@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://preview.aclanthology.org/ingest-acl/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."
}