GeoAgent: Learning to Geolocate Everywhere with Reinforced Geographic Characteristics
Fuente:
arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866915796487241728 |
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| author | Jin, Modi Zhang, Yiming Sun, Boyuan Zhang, Dingwen Cheng, MingMing Hou, Qibin |
| author_facet | Jin, Modi Zhang, Yiming Sun, Boyuan Zhang, Dingwen Cheng, MingMing Hou, Qibin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_12617 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | GeoAgent: Learning to Geolocate Everywhere with Reinforced Geographic Characteristics Jin, Modi Zhang, Yiming Sun, Boyuan Zhang, Dingwen Cheng, MingMing Hou, Qibin Artificial Intelligence 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. |
| title | GeoAgent: Learning to Geolocate Everywhere with Reinforced Geographic Characteristics |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2602.12617 |