Figureure 2 · : Overview of GeoWeaverFigure 2: Overview of GeoWeaver. GeoWeaver treats geometry as a representational prerequisite rather than a late fusion signal. A frozen VGGT encoder provides a multi-layer geometry bank, from which each visual token adaptively retrieves sparse geometric evidence via query-conditioned compact geometric grounding before entering the Qwen LLM. This pre-reasoning grounding process converts semantic visual tokens into geometry-grounded visual tokens for spatio-temporal reasoning.这张图概括 GeoWeaver 的整体方法流程。阅读时先看模块之间传递的训练信号，再看作者如何把目标拆成可优化的子问题。(b) Visualization of VGGT feature responses from different layers(b) Visualization of VGGT feature responses from different layers. Figure 1: Motivation and paradigm comparison of GeoWeaver. Top: Existing geometry-enhanced VLMs mainly introduce geometry through pre-fusion or LLM-side fusion, while GeoWeaver grounds visual tokens before language reasoning. Bottom: VGGT feature maps from different layers exhibit heterogeneous spatial responses, indicating that multi-layer geometry does not provide a uniform signal. This motivates our design of treating geometry features as a multi-level evidence bank.这张图/表用于判断 GeoWeaver 的实验收益来自哪里。重点看替换、消融或跨模型设置下趋势是否一致，而不是只看单个最高分。
