发表机构
Information Processing and Telecomunications Center, ETSI Telecomunicación, Universidad Politécnica de Madrid（信息处理与电信中心，马德里理工大学电信工程学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究如何用相对位置编码增强Transformer路由以解决团队定向越野问题，核心方法是在注意力机制中嵌入图节点空间关系，实验表明相比普通Transformer架构有改进，凸显显式关系建模对复杂组合优化的作用。
AI 中文摘要
本文探索将相对位置编码（RPE）作为Transformer架构中的附加偏差来解决团队定向越野问题。通过在注意力机制中嵌入表示路由问题的图节点间的成对空间关系，Transformer编码器可计算出更丰富的空间感知图嵌入，使解码器能更好地估计路线。对多达100个节点的实例进行实验，结果表明相比其他工作中使用的普通Transformer架构，在收集奖励和最优性差距方面有持续改进。这些发现突出了显式关系建模显著增强了复杂组合优化的可扩展性和泛化能力。
英文摘要
This paper explores Relative Positional Encoding (RPE) as an additive bias in Transformer architectures to solve the Team Orienteering Problem. By embedding in the attention mechanism pairwise spatial relationships among nodes of the graph that represents the routing problem, the transformer encoder can compute a richer spatial-aware graph embedding that allows the decoder to estimate better routes. Experimental results involving instances up to 100 nodes demonstrate consistent improvements in collected rewards and optimality gaps over vanilla Transformer architectures used by other state-of-the-art works. These findings highlight that explicit relational modeling significantly enhances scalability and generalization for complex combinatorial optimization.
CommentsThis work was accepted to be presented at the Graph Signal Processing Workshop 2026
