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

2026-04-23

AI Summary

This work proposes the first end-to-end geocoding framework based on large language models, reframing coordinate prediction as a text generation task. In contrast to traditional multi-stage approaches—which suffer from complex pipelines, error propagation, and heavy reliance on structured geographic knowledge bases—the proposed method eliminates the need for external databases by integrating chain-of-thought reasoning to enhance spatial relationship inference. Furthermore, it employs a reinforcement learning mechanism with a distance-bias-aware reward function to refine coordinate prediction accuracy. The framework not only achieves high precision in mapping explicit addresses to point coordinates but also effectively interprets ambiguous relative location descriptions and demonstrates strong generalization capabilities for non-point, region-based queries.

Technology Category

• Natural Language Processing: (Large) Language Models
• Planning, Routing, and Scheduling: Planning with Language Models
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• geocoding
• multi-stage approaches
• error propagation
• structured geographic knowledge bases
• workflow complexity

Innovation

• geocoding
• large language models
• Chain-of-Thought reasoning
• geohash
• reinforcement learning