论文提出了SGA模块，能自动修复LLM写动画代码时的几何遮挡错误，实测用GPT-5.1搭配Code2Video能提升16%的画面质量。
SGA (Symbolic Geometric Agent) 是一个即插即用模块，拦截LLM生成的Manim代码，通过部分执行提取场景图并检测几何遮挡问题。实验在MMMC-Code基准上，使用4种LLM后端和2种流水线配置，SGA在Code2Video + GPT-5.1配置下达到73.11的MVQS分数，相对原始基线提升16.1%。SGA在8种配置中有7种提升了MVQS。
SGA: Plug&Play Geometric Verification for Educational Video Synthesis
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.