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-lang...
| Authors | Wenjia Jiang, Zongyuan Cai, Yuanhang Shao et al. |
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| Primary category | cs.AI |
| Published | 29 June 2026 |
| Last updated at source | 1 July 2026 |
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