GeoX: Mastering Geospatial Reasoning Through Self-Play and Verifiable Rewards
GeoX is a new framework designed to enhance geospatial reasoning through self-play and verifiable rewards. It operates without the need for extensive human-curated data, utilizing a multimodal policy to solve spatial problems. The framework has shown improvements over traditional models, achieving better performance with less reliance on large datasets.
- ▪GeoX employs a self-play framework to acquire spatial logic through executable programs.
- ▪The system can solve spatial problems using three reasoning modes: abduction, deduction, and induction.
- ▪GeoX has improved its base models by up to 5.5 points on average compared to conventional baselines.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.20006 |
| Publication time | Wed, 20 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-20T04:04:59.484Z |
| Last seen | 2026-05-20T04:04:59.484Z |
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| Summary source text | contentText |
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| Commercial reuse | May the content be reused commercially? | Not permitted |
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Computer Science > Artificial Intelligence arXiv:2605.20006 (cs) [Submitted on 19 May 2026] Title:GeoX: Mastering Geospatial Reasoning Through Self-Play and Verifiable Rewards Authors:Kyeongjin Ahn, Seungeon Lee, Krishna P. Gummadi, Meeyoung Cha View a PDF of the paper titled GeoX: Mastering Geospatial Reasoning Through Self-Play and Verifiable Rewards, by Kyeongjin Ahn and Seungeon Lee and Krishna P. Gummadi and Meeyoung Cha View PDF HTML (experimental) Abstract:Geospatial reasoning requires solving image-grounded problems over the complex spatial structure of a scene. However, developing this capability is hindered by the cost of annotating a vast and combinatorial question space.
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