arxiv: 2604.09568 · v1 · submitted 2026-02-20 · 💻 cs.HC · cs.CL· cs.CV

Recognition: 1 theorem link

· Lean Theorem

EvoDiagram: Agentic Editable Diagram Creation via Design Expertise Evolution

Tianfu Wang , Leilei Ding , Ziyang Tao , Yi Zhan , Zhiyuan Ma , Wei Wu , Yuxuan Lei , Yuan Feng

show 8 more authors

Junyang Wang Yin Wu Yizhao Xu Hongyuan Zhu Qi Liu Nicholas Jing Yuan Yanyong Zhang Hui Xiong

Authors on Pith no claims yet

Pith reviewed 2026-05-15 21:04 UTC · model grok-4.3

classification 💻 cs.HC cs.CLcs.CV

keywords EvoDiagramagentic frameworkeditable diagramsdesign knowledge evolutioncanvas schemamulti-agent systemdiagram generationCanvasBench

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The pith

EvoDiagram generates editable diagrams by evolving design expertise inside a multi-agent system that stores guidelines in hierarchical memory.

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

EvoDiagram is an agentic framework that creates object-level editable diagrams through an intermediate canvas schema. A coordinated multi-agent setup separates semantic intent from rendering logic while a design knowledge evolution mechanism turns execution traces into retrievable domain guidelines. The system is meant to produce outputs that stay editable, structurally consistent, and visually coherent. The authors also release CanvasBench, a benchmark with data and metrics for canvas-based diagramming tasks. If the approach holds, it narrows the gap between pixel-based models that lack precision and code-based methods that limit flexibility.

Core claim

The paper claims that a multi-agent system combined with a design knowledge evolution mechanism can distill execution traces into hierarchical memory, allowing agents to retrieve context-aware expertise and generate diagrams that are simultaneously editable at the object level, structurally consistent, and aesthetically coherent, outperforming baselines on these dimensions.

What carries the argument

The design knowledge evolution mechanism, which distills execution traces into a hierarchical memory of domain guidelines that agents retrieve adaptively during diagram creation.

If this is right

Diagrams remain editable at the individual object level instead of requiring full regeneration from pixels or code.
Agents resolve conflicts across semantic, visual, and spatial layers without manual intervention.
Hierarchical memory allows the system to reuse distilled design guidelines across new tasks.
CanvasBench supplies standardized data and metrics for comparing canvas-based diagramming methods.
The framework maintains balance across editability, structural consistency, and aesthetic quality where prior methods trade one for another.

Where Pith is reading between the lines

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

The same trace-distillation approach could be tested on other layout-heavy tasks such as UI mockups or technical illustrations.
Hierarchical memory might reduce the number of agents needed for long design sessions by storing compressed expertise.
Releasing both the code and the benchmark could let other groups measure progress on editable diagram generation directly.
If the memory structure generalizes, it may support incremental refinement where users edit the diagram and the agents update the stored guidelines.

Load-bearing premise

The design knowledge evolution mechanism can reliably distill execution traces into a hierarchical memory that enables agents to retrieve context-aware expertise without introducing biases or losing critical design information.

What would settle it

Run EvoDiagram on a CanvasBench task requiring many overlapping elements and layered styling; if the resulting diagram cannot be edited object-by-object in standard tools or shows more structural errors than a strong code-based baseline, the performance claim fails.

Figures

Figures reproduced from arXiv: 2604.09568 by Hongyuan Zhu, Hui Xiong, Junyang Wang, Leilei Ding, Nicholas Jing Yuan, Qi Liu, Tianfu Wang, Wei Wu, Yanyong Zhang, Yin Wu, Yi Zhan, Yizhao Xu, Yuan Feng, Yuxuan Lei, Zhiyuan Ma, Ziyang Tao.

**Figure 1.** Figure 1: Comparison of diagram generation paradigms. Unlike pixel-based generation (limited control) or code-based synthesis (high barrier), Canvas-based creation unifies AI actionability with human-interpretable UI editing, bridging the representation gap. structures, diagrams reduce cognitive load, accelerate comprehension, and facilitate collaborative reasoning. However, high-fidelity diagram creation represent… view at source ↗

**Figure 2.** Figure 2: The overview of EvoDiagram framework. (a) Agentic Creation System: A multi-agent pipeline where specialized agents for structure, style, and layout coordinate via a shared symbolic schema, followed by a closed-loop refinement agent to resolve cross-layer conflicts. (b) Hybrid Experience Search: A structural exploration of the design space using both vertical refinement and horizontal comparison. (c) Design… view at source ↗

**Figure 3.** Figure 3: The dataset construction pipeline and the overview of CanvasBench. knowledge memory. This grounding ensures the agent exploits established knowledge to avoid historical failure modes and improve consistency. Subsequently, the system engages in the hybrid experience search to capture novel emerging patterns through directed discovery. Incremental Knowledge Update. Newly collected strategies are passed throu… view at source ↗

**Figure 4.** Figure 4: Visual comparison of different baseline methods on a representative diagramming task. 6.1. Experimental Settings Implementation Details. We utilize the popular tldraw1 library as the underlying canvas engine. See Appendix B.3 for its schema description. The agentic framework is orchestrated using LANGGRAPH and powered by state-of-the-art VLMs with a temperature of 0.7. By default, the refiner agent is inv… view at source ↗

**Figure 5.** Figure 5: Information density by diagram type. The box plots illustrate the distribution of character counts for each category, revealing high semantic variation and significant textual depth across the dataset. knowledge-intensive types such as Concept Maps, Mind Maps, and Entity Relationship (ER) diagrams exhibit substantial variance and higher upper quartiles, reflecting their role in organizing complex, multi-fa… view at source ↗

**Figure 6.** Figure 6: Hierarchical distribution of vertical domains. The sunburst chart depicts the balanced coverage across six primary disciplines and 30 granular sub-domains, ensuring the benchmark tests generalizability across diverse knowledge fields. These qualitative examples underscore that CanvasBench moves beyond rigid, synthetic templates to capture the heterogeneous layout styles and nuanced semantic logic found in… view at source ↗

**Figure 7.** Figure 7: Data examples in CanvasBench. 24 [PITH_FULL_IMAGE:figures/full_fig_p024_7.png] view at source ↗

**Figure 8.** Figure 8: A case study of refinement iterations. 25 [PITH_FULL_IMAGE:figures/full_fig_p025_8.png] view at source ↗

**Figure 9.** Figure 9: Human intuitively refines the generated diagram via UI-friendly operations in our web application. The interface facilitates a fluid transition from agentic generation to manual manipulation. 26 [PITH_FULL_IMAGE:figures/full_fig_p026_9.png] view at source ↗

read the original abstract

High-fidelity diagram creation requires the complex orchestration of semantic topology, visual styling, and spatial layout, posing a significant challenge for automated systems. Existing methods also suffer from a representation gap: pixel-based models often lack precise control, while code-based synthesis limits intuitive flexibility. To bridge this gap, we introduce EvoDiagram, an agentic framework that generates object-level editable diagrams via an intermediate canvas schema. EvoDiagram employs a coordinated multi-agent system to decouple semantic intent from rendering logic, resolving conflicts across heterogeneous design layers. Additionally, we propose a design knowledge evolution mechanism that distills execution traces into a hierarchical memory of domain guidelines, enabling agents to retrieve context-aware expertise adaptively. We further release CanvasBench, a benchmark consisting of both data and metrics for canvas-based diagramming. Extensive experiments demonstrate that EvoDiagram exhibits excellent performance and balance against baselines in generating editable, structurally consistent, and aesthetically coherent diagrams. Our code is available at https://github.com/AuraX-AI/EvoDiagram.

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

EvoDiagram adds a multi-agent system with design knowledge evolution to produce editable canvas diagrams, backed by a released benchmark and ablations.

read the letter

The main point is that EvoDiagram routes diagram generation through a multi-agent setup that decouples semantic intent from rendering via an intermediate canvas schema, then adds a knowledge evolution step that turns execution traces into hierarchical memory for agents to query. This produces object-level editable output rather than fixed pixels or rigid code. The paper ships code and CanvasBench, which includes metrics for structural consistency, editability, and aesthetics, and the ablations isolate what the evolution mechanism contributes. Those elements give the work more substance than pure generation papers that stop at qualitative examples. The experiments reportedly show better balance than baselines on the claimed dimensions, and the stress-test note indicates the full manuscript has implementation details and controls that line up without internal contradictions. A soft spot is the distillation process itself: if the hierarchical memory drops key design constraints or injects biases from the traces, the adaptive retrieval could degrade on edge cases, though the reported consistency checks appear to test this directly. The abstract's performance language is strong, but the actual numbers and baseline choices will determine how much the gains generalize beyond the benchmark. This is for HCI researchers building AI tools for visual design and diagramming. Anyone working on agentic systems or editable output formats will find the architecture and benchmark usable. It has enough concrete pieces to go to a serious referee rather than a desk reject.

Referee Report

0 major / 3 minor

Summary. The paper introduces EvoDiagram, an agentic multi-agent framework for generating object-level editable diagrams. It decouples semantic intent from rendering via an intermediate canvas schema, employs a design knowledge evolution mechanism that distills execution traces into hierarchical memory for adaptive expertise retrieval, and releases CanvasBench as a benchmark with associated metrics. Experiments are reported to show superior balance in editability, structural consistency, and aesthetic coherence relative to baselines.

Significance. If the reported results and ablations hold, the work offers a practical advance in automated diagramming by addressing the representation gap between pixel and code-based methods, while the open CanvasBench and hierarchical memory approach could support reproducible follow-on research in agentic design systems.

minor comments (3)

The abstract states 'excellent performance' without numerical values; move at least one key metric (e.g., editability score or consistency rate) from the experimental section into the abstract for immediate clarity.
Clarify the exact schema of the 'intermediate canvas' (object attributes, coordinate system, layering rules) in the methods section, as this is load-bearing for reproducibility of the decoupling claim.
In the ablation study, explicitly state the number of execution traces used to seed the hierarchical memory and any filtering criteria applied during distillation.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for their positive evaluation of EvoDiagram and the recommendation for minor revision. The referee's summary correctly identifies the core technical contributions: the multi-agent coordination via the canvas schema, the design knowledge evolution mechanism, and the CanvasBench benchmark with its associated metrics. We will prepare a revised manuscript incorporating any minor clarifications or updates as appropriate.

Circularity Check

0 steps flagged

No significant circularity detected in derivation chain

full rationale

The paper introduces an agentic multi-agent framework for editable diagram generation via an intermediate canvas schema, with a design knowledge evolution mechanism that distills external execution traces into hierarchical memory for adaptive retrieval. No load-bearing step reduces by construction to its inputs: there are no self-definitional equations, fitted parameters renamed as predictions, or uniqueness theorems imported via self-citation. The central claims rest on experimental results against baselines on the released CanvasBench benchmark, with ablations isolating the evolution component. The derivation draws from observable traces and external metrics rather than self-referential fitting, making the approach self-contained.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 2 invented entities

The central claims rest on assumptions about effective agent coordination and knowledge distillation from traces; no free parameters or invented entities with independent evidence are detailed in the abstract.

axioms (1)

domain assumption A coordinated multi-agent system can decouple semantic intent from rendering logic and resolve conflicts across design layers.
Invoked as the core mechanism for handling complex diagram creation.

invented entities (2)

intermediate canvas schema no independent evidence
purpose: Bridge between semantic understanding and precise rendering for object-level editability.
New representation introduced to address representation gap in existing methods.
design knowledge evolution mechanism no independent evidence
purpose: Distill execution traces into hierarchical memory for adaptive expertise retrieval.
Core proposed innovation enabling context-aware agent behavior.

pith-pipeline@v0.9.0 · 5525 in / 1294 out tokens · 53162 ms · 2026-05-15T21:04:55.233889+00:00 · methodology

discussion (0)

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unclear
Relation between the paper passage and the cited Recognition theorem.

design knowledge evolution mechanism that distills execution traces into a hierarchical memory of domain guidelines

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Reference graph

Works this paper leans on

22 extracted references · 22 canonical work pages · 10 internal anchors

[1]

Qwen3-VL Technical Report

Bai, S., Cai, Y ., Chen, R., Chen, K., Chen, X., Cheng, Z., Deng, L., Ding, W., Gao, C., Ge, C., Ge, W., Guo, Z., Huang, Q., Huang, J., Huang, F., Hui, B., Jiang, S., Li, Z., Li, M., Li, M., Li, K., Lin, Z., Lin, J., Liu, X., Liu, J., Liu, C., Liu, Y ., Liu, D., Liu, S., Lu, D., Luo, R., Lv, C., Men, R., Meng, L., Ren, X., Ren, X., Song, S., Sun, Y ., Tan...

work page internal anchor Pith review Pith/arXiv arXiv
[2]

Q., and Shou, M

Chen, Y ., Lin, K. Q., and Shou, M. Z. Code2video: A code- centric paradigm for educational video generation.arXiv preprint arXiv:2510.01174,

work page arXiv
[3]

and Devereux, B

Deka, P. and Devereux, B. Flowchart2mermaid: A vision-language model powered system for converting flowcharts into editable diagram code.arXiv preprint arXiv:2512.02170,

work page arXiv
[4]

Evomem: Improving multi-agent planning with dual-evolving memory.arXiv preprint arXiv:2511.01912,

Fan, W., Yan, N., and Mortazavi, M. Evomem: Improving multi-agent planning with dual-evolving memory.arXiv preprint arXiv:2511.01912,

work page arXiv
[5]

Video-MMMU: Evaluating Knowledge Acquisition from Multi-Discipline Professional Videos

Hu, K., Wu, P., Pu, F., Xiao, W., Zhang, Y ., Yue, X., Li, B., and Liu, Z. Video-mmmu: Evaluating knowledge acqui- sition from multi-discipline professional videos.arXiv preprint arXiv:2501.13826, 2025a. Hu, Y ., Liu, S., Yue, Y ., Zhang, G., Liu, B., Zhu, F., Lin, J., Guo, H., Dou, S., Xi, Z., et al. Memory in the age of ai agents.arXiv preprint arXiv:25...

work page internal anchor Pith review Pith/arXiv arXiv
[6]

Memory os of ai agent.arXiv preprint arXiv:2506.06326,

Kang, J., Ji, M., Zhao, Z., and Bai, T. Memory os of ai agent.arXiv preprint arXiv:2506.06326,

work page arXiv
[7]

FLUX.1 Kontext: Flow Matching for In-Context Image Generation and Editing in Latent Space

Labs, B. F., Batifol, S., Blattmann, A., Boesel, F., Con- sul, S., Diagne, C., Dockhorn, T., English, J., English, Z., Esser, P., et al. Flux. 1 kontext: Flow matching for in-context image generation and editing in latent space. arXiv preprint arXiv:2506.15742,

work page internal anchor Pith review Pith/arXiv arXiv
[8]

Novikov, A. et al. Alphaevolve: A coding agent for scientific and algorithmic discovery.arXiv preprint arXiv:2506.13131,

work page internal anchor Pith review Pith/arXiv arXiv
[9]

ReasoningBank: Scaling Agent Self-Evolving with Reasoning Memory

9 EvoDiagram: Agentic Editable Diagram Creation via Design Expertise Evolution OpenAI. Introducing gpt-5.2. https://openai.com/ index/introducing-gpt-5-2 , December 2025a. OpenAI. Introducing 4o image genera- tion. https://openai.com/index/ introducing-4o-image-generation/ , March 2025b. Ouyang, S., Yan, J., Hsu, I., Chen, Y ., Jiang, K., Wang, Z., Han, R...

work page internal anchor Pith review Pith/arXiv arXiv
[10]

A., Puri, A., Agarwal, S., Laradji, I

Rodriguez, J. A., Puri, A., Agarwal, S., Laradji, I. H., Ro- driguez, P., Rajeswar, S., Vazquez, D., Pal, C., and Peder- soli, M. Starvector: Generating scalable vector graphics code from images and text. InProceedings of the Com- puter Vision and Pattern Recognition Conference, pp. 16175–16186, 2025a. Rodriguez, J. A., Puri, A., Agarwal, S., Laradji, I. ...

work page 2025
[11]

Kimi K2: Open Agentic Intelligence

Team, K., Bai, Y ., Bao, Y ., Chen, G., Chen, J., Chen, N., Chen, R., Chen, Y ., Chen, Y ., Chen, Y ., et al. Kimi k2: Open agentic intelligence.arXiv preprint arXiv:2507.20534,

work page internal anchor Pith review Pith/arXiv arXiv
[12]

Wan: Open and Advanced Large-Scale Video Generative Models

Wan, T., Wang, A., Ai, B., Wen, B., Mao, C., Xie, C.-W., Chen, D., Yu, F., Zhao, H., Yang, J., et al. Wan: Open and advanced large-scale video generative models.arXiv preprint arXiv:2503.20314,

work page internal anchor Pith review Pith/arXiv arXiv
[13]

J., Zhang, Q., Xie, X., and Xiong, H

Wang, T., Zhan, Y ., Lian, J., Hu, Z., Yuan, N. J., Zhang, Q., Xie, X., and Xiong, H. Llm-powered multi-agent framework for goal-oriented learning in intelligent tutor- ing system. InCompanion Proceedings of the ACM on Web Conference 2025, pp. 510–519,

work page 2025
[14]

MIRIX: Multi-Agent Memory System for LLM-Based Agents

Wang, Y . and Chen, X. Mirix: Multi-agent memory system for llm-based agents.arXiv preprint arXiv:2507.07957,

work page internal anchor Pith review Pith/arXiv arXiv
[15]

SANA: Efficient High-Resolution Image Synthesis with Linear Diffusion Transformers

Xie, E., Chen, J., Chen, J., Cai, H., Tang, H., Lin, Y ., Zhang, Z., Li, M., Zhu, L., Lu, Y ., et al. Sana: Efficient high- resolution image synthesis with linear diffusion trans- formers.arXiv preprint arXiv:2410.10629,

work page internal anchor Pith review Pith/arXiv arXiv
[16]

A survey on agen- tic multimodal large language models.arXiv preprint arXiv:2510.10991, 2025

Yao, H., Zhang, R., Huang, J., Zhang, J., Wang, Y ., Fang, B., Zhu, R., Jing, Y ., Liu, S., Li, G., et al. A survey on agentic multimodal large language models.arXiv preprint arXiv:2510.10991,

work page arXiv
[17]

Agentic Context Engineering: Evolving Contexts for Self-Improving Language Models

Zhang, Q., Hu, C., Upasani, S., Ma, B., Hong, F., Kamanuru, V ., Rainton, J., Wu, C., Ji, M., Li, H., et al. Agentic con- text engineering: Evolving contexts for self-improving language models.arXiv preprint arXiv:2510.04618, 2025a. Zhang, Z., Zhang, X., Wei, J., Xu, Y ., and You, C. Postergen: Aesthetic-aware paper-to-poster generation via multi- agent l...

work page internal anchor Pith review Pith/arXiv arXiv 2025
[18]

Q., and Shou, M

Zhu, Z., Lin, K. Q., and Shou, M. Z. Paper2video: Au- tomatic video generation from scientific papers.arXiv preprint arXiv:2510.05096,

work page arXiv
[19]

13 A.2 Agent Memory Evolution

11 EvoDiagram: Agentic Editable Diagram Creation via Design Expertise Evolution Contents of Appendix A More Related Work 13 A.1 Agentic Media Creation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 A.2 Agent Memory Evolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13...

work page 2025
[20]

leverage multimodal agents to orchestrate scripts, visuals, and voiceovers. While existing frameworks treat media (e.g., posters, videos) as macro-containers for asset arrangement, diagrams serve a distinct role as compact, structure-dense kernels (Larkin & Simon, 1987). Functioning as embedded explanatory units, they translate abstract semantics into con...

work page 1987
[21]

diagram type diagram of vertical domain

extracts generalizable reasoning patterns from self-judged successes and failures, while ACE (Zhang et al., 2025a) treats context as an evolving playbook that accumulates task strategies. Here, high-fidelity diagramming requires multi-objective trade-offs and acquires implicit, nuanced knowledge. To bridge this, we introduce hierarchical design knowledge ...

work page 2024
[22]

Notably, 16 EvoDiagram: Agentic Editable Diagram Creation via Design Expertise Evolution Table 5.Detailed Taxonomy for Diagram Image Retrieval

The results demonstrate that CanvasBench maintains a high level of information density, with the median text length for most categories exceeding 1,000 characters. Notably, 16 EvoDiagram: Agentic Editable Diagram Creation via Design Expertise Evolution Table 5.Detailed Taxonomy for Diagram Image Retrieval. The taxonomy intersects 21 structural types with ...

work page 2024