用多智能体协作让大模型解决工程问题时更可行。
EngiAgent: Fully Connected Coordination of LLM Agents for Solving Open-ended Engineering Problems with Feasible Solutions
- 设计全连接协调器，动态调度各专业智能体协同工作。
- 在四个领域测试中，可行性显著优于现有方法。
- 适合需要可靠解的工程场景，如结构设计、系统优化。
工程问题求解是现实决策的核心，要求数学建模不仅表达复杂问题，还需在数据和物理约束下生成可行解。与预定义公式的数学题不同，工程任务需开放式分析、以可行性为导向的建模及迭代优化。尽管大语言模型（LLMs）在推理和代码生成上表现强劲，但常无法保证解的可行性，限制了其在工程中的应用。为此，我们提出EngiAgent，一个由全连接协调器驱动的多智能体系统，模拟专家工作流程，包含问题分析、建模、验证、求解与评估等专用智能体。全连接协调器支持灵活反馈路径，克服了以往流水线式反思方法的僵化，确保每个阶段的可行性。该设计不仅提升了对数据提取错误、约束冲突、求解失败等多样故障的鲁棒性，还显著改善了整体求解质量。在四个代表性领域的实证结果表明，EngiAgent相较已有方法在可行性方面有显著提升，为基于大模型的可行性导向工程求解树立了新范式。代码与数据已公开于 https://github.com/AI4Engi/EngiAgent。
原文摘要 · Abstract (English)
Engineering problem solving is central to real-world decision-making, requiring mathematical formulations that not only represent complex problems but also produce feasible solutions under data and physical constraints. Unlike mathematical problem solving, which operates on predefined formulations, engineering tasks demand open-ended analysis, feasibility-driven modeling, and iterative refinement. Although large language models (LLMs) have shown strong capabilities in reasoning and code generation, they often fail to ensure feasibility, which limits their applicability to engineering problem solving. To address this challenge, we propose EngiAgent, a multi-agent system with a fully connected coordinator that simulates expert workflows through specialized agents for problem analysis, modeling, verification, solving, and solution evaluation. The fully connected coordinator enables flexible feedback routing, overcoming the rigidity of prior pipeline-based reflection methods and ensuring feasibility at every stage of the process. This design not only improves robustness to diverse failure cases such as data extraction errors, constraint inconsistencies, and solver failures, but also enhances the overall quality of problem solving. Empirical results across four representative domains demonstrate that EngiAgent achieves substantial improvements in feasibility compared to prior approaches, establishing a new paradigm for feasibility-oriented engineering problem solving with LLMs. Our source code and data are available at https://github.com/AI4Engi/EngiAgent.
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