SpotAgent: Grounding Visual Geo-localization in Large Vision-Language Models through Agentic Reasoning

Researchers introduce SpotAgent, a new framework that improves AI geo-localization by combining visual interpretation with external tool verification through agentic reasoning. The system addresses limitations of current Large Vision-Language Models that often make confident but ungrounded predictions when visual cues are sparse or ambiguous.

• →SpotAgent uses external tools like web search and maps to verify visual cues in geo-localization tasks.
• →The framework employs a 3-stage training pipeline including supervised fine-tuning, agentic cold start, and reinforcement learning.
• →SpotAgent achieves state-of-the-art performance on standard benchmarks while reducing AI hallucinations.
• →The system introduces Spatially-Aware Dynamic Filtering to improve training efficiency by prioritizing learnable samples.
• →The approach addresses real-world challenges where visual cues are sparse, ambiguous, or highly specialized.