The rapid expansion of modern wide-area networks (WANs) has made traffic engineering (TE) increasingly challenging, as traditional solvers struggle to keep pace. Although existing offline ML-driven approaches accelerate TE optimization with deep neural networks (DNNs), they often lack sufficient expressiveness and generalization on unseen traffic patterns or topologies, limiting their practicality. Inspired by the success of large language models (LMs), for the first time, this paper investigates their potential as general-purpose traffic planners. Our contributions are two-fold: (i) Theoretically, we show that pre-trained LMs can simulate the sequential decision processes underlying TE and, crucially, exhibit parallel reasoning capabilities, making them well-suited for the task; (ii) Practically, we present LMTE, a novel LM-driven TE framework that embraces these insights through efficient multimodal alignment and lightweight configuration generation, all while preserving the model's original abilities. Extensive experiments demonstrate that fold matches top-tier performance on five datasets, achieving up to 15\% better maximum link utilization (MLU) and consistently lower performance degradation across diverse scenarios, e.g., less than 5\% with high traffic dynamics and link failures. Moreover, it achieves 10 to 100 times speedups over traditional TE solvers. To aid future works, our codebase is available at https://github.com/Y-debug-sys/LMTE.

翻译：现代广域网（WAN）的快速扩张使得流量工程（TE）日益复杂，传统求解器难以跟上发展步伐。尽管现有的离线机器学习驱动方法通过深度神经网络（DNN）加速了TE优化，但它们通常在未见过的流量模式或拓扑结构上缺乏足够的表达能力和泛化性，从而限制了其实用性。受大型语言模型（LM）成功的启发，本文首次探讨了其作为通用流量规划器的潜力。我们的贡献包括两个方面：（i）理论上，我们证明预训练的LM能够模拟TE背后的序列决策过程，并且关键地展现出并行推理能力，使其非常适合此任务；（ii）实践上，我们提出了LMTE，一种新颖的LM驱动的TE框架，该框架通过高效的多模态对齐和轻量级配置生成来体现这些见解，同时保留了模型的原始能力。大量实验表明，该框架在五个数据集上达到了顶尖性能，实现了高达15%的最大链路利用率（MLU）提升，并在多样化场景（例如高流量动态和链路故障下性能下降低于5%）中始终保持更低的性能衰减。此外，相较于传统TE求解器，其速度提升了10至100倍。为促进后续研究，我们的代码库已在 https://github.com/Y-debug-sys/LMTE 公开。