Researchers have developed the Symbolic Geometric Agent (SGA), a new module designed to improve the spatial correctness and visual legibility of educational animations generated by Large Language Models (LLMs). SGA intercepts LLM-generated code, extracts symbolic scene graphs, and refines them to resolve spatial conflicts. A new metric, the Manim Visual Quality Score (MVQS), was introduced as a proxy for spatial integrity. Experiments showed that SGA, when combined with GPT-5.1 and the Code2Video pipeline, achieved a peak MVQS of 73.11, representing a 16.1% improvement over the baseline. AI
IMPACT This research could lead to more accurate and visually coherent AI-generated educational content, improving learning experiences.
RANK_REASON The cluster contains a research paper detailing a new method and benchmark for AI-generated animations.
Read on arXiv cs.MA (Multiagent) →
- Code2Video
- GPT-5.1
- Jhon Edinson Lopez Duran
- Large Language Models
- Manim
- Manim Visual Quality Score
- Symbolic Geometric Agent
- LLMs
- MMMC-Code
- MVQS
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