{"slug": "aeroground-a-comprehensive-benchmark-for-aerial-ground-collaborative-reasoning", "title": "AeroGround: A Comprehensive Benchmark for Aerial-Ground Collaborative Reasoning", "summary": "A new benchmark, AeroGround, evaluates vision-language models (VLMs) on aerial-ground collaborative reasoning tasks, revealing a significant performance gap: the best model achieves 54.4% average accuracy versus 93.3% for humans. The benchmark, built on a simulated dataset with approximately 29,000 multimodal observation groups and 2,250 question-answering instances, tests cross-view correspondence, spatial understanding, and reasoning across 16 pretrained VLMs and two domain-adapted variants. The findings highlight current model limitations and aim to guide development of more capable aerial-ground collaborative embodied intelligence systems.", "body_md": "arXiv:2608.14721v1 Announce Type: new\nAbstract: Vision-language models (VLMs) have been widely employed in understanding and reasoning tasks for unmanned aerial vehicles (UAVs). Existing UAV benchmarks primarily focus on aerial-view scenarios. However, whether current VLMs can perform well on understanding and reasoning tasks in aerial-ground collaborative scenarios which are practical in real-world applications like rescue and infrastructure inspection remains underexplored. To address this gap, we introduce AeroGround, a comprehensive benchmark for evaluating VLMs in aerial-ground collaborative reasoning. AeroGround is built upon a simulated aerial-ground dataset containing approximately 29,000 multimodal observation groups from diverse open environments, and provides 2,250 high-quality question-answering instances covering cross-view correspondence, spatial understanding, and reasoning. Experiments on 16 pretrained VLMs, together with two domain-adapted variants, reveal a substantial gap between current models and human performance: the best model achieves an average accuracy of 54.4%, whereas humans reach 93.3%. By systematically revealing the strengths and limitations of existing models in aerial-ground collaborative reasoning, AeroGround provides a foundation for developing more capable aerial-ground collaborative embodied intelligence systems.", "url": "https://wpnews.pro/news/aeroground-a-comprehensive-benchmark-for-aerial-ground-collaborative-reasoning", "canonical_source": "https://arxiv.org/abs/2608.14721", "published_at": "2026-08-18 04:00:00+00:00", "updated_at": "2026-08-18 04:11:48.966888+00:00", "lang": "en", "topics": ["artificial-intelligence", "computer-vision", "ai-research"], "entities": ["AeroGround", "arXiv"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/aeroground-a-comprehensive-benchmark-for-aerial-ground-collaborative-reasoning", "markdown": "https://wpnews.pro/news/aeroground-a-comprehensive-benchmark-for-aerial-ground-collaborative-reasoning.md", "text": "https://wpnews.pro/news/aeroground-a-comprehensive-benchmark-for-aerial-ground-collaborative-reasoning.txt", "jsonld": "https://wpnews.pro/news/aeroground-a-comprehensive-benchmark-for-aerial-ground-collaborative-reasoning.jsonld"}}