@inproceedings{ji-etal-2026-thinking,
title = "Thinking with Map: Reinforced Parallel Map-Augmented Agent for Geolocalization",
author = "Ji, Yuxiang and
Wang, Yong and
Ma, Ziyu and
Hu, Yiming and
Huang, Hailang and
Hu, Xuecai and
Chen, Guanhua and
Wu, Liaoni and
Chu, Xiangxiang",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.894/",
pages = "17991--18004",
ISBN = "979-8-89176-395-1",
abstract = "The image geolocalization task aims to predict the location where an image was taken anywhere on Earth using visual clues.Existing large vision-language model (LVLM) approaches leverage world knowledge, chain-of-thought reasoning, and agentic capabilities, but overlook a common strategy used by humans {---} using maps.In this work, we first equip the model \textit{Thinking with Map} ability and formulate it as an agent-in-the-map loop.We develop a two-stage optimization scheme for it, including agentic reinforcement learning (RL) followed by parallel test-time scaling (TTS).The RL strengthens the agentic capability of model to improve sampling efficiency, and the parallel TTS enables the model to explore multiple candidate paths before making the final prediction, which is crucial for geolocalization.To evaluate our method on up-to-date and in-the-wild images, we further present MAPBench, a comprehensive geolocalization training and evaluation benchmark composed entirely of real-world images.Experimental results show that our method outperforms existing open- and closed-source models on most metrics, specifically improving Acc@500m from 8.0{\%} to 22.1{\%} compared to \textit{Gemini-3-Pro} with Google Search/Map grounded mode."
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<abstract>The image geolocalization task aims to predict the location where an image was taken anywhere on Earth using visual clues.Existing large vision-language model (LVLM) approaches leverage world knowledge, chain-of-thought reasoning, and agentic capabilities, but overlook a common strategy used by humans — using maps.In this work, we first equip the model Thinking with Map ability and formulate it as an agent-in-the-map loop.We develop a two-stage optimization scheme for it, including agentic reinforcement learning (RL) followed by parallel test-time scaling (TTS).The RL strengthens the agentic capability of model to improve sampling efficiency, and the parallel TTS enables the model to explore multiple candidate paths before making the final prediction, which is crucial for geolocalization.To evaluate our method on up-to-date and in-the-wild images, we further present MAPBench, a comprehensive geolocalization training and evaluation benchmark composed entirely of real-world images.Experimental results show that our method outperforms existing open- and closed-source models on most metrics, specifically improving Acc@500m from 8.0% to 22.1% compared to Gemini-3-Pro with Google Search/Map grounded mode.</abstract>
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%0 Conference Proceedings
%T Thinking with Map: Reinforced Parallel Map-Augmented Agent for Geolocalization
%A Ji, Yuxiang
%A Wang, Yong
%A Ma, Ziyu
%A Hu, Yiming
%A Huang, Hailang
%A Hu, Xuecai
%A Chen, Guanhua
%A Wu, Liaoni
%A Chu, Xiangxiang
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F ji-etal-2026-thinking
%X The image geolocalization task aims to predict the location where an image was taken anywhere on Earth using visual clues.Existing large vision-language model (LVLM) approaches leverage world knowledge, chain-of-thought reasoning, and agentic capabilities, but overlook a common strategy used by humans — using maps.In this work, we first equip the model Thinking with Map ability and formulate it as an agent-in-the-map loop.We develop a two-stage optimization scheme for it, including agentic reinforcement learning (RL) followed by parallel test-time scaling (TTS).The RL strengthens the agentic capability of model to improve sampling efficiency, and the parallel TTS enables the model to explore multiple candidate paths before making the final prediction, which is crucial for geolocalization.To evaluate our method on up-to-date and in-the-wild images, we further present MAPBench, a comprehensive geolocalization training and evaluation benchmark composed entirely of real-world images.Experimental results show that our method outperforms existing open- and closed-source models on most metrics, specifically improving Acc@500m from 8.0% to 22.1% compared to Gemini-3-Pro with Google Search/Map grounded mode.
%U https://aclanthology.org/2026.findings-acl.894/
%P 17991-18004
Markdown (Informal)
[Thinking with Map: Reinforced Parallel Map-Augmented Agent for Geolocalization](https://aclanthology.org/2026.findings-acl.894/) (Ji et al., Findings 2026)
ACL
- Yuxiang Ji, Yong Wang, Ziyu Ma, Yiming Hu, Hailang Huang, Xuecai Hu, Guanhua Chen, Liaoni Wu, and Xiangxiang Chu. 2026. Thinking with Map: Reinforced Parallel Map-Augmented Agent for Geolocalization. In Findings of the Association for Computational Linguistics: ACL 2026, pages 17991–18004, San Diego, California, United States. Association for Computational Linguistics.