Learning to Solve Compositional Geometry Routing Problems
The article discusses a new approach to solving the Compositional Geometry Routing Problem (CGRP), which encompasses various traditional routing challenges. The authors introduce DiCon, a differential attention-assisted solver that enhances decision-making in complex routing scenarios. Extensive experiments show that DiCon offers strong performance and versatility across different instances of CGRP.
- ▪The Compositional Geometry Routing Problem (CGRP) includes point-only, line-only, area-only, and hybrid task geometries.
- ▪DiCon employs a differential attention mechanism to improve action selection and reduce irrelevant options.
- ▪The framework also features a double-level contrastive learning objective to enhance representation learning.
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Computer Science > Artificial Intelligence arXiv:2605.18094 (cs) [Submitted on 18 May 2026] Title:Learning to Solve Compositional Geometry Routing Problems Authors:Mingfeng Fan, Jianan Zhou, Jiaqi Cheng, Yifeng Zhang, Jie Zhang, Guillaume Adrien Sartoretti View a PDF of the paper titled Learning to Solve Compositional Geometry Routing Problems, by Mingfeng Fan and 5 other authors View PDF HTML (experimental) Abstract:We study the Compositional Geometry Routing Problem (CGRP), a unified superclass of traditional routing problems that covers point-only, line-only, area-only, and arbitrary hybrid task geometries, providing a broad abstraction for real-world routing scenarios.
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