arXiv:2605.23898v1 Announce Type: new Abstract: Vision-Language Models (VLMs) are increasingly deployed in embodied environments, where they need produce numerical outputs such as action magnitudes and spatial coordinates. Although these numbers appear meaningful, it remains unclear whether these numerical outputs are genuinely grounded in spatial perception. Therefore, in this work, we revisit spatial numerical understanding through SpaceNum, a unified framework that captures two complementary settings: numbers as dynamic transitions during spatial exploration, and numbers as static layouts i
The increasing deployment of Vision-Language Models in embodied environments necessitates a deeper understanding of their numerical grounding for practical application.
A refined understanding of how VLMs interpret and produce spatial numerical outputs is crucial for the reliable and safe deployment of AI in physical actions and sophisticated spatial reasoning.
This research introduces a unified framework to systematically evaluate and enhance VLMs' spatial numerical understanding, moving beyond superficial numerical outputs.
- · AI developers
- · Robotics companies
- · Embodied AI researchers
- · Developers relying on ungrounded numerical VLM outputs
Improved reliability and precision of AI systems operating in physical spaces requiring numerical understanding.
Accelerated development of advanced autonomous agents capable of complex manipulation and navigation.
Enhanced trust and adoption of AI in domains where spatial accuracy and numerical reasoning are paramount, such as manufacturing and logistics.
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Read at arXiv cs.AI