A Multidisciplinary Design and Optimization (MDO) Agent Driven by Large Language Models
On the map: LLM agents and tool use → Multimodal and embodied agents
Abstract (arXiv)
To accelerate mechanical design and enhance design quality and innovation, we present a Multidisciplinary Design and Optimization (MDO) Agent driven by Large Language Models (LLMs). The agent semi-automates the end-to-end workflow by orchestrating three core capabilities: (i) natural-language-driven parametric modeling, (ii) retrieval-augmented generation (RAG) for knowledge-grounded conceptualization, and (iii) intelligent orchestration of engineering software for performance verification and optimization. Working in tandem, these capabilities interpret high-level, unstructured intent, translate it into structured design representations, automatically construct parametric 3D CAD models, generate reliable concept variants using external knowledge bases, and conduct evaluation with iterative optimization via tool calls such as finite-element analysis (FEA). Validation on three representative cases - a gas-turbine blade, a machine-tool column, and a fractal heat sink - shows that the agent completes the pipeline from natural-language intent to verified and optimized designs with reduced manual scripting and setup effort, while promoting innovative design exploration. This work points to a practical path toward human-AI collaborative mechanical engineering and lays a foundation for more dependable, vertically customized MDO systems.
Key facts. Cited by 1 paper 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 11 of its 30 references that are in the map. First posted on arXiv 6 Oct 2025.
Builds on
11 of 30 references.
References · 11
- 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
- 2023A Survey on Evaluation of Large Language Modelscited by 1,242
- 2023Vision-Language Models for Vision Tasks: A Surveycited by 839
- 2023A Comprehensive Overview of Large Language Modelscited by 648
- 2023Explainability for Large Language Models: A Surveycited by 333
- 2023ToolQA: A Dataset for LLM Question Answering with External Toolscited by 274
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- 2024Retrieval-Augmented Generation with Knowledge Graphs for Customer Service Question Answeringcited by 96
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- 2024AvaTaR: Optimizing LLM Agents for Tool Usage via Contrastive Reasoningcited by 72
- 2024A Large Language Model and Denoising Diffusion Framework for Targeted Design of Microstructures with Commands in Natural Languagecited by 6
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Citing papers per year: 2026: 1.
1 paper.
Similar, not cited
Close in wording, no citation either way
- 2024Large Language Model Agent as a Mechanical Designer
- 2025From Idea to CAD: A Language Model-Driven Multi-Agent System for Collaborative Design
- 2026TOOLCAD: Exploring Tool-Using Large Language Models in Text-to-CAD Generation with Reinforcement Learning
- 2026Physics-in-the-Loop: A Hybrid Agentic Architecture for Validated CAD Engineering Design
- 2025Toward Autonomous Engineering Design: A Knowledge-Guided Multi-Agent Framework
- 2025AI Agents in Engineering Design: A Multi-Agent Framework for Aesthetic and Aerodynamic Car Design
- 2025Automating Structural Engineering Workflows with Large Language Model Agents
- 2026COSMO-Agent: Tool-Augmented Agent for Closed-loop Optimization,Simulation,and Modeling Orchestration