ERGeoBench:A Comprehensive Benchmark for Embodied Reasoning and Geo-localization in Multimodal Large Language Models

• URL: http://arxiv.org/abs/2605.31251v1
• Date: Fri, 29 May 2026 12:49:17 GMT
• Title: ERGeoBench:A Comprehensive Benchmark for Embodied Reasoning and Geo-localization in Multimodal Large Language Models
• Abstract summary: ERGeoBench is a diagnostic benchmark for vision-driven embodied geo-localization.<n> benchmark contains 2,207 globally distributed street-view panoramas.<n> ERGeoBench provides a unified framework for diagnosing and advancing human-like embodied geo-localization.
• Score: 17.86934121595618
• License: http://creativecommons.org/licenses/by/4.0/
• 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-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/

Related papers

• Where Do Vision-Language Models Fail? World Scale Analysis for Image Geolocalization [3.713195826948382]
  Vision-Language Models (VLMs) have demonstrated strong zero-shot reasoning capabilities across multimodal tasks.<n>We present a systematic evaluation of multiple state-of-the-art VLMs for country-level image geolocalization using ground-view imagery only.
  arXiv  Detail & Related papers  (2026-04-17T17:09:14Z)
• Learning to Wander: Improving the Global Image Geolocation Ability of LMMs via Actionable Reasoning [72.13218601075958]
  We introduce textbfWanderBench, the first open access global geolocation benchmark for actionable geolocation reasoning in embodied scenarios.<n>We propose textbfGeoAoT (Action of Thought), a underlineGeolocation framework with underlineAction of underlineThough, which couples reasoning with embodied actions.<n>Experiments on 19 large multimodal models show that GeoAoT achieves superior fine-grained localization and stronger generalization in dynamic environments.
  arXiv  Detail & Related papers  (2026-03-11T06:24:10Z)
• GeoFocus: Blending Efficient Global-to-Local Perception for Multimodal Geometry Problem-Solving [55.14836667214487]
  GeoFocus is a novel framework comprising two core modules.<n>GeoFocus achieves a 4.7% accuracy improvement over leading specialized models.<n>It demonstrates superior robustness in MATHVERSE under diverse visual conditions.
  arXiv  Detail & Related papers  (2026-02-09T11:15:01Z)
• Thinking with Geometry: Active Geometry Integration for Spatial Reasoning [68.59084007360615]
  We propose GeoThinker, a framework that shifts paradigm passive fusion to active perception.<n>Instead of feature mixing, GeoThinker enables the model to selectively retrieve geometric evidence conditioned on its internal reasoning demands.<n>Our results indicate that the ability to actively integrate spatial structures is essential for next-generation spatial intelligence.
  arXiv  Detail & Related papers  (2026-02-05T18:59:32Z)
• GeoVista: Web-Augmented Agentic Visual Reasoning for Geolocalization [53.080882980294795]
  Current research on agentic visual reasoning enables deep multimodal understanding but primarily focuses on image manipulation tools.<n>In this work, we revisit the geolocalization task, which requires not only nuanced visual grounding but also web search to confirm or refine hypotheses.<n>Since existing geolocalization benchmarks fail to meet the need for high-resolution imagery and the localization challenge for deep agentic reasoning, we curate GeoBench.<n>We propose GeoVista, an agentic model that seamlessly integrates tool invocation within the reasoning loop, including an image-zoom-in tool to magnify regions of interest and a web-search tool to retrieve related
  arXiv  Detail & Related papers  (2025-11-19T18:59:22Z)
• From Pixels to Places: A Systematic Benchmark for Evaluating Image Geolocalization Ability in Large Language Models [14.178064117544082]
  Image geolocalization is important for applications in crisis response, digital forensics, and location-based intelligence.<n>Recent advances in large language models (LLMs) offer new opportunities for visual reasoning.<n>We introduce a benchmark called IMAGEO-Bench that systematically evaluates accuracy, distance error, geospatial bias, and reasoning process.
  arXiv  Detail & Related papers  (2025-08-03T06:04:33Z)
• Recognition through Reasoning: Reinforcing Image Geo-localization with Large Vision-Language Models [47.98900725310249]
  New pipeline constructs a reasoning-oriented geo-localization dataset, MP16-Reason, using diverse social media images.<n>GLOBE incorporates task-specific rewards that jointly enhance localizability assessment, visual-cue reasoning, and geolocation accuracy.<n>Results demonstrate that GLOBE outperforms state-of-the-art open-source LVLMs on geo-localization tasks.
  arXiv  Detail & Related papers  (2025-06-17T16:07:58Z)
• GRE Suite: Geo-localization Inference via Fine-Tuned Vision-Language Models and Enhanced Reasoning Chains [20.788130896943663]
  Geo Reason Enhancement (GRE) Suite is a novel framework that augments Visual Language Models with structured reasoning chains for interpretable location inference.<n>First, we introduce GRE30K, a high-quality geo-localization reasoning dataset designed to facilitate fine-grained visual and contextual analysis.<n>Next, we present the GRE model, which employs a multi-stage reasoning strategy to progressively infer scene attributes, local details, and semantic features, thereby narrowing down potential geographic regions with enhanced precision.
  arXiv  Detail & Related papers  (2025-05-24T13:48:57Z)
• Swarm Intelligence in Geo-Localization: A Multi-Agent Large Vision-Language Model Collaborative Framework [51.26566634946208]
  We introduce smileGeo, a novel visual geo-localization framework. By inter-agent communication, smileGeo integrates the inherent knowledge of these agents with additional retrieved information. Results show that our approach significantly outperforms current state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-08-21T03:31:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.