KilometerVision: A New Frontier for Large-Scale Spatial Intelligence in VLMs

Abstract:We push the frontier of large-scale spatial intelligence in Vision-Language Models (VLMs) and introduce the first benchmark that probes geographical layout understanding from real-world videos, spanning up to 1km distances. Inspired by the cognitive science literature, we evaluate models against the hierarchical stages of human spatial awareness: anchoring via landmarks, connecting them through routes, and integrating these into global mental maps. Extensive experiments reveal a fundamental divergence in how current AI models process spatial information. Instead of utilising true path integration or forming geometric survey knowledge, we find that VLMs rely almost entirely on 2D visual recognition and text-matching to bypass complex spatial reasoning. The benchmark is publicly available at this https URL.

Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)

arXiv:2609.39588 [cs.CV]

(or arXiv:2609.39588v1 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2609.39588 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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