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arXiv · 2602.12617

GeoAgent: Learning to Geolocate Everywhere with Reinforced Geographic Characteristics

Modi Jin·Yiming Zhang·Boyuan Sun·Dingwen Zhang·MingMing Cheng·Qibin Hou
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Abstract

This paper presents GeoAgent, a model capable of reasoning closely with humans and deriving fine-grained address conclusions. Previous RL-based methods have achieved breakthroughs in performance and interpretability but still remain concerns because of their reliance on AI-generated chain-of-thought (CoT) data and training strategies, which conflict with geographic characteristics. To address these issues, we first introduce GeoSeek, a new geolocation dataset comprising CoT data annotated by geographic experts and professional players. We further thoroughly explore the inherent characteristics of geographic tasks and propose a geo-similarity reward and a consistency reward assessed by a consistency agent to assist training. This encourages the model to converge towards correct answers from a geographic perspective while ensuring the integrity and consistency of its reasoning process. Experimental results show that GeoAgent outperforms existing methods and a series of general VLLMs across multiple grains, while generating reasoning that closely aligns with humans.

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Modi Jin, Yiming Zhang, Boyuan Sun, Dingwen Zhang, MingMing Cheng, Qibin Hou. 2026-02-13. GeoAgent: Learning to Geolocate Everywhere with Reinforced Geographic Characteristics. https://arxiv.org/abs/2602.12617

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