← 研踪 PaperTrailarXiv:2511.00908 · v3
Formalizing Heterogeneous Interaction Modeling for Multi-Agent Debate in Visual Geo-Localization
首次提交 2025-11-02 19:58
版本更新 2026-11-05 12:15
雷达观测 2026-11-06 11:06
摘要
Visual geo-localization requires complex reasoning to infer image locations without GPS metadata. Traditional retrieval methods are limited by database quality while Large Vision Language Models often struggle with diverse scenes. Although existing multi-agent systems improve collaboration, they treat interactions uniformly and lack effective conflict resolution. We propose GraphGeo, a multi-agent debate framework based on heterogeneous graph neural networks for visual geo-localization. The framework models diverse debate relationships through typed edges and separates supportive collaboration, competitive argumentation and knowledge transfer. We introduce a dual-level debate mechanism combining node-level refinement and edge-level argumentation modeling. A cross-level topology refinement strategy enables co-evolution between graph structure and agent representations. Experiments on multiple benchmarks demonstrate GraphGeo outperforms single model and multi-agent baselines. The framework converts cognitive conflicts between agents into higher geo-localization accuracy through structured debate.
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v3 · 2026-11-05 12:15 · 最新观测版本
Formalizing Heterogeneous Interaction Modeling for Multi-Agent Debate in Visual Geo-Localization
Visual geo-localization requires complex reasoning to infer image locations without GPS metadata. Traditional retrieval methods are limited by database quality while Large Vision Language Models often struggle with diverse scenes. Although existing multi-agent systems improve collaboration, they treat interactions uniformly and lack effective conflict resolution. We propose GraphGeo, a multi-agent debate framework based on heterogeneous graph neural networks for visual geo-localization. The framework models diverse debate relationships through typed edges and separates supportive collaboration, competitive argumentation and knowledge transfer. We introduce a dual-level debate mechanism combining node-level refinement and edge-level argumentation modeling. A cross-level topology refinement strategy enables co-evolution between graph structure and agent representations. Experiments on multiple benchmarks demonstrate GraphGeo outperforms single model and multi-agent baselines. The framework converts cognitive conflicts between agents into higher geo-localization accuracy through structured debate.
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