@inproceedings{jiang-etal-2026-perceive,
title = "Perceive to Hypothesize, Verify to Ground: an Agentic Reasoning Framework for Open-World Geo-Localization",
author = "Jiang, Yutian and
Li, Ruijie and
Lyu, Sisuo and
Hao, Xixuan and
Liu, Qingxiang and
Yu, Yongzi and
Liang, Yuxuan",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.1984/",
pages = "45132--45153",
ISBN = "979-8-89176-459-0",
abstract = "Open-world geo-localization requires models to reason over ambiguous visual cues through multi-step reasoning and external knowledge grounding. While recent large vision-language models exhibit strong multimodal reasoning capabilities, existing approaches still suffer from perceptual hallucination and context drift due to the lack of explicit evidence-grounded verification. In this work, we reformulate geo-localization as a human-like perceive-then-verify reasoning problem and propose GeoPAVE (Geo-localization Perception-and-Verification-Engine), a bi-level agentic framework that contains perception-based hypothesis generation via single-pass rollouts and verification-based evidence grounding for decision actions: support, refute, and refine. To support rigorous evaluation, we further introduce PAVED, a novel dataset derived from real-world user check-in data, equipped with comprehensive reasoning trajectories featuring multi-hop queries, multi-round tool invocations, and structured perception-verification traces. The dataset and code are available at https://github.com/Arandinglv/GeoPAVE."
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%0 Conference Proceedings
%T Perceive to Hypothesize, Verify to Ground: an Agentic Reasoning Framework for Open-World Geo-Localization
%A Jiang, Yutian
%A Li, Ruijie
%A Lyu, Sisuo
%A Hao, Xixuan
%A Liu, Qingxiang
%A Yu, Yongzi
%A Liang, Yuxuan
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F jiang-etal-2026-perceive
%X Open-world geo-localization requires models to reason over ambiguous visual cues through multi-step reasoning and external knowledge grounding. While recent large vision-language models exhibit strong multimodal reasoning capabilities, existing approaches still suffer from perceptual hallucination and context drift due to the lack of explicit evidence-grounded verification. In this work, we reformulate geo-localization as a human-like perceive-then-verify reasoning problem and propose GeoPAVE (Geo-localization Perception-and-Verification-Engine), a bi-level agentic framework that contains perception-based hypothesis generation via single-pass rollouts and verification-based evidence grounding for decision actions: support, refute, and refine. To support rigorous evaluation, we further introduce PAVED, a novel dataset derived from real-world user check-in data, equipped with comprehensive reasoning trajectories featuring multi-hop queries, multi-round tool invocations, and structured perception-verification traces. The dataset and code are available at https://github.com/Arandinglv/GeoPAVE.
%U https://aclanthology.org/2026.emnlp-main.1984/
%P 45132-45153
Markdown (Informal)
[Perceive to Hypothesize, Verify to Ground: an Agentic Reasoning Framework for Open-World Geo-Localization](https://aclanthology.org/2026.emnlp-main.1984/) (Jiang et al., EMNLP 2026)
ACL
- Yutian Jiang, Ruijie Li, Sisuo Lyu, Xixuan Hao, Qingxiang Liu, Yongzi Yu, and Yuxuan Liang. 2026. Perceive to Hypothesize, Verify to Ground: an Agentic Reasoning Framework for Open-World Geo-Localization. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 45132–45153, Budapest, Hungary. Association for Computational Linguistics.