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Creative agents need a shared editable canvas as project state, not chat logs or fixed pipelines.

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2026-07-30 18:09 UTC pith:EBPYCIWU

load-bearing objection Open canvas-as-state harness with a clean three-layer design; the comparative claim outruns the three qualitative demos.

arxiv 2607.23588 v1 pith:EBPYCIWU submitted 2026-07-26 cs.CV

JarvisHub: An Open Harness for Canvas-Native Multimodal Creative Agents

Yunlong Lin , Zixu Lin , Zhaohu Xing , Biqiang Li , Chenxin Li , Haonan Wang , Haitao Wu , Hengyu Liu
show 18 more authors
Jianghai Chen Kaituo Feng Kaixin Li Shawn Chen Shijue Huang Sixiang Chen Tsung-Yi Ho Wenxuan Huang Xiangyan Liu Xiaomeng Hu Xuanhua He Yan Sun Yunqing Zhao Zhiqin Yang Zehan Wang Zhengyang Tang Tianyu Pang Xiangyu Yue
This is my paper
classification cs.CV
keywords creative agentscanvas-native interfacemultimodal generationlong-horizon workflowsproject stateagent harnesshuman-in-the-looptrajectory recording
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

Real creative work is long-horizon: references, drafts, alternatives, failures, versions, and feedback accumulate into a project state that single prompts and linear chats cannot hold. This paper argues that the missing piece is not another generator but an open harness in which an editable multimodal canvas is the workspace, external memory, action space, and shared state for both user and agent. JarvisHub represents artifacts, dependencies, versions, and feedback as typed nodes and links, and routes agent behavior through a three-layer design—canvas state, a protocol bridge that checks reads and writes, and a runtime that plans, calls tools, and records trajectories. On narrative media, interactive web, and presentation tasks, the system shows agents planning, generating, revising, and organizing while humans can inspect and intervene. If the claim holds, creative AI research can study sustained, steerable production instead of isolated prompt–output steps.

Core claim

JarvisHub establishes that long-horizon multimodal creation can be formalized as an agent process over an editable project graph on a canvas, and that a three-layer harness—canvas state, protocol-constrained interaction, and an agent runtime with tool families, skills, memory, and subagents—lets agents plan, generate, revise, and organize multimodal projects in a way that remains inspectable and human-steerable, unlike prompt-only, chat-only, or fixed node-pipeline systems.

What carries the argument

The canvas-native three-layer harness: the canvas as typed artifact graph Ct = (Gt, Xt, Mt, Ut, Lt); a protocol bridge that issues capability manifests and execution grants and commits only checked mutations; and an agent runtime that selects granted actions, invokes tool families, and records full trajectories including feedback and repairs.

Load-bearing premise

That qualitative workspace traces and final artifacts on three hand-chosen tasks with strong external generators are enough to show the harness itself—not the backends—delivers long-horizon consistency, recovery, and competence.

What would settle it

Build a controlled project-state benchmark with fixed initial canvases, tools, and feedback events; compare JarvisHub against chat-only and fixed-pipeline agents on process metrics (context preservation, dependency correctness, local repair success) and final quality; if the canvas harness does not improve process metrics or recoverability when generators are held fixed, the central claim fails.

Watch this falsifier — get emailed when new claim-graph text bears on it.

If this is right

  • Creative agents can keep prompts, references, drafts, versions, and feedback as addressable canvas state instead of discarding them in chat history.
  • Researchers can define project-state benchmarks with initial canvases, tools, constraints, and checkpoints rather than only prompt–answer pairs.
  • Evaluation can combine final artifact quality with process measures: context preservation, tool appropriateness, dependency correctness, feedback adherence, and repair success.
  • Recorded trajectories of states, actions, observations, feedback, and repairs become training data for planning, tool choice, state tracking, and local repair.
  • Users can inspect, edit, and steer the same project graph the agent reads and writes throughout long workflows.

Where Pith is reading between the lines

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

  • Closed commercial creative agents may already use similar internal state, so open canvas harnesses could become the main way the field measures and compares long-horizon creative behavior.
  • Once trajectory datasets exist at scale, progress may shift from better single-shot generators toward models trained specifically on canvas mutations and local repair policies.
  • The same graph-plus-grant pattern could transfer to other long-horizon multimodal domains (scientific figure pipelines, game content, multi-page design systems) that also outgrow linear chat.
  • Without standardized process metrics, harness papers risk being judged only on final visuals that mostly reflect the underlying image/video models.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Circularity Check

0 steps flagged

No circular derivation: JarvisHub is a systems architecture paper whose equations define a harness and whose demos illustrate that harness, not fitted or self-justifying predictions.

full rationale

The paper’s load-bearing chain is design → implementation → qualitative demonstration, not a first-principles derivation that is then ‘predicted.’ Equations (1)–(5) are explicit definitions of canvas state C_t, typed nodes, the protocol state-transition operator F, the granted action set A_t, and the trajectory record τ; they do not claim to predict external observables from fitted constants. The three experimental cases (narrative media, web development, presentation decks) are curated workspace traces and final artifacts that show the implemented loop operating as specified; they are not held out predictions forced by a fit. Related self-citations (e.g., JarvisArt, JarvisEvo) appear as prior agent work in the same line and are not invoked as uniqueness theorems that forbid alternatives or force the canvas-native design. Comparative claims versus prompt/chat/node systems are architectural and empirical-gap arguments, not reductions of a result to its own inputs. Under the circularity criteria (self-definitional prediction, fitted input called prediction, load-bearing self-citation uniqueness, ansatz-via-citation, renaming), there is no step that reduces a claimed prediction to its inputs by construction. Residual concerns about missing baselines and qualitative-only evidence are evaluation/correctness issues, not circularity.

Axiom & Free-Parameter Ledger

3 free parameters · 5 axioms · 3 invented entities

Load-bearing content is mostly domain framing and engineered abstractions, not fitted physical constants. The central claim rests on assumptions about what creative work needs, on the adequacy of a typed canvas graph plus protocol grants as agent state, and on external generative backends whose quality is out of scope. Invented entities are the harness layers and formal state objects introduced to operationalize that design.

free parameters (3)
  • Choice of agent/image/video/eval backends (GPT-5.5, GPT Image 2, Seedance 2.0, Gemini 3.1 Pro) = Named in §3.1; versions/API settings largely deferred to GitHub
    Demo outcomes depend on these proprietary model choices; the paper does not isolate harness effects from generator strength.
  • Per-turn execution grant Ωt and capability manifest Γt contents
    What the agent may do each turn is a designed policy surface; different grant policies would change behavior and apparent competence.
  • Skill library and subagent decomposition policies
    Long-horizon sequencing is shaped by hand-designed reusable procedures and branching rules rather than a learned universal planner.
axioms (5)
  • domain assumption Real creative production is an evolving multimodal project state (references, drafts, alternatives, failures, versions, feedback), not a single prompt-output pair.
    Stated throughout §1 and used to motivate canvas-native state over chat logs.
  • domain assumption Linear conversation history and manually fixed node pipelines are insufficient representations for spatial layout, version branches, and local editable targets over long horizons.
    Comparative claim in §1/Figure 1; not experimentally stress-tested against strong baselines.
  • ad hoc to paper Checked protocol-mediated mutations (manifest + grant + validation + logging) are an adequate contract for safe, recoverable agent-canvas interaction.
    Core of §2.3; engineering design choice treated as sufficient for auditability and recovery.
  • ad hoc to paper Typed artifact graph Gt=(Vt,Et) with payloads Xt, metadata Mt, user records Ut, and layout Lt fully captures the project state agents need.
    Equation (1)–(2) in §2.2 define the representation the rest of the harness assumes.
  • domain assumption Final artifact quality may be attributed to external models while orchestration value is still demonstrable via qualitative traces.
    Explicit in §5 Limitations; separates harness claims from generator SOTA claims.
invented entities (3)
  • JarvisHub three-layer harness (canvas state, protocol bridge, agent runtime) independent evidence
    purpose: Unify shared project state, checked actions, tool orchestration, and trajectory recording for creative agents.
    Primary systems object introduced in Abstract/§2; existence is the implementation contribution.
  • Canvas-native project state Ct and trajectory record τ no independent evidence
    purpose: Make multimodal artifacts, dependencies, feedback, and repairs addressable and auditable across turns.
    Formalized in Eqs. (1)–(5); useful abstraction, but its sufficiency is assumed rather than benchmarked.
  • Tool-family partition (canvas, generation, native, recovery, MCP) under execution grants independent evidence
    purpose: Structure what agents can invoke and commit back to the canvas each turn.
    Table 2 / §2.4 design taxonomy for the runtime contract.

pith-pipeline@v1.2.0-grok45-kimik3 · 16793 in / 3695 out tokens · 78669 ms · 2026-07-30T18:09:13.245170+00:00 · methodology

0 comments
read the original abstract

Creative AI is moving from single-step asset generation toward long-horizon multimodal production. Although recent generative models can synthesize high-quality images, videos, audio clips, UI elements, storyboards, slides, and other creative assets, real-world creative work requires more than isolated prompt-output interactions. It involves references, drafts, alternatives, edits, failed attempts, version relations, tool actions, evaluation signals, and human feedback, which together form an evolving project state. Existing prompt-based, chat-based, and node-based generation systems only partially support this state, as they often discard intermediate context, rely on linear conversations, or require manually specified workflows. Recent commercial systems indicate a shift toward agent-assisted creative production, but their closed architectures make it difficult to study how agents represent context, choose tools, revise artifacts, recover from failures, and maintain consistency over time. To address this gap, we introduce JarvisHub, a canvas-native creative agent harness for long-horizon multimodal creation. JarvisHub treats an editable canvas as the user workspace, the agent's external memory, action space, and shared project state, representing multimodal artifacts, dependencies, versions, and feedback as typed canvas nodes and links. Through a three-layer architecture of canvas state, protocol bridge, and agent runtime, JarvisHub enables agents to act within an inspectable and editable creative state. This design moves creative agents beyond isolated tool use toward sustained, human-steerable creative automation, where agents can progressively plan, generate, revise, and organize multimodal projects while users remain able to inspect, guide, and intervene throughout the process.

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Reference graph

Works this paper leans on

52 extracted references · 27 linked inside Pith

  1. [1]

    Claude design

    Anthropic. Claude design. https://www.anthropic.com/news/claude-design-anthropic-labs, 2025. Accessed 2026-05-10

    2025

  2. [2]

    FLUX.1 Kontext: Flow matching for in-context image generation and editing in latent space.arXiv preprint arXiv:2506.15742, 2025

    Stephen Batifol, Andreas Blattmann, Frederic Boesel, Saksham Consul, Cyril Diagne, Tim Dockhorn, Jack English, Zion English, Patrick Esser, Sumith Kulal, et al. FLUX.1 Kontext: Flow matching for in-context image generation and editing in latent space.arXiv preprint arXiv:2506.15742, 2025

    Pith/arXiv arXiv 2025

  3. [3]

    Instructpix2pix: Learning to follow image editing instructions

    Tim Brooks, Aleksander Holynski, and Alexei A Efros. Instructpix2pix: Learning to follow image editing instructions. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 18392–18402, 2023

    2023

  4. [4]

    Seedream 5.0 Lite

    ByteDance Seed Team. Seedream 5.0 Lite. Model documentation,https://seed.bytedance.com/seedream, 2026. Accessed: 2026-06-07

    2026

  5. [5]

    Photoartagent: Intelligent photo retouching with language model-based artist agents.arXiv preprint arXiv:2505.23130, 2025

    Haoyu Chen, Keda Tao, Yizao Wang, Xinlei Wang, Lei Zhu, and Jinjin Gu. Photoartagent: Intelligent photo retouching with language model-based artist agents.arXiv preprint arXiv:2505.23130, 2025

    Pith/arXiv arXiv 2025

  6. [6]

    Unify-agent: A unified multimodal agent for world-grounded image synthesis.arXiv preprint arXiv:2603.29620, 2026

    Shuang Chen, Quanxin Shou, Hangting Chen, Yucheng Zhou, Kaituo Feng, Wenbo Hu, Yi-Fan Zhang, Yunlong Lin, Wenxuan Huang, Mingyang Song, et al. Unify-agent: A unified multimodal agent for world-grounded image synthesis.arXiv preprint arXiv:2603.29620, 2026

    arXiv 2026

  7. [7]

    Postercraft: Rethinking high-quality aesthetic poster generation in a unified framework

    SiXiang Chen, Jianyu Lai, Jialin Gao, Tian Ye, Haoyu Chen, Hengyu Shi, Shitong Shao, Yunlong Lin, Song Fei, Zhaohu Xing, et al. Postercraft: Rethinking high-quality aesthetic poster generation in a unified framework. arXiv preprint arXiv:2506.10741, 2025

    Pith/arXiv arXiv 2025

  8. [8]

    Genevolve: Self-evolving image generation agents via tool-orchestrated visual experience distillation.arXiv preprint arXiv:2605.21605, 2026

    Sixiang Chen, Zhaohu Xing, Tian Ye, Xinyu Geng, Yunlong Lin, Jianyu Lai, Xuanhua He, Fuxiang Zhai, Jialin Gao, and Lei Zhu. Genevolve: Self-evolving image generation agents via tool-orchestrated visual experience distillation.arXiv preprint arXiv:2605.21605, 2026

    Pith/arXiv arXiv 2026

  9. [9]

    Emerging properties in unified multimodal pretraining.arXiv preprint arXiv:2505.14683, 2025

    Chaorui Deng, Deyao Zhu, Kunchang Li, Chenhui Gou, Feng Li, Zeyu Wang, Shu Zhong, Weihao Yu, Xiaonan Nie, Ziang Song, et al. Emerging properties in unified multimodal pretraining.arXiv preprint arXiv:2505.14683, 2025

    Pith/arXiv arXiv 2025

  10. [10]

    Kimi-audio technical report.arXiv preprint arXiv:2504.18425, 2025

    Ding Ding, Zeqian Ju, Yichong Leng, Songxiang Liu, Tong Liu, Zeyu Shang, Kai Shen, Wei Song, Xu Tan, Heyi Tang, et al. Kimi-audio technical report.arXiv preprint arXiv:2504.18425, 2025

    Pith/arXiv arXiv 2025

  11. [11]

    MonetGPT: Solving puzzles enhances MLLMs’ image retouching skills.ACM Transactions on Graphics, 44(4):1–12, 2025

    Niladri Shekhar Dutt, Duygu Ceylan, and Niloy J Mitra. MonetGPT: Solving puzzles enhances MLLMs’ image retouching skills.ACM Transactions on Graphics, 44(4):1–12, 2025

    2025

  12. [12]

    Gen-searcher: Reinforcing agentic search for image generation.arXiv preprint arXiv:2603.28767, 2026

    Kaituo Feng, Manyuan Zhang, Shuang Chen, Yunlong Lin, Kaixuan Fan, Yilei Jiang, Hongyu Li, Dian Zheng, Chenyang Wang, and Xiangyu Yue. Gen-searcher: Reinforcing agentic search for image generation.arXiv preprint arXiv:2603.28767, 2026

    Pith/arXiv arXiv 2026

  13. [13]

    Guiding instruction-based image editing via multimodal large language models

    Tsu-Jui Fu, Wenze Hu, Xianzhi Du, William Yang Wang, Yinfei Yang, and Zhe Gan. Guiding instruction-based image editing via multimodal large language models. InInternational Conference on Learning Representations (ICLR), 2024

    2024

  14. [14]

    Advancing vision-language models in front-end development via data synthesis.arXiv preprint arXiv:2503.01619, 2025

    Tong Ge, Yashu Liu, Jieping Ye, Tianyi Li, and Chao Wang. Advancing vision-language models in front-end development via data synthesis.arXiv preprint arXiv:2503.01619, 2025

    Pith/arXiv arXiv 2025

  15. [15]

    Gemini 3.1 Pro Model Card

    Google DeepMind. Gemini 3.1 Pro Model Card. Model card,https://deepmind.google/models/model-cards/ gemini-3-1-pro/, February 2026. Accessed: 2026-05-31

    2026

  16. [16]

    Gemini 3.1 Flash Image Preview

    Google DeepMind. Gemini 3.1 Flash Image Preview. Model card, https://deepmind.google/models/ gemini-image/flash/, 2026. Also known as Nano Banana 2. Accessed: 2026-06-07

    2026

  17. [17]

    Vision2web: A hierarchical benchmark for visual website development with agent verification.arXiv preprint arXiv:2603.26648, 2026

    Zehai He, Wenyi Hong, Zhen Yang, Ziyang Pan, Mingdao Liu, Xiaotao Gu, and Jie Tang. Vision2web: A hierarchical benchmark for visual website development with agent verification.arXiv preprint arXiv:2603.26648, 2026

    Pith/arXiv arXiv 2026

  18. [18]

    ComfyGPT: A self-optimizing multi-agent system for comprehensive ComfyUI workflow generation.arXiv preprint arXiv:2503.17671, 2025

    Oucheng Huang, Yuhang Ma, Zeng Zhao, Mingrui Wu, Jiayi Ji, Rongsheng Zhang, Zhipeng Hu, Xiaoshuai Sun, and Rongrong Ji. ComfyGPT: A self-optimizing multi-agent system for comprehensive ComfyUI workflow generation.arXiv preprint arXiv:2503.17671, 2025. 13

    arXiv 2025

  19. [19]

    Hq-edit: A high-quality dataset for instruction-based image editing.arXiv preprint arXiv:2404.09990, 2024

    Mude Hui, Siwei Yang, Bingchen Zhao, Yichun Shi, Heng Wang, Peng Wang, Yuyin Zhou, and Cihang Xie. Hq-edit: A high-quality dataset for instruction-based image editing.arXiv preprint arXiv:2404.09990, 2024

    Pith/arXiv arXiv 2024

  20. [20]

    Node-based editing for multimodal generation of text, audio, image, and video.arXiv preprint arXiv:2511.03227, 2025

    Alexander Htet Kyaw and Lenin Ravindranath Sivalingam. Node-based editing for multimodal generation of text, audio, image, and video.arXiv preprint arXiv:2511.03227, 2025. Accepted to the NeurIPS 2025 Workshop on Generative and Protective AI for Content Creation

    arXiv 2025

  21. [21]

    StoryNodes: Human-AI co-creation for multimodal media generation using an agentic node-based interface

    Alexander Htet Kyaw and Lenin Ravindranath Sivalingam. StoryNodes: Human-AI co-creation for multimodal media generation using an agentic node-based interface. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pages 4540–4548, 2026

    2026

  22. [22]

    Stitch: A new way to design user interfaces using ai.https://blog.google/innovation-and-ai/ models-and-research/google-labs/stitch-ai-ui-design/, 2025

    Google Labs. Stitch: A new way to design user interfaces using ai.https://blog.google/innovation-and-ai/ models-and-research/google-labs/stitch-ai-ui-design/, 2025. Accessed 2026-05-10

    2025

  23. [23]

    Unlocking the conversion of web screenshots into html code with the websight dataset.arXiv preprint arXiv:2403.09029, 2024

    Hugo Laurençon, Léo Tronchon, and Victor Sanh. Unlocking the conversion of web screenshots into html code with the websight dataset.arXiv preprint arXiv:2403.09029, 2024

    Pith/arXiv arXiv 2024

  24. [24]

    GENEVA: GENErating and visualizing branching narratives using LLMs.arXiv preprint arXiv:2311.09213, 2024

    Jorge Leandro, Sudha Rao, Michael Xu, Weijia Xu, Nebosja Jojic, Chris Brockett, and Bill Dolan. GENEVA: GENErating and visualizing branching narratives using LLMs.arXiv preprint arXiv:2311.09213, 2024. Accepted at IEEE Conference on Games 2024

    Pith/arXiv arXiv 2024

  25. [25]

    Claw-eval-live: A live agent benchmark for evolving real-world workflows.arXiv preprint arXiv:2604.28139, 2026

    Chenxin Li, Zhengyang Tang, Mingxin Huang, Yunlong Lin, Shijue Huang, Shengyuan Liu, Bowen Ye, Rang Li, Lei Li, Benyou Wang, et al. Claw-eval-live: A live agent benchmark for evolving real-world workflows.arXiv preprint arXiv:2604.28139, 2026

    Pith/arXiv arXiv 2026

  26. [26]

    Libtv skills.https://github.com/libtv-labs/libtv-skills, 2025

    LibTV Labs. Libtv skills.https://github.com/libtv-labs/libtv-skills, 2025. Accessed 2026-05-10

    2025

  27. [27]

    Jarvisart: Liberating human artistic creativity via an intelligent photo retouching agent

    Yunlong Lin, Zixu Lin, Kunjie Lin, Jinbin Bai, Panwang Pan, Chenxin Li, Haoyu Chen, Zhongdao Wang, Xinghao Ding, Wenbo Li, et al. Jarvisart: Liberating human artistic creativity via an intelligent photo retouching agent. arXiv preprint arXiv:2506.17612, 2025

    Pith/arXiv arXiv 2025

  28. [28]

    Jarvisevo: Towards a self-evolving photo editing agent with synergistic editor-evaluator optimization.arXiv preprint arXiv:2511.23002, 2025

    Yunlong Lin, Linqing Wang, Kunjie Lin, Zixu Lin, Kaixiong Gong, Wenbo Li, Bin Lin, Zhenxi Li, Shiyi Zhang, Yuyang Peng, et al. Jarvisevo: Towards a self-evolving photo editing agent with synergistic editor-evaluator optimization.arXiv preprint arXiv:2511.23002, 2025

    arXiv 2025

  29. [29]

    Step1x-edit: A practical framework for general image editing.arXiv preprint arXiv:2504.17761, 2025

    Shiyu Liu, Yucheng Han, Peng Xing, Fukun Yin, Rui Wang, Wei Cheng, Jiaqi Liao, Yingming Wang, Honghao Fu, Chunrui Han, et al. Step1x-edit: A practical framework for general image editing.arXiv preprint arXiv:2504.17761, 2025

    Pith/arXiv arXiv 2025

  30. [30]

    Minimax hub.https://hub.minimax.io/, 2026

    MiniMax. Minimax hub.https://hub.minimax.io/, 2026. Accessed 2026-05-25

    2026

  31. [31]

    Magentic-UI: Towards human-in-the-loop agentic systems.arXiv preprint arXiv:2507.22358, 2025

    Hussein Mozannar, Gagan Bansal, Cheng Tan, Adam Fourney, Victor Dibia, Jingya Chen, Jack Gerrits, Tyler Payne, Matheus Kunzler Maldaner, Madeleine Grunde-McLaughlin, et al. Magentic-UI: Towards human-in-the-loop agentic systems.arXiv preprint arXiv:2507.22358, 2025

    Pith/arXiv arXiv 2025

  32. [32]

    GPT-5.5 System Card

    OpenAI. GPT-5.5 System Card. https://deploymentsafety.openai.com/gpt-5-5/gpt-5-5.pdf , 2026. Pub- lished April 23, 2026. Accessed: 2026-05-31

    2026

  33. [33]

    GPT Image 2

    OpenAI. GPT Image 2. OpenAI API documentation, https://developers.openai.com/api/docs/models/ gpt-image-2, 2026. Accessed: 2026-05-31

    2026

  34. [34]

    Hierarchical text-conditional image generation with CLIP latents.arXiv preprint arXiv:2204.06125, 2022

    Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen. Hierarchical text-conditional image generation with CLIP latents.arXiv preprint arXiv:2204.06125, 2022

    Pith/arXiv arXiv 2022

  35. [35]

    Dynamic story- board generation in an engine-based virtual environment for video production.arXiv preprint arXiv:2301.12688, 2023

    Anyi Rao, Xuekun Jiang, Yuwei Guo, Linning Xu, Lei Yang, Libiao Jin, Dahua Lin, and Bo Dai. Dynamic story- board generation in an engine-based virtual environment for video production.arXiv preprint arXiv:2301.12688, 2023

    Pith/arXiv arXiv 2023

  36. [36]

    High-resolution image synthesis with latent diffusion models

    Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image synthesis with latent diffusion models. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10684–10695, 2022

    2022

  37. [37]

    Photorealistic text-to-image diffusion models with deep language understanding.arXiv preprint arXiv:2205.11487, 2022

    Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily L Denton, Seyed Kamyar Seyed Ghasemipour, Burcu Karagol Ayan, S Sara Mahdavi, Rapha Gontijo Lopes, et al. Photorealistic text-to-image diffusion models with deep language understanding.arXiv preprint arXiv:2205.11487, 2022. 14

    Pith/arXiv arXiv 2022

  38. [38]

    Seedream 4.0: Toward next-generation multimodal image generation.arXiv preprint arXiv:2509.20427, 2025

    Seedream Team, Yunpeng Chen, Yu Gao, Lixue Gong, Meng Guo, Qiushan Guo, Zhiyao Guo, Xiaoxia Hou, Weilin Huang, Yixuan Huang, et al. Seedream 4.0: Toward next-generation multimodal image generation.arXiv preprint arXiv:2509.20427, 2025

    Pith/arXiv arXiv 2025

  39. [39]

    Design2code: Benchmarking multimodal code generation for automated front-end engineering

    Chenglei Si, Yanzhe Zhang, Ryan Li, Zhengyuan Yang, Ruibo Liu, and Diyi Yang. Design2code: Benchmarking multimodal code generation for automated front-end engineering. InProceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 3956...

    2025

  40. [40]

    Tapnow ai creative platform documentation

    TapNow. Tapnow ai creative platform documentation. https://docs.tapnow.ai/en/docs, 2025. Accessed 2026-05-10

    2025

  41. [41]

    Seedance 2.0: Advancing video generation for world complexity.arXiv preprint arXiv:2604.14148, 2026.https://arxiv.org/ abs/2604.14148

    Team Seedance, De Chen, Liyang Chen, Xin Chen, Ying Chen, Zhuo Chen, Zhuowei Chen, et al. Seedance 2.0: Advancing video generation for world complexity.arXiv preprint arXiv:2604.14148, 2026.https://arxiv.org/ abs/2604.14148

    Pith/arXiv arXiv 2026

  42. [42]

    Audio-omni: Extending multi-modal understanding to versatile audio generation and editing

    Zeyue Tian, Binxin Yang, Zhaoyang Liu, Jiexuan Zhang, Ruibin Yuan, Hubery Yin, Qifeng Chen, Chen Li, Jing Lyu, Wei Xue, et al. Audio-omni: Extending multi-modal understanding to versatile audio generation and editing. arXiv preprint arXiv:2604.10708, 2026

    Pith/arXiv arXiv 2026

  43. [43]

    Qwen-image technical report.arXiv preprint arXiv:2508.02324, 2025

    Chenfei Wu, Jiahao Li, Jingren Zhou, Junyang Lin, Kaiyuan Gao, Kun Yan, Shengming Yin, Shuai Bai, Xiao Xu, Yilei Chen, et al. Qwen-image technical report.arXiv preprint arXiv:2508.02324, 2025

    Pith/arXiv arXiv 2025

  44. [44]

    PromptChainer: Chaining large language model prompts through visual programming

    Tongshuang Wu, Ellen Jiang, Aaron Donsbach, Jeff Gray, Alejandra Molina, Michael Terry, and Carrie J Cai. PromptChainer: Chaining large language model prompts through visual programming. InExtended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems, pages 1–10. Association for Computing Machinery, 2022. doi: 10.1145/3491101.3519729

    arXiv 2022

  45. [45]

    Omnigen: Unified image generation.arXiv preprint arXiv:2409.11340, 2024

    Shitao Xiao, Yueze Wang, Junjie Zhou, Huaying Yuan, Xingrun Xing, Ruiran Yan, Shuting Wang, Tiejun Huang, and Zheng Liu. Omnigen: Unified image generation.arXiv preprint arXiv:2409.11340, 2024

    Pith/arXiv arXiv 2024

  46. [46]

    Show-o2: Improved native unified multimodal models.arXiv preprint arXiv:2506.15564, 2025

    Jinheng Xie, Zhenheng Yang, and Mike Zheng Shou. Show-o2: Improved native unified multimodal models.arXiv preprint arXiv:2506.15564, 2025

    Pith/arXiv arXiv 2025

  47. [47]

    ComfyUI-R1: Exploring reasoning models for workflow generation.arXiv preprint arXiv:2506.09790, 2025

    Zhenran Xu, Yiyu Wang, Xue Yang, Longyue Wang, Weihua Luo, Kaifu Zhang, Baotian Hu, and Min Zhang. ComfyUI-R1: Exploring reasoning models for workflow generation.arXiv preprint arXiv:2506.09790, 2025

    Pith/arXiv arXiv 2025

  48. [48]

    ComfyUI-Copilot: An intelligent assistant for automated workflow development

    Zhenran Xu, Xue Yang, Yiyu Wang, Qingli Hu, Zijiao Wu, Baotian Hu, Longyue Wang, Weihua Luo, and Kaifu Zhang. ComfyUI-Copilot: An intelligent assistant for automated workflow development. InProceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations), pages 632–643, 2025

    2025

  49. [49]

    ComfyBench: Benchmarking LLM-based agents in ComfyUI for autonomously designing collaborative AI systems

    Xiangyuan Xue, Zeyu Lu, Di Huang, Zidong Wang, Wanli Ouyang, and Lei Bai. ComfyBench: Benchmarking LLM-based agents in ComfyUI for autonomously designing collaborative AI systems. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 24614–24624, 2025

    2025

  50. [50]

    Scaling autoregressive models for content-rich text-to-image generation

    Jiahui Yu, Yuanzhong Xu, Jing Yu Koh, Thang Luong, Gaurav Baid, Zirui Wang, Vijay Vasudevan, Alexander Ku, Yinfei Yang, Burcu Karagol Ayan, et al. Scaling autoregressive models for content-rich text-to-image generation. arXiv preprint arXiv:2206.10789, 2022

    Pith/arXiv arXiv 2022

  51. [51]

    Magicbrush: A manually annotated dataset for instruction-guided image editing.Advances in Neural Information Processing Systems, 36:31428–31449, 2023

    Kai Zhang, Lingbo Mo, Wenhu Chen, Huan Sun, and Yu Su. Magicbrush: A manually annotated dataset for instruction-guided image editing.Advances in Neural Information Processing Systems, 36:31428–31449, 2023

    2023

  52. [52]

    Ultraedit: Instruction-based fine-grained image editing at scale.Advances in Neural Information Processing Systems, 37:3058–3093, 2024

    Haozhe Zhao, Xiaojian Shawn Ma, Liang Chen, Shuzheng Si, Rujie Wu, Kaikai An, Peiyu Yu, Minjia Zhang, Qing Li, and Baobao Chang. Ultraedit: Instruction-based fine-grained image editing at scale.Advances in Neural Information Processing Systems, 37:3058–3093, 2024. 15

    2024