ERGeoBench:A Comprehensive Benchmark for Embodied Reasoning and Geo-localization in Multimodal Large Language Models
arXiv:2605.31251v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) have shown strong potential as embodied agents, yet embodied geo-localization remains underexplored due to the lack of fine-grained evaluation. We introduce ERGeoBench, a diagnostic benchmark for vision-driven embodied geo-localization. ERGeoBench evaluates models under three progressive settings -- single-view, panorama-view, and embodied-view -- where agents may actively acquire observations through sequential changes in yaw, pitch, and zoom. The benchmark contains 2,207 globally distributed street-vie
The proliferation of advanced multimodal large language models (MLLMs) and increasing interest in their application as embodied agents necessitate more rigorous and specialized evaluation benchmarks.
Improved geo-localization capabilities for embodied AI agents are critical for their effective deployment in real-world environments, impacting a wide range of autonomous applications.
The introduction of a specialized benchmark like ERGeoBench provides a standardized framework to measure and accelerate progress in MLLMs' embodied reasoning and precise geo-localization.
- · AI developers
- · Robotics companies
- · Navigation technology providers
- · Research institutions
- · Models with poor spatial reasoning
Enhanced geo-localization leads to more robust and reliable embodied AI applications.
Greater accuracy in an agent's understanding of its physical location will enable deployment in complex and safety-critical environments.
The widespread adoption of highly geo-aware embodied AI agents could redefine logistics, urban planning, and environmental monitoring.
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Read at arXiv cs.AI