用符号几何分析修复教育视频生成中的空间错误，提升画面清晰度。
SGA: Plug&Play Geometric Verification for Educational Video Synthesis
- 拦截LLM生成代码，通过符号化场景图检测空间冲突。
- 在4个LLM和2种流程上平均提升16.1%的视觉质量分数。
- 适合做代码驱动动画的开发者或教育科技研究者。
现有工作利用大语言模型（LLMs）通过Manim等库生成教学动画代码，但难以保证空间正确性和视觉可读性，因现有框架侧重教学内容而忽略几何遮挡问题。本文提出符号几何代理（SGA），作为代码中心动画流程的即插即用模块，可拦截LLM生成代码，进行部分执行以提取符号化场景图，并在检测到空间冲突时实施针对性修正。我们还引入曼姆视觉质量评分（MVQS），一种无需渲染的确定性空间完整性代理指标。在MMMC-Code基准上对四个LLM基线和两种智能体流水线的实验表明，SGA在Code2Video + GPT-5.1配置下达到峰值MVQS 73.11，相较原始基线相对提升16.1%，并在8组配置中7组提升MVQS。
原文摘要 · Abstract (English)
Recent work leverages Large Language Models (LLMs) to generate executable code for pedagogical animations using libraries such as Manim. However, ensuring spatial correctness and visual legibility remains challenging, as existing frameworks emphasize pedagogical content while overlooking geometric occlusions. We propose the Symbolic Geometric Agent (SGA), a plug-and-play module for code-centric animation pipelines that intercepts LLM-generated code, performs partial execution to extract symbolic scene graphs, and applies targeted refinement when spatial conflicts are detected. We further introduce the Manim Visual Quality Score (MVQS), a deterministic rendering-free proxy for spatial integrity. Experiments on the MMMC-Code benchmark across four LLM backbones and two agentic pipelines show that SGA achieves a peak MVQS of 73.11 (Code2Video + GPT-5.1), corresponding to a 16.1% relative improvement over the raw baseline, and improves MVQS in 7 of 8 backbone x pipeline configurations.
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