# A Lightweight Large Language Model-Based Multi-Agent System for 2D Frame Structural Analysis arXiv: https://arxiv.org/abs/2510.05414 ยท HTML: https://mlsanity.com/paper/2510.05414/ ## Key facts - Cited by 9 papers in the map (data through 14 Oct 2026). - Placed in Multimodal and embodied agents, one of 16 directions in LLM agents and tool use. - Builds on 23 of its 34 references that are in the map. - First posted on arXiv 6 Oct 2025. ## Placed in - LLM agents and tool use / [Multimodal and embodied agents](https://mlsanity.com/agents/multimodal-and-embodied-agents/) ## Builds on (most cited first, 23 of 23) - [DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning](https://mlsanity.com/paper/2501.12948/) (2025) - [Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities](https://mlsanity.com/paper/2507.06261/) (2025) - [A Survey on Large Language Models for Code Generation](https://mlsanity.com/paper/2406.00515/) (2024) - [A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models](https://mlsanity.com/paper/2405.06211/) (2024) - [Large Language Models for Mathematical Reasoning: Progresses and Challenges](https://mlsanity.com/paper/2402.00157/) (2024) - [LLM Multi-Agent Systems: Challenges and Open Problems](https://mlsanity.com/paper/2402.03578/) (2024) - [Beyond Functional Correctness: Exploring Hallucinations in LLM-Generated Code](https://mlsanity.com/paper/2404.00971/) (2024) - [A Comprehensive Survey on Process-Oriented Automatic Text Summarization with Exploration of LLM-Based Methods](https://mlsanity.com/paper/2403.02901/) (2024) - [Metacognitive Capabilities of LLMs: An Exploration in Mathematical Problem Solving](https://mlsanity.com/paper/2405.12205/) (2024) - [Enhancing LLM Factual Accuracy with RAG to Counter Hallucinations: A Case Study on Domain-Specific Queries in Private Knowledge-Bases](https://mlsanity.com/paper/2403.10446/) (2024) - [The Ultimate Guide to Fine-Tuning LLMs from Basics to Breakthroughs: An Exhaustive Review of Technologies, Research, Best Practices, Applied Research Challenges and Opportunities](https://mlsanity.com/paper/2408.13296/) (2024) - [LEGO-Puzzles: How Good Are MLLMs at Multi-Step Spatial Reasoning?](https://mlsanity.com/paper/2503.19990/) (2025) - [Fine-tuning and Utilization Methods of Domain-specific LLMs](https://mlsanity.com/paper/2401.02981/) (2024) - [ChatSUMO: Large Language Model for Automating Traffic Scenario Generation in Simulation of Urban MObility](https://mlsanity.com/paper/2409.09040/) (2024) - [On Context Utilization in Summarization with Large Language Models](https://mlsanity.com/paper/2310.10570/) (2023) - [GIS Copilot: Towards an Autonomous GIS Agent for Spatial Analysis](https://mlsanity.com/paper/2411.03205/) (2024) - [AI Agents in Engineering Design: A Multi-Agent Framework for Aesthetic and Aerodynamic Car Design](https://mlsanity.com/paper/2503.23315/) (2025) - [Large Language Models in Wireless Application Design: In-Context Learning-enhanced Automatic Network Intrusion Detection](https://mlsanity.com/paper/2405.11002/) (2024) - [Integrating Large Language Models for Automated Structural Analysis](https://mlsanity.com/paper/2504.09754/) (2025) - [LLM Agent for Fire Dynamics Simulations](https://mlsanity.com/paper/2412.17146/) (2024) - [A Large Language Model-Empowered Agent for Reliable and Robust Structural Analysis](https://mlsanity.com/paper/2507.02938/) (2025) - [SoM-1K: A Thousand-Problem Benchmark Dataset for Strength of Materials](https://mlsanity.com/paper/2509.21079/) (2025) - [A RAG-Based Multi-Agent LLM System for Natural Hazard Resilience and Adaptation](https://mlsanity.com/paper/2504.17200/) (2025) ## Built on by (most cited first, 9 of 9) - [Automating Structural Engineering Workflows with Large Language Model Agents](https://mlsanity.com/paper/2510.11004/) (2025) - [StructureClaw: Traceable LLM Agents and an Executable Benchmark for Structural Engineering Workflows](https://mlsanity.com/paper/2607.14896/) (2026) - [Rethinking Scientific Modeling: Toward Physically Consistent and Simulation-Executable Programmatic Generation](https://mlsanity.com/paper/2602.07083/) (2026) - [VFEAgent: A Multimodal Agent Framework for End-to-End Automated Finite Element Analysis](https://mlsanity.com/paper/2605.28978/) (2026) - [A Constrained Natural-Language Interface for Variational Multi-Physics Finite Element Simulations in FEniCS](https://mlsanity.com/paper/2606.10928/) (2026) - [A Lightweight Multi-Agent Framework for Automated Concrete Barrier Design](https://mlsanity.com/paper/2606.12040/) (2026) - [An Agentic Interface for End-to-End Probabilistic Seismic Hazard and Risk Analysis](https://mlsanity.com/paper/2607.16249/) (2026) - [Automating structural reliability analysis with a multi-agent large language model framework](https://mlsanity.com/paper/2607.16580/) (2026) - [Verication-driven closed-loop multi-agent large language modelframework for code-compliant structural design](https://mlsanity.com/paper/2608.07978/) (2026) ## Abstract (arXiv) Large language models (LLMs) have recently been used to empower autonomous agents in engineering, significantly improving automation and efficiency in labor-intensive workflows. However, their potential remains underexplored in structural engineering, particularly for finite element modeling tasks requiring geometric modeling, complex reasoning, and domain knowledge. To bridge this gap, this paper develops a LLM-based multi-agent system to automate finite element modeling of 2D frames. The system decomposes structural analysis into subtasks, each managed by a specialized agent powered by the lightweight Llama-3.3 70B Instruct model. The workflow begins with a Problem Analysis Agent, which extracts geometry, boundary, and material parameters from the user input. Next, a Geometry Agent incrementally derives node coordinates and element connectivity by applying expert-defined rules. These structured outputs are converted into executable OpenSeesPy code by a Translation Agent and refined by a Model Validation Agent through consistency checks. Then, a Load Agent applies load conditions into the assembled structural model. Experimental evaluations on 20 benchmark problems demonstrate that the system achieves accuracy over 80% in most cases across 10 repeated trials, outperforming Gemini-2.5 Pro and ChatGPT-4o models.