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 in spatial reasoning. We formulate two bidirectional tasks, Num2Space and Space2Num, to evaluate how well VLMs map between vision-side spatial structure and language-side numerical representations. We systematically study whether current VLMs truly understand numerical values in spatial settings. Across dynamic transitions and static layouts, we find that models largely fail to ground numbers in spatial meaning and often perform close to random guess. Through error analysis, reasoning trace analysis, and controlled interventions, we show that current VLMs rely heavily on shallow spatial cues, struggle to build stable coordinate-aware representations, and fail to abstract structured spatial layouts from visual observations. We further show that explicit reasoning provides only marginal gains, while tuning can partially improve spatial numerical understanding and transfer to external spatial reasoning benchmarks.

翻译：视觉语言模型（VLM）越来越多地部署于具身环境中，需要输出动作幅度与空间坐标等数值结果。尽管这些数字看似合理，但其数值输出是否真正植根于空间感知仍不明确。为此，本文通过SpaceNum这一统一框架重新审视空间数值理解，该框架涵盖两个互补场景：空间探索中的数值动态转换，以及空间推理中的数值静态布局。我们构建了Num2Space与Space2Num两项双向任务，用以评估VLM在视觉空间结构与语言数值表征之间的映射能力。系统研究表明，当前VLM在空间场景中未能真正理解数值含义，其表现接近随机猜测。通过误差分析、推理轨迹追踪与可控干预实验，我们发现现有VLM严重依赖浅层空间线索，难以建立稳定的坐标感知表征，且无法从视觉观测中抽象出结构化空间布局。研究进一步表明，显式推理仅能带来边际收益，而参数微调可部分改善空间数值理解能力，并泛化至外部空间推理基准。