Empowered by large-scale training, vision-language models (VLMs) achieve strong image and video understanding, yet their ability to perform spatial reasoning in both static scenes and dynamic videos remains limited. Recent advances try to handle this limitation by injecting geometry tokens from pretrained 3D foundation models into VLMs. Nevertheless, we observe that naive token fusion followed by standard fine-tuning in this line of work often leaves such geometric cues underutilized for spatial reasoning, as VLMs tend to rely heavily on 2D visual cues. In this paper, we propose GeoSR, a framework designed to make geometry matter by encouraging VLMs to actively reason with geometry tokens. GeoSR introduces two key components: (1) Geometry-Unleashing Masking, which strategically masks portions of 2D vision tokens during training to weaken non-geometric shortcuts and force the model to consult geometry tokens for spatial reasoning; and (2) Geometry-Guided Fusion, a gated routing mechanism that adaptively amplifies geometry token contributions in regions where geometric evidence is critical. Together, these designs unleash the potential of geometry tokens for spatial reasoning tasks. Extensive experiments on both static and dynamic spatial reasoning benchmarks demonstrate that GeoSR consistently outperforms prior methods and establishes new state-of-the-art performance by effectively leveraging geometric information. The project page is available at https://suhzhang.github.io/GeoSR/.

翻译：通过大规模训练赋能，视觉语言模型（VLM）在图像和视频理解方面取得了显著进展，但其在静态场景和动态视频中执行空间推理的能力仍然有限。近期研究尝试通过将预训练3D基础模型中的几何标记注入VLM来解决这一局限。然而，我们观察到这类工作中简单的标记融合配合标准微调往往使几何线索在空间推理中未能得到充分利用，因为VLM倾向于过度依赖2D视觉线索。本文提出GeoSR框架，其核心在于通过激励VLM主动利用几何标记进行推理，使几何信息发挥作用。GeoSR引入两个关键组件：（1）几何释放遮蔽，在训练过程中策略性地遮蔽部分2D视觉标记，削弱非几何捷径，迫使模型在空间推理中依赖几何标记；（2）几何引导融合，一种门控路由机制，在几何证据关键区域自适应增强几何标记的贡献。这些设计共同释放了几何标记在空间推理任务中的潜力。在静态和动态空间推理基准上的大量实验表明，GeoSR通过有效利用几何信息，持续超越先前方法并建立了新的最佳性能。项目页面详见 https://suhzhang.github.io/GeoSR/。