arXiv:2601.19099v2 Announce Type: replace-cross Abstract: Vision--language models (VLMs) achieve strong performance on many multimodal benchmarks but remain brittle on spatial reasoning tasks that require aligning abstract overhead representations with egocentric views. We introduce m2sv, a scalable benchmark for map-to-street-view spatial reasoning that asks models to infer camera viewing direction by aligning a north-up overhead map with a Street View image captured at the same real-world intersection. We release m2sv-20k, a geographically diverse benchmark with controlled ambiguity, along w
The continuous evolution of vision-language models (VLMs) and the increasing demand for robust spatial reasoning capabilities in AI agents necessitate advanced benchmarks to identify current limitations and drive future development.
This benchmark highlights a critical weakness in current AI systems regarding spatial reasoning and alignment of different visual perspectives, which is fundamental for real-world autonomous applications.
The introduction of m2sv provides a standardized and scalable dataset specifically designed to challenge and improve AI's ability to interpret and fuse map-based and egocentric visual information.
- · AI researchers in computer vision and spatial AI
- · Developers of autonomous systems and robotics
- · Companies building advanced mapping and navigation technologies
- · AI models with brittle spatial reasoning capabilities
- · Developers relying solely on existing, less rigorous benchmarks
Improved spatial reasoning capabilities in future AI models, particularly VLMs.
Accelerated development of more robust autonomous vehicles and robotics that can better understand and navigate complex environments.
Enhanced integration of AI into applications requiring accurate real-world perception and interaction, such as augmented reality or advanced surveying.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI