This paper proposes ReaGeo, an end-to-end geocoding framework based on large language models, designed to overcome the limitations of traditional multi-stage approaches that rely on text or vector similarity retrieval over geographic databases, including workflow complexity, error propagation, and heavy dependence on structured geographic knowledge bases. The method converts geographic coordinates into geohash sequences, reformulating the coordinate prediction task as a text generation problem, and introduces a Chain-of-Thought mechanism to enhance the model's reasoning over spatial relationships. Furthermore, reinforcement learning with a distance-deviation-based reward is applied to optimize the generation accuracy. Comprehensive experiments show that ReaGeo can accurately handle explicit address queries in single-point predictions and effectively resolve vague relative location queries. In addition, the model demonstrates strong predictive capability for non-point geometric regions, highlighting its versatility and generalization ability in geocoding tasks.
@article{arxiv.2604.21357,
title = {ReaGeo: Reasoning-Enhanced End-to-End Geocoding with LLMs},
author = {Jian Cui and Zhiyuan Ren and Desheng Weng and Yongqi Zhao and Gong Wenbin and Yu Lei and Zhenning Dong},
journal= {arXiv preprint arXiv:2604.21357},
year = {2026}
}