ERGeoBench: A Comprehensive Benchmark for Embodied Reasoning and Geo-localization in Multimodal Large Language Models
Kaiwen Xue, Tao Wei, Zhonghong Ou, Guoxin Zhang, Kaoyan Lu, Yu Feng, Yifan Zhu, Haoran Luo
Abstract
Multimodal large language models (MLLMs) have shown strong potential for building embodied agents, yet embodied geo-localization remains underexplored due to the lack of fine-grained evaluation. We introduce ERGeoBench, a large-scale benchmark for vision-driven embodied geo-localization. ERGeoBench evaluates models under three progressively interactive settings—single-view, multi-view, and embodied-view—where agents actively acquire observations through sequential viewpoint changes. The benchmark comprises 2,207 globally distributed street-view panoramas and assesses four core capability dimensions: foundational perception, spatial awareness, commonsense reasoning, and geo-localization. Extensive evaluations of leading proprietary and open-source MLLMs reveal that while current models perform well in high-level semantic geo-localization, they struggle with low-level perceptual operations and maintaining spatial consistency across views. Notably, geo-localization performance exhibits a strong positive correlation with the other three capability dimensions, indicating that accurate localization emerges from robust perception, coherent spatial reasoning, and sound commonsense understanding. Overall, ERGeoBench provides a unified and diagnostic framework for advancing human-like embodied geo-localization.
BibTeX
@inproceedings{
xue2026ergeobench,
title={{ERG}eoBench: A Comprehensive Benchmark for Embodied Reasoning and Geo-localization in Multimodal Large Language Models},
author={Kaiwen Xue and Tao Wei and Guoxin Zhang and Zhonghong Ou and Kaoyan Lu and Yu Feng and Yifan Zhu and Haoran Luo},
booktitle={Forty-third International Conference on Machine Learning},
year={2026},
url={https://openreview.net/forum?id=4HV8YpgiXL}
}