Work

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

Year: 2026

Type: conference-paper

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 perceivethen-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.

Source: Conference on Empirical Methods in Natural Language Processing (EMNLP)

DOI: 10.18653/2026.emnlp-main.1984