ReaGeo: Reasoning-Enhanced End-to-End Geocoding with LLMs

Computer Science > Artificial Intelligence [Submitted on 23 Apr 2026] ReaGeo: Reasoning-Enhanced End-to-End Geocoding with LLMs Jian Cui, Zhiyuan Ren, Desheng Weng, Yongqi Zhao, Gong Wenbin, Yu Lei, Zhenning Dong 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. Comments: 12 pages, 8 figures, submitted to ACM SIGSPATIAL 2024 (under review) Subjects: Artificial Intelligence (cs.AI); Computation and Language (cs.CL) ACM classes: I.2.7; I.2.6; H.2.8 Cite as: arXiv:2604.21357 [cs.AI]   (or arXiv:2604.21357v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2604.21357 Focus to learn more Submission history From: Zhiyuan Ren [view email] [v1] Thu, 23 Apr 2026 07:18:21 UTC (3,768 KB) Access Paper: HTML (experimental) view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-04 Change to browse by: cs cs.CL References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)