3ViewSense: Spatial and Mental Perspective Reasoning from Orthographic Views in Vision-Language Models
arXiv:2603.07751v2 Announce Type: replace-cross Abstract: Current Large Language Models have achieved Olympiad-level logic, yet Vision-Language Models paradoxically falter on elementary spatial tasks like block counting. This capability mismatch reveals a critical ``spatial intelligence gap,'' where models fail to construct coherent 3D mental representations from 2D observations. We uncover this gap via diagnostic analyses showing the bottleneck is a missing view-consistent spatial interface rather than insufficient visual features or weak reasoning. To bridge this, we introduce \textbf{3ViewS
The paper highlights a growing recognition of fundamental limitations in current Vision-Language Models (VLMs) despite advancements in language understanding, suggesting a critical review of architectural approaches is underway.
This research identifies a core deficiency in AI's ability to interpret and reason about physical space, which is essential for general intelligence and many real-world applications.
The focus is shifting from simply scaling existing VLM architectures to fundamentally redesigning them with integrated spatial reasoning interfaces, potentially leading to a new generation of more capable AI.
- · AI researchers focused on spatial reasoning
- · Hardware developers for 3D sensing
- · Robotics companies
- · Companies relying on simplistic 2D vision models
- · AI approaches that ignore fundamental spatial intelligence
Vision-Language Models will demonstrate improved capabilities in tasks requiring spatial understanding and manipulation.
More robust AI systems will emerge for scenarios like robotics, autonomous vehicles, and augmented reality, which depend heavily on 3D environmental comprehension.
The development of highly capable embodied AI agents could accelerate, as better spatial reasoning is a prerequisite for effective physical interaction.
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Read at arXiv cs.CL