aiqbench analysis
This research introduces the Symbolic Geometric Agent (SGA), a module designed to correct spatial errors in LLM-generated educational animations by extracting symbolic scene graphs before full rendering. By integrating geometric verification directly into code-centric pipelines, it significantly improves visual legibility without relying on computationally expensive deterministic re-rendering for every check. The approach demonstrates that combining generative capabilities with structured geometric constraints yields measurable gains in animation quality across multiple model backbones.
SGA: Plug&Play Geometric Verification for Educational Video Synthesis
arXiv:2607.18116v1 Announce Type: new Abstract: 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.
Abstract: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.
Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Graphics (cs.GR); Multiagent Systems (cs.MA); Multimedia (cs.MM)
arXiv:2607.18116 [cs.AI]
(or arXiv:2607.18116v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2607.18116 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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