用多智能体协作解决现实图问题，提升准确率与可解释性。
MA-GTS: A Multi-Agent Framework for Solving Complex Graph Problems in Real-World Applications
- 多智能体分工协作，动态选择算法并结构化文本图数据
- 在多个真实场景数据集上达到94.2%~98.4%准确率
- 适合需要高效、可解释图计算的工程应用
图论问题广泛存在于物流、通信网络和交通优化等现实应用中。这些问题通常复杂、噪声大且结构不规则，传统算法难以应对。大型语言模型虽有潜力，但存在准确率低和输入长度受限的问题。为此，我们提出MA-GTS（多智能体图论求解框架），通过智能体协作分解复杂问题。该框架将隐含的文本图数据映射为清晰的结构化表示，并根据问题约束和图规模动态选择最优算法，确保求解高效且推理过程可解释。我们在自建的真实场景图论数据集G-REAL上验证了MA-GTS，实验结果表明其在效率、准确率和可扩展性上均优于现有方法，在多个基准测试中表现优异（G-REAL 94.2%，GraCoRe 96.9%，NLGraph 98.4%）。项目已开源：https://github.com/ZIKEYUAN/MA-GTS.git。
原文摘要 · Abstract (English)
Graph-theoretic problems arise in real-world applications like logistics, communication networks, and traffic optimization. These problems are often complex, noisy, and irregular, posing challenges for traditional algorithms. Large language models (LLMs) offer potential solutions but face challenges, including limited accuracy and input length constraints. To address these challenges, we propose MA-GTS (Multi-Agent Graph Theory Solver), a multi-agent framework that decomposes these complex problems through agent collaboration. MA-GTS maps the implicitly expressed text-based graph data into clear, structured graph representations and dynamically selects the most suitable algorithm based on problem constraints and graph structure scale. This approach ensures that the solution process remains efficient and the resulting reasoning path is interpretable. We validate MA-GTS using the G-REAL dataset, a real-world-inspired graph theory dataset we created. Experimental results show that MA-GTS outperforms state-of-the-art approaches in terms of efficiency, accuracy, and scalability, with strong results across multiple benchmarks (G-REAL 94.2%, GraCoRe 96.9%, NLGraph 98.4%).MA-GTS is open-sourced at https://github.com/ZIKEYUAN/MA-GTS.git.
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