SVG generation is typically evaluated by comparing rendered outputs to reference images, which captures visual similarity but not the structural properties that make SVG editable, decomposable, and reusable. Inspired by the classical jackknife, we introduce element-level leave-one-out (LOO) analysis. The procedure renders the SVG with and without each element, which yields element-level signals for quality assessment and structural analysis. From this single mechanism, we derive (i) per-element quality scores that enable zero-shot artifact detection; (ii) element-concept attribution via LOO footprints crossed with VLM-grounded concept heatmaps; and (iii) four structural metrics: purity, coverage, compactness, and locality, which quantify SVG modularity from complementary angles. These metrics extend SVG evaluation from image similarity to code structure, enabling element-level diagnosis and comparison of how visual concepts are represented, partitioned, and organized within SVG code. Their practical relevance is validated on over 19,000 edits (5 types) across 5 generation systems and 3 complexity tiers.

翻译：SVG生成通常通过对比渲染输出与参考图像进行评估，这虽能捕捉视觉相似性，却无法体现SVG作为可编辑、可分解、可复用对象的构造特性。受经典刀切法启发，我们引入元素级留一分析。该方法分别生成包含与剔除各元素后的SVG，从而产生用于质量评估与结构分析的元素级信号。基于这一统一机制，我们推导出：（i）支持零样本伪影检测的各元素质量评分；（ii）通过留一足迹与VLM主题热力图交叉得到的元素-概念归因；（iii）四项结构度量指标：纯度、覆盖率、紧凑性与局部性，从互补角度量化SVG模块化程度。这些度量将SVG评估从图像相似度拓展至代码结构层面，支持对视觉概念在SVG代码中表达、划分与组织方式的元素级诊断与对比。我们在5个生成系统、3个复杂度层级的19000余次编辑（5种类型）上验证了其实际效用。