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

2026-08-30

AI Summary

本文针对开放世界地理定位中的感知幻觉和上下文漂移问题，提出了一种基于感知-验证的双层代理框架GeoPAVE，并引入了新的数据集PAVED以支持评估。

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.

Problem

• open-world geo-localization
• perceptual hallucination
• context drift
• evidence-grounded verification

Innovation

• Geo-localization
• Perception-and-Verification-Engine
• Agentic Framework
• Multi-step Reasoning