这篇论文提出了ManimAgent，它能通过经验记忆自我进化，生成数学动画代码时效果比基线方法好很多。
ManimAgent是一个基于大语言模型的自进化多模态智能体，能从学术论文段落生成Manim库的Python代码以渲染数学动画。它通过双通道情节记忆库（M+和M-）跨任务保存反思经验，无需权重更新或人工种子。在固定探针评估中，相比无记忆、匹配预算的检索增强生成和混洗记忆基线，盲人Pass@1随记忆增长而上升，反思轮次下降。
ManimAgent: Self-Evolving Multimodal Agents for Visual Education
Multi-round reflection lets agents built on large language models recover from failures within a single task, but each task remains an isolated episode: lessons learned across many reflection rounds on one task are discarded before the next begins. We study this gap on a code-generation task: from a scientific paper section, the agent writes Python in the open-source Manim library to render a mathematical animation. We present ManimAgent, a self-evolving multimodal agent that carries reflection experience across tasks through a dual-channel Episodic Memory Bank grown entirely from its own task stream, with no weight updates and no human seeds. After each animation converges, a vision-language model scores the rendered keyframes; the resulting signals populate a positive channel M+ that stores success rationales as soft Reference Examples, and a negative channel M- that stores validated failure patterns as hard Known Pitfalls. On a fixed-probe evaluation against no-memory, matched-budget retrieval-augmented generation, and shuffled-memory baselines, blind human Pass@1 rises and reflection rounds fall as memory size grows. We will release the code, frozen memory snapshots, and the task stream.