@inproceedings{cui-etal-2026-reageo,
title = "{R}ea{G}eo: Reasoning-Enhanced End-to-End Geocoding with {LLM}s",
author = "Cui, Jian and
Ren, Zhiyuan and
Weng, Desheng and
Zhao, Yongqi and
Wenbin, Gong and
Lei, Yu and
Dong, Zhenning",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.1396/",
pages = "28012--28023",
ISBN = "979-8-89176-395-1",
abstract = "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."
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%0 Conference Proceedings
%T ReaGeo: Reasoning-Enhanced End-to-End Geocoding with LLMs
%A Cui, Jian
%A Ren, Zhiyuan
%A Weng, Desheng
%A Zhao, Yongqi
%A Wenbin, Gong
%A Lei, Yu
%A Dong, Zhenning
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F cui-etal-2026-reageo
%X 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.
%U https://aclanthology.org/2026.findings-acl.1396/
%P 28012-28023
Markdown (Informal)
[ReaGeo: Reasoning-Enhanced End-to-End Geocoding with LLMs](https://aclanthology.org/2026.findings-acl.1396/) (Cui et al., Findings 2026)
ACL
- Jian Cui, Zhiyuan Ren, Desheng Weng, Yongqi Zhao, Gong Wenbin, Yu Lei, and Zhenning Dong. 2026. ReaGeo: Reasoning-Enhanced End-to-End Geocoding with LLMs. In Findings of the Association for Computational Linguistics: ACL 2026, pages 28012–28023, San Diego, California, United States. Association for Computational Linguistics.