Attention-based Vision-Language Memory for Spatial Reasoning

Spatial reasoning is a critical capability for intelligent robots,yet current vision-language models (VLMs) still fall short of human-levelperformance in video-based spatial reasoning. This gap mainly stemsfrom two challenges: a semantic-geometric misalignment that preventsconsistent 3D understanding, and the absence of persistent memory toretain 3D representation and understanding across frames. To addressthese limitations, we present VLM2 , a Vision-Language Model with per-sistent Memory for spatial reasoning with a view-consistent, 3D-awarerepresentation purely from 2D videos. Specifically, we incorporate a dual-memory module consisting of a working memory that operates as a slid-ing window to focus on immediate context, and an episodic memory thatconsolidates and stores critical information across frames. This designenables bounded and efficient spatial reasoning under a fixed computa-tional cost. Extensive experiments on multiple benchmarks show thatVLM2 achieves state-of-the-art performance among video-based models,significantly advancing the frontier of visual-spatial intelligence.