ERGeoBench:A Comprehensive Benchmark for Embodied Reasoning and Geo-localization in Multimodal Large Language Models
On the map: LLM reasoning → Spatial and embodied reasoning
Abstract (arXiv)
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-view panoramas and measures four complementary capabilities: foundational perception, spatial awareness, common sense reasoning, and geo-localization reasoning. Evaluations of leading proprietary and open-source MLLMs show that current models can infer high-level geographic semantics, but still struggle with fine-grained perceptual operations, metric localization, and spatial consistency across views. We further observe that geo-localization is strongly correlated with the other capability dimensions, suggesting that accurate localization depends on integrated perception, spatial reasoning, and commonsense inference rather than isolated visual recognition. Overall, ERGeoBench provides a unified framework for diagnosing and advancing human-like embodied geo-localization. Project Page: https://kaixuewen.github.io/ERGeoBench/
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Spatial and embodied reasoning, one of 17 directions in LLM reasoning. Builds on 11 of its 34 references that are in the map. First posted on arXiv 29 May 2026.
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11 of 34 references.
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- 2025Evaluation of Geolocation Capabilities of Multimodal Large Language Models and Analysis of Associated Privacy Risks30 Jun
- 2025Recognition through Reasoning: Reinforcing Image Geo-localization with Large Vision-Language Models17 Jun
- 2025GRE Suite: Geo-localization Inference via Fine-Tuned Vision-Language Models and Enhanced Reasoning Chains24 May
Other references · 8
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- 2023Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyondcited by 2,224
- 2024Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scalingcited by 1,599
- 2025From Pixels to Places: A Systematic Benchmark for Evaluating Image Geolocalization Ability in Large Language Modelscited by 12
- 2025GAEA: A Geolocation Aware Conversational Assistantcited by 5
- 2025CreBench: Human-Aligned Creativity Evaluation from Idea to Process to Productcited by 2
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- 2025ViewSpatial-Bench: Evaluating Multi-perspective Spatial Localization in Vision-Language Models
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- 2026Contextual Observer Grounding: Evaluating Situated Spatial Reasoning in Vision-Language Models
- 2025OST-Bench: Evaluating the Capabilities of MLLMs in Online Spatio-temporal Scene Understanding
- 2025Spatial Reasoning in Multimodal Large Language Models: A Survey of Tasks, Benchmarks and Methods
- 2026GeoAgent: Evaluating VLM Geolocalization Through Embodied Navigation
- 2026Towards a Data-Centric Understanding of Spatial Reasoning in Multimodal Large Language Models
- 2026m2sv: A Scalable Benchmark for Map-to-Street-View Spatial Reasoning