scales.
  arXiv  Detail & Related papers  (2024-12-22T15:16:44Z)
• AlignSAM: Aligning Segment Anything Model to Open Context via Reinforcement Learning [61.666973416903005]
  Segment Anything Model (SAM) has demonstrated its impressive generalization capabilities in open-world scenarios with the guidance of prompts. We propose a novel framework, termed AlignSAM, designed for automatic prompting for aligning SAM to an open context.
  arXiv  Detail & Related papers  (2024-06-01T16:21:39Z)
• Zero-shot Composed Text-Image Retrieval [72.43790281036584]
  We consider the problem of composed image retrieval (CIR) It aims to train a model that can fuse multi-modal information, e.g., text and images, to accurately retrieve images that match the query, extending the user's expression ability.
  arXiv  Detail & Related papers  (2023-06-12T17:56:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.