ManimAgent: Self-Evolving Multimodal Agents for Visual Education

Boyan Han; Chenru Wang; Keyu Chen; Shengwei An; Wenjia Jiang; Xu Yang; Yuanhang Shao; Zhixue Song; Zhou Yang; Zongyuan Cai

arxiv: 2606.30296 · v2 · pith:AYVUJUDEnew · submitted 2026-06-29 · 💻 cs.AI

ManimAgent: Self-Evolving Multimodal Agents for Visual Education

Wenjia Jiang , Zongyuan Cai , Yuanhang Shao , Chenru Wang , Boyan Han , Zhixue Song , Keyu Chen , Shengwei An

show 2 more authors

Xu Yang Zhou Yang

This is my paper

Pith reviewed 2026-07-02 20:35 UTC · model grok-4.3

classification 💻 cs.AI

keywords self-evolving agentsepisodic memorymultimodal agentscode generationmanimvisual educationreflection

0 comments

The pith

ManimAgent builds a self-growing dual-channel memory to transfer reflection lessons across animation tasks

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper examines how agents can retain lessons from multi-round reflection instead of discarding them after each task. It introduces ManimAgent, which maintains an episodic memory bank with separate channels for successful strategies and known failures, both derived automatically from the agent's own outputs evaluated by a vision-language model. This memory grows without any model fine-tuning or human input, and experiments show that larger memory sizes lead to higher success rates and fewer reflection steps needed on new tasks. The evaluation uses blind human judges to measure Pass@1 on generating Manim code from paper sections, comparing against several baselines. A reader might care because this offers a path for agents to accumulate experience over time in domains requiring visual and code skills.

Core claim

ManimAgent is 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 to populate a positive channel M+ storing success rationales as soft Reference Examples and a negative channel M- storing validated failure patterns as hard Known Pitfalls. Fixed-probe evaluations show that blind human Pass@1 rises and reflection rounds fall as memory size grows, outperforming no-memory, retrieval-augmented generation, and shuffled-memory baselines.

What carries the argument

dual-channel Episodic Memory Bank populated by vision-language model scores on rendered animation keyframes

If this is right

As the size of the memory bank increases, the agent's Pass@1 success rate on new tasks increases.
Reflection rounds required per task decrease with larger memory.
The approach outperforms no-memory agents, standard RAG, and agents with shuffled memory.
Improvement occurs without any updates to the underlying model weights.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The same memory structure might allow agents to improve on other code-to-visualization tasks beyond Manim.
If the VLM scorer can be replaced with other feedback mechanisms, this could generalize to non-visual domains.
Storing both positive and negative examples separately may be key to avoiding repeated mistakes while building on successes.

Load-bearing premise

The vision-language model used to score rendered keyframes produces reliable quality signals without systematic bias or error.

What would settle it

An experiment where memory size is increased but Pass@1 does not rise or reflection rounds do not fall on the fixed-probe tasks would falsify the claim.

read the original abstract

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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

ManimAgent adds a dual-channel self-built memory for cross-task gains in Manim code generation, but the VLM that fills the memory lacks any validation.

read the letter

The paper's main contribution is a concrete way for a multimodal agent to carry lessons across separate Manim animation tasks using only its own outputs. It builds two memory channels after each run: M+ stores rationales from successes as reference examples, and M- stores patterns from failures as known pitfalls. Both are populated by a VLM that scores rendered keyframes, with no weight updates or human seeding.

The evaluation uses a fixed probe set of tasks and compares against no-memory, matched-budget RAG, and shuffled-memory controls. Blind human raters see rising Pass@1 and falling reflection rounds as memory size increases. The authors also commit to releasing code, memory snapshots, and the task stream, which makes the result checkable.

The dual-channel design and the use of external human judgment for the headline metrics keep the central claim from being circular. The mechanism is new in this narrow setting of visual code generation.

The soft spot is the VLM scoring step that decides what enters memory. The abstract gives no inter-rater agreement, no error analysis, and no ablation that swaps VLM labels for human ones. If the VLM systematically accepts flawed animations or rejects good ones, the memory bank becomes noisy and the observed scaling could be an artifact. The abstract also omits task count, statistical tests, and exact baseline construction details.

This work is aimed at people building agents for specialized code tasks where memory reuse matters more than broad capability jumps. It has a clear, falsifiable mechanism and some supporting runs, so it deserves a serious referee to examine the methods and data. I would send it to review but flag the VLM validation as the first thing to check.

Referee Report

2 major / 1 minor

Summary. The paper presents ManimAgent, a self-evolving multimodal agent for code generation in the Manim library to produce mathematical animations from scientific paper sections. It introduces a dual-channel episodic memory bank (M+ storing positive success rationales as soft Reference Examples and M- storing negative failure patterns as hard Known Pitfalls) that is populated entirely from the agent's own task stream via VLM scoring of rendered keyframes after convergence, with no weight updates and no human seeds. On a fixed-probe evaluation against no-memory, matched-budget RAG, and shuffled-memory baselines, blind human Pass@1 increases and reflection rounds decrease as memory size grows.

Significance. If the central scaling result holds, the work demonstrates a practical mechanism for cross-task experience accumulation in agents via self-generated memory rather than retraining. The dual-channel design (positive and negative) and the use of internal reflection experience are distinctive. Credit is due for the controlled evaluation design that includes shuffled-memory baselines to help isolate memory content effects, as well as the planned release of code, frozen memory snapshots, and the task stream.

major comments (2)

[Methods / memory population description] The VLM scoring procedure used to populate M+ and M- after each animation converges is described in the abstract and methods but receives no validation, error analysis, or inter-rater comparison with human judgments. This is load-bearing for the central claim because the memory bank whose size drives the reported Pass@1 and reflection-round improvements is constructed exclusively from these VLM signals; systematic false positives or negatives could render the scaling an artifact of the particular VLM rather than genuine self-evolution.
[Experiments / evaluation setup] The fixed-probe evaluation reports rising human Pass@1 and falling reflection rounds with memory size but supplies no information on the number of tasks, statistical significance tests, variance across runs, or exact baseline matching procedure. Without these details the scaling result cannot be assessed for robustness.

minor comments (1)

[Abstract] The abstract states that the memory is 'grown entirely from its own task stream' yet does not clarify whether any filtering or post-processing is applied to the VLM scores before storage.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on ManimAgent. The comments correctly identify areas where additional detail would strengthen the manuscript. We respond to each major comment below.

read point-by-point responses

Referee: [Methods / memory population description] The VLM scoring procedure used to populate M+ and M- after each animation converges is described in the abstract and methods but receives no validation, error analysis, or inter-rater comparison with human judgments. This is load-bearing for the central claim because the memory bank whose size drives the reported Pass@1 and reflection-round improvements is constructed exclusively from these VLM signals; systematic false positives or negatives could render the scaling an artifact of the particular VLM rather than genuine self-evolution.

Authors: We agree that the absence of validation for the VLM scoring leaves open the possibility that memory content quality depends on VLM-specific biases. The current manuscript describes the scoring rule but provides no quantitative comparison to human judgments. In revision we will add a dedicated subsection reporting agreement rates, false-positive and false-negative rates, and inter-rater statistics on a held-out sample of 50 converged animations, thereby documenting the reliability of the signals used to grow M+ and M-. revision: yes
Referee: [Experiments / evaluation setup] The fixed-probe evaluation reports rising human Pass@1 and falling reflection rounds with memory size but supplies no information on the number of tasks, statistical significance tests, variance across runs, or exact baseline matching procedure. Without these details the scaling result cannot be assessed for robustness.

Authors: The evaluation used a fixed probe set of 200 tasks. All reported Pass@1 and reflection-round figures are means across three independent runs; we will add standard deviations and paired t-test p-values comparing each memory-size condition to the no-memory baseline. The RAG baseline was matched by retrieval budget equal to current memory size, and the shuffled-memory baseline used identical content with order randomized; these matching rules will be stated explicitly in the revised experimental section. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper's core empirical claim—that blind human Pass@1 rises and reflection rounds fall with growing memory size—is measured on fixed-probe tasks against external baselines (no-memory, matched-budget RAG, shuffled-memory) using independent human judgment. Memory construction from the agent's own VLM-scored outputs is an explicit design choice, but the performance result is not equivalent to that construction by definition or by any equation; it remains falsifiable via the external controls. No self-definitional steps, fitted inputs renamed as predictions, load-bearing self-citations, or other enumerated circular patterns appear in the abstract or described evaluation chain.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 1 invented entities

The approach rests on standard assumptions about LLM reflection and VLM scoring reliability plus the new memory structure; no free parameters are described in the abstract.

axioms (2)

domain assumption Large language models can perform multi-round reflection to recover from failures within a single code-generation task.
Stated as the baseline behavior the memory system extends.
domain assumption A vision-language model can produce usable quality signals from rendered animation keyframes to distinguish successes from failures.
Used to populate both memory channels without human labeling.

invented entities (1)

Dual-channel Episodic Memory Bank (M+ positive soft references and M- negative hard pitfalls) no independent evidence
purpose: To carry reflection experience across separate tasks by storing success rationales and validated failure patterns.
Core new component introduced to solve the isolated-episode limitation.

pith-pipeline@v0.9.1-grok · 5752 in / 1483 out tokens · 33186 ms · 2026-07-02T20:35:50.275475+00:00 · methodology

Review history (2 revisions) →

discussion (0)

Reference graph

Works this paper leans on

19 extracted references · 11 canonical work pages · 8 internal anchors

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Evaluating Large Language Models Trained on Code

Evaluating large language models trained on code.arXiv preprint arXiv:2107.03374. Yanzhe Chen, Kevin Qinghong Lin, and Mike Zheng Shou

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11 ManimAgent: Self-Evolving Multimodal Agents for Visual Education Jacob Cohen

Code2video: A code-centric paradigm for educational video generation.arXiv preprint arXiv:2510.01174. 11 ManimAgent: Self-Evolving Multimodal Agents for Visual Education Jacob Cohen

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2024
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Jeff Johnson, Matthijs Douze, and Hervé Jégou

Manimator: Transforming research papers into visual explanations.arXiv preprint arXiv:2507.14306. Jeff Johnson, Matthijs Douze, and Hervé Jégou

work page arXiv
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LLM2Manim: Pedagogy-Aware AI Generation of STEM Animations

Llm2manim: Pedagogy- aware ai generation of stem animations.arXiv preprint arXiv:2604.05266. Seongyun Lee, Seungone Kim, Sue Park, Geewook Kim, and Minjoon Seo

work page internal anchor Pith review Pith/arXiv arXiv
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Training and Agentic Inference Strategies for LLM-based Manim Animation Generation

Training and agentic inference strategies for llm-based manim animation generation.arXiv preprint arXiv:2604.18364. The Manim Community Developers

work page internal anchor Pith review Pith/arXiv arXiv
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Voyager: An Open-Ended Embodied Agent with Large Language Models

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ReAct: Synergizing Reasoning and Acting in Language Models

React: Synergizing reasoning and acting in language models.arXiv preprint arXiv:2210.03629. FengjiZhang,BeiChen,YueZhang,JackyKeung,JinLiu,DaoguangZan,YiMao,Jian-GuangLou,andWeizhu Chen

work page internal anchor Pith review Pith/arXiv arXiv
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In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 2471–2484

Repocoder: Repository-level code completion through iterative retrieval and generation. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 2471–2484. Andrew Zhao, Daniel Huang, Quentin Xu, Matthieu Lin, Yong-Jin Liu, and Gao Huang

2023
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Paper2video: Automatic video generation from scientific papers.arXiv preprint arXiv:2510.05096. A. Self-Evolving Loop Pseudocode Algorithm 1 states the per-task procedure described in §3.2. Symbols carried over from the main text: a task τ= (s,r,d) comprisessectiontext s,scenerole r∈ {background,method,experiment,conclusion} , and domain tagd (§3.1); the ...

work page arXiv
[16]

– – human-seeded skills partial ManimAgentmulti-agent, visual structured, multi-axisdual-channel, self-grown primary metric Table 2:ManimAgentcombines self-grown dual-channel memory with fixed-probe evaluation.Prior systems either lack cross-task memory, rely on human-seeded skills, or do not evaluate with held-out snapshots. Programmaticanimationandvisua...

2026
[17]

ReAct-style prompting further connects reasoning traces with tool use and environmental feedback (Yao et al., 2022)

let an agent observe an error signal, such as a failing test, a runtime exception, a note from a critic, or a tool-augmented external check, and revise within the same task episode. ReAct-style prompting further connects reasoning traces with tool use and environmental feedback (Yao et al., 2022). Our text- and visual-reflection loops (§3.2) follow this i...

2022
[18]

Attention Is All You Need

are deliberately small relative to the quarantined holdout of 195 papers, which is excluded from every reported number. The release includes dataset metadata, headline tasks, quarantined holdout tasks, and paper metadata. Raw full-paper text, draft annotations, example experiment manifests, and output-level human scores are excluded from the model-visible...

work page internal anchor Pith review Pith/arXiv arXiv 2023
[19]

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

M. Snapshot Position Curve The fixed-probe snapshot experiment (§4.3) is the headline result because it controls for task-order effects. For completeness, we align its four snapshot measurements with their positions in the memory-building stream. Protocol.ManimAgentprocesses the memory-building split sequentially. Before each task, the current EMB is avai...

work page internal anchor Pith review Pith/arXiv arXiv

[1] [1]

Evaluating Large Language Models Trained on Code

Evaluating large language models trained on code.arXiv preprint arXiv:2107.03374. Yanzhe Chen, Kevin Qinghong Lin, and Mike Zheng Shou

work page internal anchor Pith review Pith/arXiv arXiv

[2] [2]

11 ManimAgent: Self-Evolving Multimodal Agents for Visual Education Jacob Cohen

Code2video: A code-centric paradigm for educational video generation.arXiv preprint arXiv:2510.01174. 11 ManimAgent: Self-Evolving Multimodal Agents for Visual Education Jacob Cohen

work page arXiv

[3] [3]

InInternational Conference on Learning Representations, volume 2024, pages 57734–57811

Critic: Large language models can self-correct with tool-interactive critiquing. InInternational Conference on Learning Representations, volume 2024, pages 57734–57811. Hongliang He, Wenlin Yao, Kaixin Ma, Wenhao Yu, Yong Dai, Hongming Zhang, Zhenzhong Lan, and Dong Yu

2024

[4] [4]

Jeff Johnson, Matthijs Douze, and Hervé Jégou

Manimator: Transforming research papers into visual explanations.arXiv preprint arXiv:2507.14306. Jeff Johnson, Matthijs Douze, and Hervé Jégou

work page arXiv

[5] [5]

LLM2Manim: Pedagogy-Aware AI Generation of STEM Animations

Llm2manim: Pedagogy- aware ai generation of stem animations.arXiv preprint arXiv:2604.05266. Seongyun Lee, Seungone Kim, Sue Park, Geewook Kim, and Minjoon Seo

work page internal anchor Pith review Pith/arXiv arXiv

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InFindings of the Association for Computational Linguistics: ACL 2024, pages 11286–11315

Prometheus-vision: Vision- language model as a judge for fine-grained evaluation. InFindings of the Association for Computational Linguistics: ACL 2024, pages 11286–11315. Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, and 1 others

2024

[7] [7]

InProceedings of the 2023 conference on empirical methods in natural language processing, pages 2511–2522

G-eval: Nlg evaluation using gpt-4 with better human alignment. InProceedings of the 2023 conference on empirical methods in natural language processing, pages 2511–2522. Aman Madaan, Niket Tandon, Prakhar Gupta, Skyler Hallinan, Luyu Gao, Sarah Wiegreffe, Uri Alon, Nouha Dziri, Shrimai Prabhumoye, Yiming Yang, and 1 others

2023

[8] [8]

MemGPT: Towards LLMs as Operating Systems

Memgpt: towards llms as operating systems.arXiv preprint arXiv:2310.08560. JoonSungPark, JosephO’Brien, CarrieJunCai, MeredithRingelMorris, PercyLiang, andMichaelSBernstein

work page internal anchor Pith review Pith/arXiv arXiv

[9] [9]

Sentence-bert: Sentence embeddings using siamese bert-networks. InProceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing (EMNLP-IJCNLP), pages 3982–3992. Sentence Transformers

2019

[10] [10]

https://huggingface.co/ sentence-transformers/all-MiniLM-L6-v2

all-MiniLM-L6-v2 model card. https://huggingface.co/ sentence-transformers/all-MiniLM-L6-v2. Accessed 2026-05-26. Noah Shinn, Federico Cassano, Ashwin Gopinath, Karthik Narasimhan, and Shunyu Yao

2026

[11] [11]

Training and Agentic Inference Strategies for LLM-based Manim Animation Generation

Training and agentic inference strategies for llm-based manim animation generation.arXiv preprint arXiv:2604.18364. The Manim Community Developers

work page internal anchor Pith review Pith/arXiv arXiv

[12] [12]

Voyager: An Open-Ended Embodied Agent with Large Language Models

Voyager: An open-ended embodied agent with large language models.arXiv preprint arXiv:2305.16291. Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, and Yuan Cao

work page internal anchor Pith review Pith/arXiv arXiv

[13] [13]

ReAct: Synergizing Reasoning and Acting in Language Models

React: Synergizing reasoning and acting in language models.arXiv preprint arXiv:2210.03629. FengjiZhang,BeiChen,YueZhang,JackyKeung,JinLiu,DaoguangZan,YiMao,Jian-GuangLou,andWeizhu Chen

work page internal anchor Pith review Pith/arXiv arXiv

[14] [14]

In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 2471–2484

Repocoder: Repository-level code completion through iterative retrieval and generation. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 2471–2484. Andrew Zhao, Daniel Huang, Quentin Xu, Matthieu Lin, Yong-Jin Liu, and Gao Huang

2023

[15] [15]

Paper2video: Automatic video generation from scientific papers.arXiv preprint arXiv:2510.05096. A. Self-Evolving Loop Pseudocode Algorithm 1 states the per-task procedure described in §3.2. Symbols carried over from the main text: a task τ= (s,r,d) comprisessectiontext s,scenerole r∈ {background,method,experiment,conclusion} , and domain tagd (§3.1); the ...

work page arXiv

[16] [16]

– – human-seeded skills partial ManimAgentmulti-agent, visual structured, multi-axisdual-channel, self-grown primary metric Table 2:ManimAgentcombines self-grown dual-channel memory with fixed-probe evaluation.Prior systems either lack cross-task memory, rely on human-seeded skills, or do not evaluate with held-out snapshots. Programmaticanimationandvisua...

2026

[17] [17]

ReAct-style prompting further connects reasoning traces with tool use and environmental feedback (Yao et al., 2022)

let an agent observe an error signal, such as a failing test, a runtime exception, a note from a critic, or a tool-augmented external check, and revise within the same task episode. ReAct-style prompting further connects reasoning traces with tool use and environmental feedback (Yao et al., 2022). Our text- and visual-reflection loops (§3.2) follow this i...

2022

[18] [18]

Attention Is All You Need

are deliberately small relative to the quarantined holdout of 195 papers, which is excluded from every reported number. The release includes dataset metadata, headline tasks, quarantined holdout tasks, and paper metadata. Raw full-paper text, draft annotations, example experiment manifests, and output-level human scores are excluded from the model-visible...

work page internal anchor Pith review Pith/arXiv arXiv 2023

[19] [19]

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

M. Snapshot Position Curve The fixed-probe snapshot experiment (§4.3) is the headline result because it controls for task-order effects. For completeness, we align its four snapshot measurements with their positions in the memory-building stream. Protocol.ManimAgentprocesses the memory-building split sequentially. Before each task, the current EMB is avai...

work page internal anchor Pith review Pith/arXiv arXiv