Perceive to Hypothesize, Verify to Ground: An Agentic Reasoning Framework for Open-World Geo-Localization

• URL: http://arxiv.org/abs/2608.29880v1
• Date: Sun, 30 Aug 2026 16:19:10 GMT
• Title: Perceive to Hypothesize, Verify to Ground: An Agentic Reasoning Framework for Open-World Geo-Localization
• Abstract summary: Open-world geo-localization requires models to reason over ambiguous visual cues through multi-step reasoning.<n>Existing approaches still suffer from perceptual hallucination and context drift due to the lack of explicit evidence-grounded verification.<n>We reformulate geo-localization as a human-like perceive-then-verify reasoning problem and propose GeoPAVE, a bi-level agentic framework.
• Score: 32.84908122358348
• License: http://creativecommons.org/licenses/by/4.0/
• 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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