@inproceedings{jiang-etal-2026-manimagent,
title = "{M}anim{A}gent: Self-Evolving Multimodal Agents for Visual Education",
author = "Jiang, Wenjia and
Cai, Zongyuan and
Shao, Yuanhang and
Wang, Chenru and
Han, Boyan and
Song, Zhixue and
Chen, Keyu and
An, Shengwei and
Yang, Xu and
Yang, Zhou",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.2542/",
pages = "57798--57822",
ISBN = "979-8-89176-459-0",
abstract = "Multi-round reflection lets agents built on large language models recover from failures within a single task, but 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 executable \textsc{Manim} code. We present \textsc{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 $\mathcal{M}^{+}$ that stores success rationales as soft Reference Examples, and a negative channel $\mathcal{M}^{-}$ that stores VLM-screened 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."
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<abstract>Multi-round reflection lets agents built on large language models recover from failures within a single task, but 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 executable Manim code. 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 \mathcalM⁺ that stores success rationales as soft Reference Examples, and a negative channel \mathcalM⁻ that stores VLM-screened 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.</abstract>
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%0 Conference Proceedings
%T ManimAgent: Self-Evolving Multimodal Agents for Visual Education
%A Jiang, Wenjia
%A Cai, Zongyuan
%A Shao, Yuanhang
%A Wang, Chenru
%A Han, Boyan
%A Song, Zhixue
%A Chen, Keyu
%A An, Shengwei
%A Yang, Xu
%A Yang, Zhou
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F jiang-etal-2026-manimagent
%X Multi-round reflection lets agents built on large language models recover from failures within a single task, but 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 executable Manim code. 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 \mathcalM⁺ that stores success rationales as soft Reference Examples, and a negative channel \mathcalM⁻ that stores VLM-screened 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.
%U https://aclanthology.org/2026.emnlp-main.2542/
%P 57798-57822
Markdown (Informal)
[ManimAgent: Self-Evolving Multimodal Agents for Visual Education](https://aclanthology.org/2026.emnlp-main.2542/) (Jiang et al., EMNLP 2026)
ACL
- Wenjia Jiang, Zongyuan Cai, Yuanhang Shao, Chenru Wang, Boyan Han, Zhixue Song, Keyu Chen, Shengwei An, Xu Yang, and Zhou Yang. 2026. ManimAgent: Self-Evolving Multimodal Agents for Visual Education. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 57798–57822, Budapest, Hungary. Association for Computational Linguistics.