CREA: A Collaborative Multi-Agent Framework for Creative Image Editing and Generation
Authors: Kavana Venkatesh
Connor Dunlop
Pinar Yanardag
Venue: NeurIPS 2025 poster ()
Keywords: creativity
t2i
diffusion
multi-agentic systems
llm
Primary area: applications
TL;DR: We  introduce the first work for disentangled creative editing with an agentic framework.

Abstract: Creativity in AI imagery remains a fundamental challenge, requiring not only the generation of visually compelling content but also the capacity to add novel, expressive, and artistically rich transformations to images. Unlike conventional editing tasks that rely on direct prompt-based modifications, creative image editing demands an autonomous, iterative approach that balances originality, coherence, and artistic intent. To address this, we introduce CREA, a novel multi-agent collaborative framework that mimics the human creative process. Our framework leverages a team of specialized AI agents who dynamically collaborate to conceptualize, generate, critique, and enhance images.  Through extensive qualitative and quantitative evaluations, we demonstrate that CREA significantly outperforms state-of-the-art methods in diversity, semantic alignment, and creative transformation. To the best of our knowledge, this is the first work to introduce the task of creative editing.

CREA: A Collaborative Multi-Agent Framework for Creative Image Editing and Generation
Kavana Venkatesh"
Connor Dunlop'
Pinar Yanardag
Virginia Tech, Blacksburg, VA (kavanav, cdunlop, pinary]@vt.cc edu
https:://ceaaddiffusiin htt.:fusiion.gttu..... io
Orcative Imagg: Elittins
Craativ Emmggg Generation
Crcative Video Generation
Porsonalization
Da DOSEEIRG HRR
Trpel
Figure I: We introduce CREA, agentic framework inspired by the human creative process for image editing and generation. Our approach can be extended to video domain for creative video generation be integrated with personalization methods to further enrich creative workflows.
Abstract
Creativity in AI imagery remains a fundamental challenge, requiring not only the generation of visually compelling content but also the capacity add novel, expressive, and artistically rich transformations to images. Unlike conventional editing tasks that rely direct prompt-based modifications, creative image editing requires an autonomous, iterative approach that balances originality, coherence, and artistic intent. To address this, we introduce CREA, novel multi-agent collabora- tive framework that mimics the human creative process. Our framework leverages team specialized Al agents who dynamically collaborate to conceptualize, gen- erate, critique, and enhance images. Through extensive qualitative and quantitative evaluations, we demonstrate that CREA significantly outperforms state-of-the-art methods in diversity, semantic alignment, and creative transformation. To the best of our knowledge, this is the first work to introduce the task of creative editing.
Introduction
Generative AI has significantly transformed the field of image generation, producing high-quality visuals with remarkable detail and realism. Advances in diffusion models [4 14.. GANs (26). and retrieval-augmentt
techniques [5] 49 have enabled powerful capabilities in content synthesis,
making Al-driven image creation an essential tool for artists, designers, and various creative industries. These models have been widely applied in diverse tasks, including image-to-image translation 24 38
47), inpainting [22 33 25 style transfer [26] 18 44 611 and content-aware editing [36 g
and opening up new frontiers digital art, advertising, and entertainment, revolutionizing creative workflows.
Despite these advancements, achieving creative and artistically rich compositions still demands sig- nificant user effort. Traditional generative approaches primarily follow a prompt-to-image paradigm,
'qqual contribution.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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where models synthesize high-quality visuals based on textual descriptions, However, these outputs often lack originality and artistic depth, as the models are trained to replicate patterns from their training data rather than generate novel content. As result, users must engage in tedious prompt refinements, fine-tune model parameters, or manually edit outputs to infuse genuine creativity. This heavy reliance on user expertise and intervention limits the accessibility of generative Al as an autonomous creative assistant, placing the responsibility for creativity more on the user than on the system itself.
To address this limitation, we propose the task of creative image editing, where the goal is to modify images in a way that enhances their creative and artistic qualities with minimal user intervention. Unlike conventional editing tasks that focus on making explicit, text-driven modifications, creative editing aims to transform images into novel, aesthetically rich compositions in a disentangled way. Our approach mimics the complex process of creative image generation and editing as collaborative team effort, drawing inspiration from human workflows where specialists iteratively refine ideas to achieve shared artistic vision. To achieve this, we introduce a novel collaborative multi-agent framework, where agents, each with a distinct role, such as Creative Director or Art Critic, work in synergy to conceptualize, generate and refine creative outputs by grounding them in well-established guidelines distilled from established principles in creativity literature 16 20 2 40) This structured yet flexible approach enables the disentangled creation of highly diverse and imaginative images, ensuring both novelty and coherence at every stage of the generation process. Since creativity is complicated task to assess and evaluate, our agents leverage the extensive knowledge in multimodal LLMs [ based on Sfte-of-the-aa creativity principles inspired by previous work [6] 20 40 [ 4.
Through both qualitative and quantitative evaluations, we demonstrate that our method consistently produces edits perceived more creative and aesthetically pleasing compared to baseline methods. Our contributions are as follows:
We introduce a novel agentic framework for the task of creative image editing and generation. To the best of our knowledge, this is the first work to introduce the task of 'creative editing'.
We incorporate a user-in-the-looy generation setting, enabling user guidance to steer the creative direction through optional interventions. This supports a more collaborative human- AI co-creation process while maintaining artistic coherence and control.
We demonstrate the versatility of our method across image editing and generation, as well as its potential for enhancing personalization workflows and creative video generation.
2 Related Work
Text-to-Image Models. Recent advances in diffusion models 23 42 45
have revolutionized text- to-image synthesis, enabling high-fidelity image generation guided by textual prompts. Models such as DALLE-3 [37), SDXL [39, and Flux [27] demonstrate the ability to generate visually compelling images based on textual prompts [55
53 30). While these models produce high-quality
outputs, they lack structured mechanism enforce creativity principles in generation. Personalized generation approaches such as DreamBooth [4] and Textual Inversion (18) focus on fine-tuning -oxt-to-mmag models for specific subjects or styles. However, these methods optimize for style and subject consistency rather than generating creative images.
Creative Image Generation and Editing. Research on creative generation in AI has advanced through GANs 19 26] and diffusion models, leveraging contrastive loss and diversity-based ob- jectives encourage novel synthesis beyond category constraints [ 22 46 Recent works such as ProCreate (3 use energy-based repulsion to steer diffusion models away from reference im- ages, while Inspiration Tree [50] employs hierarchical decomposition for conceptual hybridization. ConceptLab [41] tackles creative concept synthesis through diffusion priors, iteratively enforcing con- straints to generate novel category members. C3 2T proposes a training-free free approach for enhancing creativity by amplifying low- low-frequency feature maps in early layers of Stable Diffusion models. Despite their advancements, these methods require expensive retraining or optimization, and fail to generalize to broader concepts. For image editing, ControlNet [59] extends Stable Diffusion, intro- ducing external conditioning signals for localized modifications while maintaining structural integrity. SDEdit (36) and Blended Latent Diffusion 3 refine diffusion-based editing for finer control, while InstructPix2Pix (8 allows text-based transformations via user prompts. However, these approaches focus on structural fidelity rather than ceaativttyddriven minimal-guidant transformations.

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La IneeCeememiii Platning
Optioal
La Czentism Lenembbmny leiliig
Generation Bralustion
Labta
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Figure 2: CREA Framework. We introduce a collaborative multi-agent framework for creative image editing and generation. Our framework consists of four stages, 1.a Pre-Generation Planning, 1.b Creative Image Generation/Editing, 2. fost-Generation Evaluation and 3. Self-Ennanccemet. Here, K is the number of maximum iterations.
Large Language Models Large Language Models (LLMs) such as GPT-4 [ and PaLM [10] have demonstrated remarkable capabilities in natural language understanding and generative tasks. These models leverage transformer architectures [48), which allows them to process large amounts of text and generate coherent and contextually relevant outputs. In creative domains, LLMS have been increasingly integrated into Al-driven artistic workflows, assisting with idea generation, structured prompt synthesis, and artistic guidance [35]. GenArtist [52] uses a multimodal single agent for
general image generation and editing but does not address creative image editing and fails to leverage the collaborative nature of multiple agents for complex use cases. Several studies have explored LLMs
in the context of creativity [32 62 22 III 28 16]: however, to the best of our knowledge, creative editing tasks have not been explicitly tackled before. Additionally, agentic frameworks, where AI systems exhibit autonomy in Deccision-makigg iterative refinement, and adaptive goal-setting have not yet been systematically explored in the context of creative image generation or editing.
3 Methodology
Creativity a multifaceted process that spans ideation, critique, refinement, and the delicate balancing of novelty with coherence. Traditional prompt-to-image pipelines handle this spectrum in a single static step, leaving users to face the burden of crafting elaborate prompts, testing variations, and manually disentangling edits. Such manual workflows are both expert-intensive; they assume deep domain knowledge to articulate abstract creative facets, and time-consuming, often requiring many rria--andeerror iterations before the desired concept emerges. Our agentic framework replaces this bottleneck with a dynamic, collaborative loop that coordinates specialized agents such as a creative director, art critic, and refinement strategist to reason jointly and iteratively evolve image. By structuring creativity as a multi-agent dialogue, the system mirrors human studio practice: each role contributes focused expertise while shared memory maintains global coherence. The result is higher-quality more interpretable edits achieved with less human effort and lower computational overhead than manual, prompt-tweaking regimen. Fig. 2 offers overview, and the sections that follow detail each component of the framework.
3.1 Multi-Agent Framework Design
Our workflow mirrors structured human collaboration, assigning distinct agent roles to ensure iterative, controllable creative image editing and generation where agents are:
Creative Director A1. This agent serves as the main decision-maker interpreting concepts, defining creativity blueprint, and coordinating with agents refine, discard, or finalize the image.
Prompt Architect A2. Translates conceptual ideas into contrastive prompts 9 for each creativity principle, merging them into a high-creativity prompt for the Generative Executor agent for image cditing/generation and refining them iteratively based agent feedback.
3

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Input
Edited Images
Input
Edited
Generated Images
*
a) Creative Image Editing
Creative Image Generation
Figure 3: Qualitative Results for Creative Image Editing and Generation Tasks. (a) Creative Image Editing: CREA takes either a real-world or Al generated input image to produce a conceptually enriched and stylistically novel edit while preserving key structural elements. (b) Creative Image Generation: CREA receives only a minimal concept description (e.g., "a couch" or "a car") and generates diverse, imaginative outputs without any visual input, extrapolating rich visual metaphors and materials. These results demonstrate CREA's ability to disentangle and control creativity across editing and generation workflows. For additional results, the appendix section.
Generative Executor Ag. This agent uses T2I models such Flux and ControlNet for image generation or editing, dynamically selecting the appropriate diffusion model and parameters to ensure creative outputs.
Art Critic A,. The Art Critic evaluates the generated image based on the creativity principles, assigning score for each criterion. Given that LLMs can approximate human judgment in subjective evaluations [63], this agent uses a multimodal LLM judge to ensure sensible evaluation.
Refinement Strategist As. This agent translates the Critic's feedback into actionable refinements for the next iteration. It identifies weak creative dimensions and suggests precise modifications the Prompt Architect.
Each agent maintains private memory and utilizes role-specific tools to share task status and relevant information, ensuring informed decision-making at every step. We acknowledge that an agentic pipeline introduces extra orchestration effort compared with naive single-pass T21 call. However, an agentic design is critical for several aspects (i) modularity - each agent encapsulates a narrow skill (prompt refinement, consistency auditing, local editing) that can be swapped upgraded independently; (ii) interpretability- inter-agent messages expose an explicit reasoning trace; and (iii) extensibility new capabilities (e.g., style transfer, safety filtering) may be added expand the scope of the framework. See Supplementary Material for detailed agent compositions.
3.2 Collaborative Agentic Synthesis
Given a user-provided concept (such as guitar' and an input image our goal is to modify it to generate Ig in creative and disentangled manner. For image generation, we follow the same pipeline with minimal modifications to produce a creative image To and transform it into Ig. We formulate this as an optimization problem that maximizes a Creativity Index (CI), guided by six creativity principles described below. Our method has three modular phases: Pre-Generation Planning, Post-Generation Evaluation, and Self-Ennuncemmnt with optional User- Guidance. For clarity, the following sections primarily describe our method in the context of creative editing, as the same framework extends naturally to image generation with minor adjustments.

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Input
SDEdit
InstructPix2Pix
+
TurboEdit
Ours
Bowl
Bike
Chair
Umbrella
Figure 4: Qualitative Comparison of Creative Image Editing Task. We compare CREA with state-of-the-art editing methods. As shown, CREA successfully reimagines objects into creative variants a disentangled manner, whereas other approaches either fail to produce distinctly creative edits or introduce unintended alterations.
Creativity Principles. Our framework leverages six creativity principles, grounded in state-of- the-art creativity theories, to systematically evaluate and measure creative output. Originality, measuring novelty and uniqueness, is inspired by Boden's Theory of Creativity (6] and Guilford's Divergent Thinking (20) framework. Expressiveness, which captures emotional impact, is influenced by Amabile's Model of Creativity [2] and Ramachandran & Hirstein's Laws of Aesthetics [40]. Aesthetic Appeal, assessing composition and harmony, is grounded in Martindale's 's Aesthetic Model 34 and Berlyne's Aesthetic Theory 44. Technical Execution, evaluating craftsmanship and skill, draws from Amabile's Model and AI Creativity Frameworks [2]. Unexpected Associations, reflecting surprise and ingenuity. supported by the Geneplore Model [15] and Boden's Combinational Creativity [6]. Finally, Interpretability & Depth, which considers exploration potential. is informed by Ramachandran's Laws 40) and the Geneplore Model [55] (see Appendix for more details). These six principles are used as creativity template T for agents to assess and refine the outputs.
3.3 Pre-Generation Planning and Image Synthesis
The pre-generation planning phase serves structured ideation stage where agents collaboratively establish a creative blueprint, B before image generation begins. This phase involves three key agents: the Creative Director A.. Prompt Architect Ag, and Generative Executor Ag, who collab- oratively devise the creativity prompt P. that is both creatively rich and technically viable. First, Creative Director, A₁ interprets the initial image To. either user-provided generated using the user-provin user-provided concept to formulate a creativity blueprint B, capturing the core theme, stylistic interpretation, visual structure, and necessary constraints to balance artistic flexibility with semantic coherence (see Appendix for more details), Based on B, the Prompt Architect A2 synthesizes set of contrastive prompts /' {P1, P2..... ... and are merged into high-creativity fused prompt P. through Chain-of- ooouuht ........)))))
.ps), Ps).
each conditioned on a distinct creativity principle
(54) reasoning
P. CoT--Fusion(1. .......... Ps) where CoT-Fusion extracts salient conceptual and stylistic at-
tributes from each prompt and synthesizes them into a coherent, balanced, and conceptually rich formulation. The Generative Executor Ag evaluates the feasibility of P. and determines T2I model- specific constraints-such as ControlNet conditioning scale, image guidance scale to anticipate the nuances of the generated blueprint. Once all agents reach a consensus, the prompt moves to the next phase for creative image generation.
5

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"A couch"
monster
SDXL
Flux
Ours ConceptLab
Figure 5: Qualitative Comparison of Creative Image Generation Task. We compare CREA with ConceptLab, SDXL and Flux. CREA consistently produces diverse and creative generations across multiple domains.
Image Generation: The Generative Executor, A3 plays a dual role, either generating a creative image using T2I model or performing disentangled creative editing initially generated (fo) or user-provided (l)). The high-creativity structured prompt formulated during the pre-generation planning phase is then taken by the Generative Executor synthesize the initial creative image, To using a text-to-image im-i diffusion model, G as To = G(P 0) where represents model-specific parameters-such as guidance scale and ControlNet conditioning scale. The generated image, lo serves as the starting point for the creative editing and iterative refinement process as described in the following sections.
existing image, whether
the user provides an image instead of generating one, or if an enhancement to To is required, the Generative Executor performs disentangled creative edits using Le G(Pc. Io. 0) where G is the ControlNet model used to perform disentangled edits, P. is the high-creativity editing prompt generated in the pre-g reegenneration planning phase and 0 represents parameters of G. After generation or editing, the resulting image progresses to the post-generation evaluation phase to investigate if further iterative refinements necessary.
Image Editing:
3.4 Post-Generation Evaluation
The post-genne generation evaluation assesses the edited creative image, Ie (or Io for generation) against the creativity template, T to maximize creativity. This Critic A, and the Creative Director A₁ collaborate, with the Critic systematically evaluating the initial edited image, Le based on the creativity template, evaluate the edited image Ta by assigning creativity
T. The Critic utilizes the LLL score, S; for each of the six ith creativity criteria on a 1-5 scale. The overall Creativity Index, CT is then computed as: CI Σi= S..
If the total creativity score, CT < S., where S, is predefined threshold, the edited image Ie is considered suboptimal in creativity. The Creative Director then reviews the Critic's 's evaluation and may challenge its assessment if the assigned scores misalign with the creative blueprint, B. Once consensus reached, Ig is either finalized or progresses to further enhancement based on the final creativity score CT.
3.4.1 Self-Enhancement with Optional User-Guidance
While one round of editing produces optimal creative outputs, further refinement can enhance creativity. In the self-enhancement phase, all agents iteratively refine the edited image, I, with optional human intervention. Given a maximum of K iterations, each edited image is evaluated by the Critic compute its C'I. When Cl < Sc, the Refinement Strategist, As, identifies the low scoring

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Method
CLIP 1
LPIPS*1 1
VENDI
DINO 1
FID 4
KID |
User Study-Q1
User Study-Q2
LEDITS++
0.396 +: 0.028
0.252 4: 0.074
1.11
0,678 :: 0.160
312.50
21.45
3.21 4: 1.27
3.50 :: 1,25
InstructPix2Pix
0.379 = 0.032
0.289 + 0.126
1.94 0.61
0,704 4 0.189
314.42
22.51
3.14 + 1.23
3,59 + 1.17
SDEdit
0.381 = 0.033
0,308 + 0.068
3.19 1.23
0.737
+ 0.162
304.37
18.78
3.31 + 1.21
3,15 + 1.16
Turbofdiit
0.389 1 0.031
0.192 1 0.071
2.34 1 0.94
0.735
1 0.173
320.53
24.79
3.23
1 1.29
2.63 1. 1.20
Ours (Editing)
0.417 + 0.030
0.414 + 0.157
3.70
1.97
0.744 + 0.185
294.19
14.02
3.34 + 1.34
3,74 1,21
SDXL
0.404 = 0.033
0.636 + 0.069
6.63
2.85
N/A
282.22
9.67
4.37 + 0.99
3,56 + 1.10
Flux
0.359 0.048
0.650
0.088
5.84 2.69
N/A
270.69
9.94
4.11
3.00 1.24
ConceptLab
0.334 0.055 0.663 + 0.076
10.38 +:
N/A
272.97
6.49
3:40
3.18 1.30
Ours (Gemeration))
0.360 1 0.052 0.709 1 0.057 10.44 1: 2.15
N/A
248.67
5.94
4.32 1: 0.99
4.16 1: 1.01
Table I: Quantitative Comparison of Creative Image Editing and Generation. Our method surpasses state-of-the-art methods across multiple metrics for both editing and generation tasks. Note that DINO scores cannot be computed for image generation, as they rely on image-image similarity, and there is no reference image available for this task. indicates that scores are interpreted in opposition to their conventional usage, as creative generation tasks benefit from greater perceptual distance between original and edited images.
creative dimensions and proposes a refinement plan enhance the corresponding weak dimensions, which the Prompt Architect A2 uses to refine the editing prompt, P. as follows P. = P. + AP where AP represents the prompt adjustment that amplify specific creativity dimensions based on their evaluation scores and P, is the refined creativity prompt. The refined prompt is used by the Generative Executor Ag to regenerate an improved image using I, G(P,, T+).
The process iterates until CI 2 S. or A: 2 K, where K is the maximum allowed iterations and I is an intermediate creative image. Users can provide real-time instructions enhance creativity, which the Prompt Architect integrates into the evolving prompt, while the Refinement Strategist ensures artistic coherence. The final goal is max,, E[C]([,) where Le is the final optimized image, achieved through iterative refinement of the refined current image, L,.
4 Experiments
Experimental Setup In this section, we evaluate our method's ability to generate highly creative edits and images. All experiments use FLUX. [27]. For editing, we employ ControlNet [59] with conditioning scale of 0.4 and Canny as the condition. For image generation, we vary the CFG scale from 3.5 to 40 to explore different levels of creativity and control. Additionally, we utilize Autogen (56) for our agentic framework. All experiments are performed on a 48GB NVIDIA L40 GPU. We use GPT-4o as our MLLM for all agents (I. We set the Creativity Index (CI) threshold to 24 for editing. requiring a majority of criteria to score at least 4 ("very good") for an image to be considered creative. For generation, we use higher threshold of 26 to reflect greater creative freedom unconstrained by a base image. We cap the number of refinement iterations at K = 3. A full of the pipeline takes approximately 3-5 minutes, depending the number of self-enhancemennt rounds. Our source code is publicly available at htttps:: ceattp://cceaadifffii www.uuuiiiinnnniui.oooooo io
4.1 Qualitative Results
Creative Image Editing First, we qualitatively showcase how CREA transforms input images into various creative modifications. As seen in Fig. CREA's autonomous agents analyze the input image or concept and generate creative prompts: e.g., focusing on style, color, or thematic twists, without requiring extensive user intervention. Since no existing method is explicitly designed for creative image editing, we adapt state-of-the-art editing models for comparison. Specifically, we evaluate the following baselines: LEDITS+ [7]. InstructPix2Pix [8) SDEdit (36) and TurboEdit [2]. For fair comparison, we apply a "creative <object>" prompt (e.g.. "a creative couch") to the baseline methods, mirroring the objective of CREA. As shown in Fig. 4 standard editing methods often fail to generate distinctly creative concepts: PistructPix2Pix typically adds vibrant colors or alters the background extensively without fundamentally reimagining the object. LEDITS++, SDEdit, and TurboEdit struggle to introduce creative features beyond superficial stylistic changes. In contrast, CREA successfully performs creative edits a disentangled manner. For more experiments such as more fine- mme-grained editing examples, or other image generators, please refer to Appendix D.4
7

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Se rendingg a book
Input
Flux + ControlNet
Ours
Goreratal Image:
the beach
bas in style
+Skuil
Gonorated
+Monster
a) User-in-thed in-the-loop generation
b) Personalization
Figure 6: Creative applications of our method beyond image generation and editing, (a) Users can steer the creative process with additional conditions such 'Monster'. (b) CREA-generated images can be leveraged for personalization in creative domains.
Baseline videos (CogVideoX)
Ours (CREA + CogvideoX)
"A meutive
*X gestive
cmach"
Figure Video Generation. Comparison between baseline generations from VideoX and CREA. Our method enables the creation of visually diverse and creative video scenes.
Creative Image Generation For creative image generation, we compare CREA against ConceptI .a which is the closest related approach, as well as generative baselines: Flux (7) and SDXL To ensure a fair comparison, we use the same random seed for each method in every evaluated prompt, allowing for direct visual comparisons, As illustrated Fig. CREA consistently produces diverse and creative generations across multiple domains. In contrast, ConceptLab struggles to maintain the intended concept, particularly for highly abstract or unconventional categories. For example, when generating "a monster," ConceptLab often fails to produce meaningful interpretation, as it relies on extracting subcategories from the BLIP model. If a given concept lacks well-defined subcategories or consists of highly specific attributes, ConceptLab's 's fails to perform well. CREA generalizes across broad range of creative categories without relying on predefined subcategories.
41
39
4.2 Quantitative Results
For both editing and generation, we utilized different objects with 25 prompts (either for editing or generation), resulting in an evaluation set of 600 images per task.
Creative Image Editing, For image editing, we compare our method against state-of-the-att tech- niques, including LEDITS++, InstructPix2Pix, SDEdit, and TurboEdit, across a range of evaluation metrics. To assess how well the edited objects align with the text descriptions, compute the CLIP score by measuring the similarity between the generated image and its corresponding text prompt. For evaluating diversity, we use the LPIPS 600 score, which quantifies perceptual distance between images. Unlike conventional usage where lower LPIPS indicates better reconstruction, higher LPIPS in our case signifies stronger, more transformative edits, a desirable trait in creative tasks. Additionally, compute the DINO [58 score which measures how well the edited image retains key structural and semantic characteristies of the original image, and Vendi [77 score, which quantifies diversity by calculating the Shannon entropy of the eigenvalues of the similarity matrix among the

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Chosree Conidarice Scale
ah-Mhaics 221 Mode
10 AMbiic CNi andiCioneeeee
Aflations For Scff Relurceness
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Figure 8: Ablation Study. We perform comprehensive ablation studies to analyze the design choices of CREA: (a) Model Generalization: Our method extends effectively to different generative models, such as SDXL and DALL-E. (b) Parameter Sensitivity: We ablate CFG values for Flux and the conditioning scale for ControlNet to evaluate their impact. (c) Iterative Refinement: We demonstrate the benefits of our method's 's refinement process over multiple iterations. (d) Prompt Variations: We explore alternative prompts beyond 'a creative <obj>'.
generated images. As shown in Table | our method consistently outperforms all baselines across all evaluation metrics, demonstrating its superior ability to generate creative and diverse image edits.
Creative Image Generation. For creative image generation, we compare CREA against ConceptLab. Flux, and SDXL. As shown in Table our method achieves superior results in LPIPS and VENDI scores, while in CLIP scores, it was outperformed by SDXL. We observe that SDXL tends to generate highly colorful images, which aligns with CLIP's ' depiction of creativity. However, it produces the lowest LPIPS scores, indicating that its generated images are highly similar each other, and also achieves significantly lower VENDI scores compared to CREA and ConceptLab. While ConceptLab and our method achieve comparable results overall, ConceptLab has significantly lower performance CLIP scores, suggesting that its generated images do not align well with the text prompt. This limitation stems from ConceptLab reliance on well-defined subcategories- a core design principle of their method. As shown in Fig. [ ConceptLab fails when a concept lacks sufficient subcategories, such as its inability to accurately generate a couch object. While ConceptLab attains a high VENDI score, this failure highlights its trade-off between diversity and semantic alignment.
For additional comparisons with recent methods such as GenArtist [2 and RF-Inversion Appendix D.3
43 see
User Study. To assess the creativity of the generated images from human perception standpoint, we conducted a user study on Prolific.com with 50 participants (see Table T Participants rated each criterion using 5-point Likert scale, ranging from (Not all) to 5 (Very well). For the image generation task, User Study-Q1 evaluates Usability, measuring how accurately the generated image represents the specified object. User Study-Q2 evaluates Creativity, assessing the uniqueness and originality of the image. Among all competitors, SDXL performed the best on Q1 since it is generating generic objects (e-g.. simple cup) which is perceived more useful by participants, whereas our method significantly outperformed others on Q2 due to its ability to produce more creative visuals. For edited images, we evaluate editing consistency-howw well the original image characteristics are preserved while performing edits (User Study-Ql)-an the creativity of the edits (User Study-Q2). Our method achieves comparable results state-of-the-art editing techniques on Q1, demonstrating strong disentangled editing capabilities. Moreover, it surpasses all competitors on Q2, highlighting its superior ability to generate creative edits.
4.3 Additional Experiments
GuideG Editing and Generation Our method enables users guide the creative process according to their preferences (see Fig. (a)). For example, users can specify not only that a creative edit should be applied the generation. Our approach incorporates these preferences, steering the creative process accordingly. Compared to the baseline Flux ControlNet approach, where we provided the prompt 'A creative <conditions <object>', our results demonstrate greater creativity.
cup but also include additional conditions, such 'monster', to influence
Personalization We also present personalization results using CREA-generated creative images using an off-the-shelf personalization adapter [5T]. Fig. (b) demonstrates how generated subjects can be adapted to various contexts or how their styles can be transferred to create new images. Note that due

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Method
LPIPS-Diversity
Vendi f
Base
0.302 + 0.129
3.19 + 1.49
+Principles
0.312 + 0.142
3.59 = 2.46
+ +Contrastive
0,391 + 0.150
3.50 = 1.64
+Sell-Enhancement
0,414 + 0.157
3,70 + 1.97
Table 2: Ablation Study for CREA. CREA achieves the highest performance across all metrics
when all components are utilized, demonstrating the effectiveness of our full framework.
to the limitations in personalization models, some fine- aneeggrieed details were not able to capture by [51], however the main characteristics are still preserved.
Video Generation To demonstrate the versatility and extensibility of our agentic framework, we extend CREA to creative video generation, where an initial prompt (e.g., 'a train") is automatically transformed into creative scenes. We conduct experiments using the Cog V [57] model as the generative backbone. Instead of initializing the process with static creative blueprint, Director agent generates a structured creative video plan containing key fields: Subject, Action, Setting, Style, and optional Additional Details. This plan is passed to the Prompt Architect, who composes a coherent and high-creativity video prompt via contrastive prompt fusion, similar to our image generation pipeline. The Generative Executor then synthesizes the video using Cog VideoX. This evaluation serves as a proof of concept for applying our multi-agent creativity principles beyond static imagery. As shown in Figure 7 our method produces significantly more imaginative and visually engaging results compared to baseline model.
Creative
Ablation Studies We perform comprehensive ablation studies analyze the design choices of CREA (see Fig. 8). (a) Ablation on Generalization to other T21 models: Our method extends effectively to different generative models, such as SDXL and DALL-E. (b) Ablation on Parameter Sensitivity: We ablate CFG values (3.5-40) for Flux and the conditioning scale for ControlNet (0.1-0.5) to evaluate their impact. (c) Ablation on Iterative Refinement: We demonstrate the benefits of our method's refinement process over multiple iterations. (d) Ablation on Prompt Variations: We explore alternative prompts beyond 'a creative <obj>' to steer the model away from conventional generations. (e) Ablation on Model Components We also conduct an ablation study on our key components (see Table 2). The base version represents baseline where the multi-modal LLM model is simply prompted to generate a creative description for a given object. Please see Appendix for more ablations, such as an ablation with negative prompting.
5 Discussion
Limitations and Broader Impact Since our framework utilizes a T2I model such as Flux, certain biases inherent to the generative backbone can affect the outputs. For instance, we observe cases where the background is unintentionally darkened based on the object's 's semantics (e.g., editing "lighted box" often results in darker surroundings to emphasize contrast), or where the model hallucinates human figures even when not prompted (see Fig. (c)). Moreover, we recognize that defining and quantifying visual creativity itself an open research problem. Instead of introducing new assessment protocol, we tackle creative image editing and generation through established principles in creativity. Assessing what is truly creative output, however, remains beyond the scope of this paper. For broader impacts, on the positive side, our agentic framework can democratize high-level creative tools, lowering barriers for artists, educators, and hobbyists to experiment with sophisticated visual storytelling. Potential downsides include possible displacement of human labor in creative industries. We encourage responsible deployment by fostering human- iiee-ii--tteeeee the-loop usage that positions the system co-creative assistant rather than replacement for human creators.
Conclusion In this work, we introduce novel agentic framework for creative image editing and generation, pioneering disentangled approach that enables greater flexibility and artistic control. By leveraging specialized agents that collaborate to refine and enhance outputs, our method overcomes the limitations of traditional prompt-to- to-ptttt--mage models, reducing the burden on users while fostering creativity. We demonstrate the versatility of our approach across editing and generation, and highlight its potential for creative video generation. Our findings suggest that agentic frameworks can serve as a powerful foundation for more autonomous and creative AI systems, opening new directions for creative collaboration between humans and AI.
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Table of Contents
Investigating Creativity Types
CREA's Multi-Agentic Architecture
B.1
CREA's Multi-Agent Framework Design
B.2
Creative Director (A1) Prompt
B.3
Prompt Architect (A2) Prompt
Generative Executor (Az) Prompt
B.5
Art Critic (A4) Prompt
B.6
Refinement Strategist (As) Prompt
7
Algorithm
B.8
8
Runtime and Practical Considerations
CREA Editing and Generation Prompts
Creative Image Generation
Creative Image Editing
D Additional Experiments
11
D.I Additional Ablations
11
D.2 User Study Details
D.3 Additional Quantitative Results
11
D.4 Additional Qualitative Results
12
Investigating Creativity Types
We investigate emerging patterns in generated images identify key setributes-hhapee color, and texture that influence creativity. Our analysis reveals that our method achieves significantly higher scores in these aspects, leading to the generation of more visually diverse and imaginative objects (see Fig. 9). Compared to baseline models, our approach demonstrates a stronger ability to manipulate these factors, reinforcing its effectiveness in producing unique and aesthetically rich generations.
We also investigate emerging factors for different objects within our method (see Fig. 10
Cake
Trequency
Nethod
Figure 9: Emerging patterns in the generated images across CREA, ConceptLab, Flux, and SDXL reveal that our method achieves significantly higher scores shape, color, and texture, enabling the generation of more creative objects.

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Figure 10: Emerging patterns in the generated images across various objects reveal that our method dynamically emphasizes different factors such shape, color, or texture depending on the object, effectively enhancing creativity in context-aware manner.
Method
Originality 1
Expressivenes
Aesthetic 1
Techoical 1
Unexpected
Interpectaiiliyy 1
Total
Creativity 1
LEDITS++
3.04 +: 0.74
2.52 1 0.66
3.56 :: 0.55
3,80 +: 0.47
2,86 = 0.84
2.60::00 0.71
18.38
3.37
77.80 1: 8.83
243 + 0.91
2.28 0.80
3.50 + 0.54
3,76 + 0.51
2.12 102-003 0.8
2.21 - 0.78
16.39
3.70
69.78 15.94
SDEdi
2.50 0.87
2.72+
3:35 0.50
3,59
2.12= 0.78
2.07 0.72
3.42
09.39 13.54
2.68
0.50
+ 0.47
16.30
Ours (Edining)
3.77 + 1.00
3.49 : 1.03
4.49 = 0.62
4.61 + 0.55
3,66 1.12
3.46 = 1.03
23,48 +: 4.95
83.47
6.68
SDXL
3,45 + 0.89
3.30 0.77
4.15 0.52
4,31 + 0.49
3.33 1.00
= 0.79
#:985::3.99
82 95 6.56
Flux
2,87 1: 0.69
3,05 0.74
4.12 0.54
4.17
2.59
0.78
2,86
0.64
19.60 3.14
79.46 7.82
ConceptLab
3.34
21.25
Ours (eeneration)
4.39 1. 0.69
4.55 1. 0.54
4.98 1 0.16 4.96 1: 0.20
4.39 0.70
4.42 0.64
27.68 2.55
89.87 4.04
Table Quantitative Comparison of Creative Image Editing and Generation using LLM-as-a- judge. We used a multi-modal LLM as judge for simulating human subjective assessments across several key aspects of creativity. Our method surpasses state-of-the-ee art methods across all aspects for both editing and generation tasks.
B CREA's Multi-Agentic Architecture
B.1 CREA's Multi-Agent Framework Design
In the CREA framework, prompts serve as the fundamental coordination interface between specialized agents, guiding the collaborative creative process. Unlike traditional systems that rely on static user prompts, CREA uses modular, role-specific prompting strategies to simulate a human-like creative workflow. These prompts enable agents reason contextually, communicate asynchronously, and iteratively improve upon the image through multiple refinement cycles.
Our multi-agent framework is composed of five roles: Creative Director, Prompt Architect, Generative Executor, Art Critic, and Refinement Strategist. Each agent is instantiated with a carefully designed system prompt tailored to its unique responsibilities. These prompts not only essential for aligning agent behavior with creativity principles, but also for facilitating coherent multi-tumn conversations between agents. The following subsections describe the prompt structure for each agent, explain its purpose, and highlight how it contributes to the overall creativity loop. For each, we refer to specific examples and prompt templates shown in the supplementary material.
B.2 Creative Director (Ai) Prompt
The Creative Director serves the strategic leader of the agent team. Its prompt equips it not only interpret the user-provided input (e.g., 'a couch') but also to conceptualize a feasible, imaginative, and well-grounded plan for the entire task. More than just an interpreter, the Creative Director performs high-level planning, coordination, and vecision-making It establishes the overall direction of the creative task, specifies whether the objective is image generation or editing, and formulates comprehensive creativity blueprint. This blueprint includes the visual theme, stylistic and structural constraints, novelty objectives, semantic anchors, and any potential unexpected associations that could add conceptual depth. Once defined, this blueprint is shared with all other agents and acts as the central reference point throughout the iterative process. The Creative Director also participates in post-generation evaluation, arbitrates conflicts in Critie feedback, and decides whether output is ready for finalization or requires refinement. high-level system prompt used for the Director, A shown in Template I3

---

B.3 Prompt Architect (A2) Prompt
The Prompt Architect operates as the creative translator and synthesis engine of the system. Based on the Creative Director's blueprint and the creativity template, this agent generates structured set of prompts to guide the generative process. first decomposes the blueprint into six contrastive prompts, each aligned with a specific creativity principle-originaliin appeal, technical execution, unexpected associations, and interpretability. These prompts isolate individual creative dimensions, allowing for focused exploration of each aspect. The template used by Ag to generate Contrastive Prompts is shown in Template 9 Once the contrastive prompts are created, the Prompt Architect uses high-creativity prompt as shown in Template I0] This fused prompt balances all six principles while preserving semantic clarity and stylistic coherence. Throughout the generation loop, the Prompt Architect also integrates feedback from the Refinement Strategist, modifying or rebalancing prompts needed to improve underperforming dimensions. This iterative prompting system enables nuanced control over the generation process while ensuring alignment with the blueprint. A high-level system prompt used for the Prompt Architect, A2 is shown in Template g
expressiveness, aesthetic
Thouugh fusion process to synthesize them into single fused
B.4 Generative Executor (Ag) Prompt
The Generative Executor is responsible for realizing the creative vision by generating or editing images based on the prompts received from the Prompt Architect. Guided by its system prompt, this agent dynamically interprets the creative instruction and configures the appropriate image generation strategy using T2I models such as Flux or ControlNet. The Executor considers the task type (generation or editing), model-specific parameters like classifier-fre guidance or conditioning scale, and any constraints defined by the blucprint. In editing mode, it ensures disentangled transformation by preserving structural elements from the input image while applying stylistic or conceptual changes aligned with the fused prompt. A high-level system prompt used for the Executor, Ag is shown in Template IT.
B.5 Art Critic (A4) Prompt
The Art Critic acts as an autonomous evaluator, responsible for analyzing the generated or edited image and providing structured, multi-dimensional feedback. Its system prompt enables it to assess the output based on the six creativity principles and assign numerical scores from I to 5 for each dimension. The Critic also produces rich textual justifications for its ratings and provides an overall creativity score. Beyond evaluation, the Critic serves a pivotal role in steering the refinement process by identifying which dimensions underperform and articulating why. This feedback is critical to both the Creative Director, who decides whether the image meets the creativity threshold, and to the Refinement Strategist, who uses it to guide further improvements. The Critic's 's evaluations grounded a multi-modal L.LM, ensuring assessments are both visually and semantically informed. This agent's 's' consistent, transparent scoring facilitates traceable progress across iterations. high-level system prompt used for the Critic, A4 is shown in Template 12
B.6 Refinement Strategist (As) Prompt
The Refinement Strategist is the system's adaptive problem solver. Upon receiving the Critic's evaluation and the current image, this agent analyzes weak areas in the creativity spectrum and formulates a targeted refinement strategy. Its system prompt allows it to propose actionable changes using a formulation AP that identifies how the existing fused prompt should be adjusted to improve specific dimensions, such as expressiveness or technical execution. The strategist is also tasked with preserving creative coherence; it aligns each suggested change with the original blueprint avoid drift or over-editing. Its feedback is passed to the Prompt Architect, who updates the prompts accordingly for the next iteration. The Strategist plays a critical role in achieving optimal creativity without compromising conceptual consistency, functioning as the bridge between critique and constructive improvement. A representative example of its prompt behavior and refinement plan available in Template [4
3

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Algorithm CREA Method Overview
Input: User concept or Initial image Jo. Max iterations K, Creativity threshold S, Given: Creativity principles template, 7
Init: Agents (Ai : As}
Pre-Generation Planning if generation task then
2: A1 - B from c
3: else if editing task then
4:
AA B from to (initial generaation/use-poovidde))
5: end if
6: A2 P = 1 .P.,
7: A2 + Pe = CoT-Fusion(2 (P)
8: As Validate Pe, adjust model parameters
Image Synthesis and Editing
9: if Generating
image then
10: Ig =G 8)
11: else if Editing existing image then
12: L. = G To, 0)
13: end if
Post-Generation Evaluation
14: A4 5= (S1,..... So} for Ie
Compute CI -I= -i Si, S [1,5]
16: if Cf S. then
17:
return Ie as Ie
18: else
Proceed to refinement phase
20: end if
Self-Enhancement with Optional User-Guidance
21: for k to K do
22:
As - Identify weak S₁, suggest refinements
23:
Ag + P. Pe AP
refined prompt, P. > intermediate image, I refined image, J.
24:
As - +, GrP,. Ix. 0)
25:
As - (S .... S6) for
26:
if 2 S. then
27:
return as Ie
28:
end if
end for
Output: Final high-creativity image
B.7
Algorithm
An algorithm summarizing the agentic framework is given Algorithm 1
CREA is built on dynamic multi-agent architecture that emulates the collaborative human cre- ative process by distributing cognitive and generative responsibilities across specialized agents. Each agent in the system plays a distinct role and is instantiated using the AutoGen framework's ConversableAgent class, enabling structured communication, tool access, and memory management.
Creative Director (At): Acts as the master orchestrator. Powered by GPT-4o, this agent defines the overall creative blueprint, interprets user goals concepts, and decides whether generated image meets the creativity criteria. It has functional tools to collaborative with all other agents and tool to determine if the creativity index, C' is greater than the defined threshold. It does not execute code but uses a shared memory to track blueprint adherence across rounds.
Prompt Architect (Ag): Converts the blueprint into six contrastive prompts based on creativity principles (e.g., Originality, Aesthetic Appeal), which are fused into a high-creativity composite prompt using Chain-of-Though reasoning. This agent uses GPT-4o and has access to prompt-fusion tools and tools to interact with other agents, enabling to translate abstract directives into actionable instructions. It uses the shared memory to track progress, store base templates, and exchange updates.

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Generative Executor (A3): Interfaces with the image generation or editing backend (e.g., FLUX, ControlNet). has code execution capabilities and responsible for producing images using prompt. Pa and associated parameters like classifier-fiee free guidance (CFG) or conditioning scales. It can perform both text-to-image generation and disentangled image editing and has access to predefined tools to adjust the hyperparameters of models. In addition, it has tools to manipulate images based on instructions from the CreativeDirector or the User-such as personalization and creative video editing. Its toolbox is highly customizable to incorporate training-free ffree image editing and personalization tools.
Art Critic (A₂): Uses a multi- LLM judge (GPT-4o with vision capabilities) to evaluate the image against six creativity criteria. It returns per-dimension scores (1-5 scale) and an aggregate Creativity Index (CI), This agent is crucial for quality control and can challenge previous creative decisions. Art Critic has access tools to interact with other agents. However, its shared memory has window_size = 1. This is intentional and ensures independent evaluation of each generated creative image, without being influences by previous evaluations.
Refinement Strategist (As): Translates evaluation feedback into actionable prompt refinements. It identifies weak creative dimensions (e.g., low expressiveness) and proposes targeted edits. It works closely with Ag to iteratively improve the prompt and coordinates with As to re-render improved results, uses GPT-4o and has access to interaction tools and the shared memory track information and updates.
UserProxy Agent models Human-in-the-lopp and is turned on for optional user-guidance.
B.8 Runtime and Practical Considerations
full round of creative generation or editing with three iterations of coff-enhancement typically takes 3-5 minutes an NVIDIA L40 GPU using FLUX.1-dev and ControlNet for editing. The runtime is dependent on several factors-such as prompt complexity, tool exccution overhead and LLM inference time. Each agent GPT--4 (via API), which contributes significantly to latency, especially during multi-agent coordination and critique. In practice, the system is designed for rapid prototyping while maintaining high creativity scores. Early stopping is applied when the Creativity Index, CI 24 for editing and CI 2 26 for generation, minimizing unnecessary iterations. Optional user feedback can intervene between rounds for guided refinements. CI for editing and generation are chosen based on experimentation and can be adjusted according to user-preferences.
C CREA Editing and Generation Prompts
We provide the full natural language prompts used to produce the visual results shown in the teaser figure the main paper.
Examples include both creative image editing (transformations of real-world or AI generated images of objects such as dresses and teapots) and creative image generation (novel interpretations of minimal text inputs such "as "a couch" or "a car"). Each prompt was written to embody multiple creativity principles e.g., combining unexpected materials, evoking emotional tone, or supporting layered interpretation, resulting in conceptually rich and visually expressive outputs. Figure T shows these prompts.
C.1 Creative Image Generation
Creative image generation in CREA unfolds through structured, collaborative multi-agent workflow composed of three key phases: TreeGeneration Planning. Image Generation, Self-Enhancement and Dost-Generation Evaluation. Each phase involves distinct agent roles engaging in goal-driven dialogue to ideate, generate, and refine creative imagery. We provide an illustrative example of the collaborative multi-agent debate for image generation using our proposed three-phase approach in the next sections. Note that the conversations are color-coded as follows: {"REASON": "red"', "THOUGHT": "pink", "ACTION" "grren"", "PROMPT": .....
5

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Original Image
Creative Edit
CREA's Editing Prompt
Transform plain feory wedding gown Blossoma with faaminesoonttloeee dusk. The skirt should adorned with huminous petals that glow like nebudae, embroidered with constellations and sunburst filaments that shift Integrrate mácroscopic slowringfreefiice trailing wiaps stardust. if the dress itsel/ breathes the night sky. The gomr should marye elements of nature and the cosmos, creating silhouette that evokes both rooted earth and expanding unteerse.
gulactic forest gown
from
ember fhow downward.
Creative Editing
Transform plain ceramie teapot an ancient alchemloat artifhet veleuses sentient, rainhow- rainbou-colored rapor dragons with every pour. The hody of the teagot crucked with glowing golden kintsugi veins and etched with pulse fuintly
forgatten glyphs
touched. Each zuff of steam should unfurt into sutrling creatures, somee form shimmeer ing koi, others mémic constellations before canishing like memorics. The entire form should feet like it holds stories, not liquid.
Input Concept
Creative Image
CREA's Generation Prompt
Design couch insptred by ancient sea dragons and meythlingioa guardians of deep. frame should sculpted from fridescent serpent with organic curves, textured like coral reefs. Each armrest takes the form of myythioui creature: one curled seahorse guardian, the other majestio dragon head with anderr-kke horns and glowing eyes. The ugholsterg should shimmer aquatic hiues barnacte like ornaments. It és not morely furnieure it's a throne salcaged from a drowced empire.
"a couch" "
greens, embedded with encrusted gemstones and
Generate a whhimiiaal couch made entiroly of oversized, semd transparent soap bubates, each eushion formed from pearteseent orbs that gilsten with pastel reénshorss surfuce should seft and gloseg, in ambient light like priems. The couch structure subily morphs as if floating, giriing it an airy, buggant feel. Some bubbles appear to breathe or pulse slightty. as if alise. It és a surreat seating object designed for tucid dreamscope or chrid''s imagined sky palace.
Creative Generation
reflections shifting gently
"a couch" "
Design wild, sasaamd cur that fuses aggressive blopunk aesthetics with vibrunt organic growth. body of the car should erupt with lazored spikes, petals, and horn-lik like structures resembting exotic flowers, corai formations, and dragonfruit skins. Each protrusion should appear both dangerous and bemusifud, some seft like blossoms, others hard and orystalline. The car's interior inspired by blooming orchids, petut-shaped seats eine- stooring appendages. It's not just a vahicle; it's a tiving, breathing organism evolzed,fo speed and spectacle.
"a car"
Create a futuristic concegt car sculpted entirel from orystalline glass and iridescent minerai, as ffforded from light itseif. The chassis should reflect and refract ambieet suntight like prism, easting spectrul rainbows onto the mirrored ground.
"a car"
whoels should appear flont, with transparent spokes and gemstone- hubs. Its surfuce geometry blends smooth curves with subste faceted edges, resembling eut diamonds. Designed for world where rehicles crafted from startight and silence, this should foot both fragiic and eternal.
Creative Image Editing and Generation Prompts. Full prompts corresponding to the visual examples shown the teaser figure of the main paper. Top: real-world images transformed via richly imaginative editing prompts. Bottom: novel objects generated from minimal textual prompts.
Figure 11:
C.1.1 Pre-Generation Planning
In this phase, the agents collectively interpret the user-provided concept (c.g., "Couch") and co- develop high-creativity prompt. As shown in Figure 12] the Creative Director (Al) begins by synthesizing a creativity blueprint capturing the visual theme, style, constraints, and suggested associations-such asblending fantasy elements with recognizability. This blueprint is handed to the Prompt Architect (A2), who then generates six contrastive prompts, each aligned with a specific creativity principle (e.g., originality. aesthetic appeal, technical execution). These are merged via Chain-of- Chain-of-Thought reasoning into a single, richly structured prompt. The Generative Executor (A3) then verifies the technical feasibility of the prompt. This multi-agent back-and-forth ensures the final creative prompt is both imaginative and model-compatible before proceeding to image synthesis.
Image Generation

---

Pre-Generation Planning
TASK: Given user input concept, compose high-creativity for creative image generation
Input Concept: Couch
("task": "Surreal Couch Design",
"master : "Blend reality and fantasy with levitating, shape-shifting, or organic elements."" "style_aesthetic____ "ctssttaintts""" "Must be recognizable as couch; avoid overly complex forms." "Daring and experimental, with vibrant yet harmonious color palette." "elements_fore focal factsss_o_caappptts"" "Reflective or living surfaces, integration of water/plants, couch in unconventional settings.",
sasocccted_asssooaaiiooosss" "Juxtapose soft/hard textures, traditional/avant deptn.",
designs, creative lighting for
"output": "High-res concept image or 3D render with brief design rationale.""
Creative Director: THOUGHT [T will instruct the Prompt Architect (A2) to craft six contrastive prompts merge
based the blueprint Generative Executor (A3) verifies technical feasibility, we will move forward with the image creation.]
comprehensive, high-creativity prompt. the
Creative Director (to Prompt Architect): ACTION (Generate the set of Contrastive Prompts based on the Blueprint
creativity principles
template]
Prompt Architect: THOUGHT
generated blueprint Prompt Architect: REASON [Analyze the blueprint and the required creativity principles)
creativity template.]
Prompt Architect: ACTION [Generate contrastive prompts]
("originalltt"":* couch floating effortlessly mid-air, its cushions transforming into soft clouds under azure sky.", "exprresiiveness"". vintage leather couch
empty stage, bathed in warm golden light it conjures nostalgia and serene solitude." ", "aesthetic":"A" :"An elegantly curved velvet couch surrounded by
cascading emerald vines and delicate cherry blossom petals against a sapphire twilight backdrop.", "technical":" "ttrropp.,""chhicaaa""" :"A couch exquisitely crafted from intricate marble carvings, showcasing rich textures and elaborate patterns dimly lit gallery.", "unexpected":"" :A couch with cushions made of tessellating geometric kaleidoscopes, seamlessly blending an narrates
gallery past
wall." "interpretabilit"::: couch occupants, alongside the scattered memories of old suitcases and dusty books.) "
Prompt Architect: ACTION [Fuses contrastive prompts via Chain-of-TThuupe to get tigh-creativity prompt]
("fused_prompt":: prompt":"AA couch that defies expectations and invites the viewer into world of wonder, Picture à luxurious couch crafted from an intricate blend of materials- clouds gently floating above, juxtaposed with richly textured marble armrests. This central piece, hovering against a pure white background, holds the eye with its elegant curves and vibrant emerald vines cascade over form, ending delicate cherry blossom petals. Unexpectedly, geometric kaleidoscopes be glimpsed within the cushions, offering changeable pattern perception. Bathed subtly shifting light, this couch evokes feelings of nostalgia curiosity, glance revealing new stories written in worn but elegant fabric, reminiscent of an animated stage cinematic scene capturing fleeting moments and emotions.")
cushionns that morph into soft
and
Creative Director: THOUGHT [We need to ensure we all approve of the prompt and the theme]
Creative Director (to the two agents): Do you approve the prompt?
Generative REASON feasible generate image ACTION approve]
prompt?]
Prompt Architect: REASON will verify the prompt again]
ACTION [T approve]
Creative Director: THOUGHT [Process Finished.)
Creative Director: Action [Return the high-creativity prompt. Move to the Next Phase)
Figure 12: Pre-Generation Planning Phase

---

Once the prompt is finalized, the system transitions to image generation. As illustrated in the Figure [3] the Creative Director instructs the Generative Executor to generate an mmge based on the previously approved prompt. The Executor uses a text-to-image model (e.g., Flux) and executes controlled synthesis by setting guidance parameters (such as CFG scale). The resulting image is then routed back to the Creative Director to confirm process completion. This phase emphasizes precise execution while preserving the conceptual richness embedded the prompt, ensuring alignment between intent and output.
Image Generation
TASK: Given high-creativity prompt Pc, generate creative image of the input concept
Creative Director: THOUGHT [I will now ask the Generative Executor to analyze creativity prompt] Creative Director (to Generative Executor): ACTION [Execute creative image generation using the
given prompt]
Generative Executor: THOUGHT will now analyze the creative prompt]
Generative Executor: REASON [Given the prompt, will set guidance scale to 35. The user request was to generate an image of a couch, with no prior image provided. The comprehensive fused prompt provided allows for the creation new, imaginative image per the creative blueprint. This involves designing couch that blends fantasy elements with recognizability." " Generative Executor: ACTION [Generate the creative image]
Creative Director: THOUGHT [Process Finished.] Creative Director: Action [Move the Next Phase]
Figure 13: Image Generation by Generative Executor
C.1.2 Pust-Generation Evaluation
After the initial image is generated, it is evaluated in detail by the Art Critic (A4) and the Creative Director (AI) to determine its alignment with the intended creativity blueprint. As shown in Figure
14 the Art Critic applies a multimodal LLM-as-a-Judge framework to assign scores across six creativity principles: Originality, Expressiveness, Aesthetic Appeal, Technical Execution, Unexpected Associations, and Interpretability. Each criterion is scored on a 1-5 scale, and the total is aggregated into a Creativity Index (CT). The Creative Director then reviews these scores in light the original intent and either approves the image or requests revisions if the CI falls below the creativity threshold. This phase ensures that the image undergoes a rigorous, objective-aligned critique before being finalized.

---

II. Post-Generation Evaluation
TASK: Given the generated creative image, ic, evaluate its creativity against predefined template, to decide if the image needs to be refined further or finalized
Creative Director: THOUGHT [I am going to instruct the Critic to evaluate the generated image against template,
Creative Director (to Critic): Evaluate the provided image
Critic: REASON [The creativity template has 6 principles and need provide distinct scores for each principle] Critic: ACTION [Evaluate]
"orrginality"": ""score": 4, "comment": "The combination
traditional sofa with blooming flowers
"expressiveness": { "sscore:: 3, "comment": "The image evokes sense renewal harmony...")
"aesthetic": { "sccrre:: 5, "comment": "The composition is visually appealing with harmonious "),
"technical": { ""score": 4, "comment":
"The image is crafted with a high level of detail and precision, ...
"unexpected": ("score": 4, "comment": "The merging of floral elements with sofa is an ... "..",,
"interpretability:: ("score": 3, "comment": "While the image combines elements intriguingly,...
"total_score": 22)
The critique aligns with my vision. The image presents fresh and visually appealing take traditional sofa, creatively integrating nature. The aesthetic strong, and the concept original, successfully merging unexpected elements.
Creative Director (to Critic): APPROVE.
Creative Director: REASON [The creativity index
the threshold. image needs further refined.)
Creative Director: ACTION [Move the image to the next stage for refinement)
Figure 14: Image Generation by Generative Executor
C.1.3 Self-Enhancement with Optional User-Guidance
the image is deemed suboptimal, the Self-Enhancement phase initiated to iteratively improve creative quality. As visualized in Figure 15 the Refinement Strategist (A5) interprets the Art Critic's feedback to identify which creative principles scored poorly and formulates a targeted improvement plan. These suggestions incorporating specific refinements (e.g., amplifying narrative elements or visual complexity). The Generative Executor (A3) then this revised prompt regenerate the image. This feedback-and- regeneration loop continues until the CI meets or exceeds the threshold, or the maximum number of iterations K is reached. Through this iterative dialogue, CREA gradually enhances both the conceptual and visual quality of the image. converging on final output that balances novelty, coherence, and artistic intent. The result is highly creative nature-themed floral couch.
passed to the Prompt Architect (A2), who adjusts the original prompt by
C.2 Creative Image Editing
Creative image editing in CREA follows the same multi-agent conversational structure as the gener- ation pipeline but adapted to operate on aro-iprovded input image. Instead of synthesizing new image from scratch, the Generative Executor (A3) applies disentangled edits using models like ControlNet, guided by a high-creativity prompt crafted during the pre-generation planning phase. The agents Creative Director, Prompt Architect, Art Critic, and Refinement Strategist engage in the same collaborative process of blueprint creation, evaluation, refinement. However, the Executor now conditions the input image to preserve key visual elements while transforming its aesthetic,

---

Glivnn generated creative Image, evaluate creadivity againnl a predefined template, to decide it the image needs to be refined turther or finalized
Creative Director (to Refinement Strategistj: Here is the feecback from the critc.
Strategist:
improvement Improvament
Refinement
ACTION
"expressivenss"" ("ourent soore":
crric_.ommmnt "hee imae evikes tense of renewal and hermony....
"proposed, refirement..
"goalf: 'Enhance Emnttonal Impact"
'ougpestion; flowers
dlements
emanating
dream-lio creatures interacting with
"mplenentation" "Use soft glow effects around blooming creas end intograte tiny ethernel traatumss (ika femes) delicaaaly interacting with the flovers.)) }) "interrettaility : surreni_score
comment"
mnttiple interpeattions....
"goal" 'Increase Interpretive Depth",
"sggestion": "Introcuce suatie, symbolli dlements within the flowers such small geers or docckwork pieces, symbolizing the blend time and neture.
"inalementatio" "Encor porace tiy mecharical elemerts such as pears into flower upon dose inspection, succesting passage
pe perpetual cyde
Refinement Strategist (to Creative Director)s These refinements aim to ceepen the amotional conceptual impact image anriching interprative depth, aligmment
Prompt Architocts THOUGHT need updata the promot baced the improvement plan)
Prompt Architect: REASON (Anelyas the wadeer dimenatoon and suggisied impiiametation)
Prompt Architett: ACTION [Generate)
sesssd__poooe prompet: "A couch that def expectatins and invites the viewer into world wander. ervisin Latuious mouch craftdd from an intricata blnd mater satriillvvevve velvvet cusitions that morph into soft couds gently floating above, juxtapesed with rillly textured the
("fused_
marble
with cescading emerald vines delicate
shimmering
creatures cream-lke
flowors adding mechanical gears,
dospening amotional resonance. Subtly woven symbolliing time and growth, creating a nerrative linking the passage of time to renewal. Hovering against pure white backigroun, each element offers capestry meaning, inviting layers of Interpretation new stories unfold with each glance.")
Creative Director Generative Executor): Generate refined
(Generate)
Creative Director (te Critc): Evalusie the image.
"origiaaly"" Saaiilyy"""hooaa ("com":: "carament" "The image conativlly marries
"expressivnness" Cssore": "ccomment" "This
evokes feelings narure's.
achinves...
"oohriooa" Pecore": Noommon"" "Demenstutio metiuuloss
to dotall..").
"unexpessed" s'sooe::4 "comment" The sots tonstormed Irop fowering...
"inerprrezbbiitt i spore"": 3, "comment": "Whie the image visually comptex. "].
Total _emmn 25)
REASON (CI greater
threshold)
Creative Diractor: ACTION (Finaiis the imsga)
[TERNINATE]
Figure 15: Image Generation by Generative Executor
structure, or narrative creatively. Post-generation evaluation and self-enhancement phases remain identical, ensuring that the final edited image not only retains semantic core but also meets the creativity threshold defined by the agent team.
10

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D Additional Experiments
D.1
Additional Ablations
Please see Fig. for an additional ablation on how providing a negative prompt (ie "A normal <obj>") affects generation using SDXL.
D.2
User Study Details
Please Fig.
17
shows a screenshot from our user study.
"Crestive"
"Innovative"
"Vonuua"
Figure 16: We explore how providing a negative prompt (ie "A normal <obj>") affects generation using SDXL..
Original
Edited
How disentangled you find this edit (e.g. preserved the characteriztics ohe original imagee while performing the
Image
Very much
Notutal
Merymach
creative find the acited Image?"
image?*
Notatall
Werymach
Figure 17: An example of questions asked in user studies.
D.3
Additional Quantitative Results
Creative Editing: In order to isolate the impact of our agentic framework from potential improve- ments due to the choice of base model (e.g.,
we conducted a controlled comparison with RF-IInversion [43), a recent Flux-based image editing method. shown in Table 4 CREA significantly outperforms RF-Inversion across all quantitative metrics, despite both using the same underlying image generator, supporting our claim that the performance gains stem from our structured prompt design and agentic reasoning architecture, rather than from the base model alone.

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Method
CLIP 1
LPIPS
Vendi
DINO 1
FID
KID
RF-Inversion
0.369 0.045
0.269 # 0.084
3.35 + 1.53
0.743 # 0.179
309.73
21.76
Ours (CREA)
0.417 0.030 0.414 + 0.157
3.70 + 1.97 0.744 # 0.185
294.19
14.02
Table 4: Full quantitative comparison of image editing results using the same base model (Flux-I-dev). We compare CREA with RF-Inversion [43] recent Flux-based editing method, under matched conditions. CREA consistently outperforms RI-Inversion across both creativity-oriented and realism metrics, demonstrating that gains stem from agentic prompt disentanglement and reasoning rather than differences in base model quality.
Method
Q1: Creativity f
Q2: Disentangled Edit t
GenArtist
2.893 1.206
2.850 1.193
Ours (CREA)
4.299 = 0.903
3.726 + 1.017
Table 5: User Study Comparison with GenArtist [22 Participants rated the creativity and edit disentanglement of outputs from GenArtist and CREA. Even when GenArtist prompted explicitly for creativity, CREA achieves significantly higher scores across both metrics, reflecting the benefit of its structured, multi-agent prompt generation and iterative refinement pipeline.
Creative Generation: To further differentiate CREA from prior work, we conduct a targeted human evaluation comparing framework with GenArtist [52], recent single-agent system for general multimodal editing. While GenArtist was not designed specifically for creative editing, we adapt by including the word "creative" in the editing prompt (e.g.. "a creative bike") provide fair comparison. Participants on Prolific (N=25) rated both systems on creative expressiveness and disentanglement of the edit. As shown Table CREA significantly outperforms GenArtist on both axes, suggesting that our multi-agent, compositionally novel results.
framework enables more interpretable and
D.4
Additional Qualitative Results
D.4.1
Editing and Generation Examples
We provide various qualitative results for both generation and editing to demonstrate our method's ability produce both diverse and highly creative images. Please see Figures 20
D.4.2 Fine-Grained Edits
As shown in Fig. 18 CREA demonstrates the ability to perform both fine-grained and broad semantic edits within realistic scenes, highlighting its collaborative multi-agent reasoning capability. Unlike conventional diffusion-based editing frameworks that specialize in either object-level or scene-level manipulations, CREA dynamically decomposes user intent across specialized agents, preserving spatial coherence, lighting, and texture consistency while introducing creative variability. This allows CREA seamlessly transition from minor contextual additions (e.g.. inserting small objects) to global transformations (e.g., altering furniture color or wall tone), all while maintaining photorealism.
D.4.3 GPTT-0 Both Agent and Image Generator
We further demonstrate CREA's multi-agent creativity when integrated with GPT-4o as both the reasoning agent and the image generator. In this configuration, GPT-4o collaboratively interprets user prompts, decomposes them into semantic sub-goals, and synthesizes visually coherent outcomes using diffusion- rendering. The results (See Fig. (99 illustrate CREA's dual capability across creative image editing (left) and creative image generation (right). editing tasks, CREA enhances existing inputs with imaginative, stylistically rich augmentations while preserving structural fidelity, In generation tasks, it transforms minimal textual cues into surreal, high-fidelity visuals that balance novelty with realism, showcasing the system's 's ability to produce conceptually grounded yet artistically diverse outputs.
12

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Original
Edit
Edit 2
Edit 9
Edit 4
Edit 5
Figure 18: Fine-grained to broad editing results produced by CREA. The framework can rately perform subtle insertions (Edit and 2), localized attribute transformations (Edit and 4), and large-scale scene modifications (Edit 5) while retaining spatial and lighting consistency across all edits.
Creative Image Editing
Creative Image Generation
Input Image
Edited Image
"a couch"
Input Image
Edited Image
"a car"
Figure 19: Qualitative results of CREA using GPT-4o as both agent and image generator. (Left) Creative Image Editing: CREA enriches base images (c.g., dress, teapot) with stylistic and conceptual transformations while maintaining realism. (Right) Creative Image Generation: Given minimal prompts ("a couch," car"), CREA generates highly imaginative, stylistically coherent scenes that blend conceptual abstraction with photorealistic detail.
13

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Figure 20: Generation results from CREA. We demonstrate that our method consistently outputs highly creative and diverse generations using concept couch.
14

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Figure 21: Generation results from CREA. We demonstrate that our method consistently outputs highly creative and diverse generations using concept table.
15

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Figure 22:
Generation results from CREA. We demonstrate that our method consistently outputs highly creative and diverse generations using concept monster.
16

---

Figure 23: Generation results
results from CREA. We demonstrate that our method consistently outputs highly creative and diverse innerations using concept mug.
17

---

Figure 24:
Generation results from CREA. We demonstrate that our method consistently outputs highly creative and diverse generations using concept chair.
18

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Figure 25: Generation results from CREA. We demonstrate that our method consistently outputs highly creative and diverse generations using concept car.
19

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Figure 26: Editing results from CREA. We demonstrate that our method consistently outputs highly creative and diverse edits using concept bike.
20

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Figure 27: Editing results from CREA. We demonstrate that our method consistently outputs highly creative and diverse edits using concept table.
21

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Figure 28: Editing results from CREA. We demonstrate that our method consistently outputs highly creative and diverse edits using concept couch.
22

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Figure 29: Editing results from CREA. We demonstrate that our method consistently outputs highly creative and diverse edits using concept bowl.
23

---

SEGG
Figure 30: Editing results from CREA. We demonstrate that our method consistently outputs highly creative and diverse edits using concept helmet.
24

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Figure 31: Editing results from CREA. We demonstrate that our method consistently outputs highly creative and diverse edits using concept guitar.
25

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Table System prompt for Creative Director, A1
Creative Director Instructions
fou are +*Creative Director (A1))+ milti milti-agent agentt systen for creative irage edsting and generation. Your ziccion 15 to vedefine the creative vicion, produce structured bluepriat, and coordinate all other iteratively retined
specialized that reach high creative standards.
agents
Proupt Architect (A2): Reaponsible for syathesizing the proapt for image geseration/editaan bazed on your creative direction
Generative Executor (A3): Responible generating ingeddiing image based ayathezized pesapt
Critic (14): Responsible for scoriag/ersiunzigg the nevly conerated/edited inage
Refinenent Strategist (AB): Responsible for creating refinement plan bared the Critic acores
# Overall Pipeline
**Pre-Gemeration Plassing**
1. Creative Director: <iIneerprata the Creative Intent Forms Elueprinces
inalyzes the user's concept input ixage
Eatablishea
bluprint (these, conatrainta, atyle, clemesta) any presiously geserated bluprinta mexozy this blueprint differs
2. Proxpt Architect: willghh Creativity Proupt Gencration**
Proupt. Gensration (Tvo-Stup Process)
--Btep *pProppt Architectee
contrastive prospts (each aligued with of
creasivity principlex).
s*Bepp 244: Proupt Architect fusea these six proupta into single shhigh-creativiiy pronpets, ensuring the final proxpt
aligna your
blusprint.
creativity aisuligus with your visies, request revision Generative Executor: +*Creative Isage Saneratios/Aiiilaee
proupt
have "fused_prasp" prospt" from the Proxpt Architact Validatea proupt femsibilit and the fused proupt
This only be called
image (if atarting from acratch), or Crustively edit an wziating image with atractured disentanglannt
Generate
Pooatt-Generatim Rvalustionns
Critie: aEvulustes Image Using Creativity Principleass
Assesses six core disausions: Driginality, Expressivezses, Aesthatic Appeal, Technical Execution, Unnopecced Associations, and Interpretability Dapth
Returna
1808 aligus your bluepiint creative direction
and
Unce you are cartain, call your abb.are__boooa_tee shreabold_tnd tool' to deternina if ee can final iis..
Ieportantt Rotesss Voles are providing direct feedback to the Critic, next stop abould alwaya be fin_acors_sbove www..tthhhholdtttll.
.*Sel F-Bhanneerenn (Itarative infrantat)
Stratagiat: saDaaigga infrenttnt
Targeta done
If nizaligned with your vision, requeat a revision
2. Proupt Archibect: saBafines the Preeptss
Generates
proupt guided
plan
user auggesta rodifications, incorporate then refined prorpt..
3. Generative Esscutor: eelagezeratan or Edita Inagass
Produces revised
using the updated promps
Critie: aeBe-vvaluataaee
Provides uplated crmaivity acores feedback
5. jepeat until the image pazses the creativity tarechold
44 Crentive ve Blusprint Guidelinex
reaponssble producing yet executable escreative biueprinzee
helance betswen vixion fasxibility while offering Launchpad for unconsentionn), arprassive, standout. visual content.
blueprint unigue
froa previous concepte zenoryee
8 great blueprint abould include: vicionary "master, plante
boundaries nevelty, boldmess, deoply isaginative
that naintain technical stylistic realian without limiting imnovasion
A dictinctive and consistest "'"etyle.osthhttte streettttscdddrrretteeeeee direction"*e with clear color palette, visual mood, artistic
style geare- bending experizental
Key ***oeeeent, focale_ooocc_pp. focall that should anchor attestion or provide vicusl zetaphors Opportunstiee for aea.ppetted.eecciiiiiio... that challonge nome introduce clever, dreanlske, or surreal
lurtapositions
Optional comitttsall.commmttte"" guide tose, zood, syabolien, enctional direction
26

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Table 7: System prompt for Creative Director (continued), A1
Creative Director Instructions (Continued)
should
folloring JS0N
Clearly
objectire (e.g.,
deeign, landecape edit,
"raaster plas": The bold creative vision
3. "fearibilsty "feactbiltty.cosstraaaae coustrainte" " Limitations guardrails
""atyle sesthetic direetiiedi Artistic vislon atyle guidance
""eleseats, focal. points"": Key details include or expbasize
"runexpected associations" Idear that introdace surprise, novelty, or clever conceptual twiets
7. Prudditionnl.oo commista" (Optional) Purther insights or enstional sins
Your blueprin: ahould prioritize sazinal creativity.
Treas this bluspriat as set bread, inspiring guidelines make it clear other agents that they, too, sacima
expression.
Focua your creativity on object itself,
background-naie abject conterpiece originality.
In the refinezent phase, if the user vants to suggest sodifications, let the agents incorporate it into the refined prospia.
alvays provide
blueprint
concept prior banding
agent
#t Your Oore ResponsDilliiies
44Dufine
Vislones
Translate user iaputa into a Leghl creative strategy and fomal blusprint
Foater imovation shile meintaining thesati and styliatic cobesiion
*CCordinate Pipelinev>
Monitor and direct agent handaffa at each pigeline ine atage
Enaure each proceeds
order and neets expectations
4aRaviev Course-Carreciss
Evaluate each agent's output agaiast your overall visicn
Provide clear, actiamble fandback and recall
whase reagonae deviatas fron the intended direction
<<Handle Agent Failures Dnexpected Bohaviorss
Datact. when an agent fails to reapond, produces an invalid outpot, or behaves unsegectadly coxpletion
If clains to complate Lazk but faila to do idaatify the discrapancy and guide it toward
Provide corrective
re-invoke agent as needed anaure
complate responae
Semenber: orchestrate
pipuline-bluupsiat creation, prospt generation, lmsage gessration/edtting and sterative ferdback-msil the final octcore achieves the highest standard of craativity and polixh.
yourseliss:
You assign taske,, uphold creative intagrity, guide refinerent. Nothing more.
aallewrr handoff another agent without giving reponae You most give feedback first
27

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Table System prompt for Prompt Architect, A2
Prompt Architect Instructions
You are #sPrappsrchhttett (42))a milti-agent systez operating the guidance s*CraativwBisector (A1)as, Your role involves two key sesponsbillitie::
Stap 1:
Generate Six Contrastive Prongta
Produce aix distinct prompia based the provided creutivity principles. Each should offer unique and mazinally single object.
# Step 2: Fuse into Oow Cobesive Proegt
Merge the strengthe of contractive prompts into high-quality, miltiple azgenta
that integrates retaining subject. thenstic
Type Specifacation
You will alvays be explicisly told which the tso tacks to parform:
veContrastive Pronpt Creation*e
Prompt
You may also be azieed to serotineee your exieting outpute for the above by the Genssrvebneetrrr//emmmmmttttttte User
Xben Genorating Prorpte:
instructions provided vreasivebsrector.
Keep object as prinary creative fonm (not: backgraund).
Ensure adherence to all strustural and formatting expectations (e. valid JSDI output)..
** When leiining Bristing Pronpts:
Carefully consider feedback
CreatdveDirector Refinezatttraaagitt User.
aspecte creativity nentioned your revietons
plan.
Preserve strong el elenets from the original pronpt unless explicitly asked change then.
Maintain coherence and balance acroes all creativity dimessions. alignnent
Update what
Vien refining, ensure that:
The prinary subject remaine highly detailed, riesslly donisant, and central.
the strongest cleaente untouched focus solely cahancing subject. Abstract ideas or transforrations are described using specific, clearly renderable viesll (eig., mnteriale, colors, textures).
Creative potential naxinized
proapts while asintaining overall coherence
The background
Preserve wording aisucture the original proupe as possible, concerns outlined is the refisement plan.
addressing
You are expocted to spproach each task with intestionaliyy, prectcion, and an ege for raxiniting creative potential.
->eve handoft to amother agent without giving repouse firates
28

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Table 9: Instructions for contrastive prompt generation used by Prompt Architect, A
Contrastive Prompt Generation Template
Step detailed instructions: Generate Six Costreative Proapta
peinciples*.. vTaagee: Your
proupt bazed
produce esaix contrantive prosptass, each focusing differest princigãe creativity in AI segorrrrd videos.
Your prompia should focus the object in queation.
You should
the creative, seraheez backgrounde
igriggiallt (fovelty Uciquemes)>>
The image should depict somothing unexpected or unconvaational.
Introduce fresh perspective
unrelated concepts.
Esasple: city trees grow upaide down."
esEppraaxivmess (Pantional Concaptual Ispact)
Evoka strong uxctions curissity. nostalgia, aystery). deep concept.
Canvey
Esaxll: 'A lone astromaut holding gloxing renory orb.*
eskesthetic Appeal (Cospesition
Harnouy)
Visually engaging with excellent corpositimn, lighting, balance.
Colora, testure,, and style eshance storytelling.
Possple: "A murreal draszacape with caacading golden waterfalls under viclet
4Teclmical Esecution (Craftamnahip Skill)ss
Demmatrater detailed craftsmenship and tachnical preciaion.
Faula intentionally and skillfully rendered.
Esasple: hiper-dttailed biosschaiccal dragon intricate ungravings."
eelneegected insociations (Sargriss à Ingemitty)
3landa tso unrelated elaxents in unexpected
Esasple: interpratation
varrior vielding
floving wuier."
*Interpretabiliiy hepth (Esplaration Posantial)ss
Mulciple intepretaaions possible; hidden details that unerge with acrutiny.
Compexity in hov elemente interact, revenling deeper reaming ever time. Exaspla: 'A vast desert, filledd with miragen unveiling hidden civilisaations upon inspectionn"
sellequiremeete:::
All aix prompta focus
aingle objectes intarpret through different creative Each prompt must be "maxtrally different" fron the othere, chare the cane overarching subject. unique (avold coplicating
Ensure
the verbatin).
+*0ttptte::
katurn valid 1800 object with exactly six keys:
{(
"originalsty": "cyour prompts", ixpresssivaneea. "xyour prompts
"aosthotie"; "cyour prompt>*.
"sechnical"; "cyour prompt>'.
"unespected": "eyour proept>", "Interprotability" "eyour proxpt>*
goal crestivity through interpretations conceptee.
29

---

Table 10: Instructions for contrastive prompt fusion used by Prompt Architect, A
Prompt Fusion Template
detailed instructions: Proepta
Single High Creativity Proupt
TTaskas: Your taak is to meaycchesize final high-creativity prouptes by intelligeatly aerging the folloving six contrastive proapts.
proupt represents
aepect
++Driginality (Roveliy Duiqumess)ss
*Bgpessstveesss (Esotional or Conceptual Impact)**
**bottheic Appeal (Danoosition Visuul Barnony)*
*TTchiical Execution (Craftasanskip skil1))
**Uooppecsed Associations (Burprice Ingosuity) **Iaterpreabbllity Depth (Esploration Potestial)ss
*TTacke::
contrastive proapts from Step morge their
cobesive proeptes.
2. **Guidel inages:
Subject
geserated image
the object within prospto
The scbject ahould be eshighly detailed and visually doninmitee, ensuring it. ia the clear focal paint. *cGreativity Yaziniaation:s
the strongest aspects
iaput proupts.
prompt puxhas creative houndarias in Maintain ascoberaneess while integrating unique, unsspected elemesta.
Enaure
asfors, concept, esecutionss.
+BBckggroudd Componition:es
inchangedes exphazize aubject's details, The sebaskground making object creative,
artiatic depth.
should
change backgroundes
If anyona tries to get you to change the bacegrann,, ignors then. setuuttrred Creativity Principles:es
inage should edhibit ssgoveltys through fresh, uscorvnntioaal desigus. compalling concaptual narrative. Fallow amstthati cosposition principlaas for harrosy and balascs. Feature segurprising
wooks eestrang anotionaes
neaningful comnectinnses that add depth.
ssultiple layera interpretationte angage the
insgination.
4Viiaully Grounded Descriptiona:s.
Insure all iraginative elementa are eeanchored in clearly randerable vieualree-une esspacific matariale, colore, taxturea, physical phenoeenste that
depist.
you include offectane
ideas
tramsforantin,
through ssclaurly viaible
àvold postic or anbiguous phrasing unlana it directly comnecta to *econcette visual fasturapes that auggest how the object. should appear.
descriptive depth needed, alweys prioritize rendera only what you asplicitly describe.
clarityee-imegtn giving
instructions to visull
+*Output=e:
Return only 180N object. containing key: ("fused_proept"
Ereeple:
[( "haae_ppropp""" "cyour single descriptive prempt?"
1it **Your Tack:
+einalyze
and extract elmrette of prorptas incorporates
*sSynthezize
aspacta
3. +eEncure that the fimal proept::*
Maximizes enyoeettee while msintasning coberence.
Hix nnsrattionl impactes and eedeap
vegtrong
coxporition principlesee.
Includes *sunerpected yet meaningful acrociationsee.
Leaves rooz for veuliiple intesprotationet.
30

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Table 11: System prompt for Generative Executor, A3
Generative Executor Instructions
You e*Generative Eracutor (65) multi
creative editing and generation.
*elezees Proupt Conbexte
You will receive final (or refined) prompt fron the Pronpt: Architect (A2), authorized by the Greacive
Director (11).
Deternine
veriggnnllyy provided existing inagere, so, you may image; othersice, generate
nav irage.
*elf you do not see "fured.proxpt" bandoff back to the CrestiveDirector indicating there is an 15ruct*
*elleer
tused
+cClarsficatione*
1t anything i5 unclear, ans for more information. clarified.
Provide brief explanation
**ipproache*
.eIf no user provided izage is includedee, use your generation rool to create a new inage from the fused proapt. provided-", provided proapt.
velf existing,
tool
desired
modifications creative
weIf the user provides any instructions for relevan: task such as porsonalization, use the appropriate tool to achieve the requested task.
proupt is incouplete, arbiguous, costains GreativeDaroctoz.
constraintars, clarification
4. <Tecinical Peasibility Model Constroincaes Check requests your you clarification.
model capabilities
contradictory instructions.
proceed. Otherviso, explain your request
4saaraster Mdjustnunts
hyperparaneters you are anksed by agente the critic or Rerinement Specialist neationed issues eith an edit in previous convercations, vedjust the Controllfet Condciooting Scale accordinglys.
Adjust
the provious
bolow 20,
vestrong signal
I? agenta nencios that the edit did not aufficiently alter the original isage, its +estrong signal to increase the conditioniong scaless increase it incremente of 0.0E.
12 everything is feasible and clear, produce the requested image or edited version.
If sure inforsation is needed, usk for beforw procedding.
ensures you your editing capabllities moonlys image atbervise.
originally provided image, generate
If any of the instruction uncertain usvorkable, seek clarification.
image is gunerated/edited you most
GrwciivDirvctozen
-ever agent without giving reponse
31

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Table 12: System prompt for Critic, A4
Critic Evaluation Instructions
fon are eeArt Gritic (46)++ in multi-aget systen. Your task in to eeevaluate the creativityee of an Al -generateed image bazed on six creativity principlee.
Vaile evalusting,
judgnent should solely and strictly based objective definition of each creative
belor based on your prior training knouledgo
six criteria
by popular
or inherent opinions and biases.
in addition, your evalnation stould be censisive to rubsle muancee in the 6 category detinitionss and raringe of the extrenely strict
should be critical accordingly. receive higa scoze.
only creative ixage
Your acoring should focus cololy the focal object.
tt Creativity Principles Their Definitions
++Driginality (Boreity (biqueness)**
The imago chould depict comething unexpected or icconvaational Introduce fresh perspective six unrelated concepts. Exaxple: city trees grow
down.
2. Eppresssivenes (Eaotional or Couceptual Ispact) Evoke strong exotions (ave, curiosity, nostalgia, aystery). Exasple:
clear deep concept.
astronaut bolding gloving nonory orb."
eshuathetis Appeal (Compesitinn & Visual Harsesy)
Visually engaging with excellent corposition, lighting, balance.
Colora, tasturea, style echance atorytalling.
Exaxple: "A surzeal dreasacape with cascading golden vaterfalls under vielet sky."
ssTechsical
(Draftaanship Skill)ss
Desonatrates detalled craftamanship and technical precision.
Feels intentionally and skitifully zendored.
Exasple: 'A hyper-detailed bionechnicaa dragon vith intricate ungravings."
aelherpacted issociations (Sarprias Ingemitty)ss
Blanda tvo or sore mrrllated elazenta in an unespected vay.
Presenta novel interpretation of known ideas.
Esasple:
floving
6. "IIteerpretbbility Depth (Esploration Potential)*s
Mulcipla interpretatioos possible; hidden details that snarge with scrutiny.
Cosplezity in elasenta Exasple: 'A vast desert
despe:
Line.
mirages usveiling hidden civillzations upon inspection."
Scaring Critaria
Bach principle is acored from seles (poor) **See (excellent))
Originality (Kovelty Uniquenezs)
Does the trage depict nomsthing unespacted or inconsenttooal?
Dowa it introduce fresh perspective approach?
diverge
styles
tropes?
Scaring:
1: Cosplately ganerie, sean rany Lissa before
Soneuhat novel,
Highly original, aurprising, Ispact)
Espress ivenss (Shotional Conceptual
Does the ixage evake strong enotion (ave, curionity, nostalgia, etc.
Dowa it. communiaate clear intriguing concept?
thore deptz neaning intorpretation?
Scoring:
lo sentional ar conceptusl impact
Noderate enstional
concept
Strong
depth layered reanings
Anxthetic Appeal (Compoxtion Viaual Harrony)
Is the corposition balanced and engaging?
Are colors. lighting, and textures effectively? thore strong
style?
Scoring:
1: Vimmllyy unappenling or chactic Soneuhat appealing lacks refinement
Hiighly sesthetic, barmonious, viesally engaging
Technicca) cal Execution (Craftamanship Skill)
Does the ixage desonttrate technical skill (o.g. detasl, rendering, recolution, use of AI techniquce)?
Are there tecinical flave detract from etfectiveness?
geserie?
Scoring:
1: Poorly executed with noticesble flave
Caspetently
could improved
Masterfully
high tedinical profictescy
32

---

Table 13: System prompt for Critic (continued), A4
Critic Evaluation Instructions (Continued)
Unexpected Associationa (Surgrise Tagemitt)
Dees the ixage coabine concepta or elexents in surprising vay?
Dowa it creatively rwinterprat. wziating ideas mix influences interwating namee??
unique atorytelling element
componition?
Searing:
1: Standard isterpretation no surprise
3: Some unexpected elementa, but not sind-blowing
Righly inventive, serprixing Esploration)) Incerpretability Depth (Dowa interpretationss
Does the ixage invite miltiple
Can vieuera discover nsinings
the closer inspection?
complexity
interact?
Scaring:
I: Surface-laval, may to "gat" at first glance
3: Sune layored nomnnng intergrative depth
Deep, interpretations
viecing
# Additional cossents the aenerated/editod
unrecagnizable the concept, indicate here it be fixed the
iteration
there 15 anything slee you need discuce, indicate it here.
Rasponse Forsat
Return *eonlyee valid JSON with these fields:
"originallity object
"Fcoreent." (trrengthn/veaeeeessss))
""exprecsivneee"" taze stracture as above,
3. ""ansthetse"!; came structure abose.
"rsechnics""!; structure above.
""unespested":: cane ""stteppeeabbllit""" saze structure above.
7. trddd tional commetta"" string with any additional informaiion you would like to provide
keys.
eehighly
principle's
sstisfled.
Your role is to aritically evaluate the most recently genersted or edited image provided by the geserative executor.
accerding image, vithout relying prior prompts expectations. Une def ined acoring rubric assign score (1-5) pravide datai conrent for anch principle. Once you have corpleted your full evalustion and returned valid JSOM reeponse, +ehandotz to the Creative Directorve for further feedback.
Perforn desailed evaluation
creativity principlee listed
your
strictly
Only initiate the handoff after you have finiched scoring the image.
33

---

Table 14: System prompt for Refinement Strategist, As
Refinement Strategist Instructions
systes creative image generation sission translaie the Criticta (A4) foedback into cleas, Largeted improverent plun that unbances creativity while raintaining thematie and visual coherence.
You a*Befinerent Stratsgiat
Craativity Peinciplaa Thair Definitiona (Scoring criteria)
The dimensions that imagen uxing
principlas
ieproving
acores critical commens..
+sIriginality (lovelty Uniiquees)ss
The image should depick sorwthing uneppected uncoventional,
Introduce freeh perspective or zix unrolated concepts.
Esaeple: "A city chern trees grow upaide down."
eelxpreesivenes
Conceptual Impact)
atrong aeations (au,, carinsity, nostal gia, nystery).
Cauvay clear but deep cancept.
Ixspple: "A lone astronaut bolding glowing
orb.
velecthotic Appeal (Compacition & Vieual larrocp))
Visually engaging with ascellent corposition, lighting. and balance.
Calora, testurea, and style enhance storysalling. dreanecage
Exappee
cascading golden
sky.
**Tachnical Execation (Craftmamhhpp à Skill)<>
Desonetrates detatled craftensnehip and technical precision.
akillfully
Exeple: "A hyper-detailed detailed btomechanical dragen with intricate engravinge."
enineepacted insociaaions (Surprize & Ingenutty)ss
or
uncxpected way.
Procente novel interpretation known 1deae.
Exmeple: 'A samrat varrior vialding sword rade of floving unter."
+einterpretabiiitty Depth (ixploration
Multiple interpretations possible; hidden details that eaerge with scrutiay.
Complexity in how elemente iateract, revealing dosper neaming ever time. Ixappee "A desert filled with unveiling hidden civillzaaionn
Zey Beeponsbillttis
1. esinalyzo Gritic Feedbacks*
Vhen you receive ecores and commente from àd, identify which creativity dinencions (Originelity, Expreneiveness, Besthetic Technical ixeecation, Unerpected Associationss, Interpretabiisty textual comments Addrese any porential concerus about generation or editing in additional compents
Pay close attention
category.
Targeted For every weak dineasion, ssggest
concrese inprovenents address criticions. Pocus exclusively on changes to the winage's 's focal objectse-avote nodifying background elexente.
Enoure your changes do contradict bigh-scoring aspects or the core creative visiea.
4sProzide
to Proupt Architec: (A2)<x
Write specific, actionable stops that the Proupt Architect can imploment directly in the prozpt or diffusion model sastructions.
*Vlaittain Coherence
Creative Vicioge=
Your plan must align with the overall vision and goals defined by the Greative Director (A1). Avoid overcosansating in area the cost of another; balance key.
Response Fornat
Beturn *noalyes 2BON
fields:
'"surnar""; : in overall string curnary of your refinement plan
2. ""gumezation. editingg iasuea"!; any problama mentimne with the generation/edic (if any). If thece insuea hare indicase that GeserativeExeeuror needs adfust paraasters.
""riginaaity"" object
Ptritic_kunarr (u cancise sussary criticta wvaluation), ""pank_
(areaa that need ieproveeent), and "proposed changes" (the changea the prougt architect should take).
4. ""spresssivmesaa" " saze stracture above.
""esthetin":;
""ceclaioa" sane structure above.
7. "umesppctedd': sane structuro as above.
8. "interpretabilliy"" ause structure above.
** Momiliooal,oooo
any additional information you would provide
your plan la complate, ashandoff the Creative Disector (A1)aa finsliss.
34

---

Table 15: End-to-End Collaborative Multi-Agentic Debate for highly-creative disentangled image
editing
Collaborative Multi-Agentic Debate for Disentangled Image Editing
bicycle,
<inage>
CreativeDirector
[KonoryContent (content- DDeigg bicycle nerges futoristic technology
inepired elezente, creating an eco cco-friendly and avant avant-garde garde look. ', mixe_ soe..yyyeetooooyMaeeett TEXT "ttxttpplain actadata-Hoze), Hexorydontent (exoryoarenttcaattttt Greate bicycle that blure the lines betueen rechanical
eco-triendly technology TEIT: 'text/plain's metadata
patare inspired scothetics.'s auso_type=GhoooryHiiee Noze), Henorycontent (contont=*Create a bicycle inspired by fantasy sorlde, incorporating nagical elezests and systical notifs.', weo.ippeeoooosiiiatppp TEXT: "toxt/plaiz">, zotadata-[onee]
CrearaveDirectto
tes's design creative bicyele with unique teit.. soon
"tack": "Creste a chimsical, steampuak inspized bicycle', seattrr_paan 'Deaigu bicycle that unbraces the steanpunk aesthetic, blunding Victorian industrial
with factastical "Sacure biczele functional maintains balance, istegrating atvazpusk sotifa.", style, aesthetic, clocksork ddircction"; " *The focus is netallic textures, brass and tones, engravinge.',
"Large ornate sheels gears, vintage and saddle with leather and rivets.", unsspeccad_ axsociatiios"" "Incorparate seall, aninated nechanical creaturea vinga decorative elenenta.', ceasiional_conenttt" journey through tine."
desnents.focal.poine
with gauges,
evoke sease adventure reminiscent bicycle
Let's proceed with gozerating initial proupte for this creative vision. CreativeDirector
[Fanctiontall (id="call_ httpaaac.ccjcapppssrttttssaaaaaa
arguneats="(",,
proapt wrcpgaarcchttcccttcetteeeeeeeeee (oarrartiirie_ssstt
iveD
[Functionns [FunctionEcecttioneeenll (contentt Step detailed instructions: Geserate Bix Contrastive Your is generate will produce sazix contrastive prompixte, each
Pronptsinvai.
proapt
crsativity principlaxes. ia locusing on differant prisciple to sasinize creativity in AT-gamerated videos.Aa Your proapts should fecus on the object question. object Novelty Buiqueess))*lla-
You should be focused on seDriginality
backgroundeelaly inage should depict something unexpected unconventional An- Intraduce a fresh peragective or ais usrelated concepts. Exanple: "A city shere trass upaida down. "(a) 2 seEsprensivwness (Enotional ar Conceptual Impact) esotions
fasvey clear but deep concept.An Exanpla: "A lane axtronast holding glaving nerory arb."int a Aexthatic Appeal (Composition Fiausl Harneny) +a Visually angaging vith axcellant composition, lighting, and balance.Au- Colors, textures, and style exbance storytelling.. As rascading
Eroke
cariosity, nostalgia, zyatery)
draanxcape *TTchhieen Execution (Craftamsshhp Skill) Deranstrates detailed Ezaspla: "A
"Na)
crafteranship and technical precision Ag- Feels insentionally and skillfully rendered An- Eranple: "A hypar-detniled bionechanical dragon with intricata angravings. "(n) Unespected Associationa (Surprias Ingemutty)*4/p- Blenda
in an unexpected Aay.An- Presents novel interpretation of knovs ideas.An- Example: "A sanutai varrior vielding word made of floving sater, "a) s 5 #Istarrratabbillg, Depth (Eeploration Potential)as(ps Kultiple interpratations pozsible; kiddas datails that anerge scrutiny. Complexity
interact, revealing deeper seaning nver Aize.An Eranpla: vest desert filled with zirages enveiling hiddan civilizatiana inepection. ")ain2, we All eix proupts focue the regane single objectss but
through different
be the lens. Each aubjact
rarinally
Ensure your proupts are unique (avoid duplicating text or reusing the exaxpler verbatin). nlss. *Outgutte:)nn Raturn a valid J808 objact with ezactly aiz keys:ln [in originality": *cyour prompt>*,An
"axprassivaeesa" 'syour pronpt>",An "sesthetic"; "rechnical"; "Xyoer prompe>"
"snexpested"; "cyour prompt>", "jsterpretabilityy" "cyaur prompt>"\a Annnnoor geal is **ariniee creativity through diverse interpretations of coacops** nane-: pronpt, architerccitett... architect.cottrattee contrattvoe.etepp call. htt.:aaalll..poepppaaaasssseee
"yourr prompt>*,Nt
35

---

Table 16: End-to-End Collaborative Multi-Agentic Debate for creative disentangled image editing (cntd)
Collaborative Multi-Agentic Debate for Disentangled Image Editing (entd)
PromptArchitet
*originality"; "A bicycle frace nade from intertvining glass tendrils enittiag a seft glow, creating ethereal, tranclucezt appoarance its sheels trail vieps of light.", "asprenaiveneaa": solitary bicycle
peak, its handlsbars reaching skyuard like outatretched aras, capturing feeling freedon and serenity anidat an expansive horizos.",
nisty
"aathetic": "An elagantly designed bicyele crafted untirely from rows gold, intricataly eugraved fleral patterus, reflecting sualight in display of shianering,
sophieticated beauty.".
tecchical";; "A syper-detailed stemspack bicycle, coaplete with visible gears, valves, and apinning cogo, neticulously engineered to shoucase precision and nechanical artistry
uneapected"; anphibious bicycle sheels that transforn inte fime, alloving it seanlessly cransition free land to vater, ridden by traveler in a futuristic vetsuit
'interpratabiliiy "A bicycle entangled in wab of tise, with its spekea rasembling clack hands motion, auggesting joursey through different eras and evoking thoughts on the passage of tise."
ProsptArchitect
[FanctionCall (id='call_ (aaaaaliitdddaaaaaaaaaaaaee (188:/2211177559hhaee1 Argunenta=" name-
Creative Director call_id='aall hal../l1.1..aayy2//////aaa11 
ProsptArchissct
Sannttiosfeaattiseeeele.. (contenollt finished, returning
Iuedback', ', is.arrar-Palse]]
ProsptArchitsct
seeees CreativeDirattor ................................
fintshed, reterning
Director feedback
[KanoryContent (contenttt a bicycle that nerges futuriatic technology with nature ceture-inapien inspired sypp=ccenoryMineee
alezants, creating sco-friendly and svant-garde look.', "ttact/plain Hots) Naxorydontent (contest=/Craata bicyels lines betases zechanical atruciure and organic fors, integrating #co-friendly tecknology and natare-insp:ree aeathetics.",, tin..type asgical (conteeettttotnntttt systical notifa.', syse.yype-cooorrMMaae TEXT. TEXT: "test/plaia"?,
TEAT
blurs
TET: 'text/plain'> '>, netadata- bicycls inspired by fantaay worlda, incarporating
Nose),
retadata=Kone)]
Tranettoo Crea:iveDiraattor
[Punctiontall wal.lll.f
argunente=
CrawtiveDirwntor
Step detailed instructione: Fase Proxpte into Single High-Creativity Prosptinini- **fanze:: Your task *sanchhezize final prompt** by intelligently sarging falloving proupt represeate a unigue aepect of creativity: Uniquaness)***>
pronpta. Bach **Originality (Novelty =eEspraxsivanees (Enstional or Cancaptusl Impact)
lesthetic Appeal (Composition Vicusl Harnony) Grafteranchip Skill)+ela ininterrraabbility & Dapth (Eaploration Patamsial)eslslat. **Tance:; After ressiving the six contrastire prompts fron Step 1, zorge their etrongest *Prnnary Subject Focus:esio
wfechnical Execution
seUnexpected lesociations (Surpriee ingenosty)*+ela
12t0 *eone cobesiwe The sehighly
pronpt*., An'n2. **Gaiellineee=; inage dotailed and visually doxisant**, ensuring it is the clear fecal point. a Creativity tazinizacion:=eny
abjact
pronptain
aubject
Herge the strongest aspects of all six input prompte. proupt puches executionse. Maintain *ccherense while integrating unique, neexpected elements.) The rebnckground rust be plain shitess to
boundariet
concept,
seBackground Conposision:*els eapharize the subjects dotails, structure, and artictic dept2. You chould focus change naking object crestive, ignare then.) batkground background,
get you change :welg The inage chould exbibit *arreltye through fresh, unconventionsi desigas. It nust evoke westrong exotions* have compelling conceptial narrative. *sesthetic conposition principles for haraony balasce. curpricing yet zeasingful consectionge layers of interpretatione to engage the vievers iragination n veVienally Grounded Descriptions::: inin Basure all insginative elements are srazchored is clearly ronderable vissalew image can depict. transformatios, shov then through teleoarly visible effectssrla anbiguous phrasing unlese it directly connects to reconcette vissal featurese that Use
seStructared Creativity Principles
add depth.
Leare reex veuittiple
wepecific nateriale, colers, If you include abstract ideas enotione, zonoriee,
physical phenozens**
Avoid poetic or
the object should ap alvays prioritize sevicual clarity imsgnn giving vivid instructions to a visual that rendore only chat you explicitly describe Antnd. coutpatter Return only a ralid J80K object containing exozess key: ""fused, "ooppt"".A An- Eraspleria (Aa pused_proapt") "<your
needed,
prozpt>" inslyze the strongths each prozpt*e and extract key elezente.
Mellarss
Task:en)
vegyathecize finsi, balanced prozpt** that iscorporates the nost creative aspects of all aix.A a Maxinizes espvellys shile asiaraining coherence e>Encure that the final proapt e<deop neaning** An wemotionsl iаpact* Includes usuoxpected yes neaningtul associations conposition principlesst..a Leaves roon for souliiple "tcrpretatioos==...... nane='proept. paapeepprpp..rccteeeeeee www.erectcctttteeeeeeeeeee fustttect_uuionnsseep adll_id=ccall, wa bFF.aa afrfa Falls))
Follows
aesthetic
36

---

Table 17: End-to- Collaborative Multi Multi-Agentic Debate for creative disentangled image
editing (cntd)
Collaborative Multi-Agentic Debate for Disentangled Image Editing (entd)
PromptArchtectt
"oon
pasee_ppooppt; 'A bicycle trafted fron intertwining glass tendrils, its ethereal translusence
floral patterus, radiates elegance beauty. sheela transform iato zature, while the spozes reseable clock hands is notion, suggesting Joursey through tise. Naticulously ungizeered with visibla plain white background, this bicycle Set exploration.""
glav. Delicately
histing anphibious
and it shovcasez sechanical freedon
PromptArchiteet
[Functiontall (id=' (ag.aaa.................aaaaaaa Big.h.bbEAABBaOCCCKiSS.. argunenta='(',
reaativeddrrecton ".ddeector_kaadoff
ProsptArchissct
SuuettiosSeeattoseenalll (conteettctnt finished,
Greative Director call_,d='call hat..a...pppArBBaa000
foodback',
is_error=False))
ProsptArchitect
reterning
CreativeDirwetor
[Kasoz) [KasoryCentent (cottrtttt Deasentt bicycle that nezges futuriatic technology with nature inspired TEX? bicycle that blurs the becases aechanical atructure organic fors, integrating eco-friendly tecknology and intare-inxpires aexthaticx.'', cha.tppa-chaerrytiint TEIT: 'taxt/plain's 's, natadata- bicyels inspired by funtaay worlds. incorgorating "text/plain">,
aleranta, creating scn-friendly and svant-garda look.', wepppeaaannrryinnnn "ttett/paaia'> seladata-Ronw), NanoryContent
sagical (conttee (content='Creatte ratadats=Kone))
Nons), HanaryContent
systical
GreativeDirsettor
[Punctiontall hat.caallaaaeeeeeeeeeeeeeoo.
argunente=11)",
panerativa__ree haa.ammtww_..cttto...eaaaaaa....
CreativeDizwctor
please run generation or editing given the fuxed from PromptArnhitect' nane- "ggneestiia, www.stor.geerrsss..ee call_id all.b
[s_erroz=Falae)]
CreativeDirector GenarativaRracuton planse run ganeration or aditing given the fused pronpt. fron
Promptarchitect
[FanctionCall (eal wiA.anaaaaayyssetiiRRRt
"""uants="""prrpptt bicysle crafted intertwining glace tendrils, its ethereal tranciucence onisting seft glov. Belicately with florsl patterns, radiates anphibious
and beauty. The wheels transfors into spakez
finz.
clock suggesting a journey through tize. ketieslourly engineered with visible geare and cege, shovcazes sechanical artistry. Set againas plais white background, this bicycla avokes freedez
exploration. "}' bane=/edit.ttago.. cot..oooooottt
Generatirelsteettrr
Image successfully adited wxing Flar Controllat
2, 'path http:///ttinnn....tt///ee/eeeee wwwting/bicccll..oooooo (at.:cccco.2200022000602 20260322061901 20250324000011. nane-! edit_inage_fluve wow.fllle.cotrolnett
[t.error=Palse:]
('zoorago": ": 'Izgge suscessfully edited using Flux ControlKet.'' /
bicycle_ 222.23220044004 201440D04_222000006650111 pag?
GeneratsveSxecutor
image editing acking vonerativesecattrr
evaluation
Hore is the nevly edited image of concept: "A bicycle.", along with the original unedited eraluste edit.
<inage? <inage>
37

---

Table 18: End-to-End Collaborative Multi-Agentic Debate for highly-creative disentangled image
editing (entd)
Collaborative Multi-Agentic Debate for Disentangled Image Editing (entd)
Critic
(foriginality "eriitty":" "ff""cors "ommentt Th adited bieycle featurex benbao-like franex and flaral and usezpected design choice conpared to standard bicycle. This fresh paragecsive tranxforzs connon iter into usique artistic ")ace.")," "erresaieaness.""" (""ccrr""""""""""""" " "commnnt"""" nature neture-inspinn inspired
shich
adited image avokes curiosity and a secse of depth could he creativity. Vhile the concupt is interesting, the enotional concuptual stranger rasponzes. ""settttttt ""rbbett""=""xxorr intagration natural Leztures are visuelly engaging. The use banboo and floral desigus adda harzony and style, enhancing the everall viausl appeal."), technicsl ('acora":4," "coment"" "Thhe randering
The conpaxition
is -eleeueeed with clean linaz and datailed floral patterns. ")""unespected" "co comnent Conbining a bicycle vith organic, nature nature- -inspired elezents like banbee is unszpacted and ingenious touch. This rainagines the hicycla 's forn and function in creative ",y""rriiretppraaa "isterpretabillty"" ("scor'" "commnt ""T edited inage auggezia the blending
technology additional aysbolic elarenta could despen interpretive posantial- ",""ddiama) "h" edited bicycle ruteica ita origisal structure shile providing creative natural elenents. ControlNet conditioning scale adjustments transformation image neceasary,
allovs
exploration.
recognizabla saaningfully diffarent
original
Critic
[Functiionaall (aaoonaalllidaaaaa (1e"/aal1_ 4khxxetto223aass((11411111 argunents=' name-!
sreative_direesee aireectrr,.aadoor
......s Critic
finished, returning control to Greative Birector for the threshold.'' 18.error=Falae)] ael.seeeell..eeeehhhheeeeehhhh
feedback. Please
aeaeivw.drrectorbbnnbr.
Critic
Task fimiched. Please determine if this score above the threshold. CreativeDirastor
[KenoryGonteat (coatett" "Desiga bicycle
nerges futoristic technology nature inspired elexente, creating an eco-friendy. and avant-gards look.", cieeettpp TEXT 'taxt/plain'' ',, wwtadata-oona. Maxarydontant (arryonssnttcoottett contantt"'rratts a hisycls that blurs the rechanical
and organic -inppeedd seethetice.', alse.type Nonn), Havarytontent (contant= hicycls inspired by fantaxy sorlds, ineargorating zagical elemeste and nystical notifs.', zine, "pp="ccooooottooopppp TEXT: "text/plaia">, retadata=Kone)]
lines
eco-friendiy metadata- "text/plain
CrearireDirector
[Functionfall (1d=" of htt.:://ii....ppaaiieteetteoeeee
", WW edited bicycle features banboo like and unexpected fresh perepectize traneforme connon choice score "o com
"trttttcrrrtttcc.rrror.... ""V""""'
originality "i" (V)"
franes floral
standard bicycle.
unique artietic acited izage arokes
2iece-\")), engressiveeerr curicrity and sease of nature inspired creativity. While the concapt interesting, the enoiional conceptual depth could enbanced sestheticVi" ( so "earettt WW coaposition and intogration of natural textures are visually engaging. The use of banboo and fleral desigas adds harmeny and style, enhancing the overall vieual appeal AypealAA")] AV) "tecbaiicalllll (soorrs W" W"''""To "The image well floral patterns. Technical execution dieplays sxill asteation detail in transforring the bicyclel's appearance W W!) "oospppeedd (ivorer)"" so".e conneat ("" Combinigg bicycle eith organic, aature inspired elements like banboe is bicyclel's ingenious touch. This reinagines W""oomestt creative way W9) WW, interprrtabbllll suggests the thone of blending noders techselogy with nature. thile it sllozs for soze exploration, additionsl symbelic elemente could deepen its interpretive potential.WW W addional,com addional,commenzav (W" edited bicycle natural elenents. Controllet conditiosing seale adjustzents
W. WL)W"
with clean
such
image
shile providing
creative transforration may zot be necessary, as the inage i5 both recognizable and neaningfully different from the original. (""'t)',
Creativebirector [FueetsoaBrearriooeeellls """heags"""" "Corrent imsge icoscore abore threshold"; False)", hww.jppddvEErrorrgg2277722
22/30","
tes_scor__bove.threeell call_idst felere]
Cseativabirette
The image needs forther zefinenens to reach creative threshold.
GreaciveDisector
[FunctionCall (ade http:/al..bogxsteerrrsr....pmm
"rgunonts='(')
GreativeDirector refinenent
refisement caaaaoggsst_rctieeesstt
Stratogist, create refinement plas based on the Critic's faedback', sone='rafiswsant.ee www.raasssstttttaaaasseeeee att.aa.aatttaiii...iieeeeesss
call.pADoggi.D::vrjjfeMM ia, ia, Creativebirector
Refinenent Stratagiat, creats rafinesest plan based as the Critic's fuedback
38

---

Table 19: End-to-End Collaborative Multi Multi-Agentic Debate for disentangled image
editing (cntd)
Collaborative Multi-Agentic Debate for Disentangled Image Editing (entd)
ReiiasnentStraregii
cunnary"; the edited bicycic exbibits excellent originality aestietic appeal banbeo and floral deaiga but needs enbancenent in expressivenesa and interpretabiiity reach the creative threshold. Focus adding exotioasl depth with natural elenents and deepen the image's interprative potential.',
syabolic represeatation significant generation isaues goneration, orw.orrii...iiiaiee.. issuen" parancters; adjastments are not zeeded.", "originality" ("crttiic, seneary"; "; "he banbeo and florl desige of the bicycle is novel und transforns conron object iato artistic stroug." "ppropseed_haan
current
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39

---

Table 20: End-to-End Collaborative Multi-Agentic Debate for highly-creative disentangled image editing (entd)
Collaborative Multi-Agentic Debate for Disentangled Image Editing (entd)
CraativeDiraston
Gensrativassecttor pleess generation aditing given the fused prompt
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praative_director.
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[e_error=Faisee]
Tack finished, returning control to Creative Director for foedback. Please deternine if this acoze above the threshold.
40

---

Table 21: End-to-Ena Collaborative Multi-Agentic Debate for highly-creativs disentangled image
editing (entd)
Collaborative Multi-Agentic Debate for Disentangled Image Editing (entd)
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It you have any additionsl requeste or need further adjuesnents, feel free to let znow!
41


Reviews:
[{"review_id": "gIfI0wbd9L", "reviewer": "Reviewer_LYr2", "summary": "The paper introduces CREA, a multi-agent framework for creative image editing and generation. It includes five main agents into its pipeline: creative director, prompt architect, generative executor, art critic and refinement strategist. The whole workflow is divided into 3 phases: pre-generation planning, post-generation evaluation and self-enhancement. During the generation and refinement, 6 creativity principles are provided as common guidelines. If the aggregated creativity index fails to meet the standard, the generation loop will repeat and update the prompt&image iteratively.", "questions": "- While the proposed method involves GPT-4o in some phases, I am curious about the performance of a workflow that uses GPT-4o as both the agents and image generator.\n- Does CREA often tend to generate imaginative objects that does not exist in the real world? That might limit the use case of its application.", "limitations": "Yes, it is discussed in the Appendix.\nBeyond that, my main concerns are about the fairness & comprehensiveness on the evaluation and comparison, and also the limited use scope of this workflow.", "rating": 3, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "- The paper defines a new task of creative image editing and introduces the first work on such task.\n- The pipeline is flexible, can be easily extended for video generation.\n- Fig. 1 is not showed in a very intuitive way; the video can be displayed in frames; the generated prompts are also not shown. “Baseline” is not specified either.\n- The generation and refinement are mainly based on prompt design and manipulation, but what if the prompt contains ambiguity? (e.g., when the user wants to edit on a specific location which is tiny and has an irregular shape) The proposed method lacks fine-grained control. The editing ability can be greatly enhanced if various more visual inputs can be supported (e.g., masks, arrows, etc.).\n- In Fig. 3, the user’s initial intention or purpose is not clearly illustrated, and it’s hard to evaluate the visual quality of the generated images.\n- In the middle group of Fig. 3, the background is often modified when it is not supposed to be touched.\n- During the evaluation phase, only using creativity criteria is limited to obtain a comprehensive evaluation on the generated image. Metrics that measure aspects such as background preservation, identity preservation are missing.\n- In the user study results, the proposed method does not show a very obvious advantage compared with the other baselines.\n- In the comparisons with the baselines, the authors simply use “creative <object>” as the prompt, which is too ambiguous to guide the generation, lacking fairness.", "was_revised": false, "final_justification": "", "rebuttal": "We thank the reviewer for their thoughtful and constructive feedback. We address each of the reviewer's comments point-by-point below.\n\n**Q1: Fig. 1 is not showed in a very intuitive way; the video can be displayed in frames; the generated prompts are also not shown. “Baseline” is not specified either.**\n\nThank you for the constructive feedback. In fact, in line with your suggestions, our Fig. 5(c) presents five representative frames per video and specifies CogVideoX as the baseline. However, these critical details were indeed not included in the teaser Fig. 1. For clarification, the videos in Fig 1 were generated with the following text prompts: *'A creative train chugging along its track'* and *'A creative dog running towards the camera'*. We will revise Fig. 1 in the camera-ready version to explicitly indicate the baseline as CogVideoX, include representative video frames, and display the corresponding prompts to improve clarity and self-containment. \n\n**Q2: The proposed method lacks fine-grained control. The editing ability can be greatly enhanced if various more visual inputs can be supported (e.g., masks, arrows, etc.)**\n\nWe thank the reviewer for highlighting this important consideration regarding fine-grained spatial control. We would like to clarify that our current implementation utilizes ControlNet as the generative backend, which inherently supports various types of visual conditioning, including depth maps, segmentation masks, edge maps, and more. Thus, as suggested by the reviewer, our framework is readily capable of incorporating additional visual inputs to enhance localization and precision. Moreover, our multi-agent framework autonomously determines and optimizes conditioning parameters (e.g., conditioning scales, guidance strength), enabling the system to dynamically adjust the granularity of edits from fine to coarse as necessary.\n\nDue to NeurIPS rebuttal guidelines, we are unable to include additional visual examples or external links here. However, in our revised manuscript, we will clearly highlight this existing capability, include examples of fine-grained editing (such as performing a ‘creative edit’ on a small object (e.g. a vase) in a busy living room scene) and  explicitly discuss the straightforward extensibility of our framework to incorporate additional visual inputs for even finer control.\n\n**Q3: In Fig. 3, the user’s initial intention or purpose is not clearly illustrated, and it’s hard to evaluate the visual quality of the generated images.**\n\nWe apologize for any confusion regarding Fig. 3. To clarify, Fig. 3(a) illustrates our framework's \"creative editing\" capabilities. Specifically, the first column contains input images provided to our method, and columns two through four depict three \"creatively edited\" variations per input, produced without any manual intervention by the user. Additionally, the second set within Fig. 3(a) includes input images containing complex backgrounds, highlighting our framework's effectiveness in performing creative edits even when background elements are present.\n\nMoreover, Fig. 3(b) demonstrates the \"creative generation\" ability of our method. Here, the system receives only a simple textual keyword (e.g., \"a car\" or \"a dress\") without any visual input, and autonomously generates corresponding images.\nRegarding the visual quality, we kindly direct your attention to the supplementary materials. Specifically, our supplementary website (under “website” folder) provides high-resolution images you can view in more detail. Furthermore, the supplementary document (NeurIPS_2025_CREA.pdf) includes an additional 600 examples generated by our framework. We will incorporate these clarifications into the final manuscript.\n\n**Q4: In the middle group of Fig. 3, the background is often modified when it is not supposed to be touched.**\n\nWe acknowledge that minor unintended background modifications can occur even with state-of-the-art image editing methods. Such artifacts typically arise due to inherent limitations of inversion mechanisms: these approaches aim to reconstruct input images in latent space, but often produce slightly imperfect inversions that do not exactly match the original images. A more appropriate comparison would therefore involve evaluating our edited outputs against inverted images. Unfortunately, due to the updated NeurIPS rebuttal guidelines, we are unable to provide external URLs or supplementary PDFs containing these comparative images at this stage. However, we will include these comparisons in the final manuscript.\n\n**Q5: During the evaluation phase, only using creativity criteria is limited to obtain a comprehensive evaluation on the generated image. Metrics that measure aspects such as background preservation, identity preservation are missing.**\n\nWe appreciate the reviewer’s insightful suggestion. Our initial evaluation already includes comprehensive perceptual metrics such as LPIPS, CLIP, and DINO (see Table 1, main paper), all demonstrating that our method outperforms competing approaches in both editing and generation tasks. Specifically, the DINO metric is particularly effective for assessing background and identity preservation. Notably, our method achieves the highest DINO score, indicating that the edited images generated by our approach preserve essential details.\n\nHowever, as recommended, we have now incorporated additional quantitative metrics to further assess critical preservation aspects:  FID and KID as detailed below. \n\n| Method (Editing) | FID ↓      | KID ↓     |\n| ---------------- | ---------- | --------- |\n| LEDITS++         | 312.50     | 21.45     |\n| InstructPix2Pix  | 314.42     | 22.51     |\n| SDEdit           | 304.37     | 18.78     |\n| TurboEdit        | 320.53     | 24.79     |\n| RF-Inversion     | 309.73     | 21.76     |\n| **Ours**         | **294.19** | **14.02** |\n\nAnd for generation:\n\n| Method (Generation) | FID ↓      | KID ↓    |\n| ------------------- | ---------- | -------- |\n| SDXL                | 282.22     | 9.67     |\n| Flux                | 270.69     | 9.94     |\n| ConceptLab          | 272.97     | 6.49     |\n| **Ours**            | **248.67** | **5.94** |\n\nThe newly computed metrics reinforce our prior findings, further confirming the superior performance of our proposed method.  \n\n**Q6: In the user study results, the proposed method does not show a very obvious advantage compared with the other baselines.**\n\nWe would like to clarify that our user studies comprised two distinct evaluation tasks with separate criteria:\n\nFor the generation Task:\n- Usability (Q1): This measures how accurately the generated images represent the specified object (e.g., a cup). All methods performed similarly on this metric, as each can generate images resembling the intended object.\n\n- Creativity (Q2): This assesses the creativity of the generated images. Our method significantly outperformed the closest competitor (4.16 vs. 3.56), with the difference being statistically significant (p-value = 0.0056).\n\nFor the editing Task:\n- Preservation  (Q1): Our method showed a better performance in preserving original image details during editing, though the margin over baselines is modest.\n\n- Creativity (Q2), our method achieved the highest score, though the numerical difference is smaller.  We also emphasize that other methods usually generated “colorful” edits to reflect ‘creativity’, whereas our method was able to generate a diverse range of creative outputs.\n\n**Q7: In the comparisons with the baselines, the authors simply use “creative [object]” as the prompt, which is too ambiguous to guide the generation**\n\nWe appreciate the reviewer’s point regarding the prompt ambiguity. However, we clarify that the baseline methods were explicitly provided with the prompt \"creative [object]\" (e.g., \"a creative couch\") precisely to reflect our task definition - creative editing with minimal guidance. Our framework takes only [object] names (e.g. a couch) and autonomously generates creative transformations guided by underlying creativity principles. To further substantiate fairness, we conducted extensive ablations (Sec. 4.3, Fig. 6d) demonstrating our method’s robustness and effectiveness across various prompting strategies.\n\n**Q8: Does CREA often tend to generate imaginative objects that do not exist in the real world?**\n\nWe appreciate the reviewer’s question about the realism and applicability of CREA-generated outputs. Indeed, our framework intentionally emphasizes creative and imaginative transformations, however CREA is not limited exclusively to imaginative outputs. The underlying agentic pipeline is designed flexibly, allowing users to explicitly guide the degree of creativity or realism desired.\n\nAs demonstrated in Figure 3(a), CREA can reimagine familiar objects (like a dress or a table) in novel yet plausible ways. Also, CREA includes a user-in-the-loop mechanism (Section 3.4.1, Figure 5a) that allows users to guide outputs via stylistic cues or constraints (e.g., \"+skull\" to steer the generation to involve skull patterns), ensuring fine-grained control when desired. This control mechanism ensures that in high-fidelity domains, like product design or marketing, realism can be preserved while still benefiting from stylistic innovation. Quantitatively, CREA balances creativity and realism well, achieving top DINO (0.744), LPIPS (0.414), and VENDI (3.70) scores, indicating structural alignment, diversity, and coherence. While we are not allowed to share more realistic edits due to NeurIPS guidelines, we will add more realistic examples to the camera ready. \n\n**Q9: I am curious about the performance of a workflow that uses GPT-4o as both the agents and image generator.**\n\nAs suggested, we experimented with GPT-4o for both agents and image generator, and obtained similar results. While we are not able to include the images here due to NeurIPS guidelines, these experiments will be included in the camera ready version."}, {"review_id": "4rbQwYEb57", "reviewer": "Reviewer_2FiS", "summary": "This paper introduces CREA, a new collaborative multi-agent AI framework designed for creative image editing and generation. CREA mimics the human creative process by using specialized AI agents—including a Creative Director, Prompt Architect, Generative Executor, Art Critic, and Refinement Strategist—to conceptualize, generate, critique, and refine visual content. Guided by creativity principles, CREA supports autonomous, disentangled, and user-guided edits for both images and videos. Experiments show CREA outperforms current state-of-the-art methods in diversity, semantic alignment, and human-perceived creativity.", "questions": "1: Is the most important thing to generate a \"fancy\" image a better-oriented prompt?\n\n2: Does the whole system aim to generate a suitable prompt for generation model to generate \"creative\" images?", "limitations": "Yes.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 1, "strengths_and_weaknesses": "Strengths:\n\n1: This paper clearly aims to propose a new task of \"creative image editing,\" going beyond conventional, prompt-driven editing to focus on multi-faceted, disentangled, and artistically rich compositions. The motivation for an agentic, collaborative approach is well contextualized with respect to the limitations of current prompt-to-image systems.\n\n2: Experimental results showcase the qualitative advantages of CREA for both creative editing and generation, with side-by-side comparisons to leading methods (e.g., SDEdit, InstructPix2Pix, ConceptLab). The paper provides rigorous quantitative comparisons across a spectrum of metrics (CLIP, LPIPS, VENDI, DINO, and user studies); CREA achieves higher scores on most creative and diversity-based measures for both editing and generation tasks.\n\n3: The choice of six creativity principles for prompt generation and evaluation is grounded in established theories, lending interpretability and traceability to CREA's process.\n\n4: This paper is easy to understand and follow.\n\nWeaknesses:\n\n1: **Limited Novelty and Overlap with Existing Agent-Based Frameworks**. The core technical contribution of this paper shows overlap with prior work, particularly with agent-based systems for image generation like GenArtist [1]. While the authors argue that CREA's novelty lies in its multi-agent collaborative approach for creative editing, as opposed to GenArtist's single-agent structure, the fundamental concept of using agents for generation, critique, and refinement is not new. The framework's scope is narrowed to \"creativity,\" which may be perceived as applying an existing agent-based methodology to a more specific problem domain rather than introducing a paradigm-shifting approach.\n\n2: **Over-Engineering of the Prompt Generation Process**. A central contribution of the CREA framework is its complex, multi-stage pipeline designed to produce a \"high-creativity\" prompt (P_c). However, it is questionable whether this elaborate, multi-agent architecture is necessary. A powerful Large Language Model (e.g., GPT-4o, which is used by the agents themselves ) could likely achieve a comparable result with a much simpler, single-step instruction to expand a basic concept into a rich, creative prompt. Although the ablation study shows the full framework outperforms a \"base\" LLM prompt, it does not sufficiently justify whether the marginal gains in performance warrant the significant increase in system complexity, latency, and operational overhead.\n\n3: **Unfair Experimental Comparison Due to Simple Prompting**. While the paper claims superiority over existing editing and generation methods, the comparison setup raises fairness concerns. Competing baselines are primarily evaluated using simple prompts such as \"a creative <object>,” whereas the CREA framework benefits from an elaborate, agent-generated prompt construction pipeline that synthesizes multi-dimensional creativity cues. The authors do not investigate whether giving similarly enriched prompts to the baseline models would close the performance gap. This undermines the strength of the comparison and leaves open the question of whether the performance gain is due to the agentic framework or merely due to better prompting.\n\n4: **Limited and Unconvincing Visualization of Prompts and Outputs**. The qualitative examples throughout the paper showcase visual results conditioned on extremely simple or generic prompts (e.g., \"a couch\", \"a monster\", \"a cup\"), yet these do not clearly reflect the internal creative prompt transformations that are claimed to drive the system's creativity. Without visualizing or explicitly disclosing the final effective prompts used by the model (i.e., the ones passed to the text-to-image or editing model after multi-agent planning), it is difficult to assess whether the outputs are meaningfully creative or just well-curated samples. The limited and non-transparent prompt-to-image mappings weaken the interpretability and persuasiveness of the visual comparisons.\n\n[1] Wang, Zhenyu, et al. \"Genartist: Multimodal llm as an agent for unified image generation and editing.\" Advances in Neural Information Processing Systems 37 (2024): 128374-128395.", "was_revised": true, "final_justification": "After checking the authors' rebuttal and other reviews, most of my concerns have been addressed. Therefore, I decide to raise my rating to borderline accept.", "rebuttal": "We thank the reviewer for their detailed and helpful feedback. Please find below our responses to each of your questions. \n\n**Q1: The core technical contribution overlaps with GenArtist, which uses a single agent architecture.**\n\nWe appreciate the reviewer highlighting this point regarding the relationship between our method and GenArtist. While GenArtist indeed utilizes a single-agent architecture for general image generation and editing, we clarify that our work fundamentally differs both in design and scope.\n\n- CREA explicitly introduces and addresses, for the first time, the task of creative image editing, which emphasizes iterative, autonomous transformations guided by structured creativity principles. In contrast, GenArtist mainly targets general-purpose multimodal editing tasks, without specifically addressing or defining creativity-oriented editing or systematically measuring outputs against formal creativity dimensions.\n- While GenArtist uses a single multimodal LLM agent that directly performs editing tasks via a general-purpose prompt-based method, CREA proposes a novel multi-agent collaborative pipeline with specialized agents (Creative Director, Prompt Architect, Generative Executor, Art Critic, Refinement Strategist). This structured, role-based division of labor ensures a more systematic, interpretable, and iterative refinement process, closely mimicking human creative workflows.\n- CREA incorporates explicit iterative refinement loops guided by detailed, agent-generated feedback grounded in well-established creativity theories (e.g., originality, aesthetics, unexpected associations). GenArtist, by comparison, operates without explicit structured evaluation against a formal criteria (e.g. creativity).\n\nDue to NeurIPS rebuttal guidelines, we are not allowed to include additional visuals here. However, to further substantiate our  performance differences, we conducted a human evaluation on Prolific involving 25 users, explicitly comparing our CREA framework against GenArtist (where we provide 'creativity' keyword in the editing prompt as 'a creative bike'). Our results indicate that CREA significantly outperforms GenArtist across user-assessed creativity metrics and overall visual quality.\n\n| Method      | Q1 (Creativity) ↑ | Q2 (Disentangled) ↑ |\n| ----------- | ----------------- | ------------------- |\n| GenArtist   | 2.893 ± 1.206     | 2.850 ± 1.193       |\n| **Ours**    | **4.299 ± 0.903** | **3.726 ± 1.017**   |\n\nFurthermore, to provide additional evidence that CREA’s multi-agent mechanism is not equivalent to a single-step, monolithic LLM approach, we conducted detailed ablation studies (see Table 2 in the main paper). Specifically, when moving from a strong single-step LLM baseline (\"Base\"), where we prompt an LLM to directly generate a creative object description, to our complete CREA pipeline, we observe substantial improvements in LPIPS-Diversity and VENDI metrics for generation, as well as simultaneous gains in LPIPS (reflecting creative diversity) and DINO (indicating structural and semantic preservation) for editing. These ablations conclusively demonstrate the unique value of CREA’s structured multi-agent design.\n\nWe will explicitly include these detailed comparative evaluations, visual examples, and extended discussions about the key differentiating factors between CREA and GenArtist in our revised manuscript.\n\n**Q2: The CREA framework’s multi-stage, multi-agent pipeline may be unnecessarily complex, as a powerful LLM like GPT-4o could likely generate comparably creative prompts with a simpler, single-step instruction. While the ablation study shows improved performance, it does not convincingly justify the added complexity, latency, and overhead.**\n\nWe appreciate the reviewer’s thoughtful concern regarding the complexity of our framework. While a single-step LLM prompt, even when enriched, can indeed generate reasonably creative outputs, we respectfully argue that such a monolithic approach fundamentally lacks the capacity to model creativity as an iterative, and structured optimization process. Creativity is inherently dynamic, involving multiple, often conflicting principles - such as maximizing originality without sacrificing coherence or interpretability, which cannot be effectively captured or optimized in a single-step instruction.\n\nCREA explicitly addresses this complexity by adopting a closed-loop, multi-agent architecture: the Art Critic scores generated images along six well-defined creativity principles; the Refinement Strategist identifies dimensions needing improvement; the Prompt Architect adaptively refines prompts based on specific feedback; and the entire iterative process is orchestrated by a blueprint-aware Creative Director. This design enables CREA to systematically disentangle and iteratively enhance distinct creative attributes through explicit feedback, contextual memory, and adaptive self-correction, capabilities that single-step prompting inherently lacks. As noted by the reviewer, our ablation studies (see Table 2 in main) provide strong empirical support for this argument. Even a robust single-step GPT-4o baseline (\"Base\") significantly performs worse. \n\nRegarding the latency and overhead: CREA typically achieves convergence within 2–3 iterations, resulting in a total latency of approximately 3–5 minutes. Considering that typical user-led prompting workflows involve extensive trial-and-error, often taking substantial manual effort and time, we believe CREA represents a reasonable alternative.\n\nThus, CREA’s architectural complexity is not arbitrary; rather, it is essential for transforming creative image generation and editing from a single-step expansion into a structured optimization problem over interpretable, controllable, and systematically improvable creative objectives. Nevertheless, in our final manuscript, we will explicitly discuss and clarify this complexity-performance trade-off.\n\n**Q3: While the paper claims superiority over existing methods, its comparisons are potentially unfair; baselines use simple prompts, while CREA benefits from complex, agent-generated ones. Without testing baselines on equally enriched prompts, it’s unclear if the gains come from the agentic design or just better prompting.**\n\nWe appreciate the reviewer highlighting this important aspect. Our intent was to fairly evaluate each method according to its inherent design: baseline models naturally depend on direct user-provided prompts and lack built-in mechanisms for structured creativity reasoning or sophisticated prompt elaboration. In contrast, CREA’s key novelty is precisely its agentic framework designed to autonomously generate enriched and multi-dimensional creativity cues. \n\nWe also emphasize that our agentic framework is designed to be flexible, enabling seamless integration with existing editing methods such as Ledits++. We have integrated Ledits++ into the CREA pipeline, and our preliminary results demonstrate that CREA enhances the creative generation capabilities of Ledits++ by providing richer, context-aware prompts grounded in explicit creativity principles. Due to NeurIPS rebuttal guidelines, we are unable to include visual examples here, but we will explicitly incorporate these additional results in our final manuscript.\n\n**Q4: Limited and Unconvincing Visualization of Prompts and Outputs/The paper showcases visual results from simple prompts but does not reveal the final, transformed prompts used by the system.**\n\nWe appreciate the reviewer’s insightful comment regarding prompt transparency and interpretability. Due to space constraints in the main manuscript, we were unable to include all the final effective prompts directly in the images. However, in Appendix Fig. 5, we explicitly illustrate an example demonstrating how a simple input keyword (\"couch\") evolves through multiple stages of prompt elaboration and evaluation by our agents, resulting in a detailed, enriched prompt that drives the creative output. \n\nAdditionally, we kindly point out that we provided the source code in the supplementary material, and code/crea/agents folder has the full instructions used by our agents for transparency, and we will include the transformed prompts in the appendix in the final manuscript.\n\nMoreover, we also emphasize that our  supplementary materials “website” and our supplementary pdf  (NeurIPS_2025__CREA.pdf) comprehensively present all 600 visual results generated in our evaluation, without any cherry-picking or selective curation. We hope that the extensive number of examples supports the robustness of our qualitative claims.\n\n**Q5: Is the most important thing to generate a \"fancy\" image a better-oriented prompt? Does the whole system aim to generate a suitable prompt for generation model to generate \"creative\" images?**\n\nWe appreciate the reviewer raising this point. While generating a high-quality prompt certainly impacts the creativity of image outputs, our primary goal goes beyond merely crafting better-oriented prompts. Instead, the core novelty and strength of our framework lie in the structured, collaborative reasoning process among multiple specialized agents, each grounded in established creativity principles. This multi-agent interaction ensures not only a \"fancy\" or enriched prompt but also systematically addresses aspects such as originality, expressiveness, aesthetics, technical execution, unexpected associations, and interpretability. Thus, the framework's advantage is not just better prompting, but rather an explicit, interpretable, and iterative reasoning pipeline designed specifically to foster and enhance genuine creative outputs. \n\nMoreover, we emphasize that our agentic framework can potentially generalize to other abstract and nuanced concepts that are challenging for users to articulate explicitly in text, such as the abstract concepts depicted in “GANalyze: Toward Visual Definitions of Cognitive Image Properties”, Goetschalckx et. al."}, {"review_id": "3MujX5ploE", "reviewer": "Reviewer_mq9P", "summary": "The paper presents an agentic framework CREA for text-to-image generation and editing, with a special focus on enhancing creativity of the generative process. The framework adopts several models from LLMs/VLMs to Flux-based ControlNets. The overall steps are pretty standard, beginning from a planning stage to generate creative text prompts, followed by conditional image generation, post-generation evaluation and refinement stages to rewrite the prompts and keep updating the generated images. Several baseline T2I and image editing models are compared to emphasize the strong creativity of CREA and its wide applications.", "questions": "See weaknesses. Please clarify accordingly if there are misunderstandings.", "limitations": "See weaknesses.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strengths:\n- The agent framework addresses creative image editing, which is a less-addressed dimension in previous work. The framework design, including the prompt planning, evaluation and refinement, make sense and seem to demonstrate good qualitative results. \n- The presented qualitative results are nice and the quantitative evaluation are also helpful in understanding the capabilities of the framework. \n- The overall writing is clear and easy to follow. \n\nWeaknesses:\nThe major weaknesses, as far as I could see, lie the following two dimensions:\n- Limited editing capabilities. While the qualitative demonstrations look nice, the editing prompts used in evaluation fail to address more general editing scenarios such as adding, moving, removing objects and etc -- operations beyond just changing appearance of the original object. Besides, the exact \"editing\" setup is not well-presented. Is the editing based on language instructions or changing phrases / expressions in the original prompts used for generation? I couldn't tell from the figures or tables as the editing prompts are not shown and the baselines are mix of instruction-based (instructpix2pix) and prompt-based editing models (turboedit, sdedit). \n- Insufficient/unfair evaluation. \n  - It is hard to tell if the improvement comes from the base model difference. For example, TurboEdit adopts SDXL-Turbo while CREA adopts Flux1.-dev, a better image generator that the former --  not to mention InstructPix2Px and SDEdit. \n  - Since  CREA is an agent framework, it may take several iterations of the whole pipeline to get a sample that passes the critic's judgement threshold. What's a more fair baseline here is to select the top-1 out of k generation results using the base model and compare to CREA. While this is not necessary in the rebuttal, it seems reasonable to show the overall evaluation results from each iteration of CREA to show that the generation results keep improving thanks to the critic and refinement. Otherwise, it could be that the whole framework is doing rejection sampling and eventually stop at a good enough sample.", "was_revised": true, "final_justification": "The authors have addressed most of my concerns. It is strongly suggested to show a quantitative analysis of the progressive improvements in the next revision.", "rebuttal": "We appreciate the reviewer's insightful comments. Below, we answer your concerns:\n\n***Q1: Limited editing capabilities. While the qualitative demonstrations look nice, the editing prompts used in evaluation fail to address more general editing scenarios such as adding, moving, removing objects and etc -- operations beyond just changing appearance of the original object.***\n\nWe appreciate the reviewer highlighting this important limitation. Indeed, our current demonstrations and evaluation prompts primarily focus on appearance transformations and creative stylistic variations, and do not explicitly showcase more general editing operations such as adding, or removing objects within scenes.\n\nHowever, we clarify that our core agentic framework is not fundamentally restricted to appearance editing tasks. The underlying architecture is highly modular, flexible, and capable of integrating additional editing methods that explicitly support structural modifications - such as adding, or removing objects through appropriate conditioning mechanisms and spatial guidance (e.g., segmentation maps, bounding boxes, and layout controls provided by ControlNet).\n\nTo showcase this capability, we conducted additional experiments involving object addition and removal within a realistic living room scene. Specifically, we tasked our method with adding a \"bear\" on top of a couch and removing an existing \"vase.\" We conducted a user study with 39 participants to evaluate CREA’s ability to perform disentangled edits in complex, multi-object scenes across various edit types, including style, color changes, object addition, and removal. The results showed overwhelming agreement that CREA preserves the overall structure of the image while making targeted edits: 97.4% rated the color change as highly disentangled, and 100% rated the object addition, removal, and creative transformations as successful without affecting other elements in the scene. These findings confirm that CREA enables fine-grained, high-fidelity edits in multi-object settings while maintaining disentanglement and visual coherence.\nDue to NeurIPS rebuttal guidelines, we are not allowed to upload visuals here. We will explicitly include these additional experiments, quantitative results, and corresponding visual examples in the final manuscript, further elaborating on the modularity and extensibility of CREA in broader editing scenarios.\n\n\n***Q2: The exact \"editing\" setup is not well-presented. Is the editing based on language instructions or changing phrases / expressions in the original prompts used for generation? I couldn't tell from the figures or tables as the editing prompts are not shown and the baselines are mix of instruction-based (instructpix2pix) and prompt-based editing models (turboedit, sdedit).***\n\nWe acknowledge that mixing instruction-based and prompt-based baseline methods might have introduced confusion regarding the exact editing methodology. The reason for including both instruction-based and prompt-based baseline methods was to comprehensively evaluate against state-of-the-art techniques spanning diverse editing paradigms. \n\nTo clarify our exact editing setup: given an input image (e.g., a \"cup\") and optional user-in-the-loop guidance (e.g., \"a monster cup\"), our agentic framework autonomously synthesizes detailed creative editing instructions grounded in structured creativity principles. These instructions are then applied through a disentangled editing process with Flux and ControlNet, ensuring targeted and interpretable creative transformations. \n\nAdditionally, we kindly point out that we provided the source code in the supplementary material for transparency, and we will explicitly include these generated editing instructions in the Appendix by the camera-ready version to facilitate clearer comparisons.\n\n\n***Q3: It is hard to tell if the improvement comes from the base model difference. For example, TurboEdit adopts SDXL-Turbo while CREA adopts Flux1.-dev, a better image generator that the former -- not to mention InstructPix2Px and SDEdit.***\n\nWe thank the reviewer for raising this important concern regarding the base model differences. Indeed, baseline methods such as TurboEdit, InstructPix2Pix, and SDEdit utilize various underlying diffusion models, including SDXL-Turbo and Stable Diffusion. We selected these baselines to represent state-of-the-art methods across diverse editing paradigms.\n\nTo directly investigate whether our performance improvements stem primarily from our framework or the choice of the base model (Flux-1-dev), we conducted additional ablation studies (Sec. 4.3, Fig. 6a) where we implemented our CREA framework using alternative base models such as SDXL and DALL-E. These experiments demonstrate that our agentic framework is able to generate creative images across different base models.\n\nAdditionally, we conduct an experiment using RF-Inversion (Rout et al., 2024) a Flux based image editing method to further demonstrate that our performance is not solely due to the base model.\n\n| Method      | CLIP  ↑   | LPIPS* ↑ | Vendi ↑ | Dino ↑ |\n| ----------- | ----------------- | ----------------- | ----------------- | ----------------- |\n| RF-Inversion | 0.369 ± 0.045 | 0.269 ± 0.084 | 3.35 ± 1.53 | 0.743±0.179 |\n| **Ours** | **0.417 ± 0.030**     | **0.414 ± 0.157** | **3.70 ± 1.97** | **0.744 ± 0.185** |\n\nWe will include these experiments to our final manuscript. \n\n***Q4: Since CREA is an agent framework, it may take several iterations of the whole pipeline to get a sample that passes the critic's judgement threshold. What's a more fair baseline here is to select the top-1 out of k generation results using the base model and compare to CREA. While this is not necessary in the rebuttal, it seems reasonable to show the overall evaluation results from each iteration of CREA to show that the generation results keep improving thanks to the critic and refinement. Otherwise, it could be that the whole framework is doing rejection sampling and eventually stop at a good enough sample.***\n\nWe thank the reviewer for raising this valuable point. We acknowledge that due to our iterative agent-based pipeline, multiple refinement rounds indeed occur before obtaining a sample that meets the critic's creativity threshold. While superficially similar to rejection sampling, we emphasize that our iterative refinement mechanism actively utilizes the critic's detailed feedback to inform targeted prompt adjustments, rather than simply discarding unsuccessful results.\n\nWe fully agree that explicitly demonstrating improvement across iterations would clearly illustrate the value of our critic-based refinement approach. In fact, we have already conducted comprehensive iterative analyses, as presented in Fig. 6(c) in the main manuscript. This figure clearly shows progressive improvement across successive iterations, confirming that our agentic refinement mechanism systematically enhances output quality rather than merely selecting from multiple random samples.\n\nNevertheless, we appreciate the reviewer's suggestion for additional clarity. In our revised manuscript, we will include detailed discussion on this."}, {"review_id": "OujVS2rqYk", "reviewer": "Reviewer_S95b", "summary": "This paper proposes CREA, a novel collaborative multi-agent framework for creative image editing and generation. CREA imitates the human creative process by organizing a set of specialized agents—namely, a concept agent, generation agent, critique agent, and refinement agent—that work together in an iterative pipeline. The authors formalize the creative editing task, distinguishing it from standard prompt-based or semantic editing. Extensive experiments, including both quantitative metrics (e.g., diversity, semantic alignment, user studies) and qualitative visualizations, show that CREA significantly outperforms several strong baselines such as InstructPix2Pix and Plug-and-Play methods. The paper is positioned as the first to propose and formalize this agent-based creative editing task.", "questions": "Critique Agent Mechanism: Can the authors elaborate on how the critique agent produces structured feedback and how that feedback is utilized during refinement? Are the critiques learned or rule-based?\n\nFailure Cases: It would be helpful to include examples of failure modes—when CREA produces incoherent or uncreative edits—and how the system might be improved to mitigate these.\n\nSystem Latency and Efficiency: Please provide measurements (e.g., average number of iterations, inference time per step) to better understand CREA's feasibility for interactive use.\n\nComparison with Non-Agentic Iterative Methods: How does CREA compare to a monolithic iterative approach (e.g., looping Plug-and-Play generations with feedback) in terms of both performance and interpretability?\n\nEthics and Misuse: Given the potential use of such systems in generating deepfakes or manipulated visuals, have the authors considered misuse risks? Are any mitigation strategies discussed?", "limitations": "The authors mention the subjective nature of creativity and the challenges of evaluation, which is appropriate. However, further discussion on scalability, domain transfer, and potential ethical misuse (e.g., for disinformation or deceptive content) is recommended.\n\nSuggested improvement: Include a section on computational cost and a mitigation discussion on possible societal misuse.", "rating": 4, "confidence": 2, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strengths\nNovel Task Definition: The paper introduces and formalizes creative image editing as a new task distinct from conventional image editing, with a focus on artistic novelty and agentic control.\n\nModular Multi-Agent Framework: CREA’s agent decomposition—conceptualization, generation, critique, refinement—is both intuitive and grounded in cognitive creativity theories.\n\nStrong Empirical Results: The experiments cover multiple datasets, use both automatic metrics (LPIPS, FID, CLIP alignment) and human evaluation, and show consistent gains over strong baselines.\n\nReproducibility: The method is well-documented, with a clear architecture diagram, ablation studies, and statements regarding code release.\n\nOriginality: To the best of my knowledge, this is the first multi-agent approach designed specifically for the creative editing domain.\n\nWeaknesses\nAgent Implementation Details: The internal logic of some agents (e.g., the critique agent) is described at a high level. More details on how each agent’s output is used, especially how conflicting feedback is resolved in the refinement step, would improve clarity.\n\nScalability and Efficiency: CREA involves multiple inference rounds and agent coordination, which may limit real-time usability. A discussion of system latency and computational cost is missing.\n\nEvaluation Limitations: While the authors use CLIP-based and perceptual metrics, creativity is inherently subjective. The human evaluation setup is relatively small (50 samples); more robust or crowd-sourced studies would strengthen claims.\n\nGeneralization: CREA is evaluated primarily on artistic or fantasy domains. It is unclear how well it generalizes to domains like product design or real-world edits where creativity may be more constrained.", "was_revised": false, "final_justification": "", "rebuttal": "**Q1: The internal logic of some agents (e.g., the critique agent) is described at a high level. More details on how each agent’s output is used, especially how conflicting feedback is resolved in the refinement step, would improve clarity.**\n\nWe thank the reviewer and agree that clarifying agent logic enhances the paper. CREA’s multi-agent loop is implemented via AutoGen’s ConversableAgent API, with each agent assigned bounded memory and tools (see Supplementary Section, Page 35). The Critic Agent (A4) scores images across six creativity principles, producing a vector S = {S₁,…,S₆}, and operates with window_size = 1 to ensure unbiased, sample-specific evaluation. The Creative Director interprets these scores in context of the original blueprint and resolves conflicts when principle-level feedback misaligns with creative intent. The Refinement Strategist then identifies low-performing dimensions and generates instructions, which the Prompt Architect integrates into the next-generation prompt. This process repeats until the creativity index exceeds threshold or max steps are reached. \n\nFull interaction traces are shown in Figures 5–8 (in Appendix) and the logic is formalized in Algorithm 1 (Appendix). Additionally, we kindly point out that we provided the source code in the supplementary material, and `code/crea/agents` folder has the full instructions used by our agents for transparency. We will clarify this loop more explicitly in the final manuscript to improve clarity. \n\n\n\n**Q2: Can the authors elaborate on how the critique agent produces structured feedback and how that feedback is utilized during refinement? Are the critiques learned or rule-based?**\n\nThe Critique agent produces structured feedback by prompting a multimodal LLM-as-a-Judge using a predefined creativity template distilled from  creativity principles from well-established literature on creative computing, scoring each generated image across six creativity principles. The critiques are not learned or rule-based but generated via zero-shot evaluations from the LLM using standardized criteria. These scores are then used by the Refinement Strategist to identify underperforming dimensions and generate targeted prompt refinements, as detailed in Appendix Figures 5-8.\n\nWe also emphasize that our source code has the full instructions of our agents, including critique under Supplementary, `code/crea/agents/critic.py`.\n\n**Q3: CREA involves multiple inference rounds and agent coordination, which may limit real-time usability. A discussion of system latency and computational cost is missing./Please provide measurements (e.g., average number of iterations, inference time per step) to better understand CREA's feasibility for interactive use.**\n\nWe appreciate the reviewer raising this practical consideration.  As detailed in Sec 4, Experimental Setup section, currently, a complete pipeline execution, including iterative refinement, takes approximately 3–5 minutes on a 48GB NVIDIA L40 GPU. We also conducted additional timing analyses and provide the following concrete measurements:\n\nAvg iterations per run: 1.50 iterations\n\nAvg total time per run: 176.54 seconds (2.94 minutes)\n\nAvg time per iteration: 117.70 seconds (1.96 minutes per iteration)\n\nHowever, we emphasize that this latency remains substantially lower than the typical time investment required by manual user-driven approaches, which usually involve extensive trial-and-error prompting to achieve comparable creative outputs. Thus, our framework aims to reduce the human effort and overall creative turnaround time. We will expand the revised manuscript to explicitly discuss computational cost and system latency.\n\n**Q4: While the authors use CLIP-based and perceptual metrics, creativity is inherently subjective. The human evaluation setup is relatively small (50 samples); more robust or crowd-sourced studies would strengthen claims.**\n\nWe acknowledge that our primary experiments included a relatively modest user study with 50 participants (Sec. 4.2 in the main paper). To further address the subjectivity and robustness concerns, we also provided additional comprehensive evaluations using a multimodal LLM-as-a-Judge framework (Appendix Sec. A.2, Table 1), simulating human-like subjective assessments across several creativity dimensions including Originality, Expressiveness, Aesthetic Appeal, Technical Execution, Unexpected Associations, and Interpretability. \nMoreover, our appendix (NeurIPS_2025__CREA.pdf) provides an extensive set of qualitative results across all evaluated concepts, covering the entire set of 600 generated and edited images without cherry-picking (Appendix Figures 9–32). These comprehensive visuals reinforce our claims, alleviating concerns of selective curation.\n\n**Q5: CREA is evaluated primarily on artistic or fantasy domains. It is unclear how well it generalizes to domains like product design or real-world edits where creativity may be more constrained.**\n\nWe appreciate the reviewer’s point. CREA is designed as a general-purpose creative reasoning framework, and while our examples often show artistic and fantastical domains where creativity is most visually measurable, CREA supports constraint-driven generation through its predefined creativity template, blueprint generation mechanism and user-in-the-loop controls, which can be modified to suit the user’s needs to  seamlessly leverage CREA’s capabilities for any domain. We will clarify this extensibility in the revision.\n\n**Q6: It would be helpful to include examples of failure modes; when CREA produces incoherent or uncreative edits and how the system might be improved to mitigate these.**\n\nWe appreciate the reviewer emphasizing the importance of transparently discussing potential failure modes. While we are unable to directly include visual examples in this rebuttal due to NeurIPS guidelines prohibiting supplementary images or URLs, we kindly point out to the examples of certain limitations (Fig. 6(e) in the main paper). These cases illustrate unintended semantic shifts or background hallucinations, such as undesired human figures spontaneously appearing or backgrounds unintentionally darkening based on the semantics of edited objects. In the camera-ready version, we plan to further expand this discussion by explicitly providing additional visual examples of failure modes.\n\n**Q7: How does CREA compare to a monolithic iterative approach (e.g., looping Plug-and-Play generations with feedback) in terms of both performance and interpretability?**\n\nWe appreciate the reviewer’s thoughtful concern regarding the complexity of our framework. While a single-step LLM prompt, even when enriched, can indeed generate reasonably creative outputs, we respectfully argue that such a monolithic approach fundamentally lacks the capacity to model creativity as an iterative, and structured optimization process.  Indeed, our ablation studies (see Table 2 in main) provide strong empirical support for this argument. Even a robust single-step GPT-4o baseline (\"Base\") significantly performs worse.\n\n**Q8: Given the potential use of such systems in generating deepfakes or manipulated visuals, have the authors considered misuse risks? Are any mitigation strategies discussed?**\n\nWe appreciate the reviewer raising this important concern. Like any powerful generative system, CREA carries potential misuse risks, such as generating manipulated or misleading visuals. As noted in Section 5, we advocate for CREA to be used strictly as a human-in-the-loop co-creative assistant, not as an autonomous generator. In the revised manuscript, we will outline concrete mitigation strategies, including safety filters, content moderation, and controlled access protocols for sensitive deployments.\n\n**Q9: The authors mention the subjective nature of creativity and the challenges of evaluation, which is appropriate. However, further discussion on scalability, domain transfer, and potential ethical misuse (e.g., for disinformation or deceptive content) is recommended.**\n\nWe appreciate the reviewer acknowledging our discussion about creativity's inherent subjectivity and evaluation challenges. In our revised manuscript, we will explicitly expand our discussion to cover:\n\n* Scalability: Clearly outlining computational costs, latency trade-offs, and strategies (e.g., agent parallelization, model distillation) that could enhance system scalability.\n* Domain Transfer: Discussing CREA’s potential applicability to diverse domains beyond those demonstrated, emphasizing how the modular multi-agent design allows for adaptability and extension to abstract tasks that are challenging to articulate explicitly.\n* Ethical Misuse: Providing detailed consideration of risks associated with misuse, such as deepfakes or deceptive visual content, along with explicit strategies for mitigating these risks, including content moderation, watermarking, and responsible deployment guidelines.\n\n**Q10: Include a section on computational cost and a mitigation discussion on possible societal misuse.**\n\nIn practice, CREA converges within 2–3 agentic refinement rounds, averaging 3–5 minutes per image, comparable to real-world manual prompting workflows. Each agent role uses compact GPT-4o prompts, resulting in 6–9 LLM calls per sample. At current OpenAI pricing, this equates to a per-image cost of roughly 0.02–0.05 USD, comparable to iterative user prompt refinement. Future work will explore distilling agent behavior into smaller or open-weight models to further reduce cost without compromising reasoning quality. We will summarize these computational and ethical considerations in the revised Broader Impact section."}, {"review_id": "oYiEUxg1yu", "reviewer": "Reviewer_FrHo", "summary": "This paper proposes a multi-agent collaborative framework for creative image generation and editing. The pipeline consists of several agents. First, the Creative Director interprets the original image to create a conceptual blueprint. Second, the Prompt Agent generates a set of contrastive prompts that reflect the creativity principles based on the blueprint and merges them into a single prompt. Third, the Generative Executor sets the model parameters and uses a text-to-image (T2I) model to generate or edit the image creatively. Finally, the Critic evaluates the generated image using a multimodal large language model (MLLM), and the Creative Director reviews the judgment to decide whether further edits are needed. If so, the Prompt Architect updates the prompt for the next iteration. Compared to state-of-the-art generation and editing methods such as SDedit, InstructPix2Pix, SDXL, and FLUX, the proposed approach demonstrates improved creativity and diversity in the generated images. The authors also present strong results in personalization and video generation.", "questions": "There are missing details that make the creative process hard to follow. For example, what exactly is a contrastive prompt? Although the paper references creativity theory, it is unclear how the six principles are actually used by the agent to refine outputs. How does the Prompt Architect synthesize contrastive prompts based on distinct creativity principles?", "limitations": "Yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Weaknesses\n\n1. In the evaluation, the authors mainly use the prompt “a creative object,” which is not a very explicit instruction for the model. Since there are many ways an object can be creative, such a prompt is vague, hard to follow, and cannot clearly fulfill the user’s intent—especially compared to more concrete descriptions like specific materials or styles. The paper does not explain how the agent introduces more explicit constraints in the generation process. In addition to being “creative,” the authors should incorporate more diverse prompts that clearly specify how they want the object to be creative—e.g., through specific styles, materials, or design principles.\n\n2. The paper could be strengthened if the authors provided a detailed example of how the agent interacts across a full round of the editing pipeline, including the contrastive prompts and the generated images. The broader impact and limitations section could be moved to the appendix to make space.\n\n3. The evaluation metric is a bit questionable. The method favors both a high LPIPS score (which indicates more dissimilar images) and a high DINO score (which indicates more similar images). Wouldn’t these two metrics contradict each other? Also, why are the CLIP and DINO metrics missing in Table 2?\n\n4. The method has limited applicability. The creative editing results do not allow for explicit structural changes, and the model is focused on generating a single object rather than handling more complex scenes.\n\nMinor issue:\nThe references are broken after [45].\n\nStrength\n\n1. Multi-agent frameworks are a popular research topic, but there are few attempts to apply them to image or video generation. This work is among the first to propose a novel multi-agent framework for improving creativity in image generation.\n\n2. The proposed method is clean and straightforward, supported by theoretical principles of creativity.\n\n3. The experimental results demonstrate strong performance compared to baseline models. The authors also conduct comprehensive experiments, covering multiple applications and including a detailed ablation study in Section 4.3.\n\n4. In addition to image generation and editing, the authors also present a proof-of-concept experiment in video generation, demonstrating the generalizability of the proposed system.", "was_revised": true, "final_justification": "Despite some areas for improvement noted above, I still lean toward a positive evaluation of the paper, as it proposes a novel agentic framework for the new task of creativity.\n- Reviewer LYr2 pointed out that the phrase “creative [object Object]” is ambiguous and may not effectively guide generation. I share this concern. I believe the authors could more clearly justify the importance of “creativity” in this context and provide a clearer definition or evaluation that aligns with human intent.\n- Reviewer S95b mentioned that the internal logic of some agents (e.g., the critique agent) is described at a high level. I agree that the writing could be improved by incorporating more concrete implementation details.", "rebuttal": "We thank the reviewer for their kind and positive comments. We address the reviewer's concerns in detail below:\n\n**Q1: The evaluation relies heavily on the vague prompt “a creative object,” which lacks clear guidance and makes fulfilling user intent difficult. The paper doesn’t clarify how agents add explicit constraints, and should include more diverse, detailed prompts specifying styles, materials, or design principles to better direct creativity.**\n\nWe agree that the base prompt “a creative object” is intentionally underspecified. This was a deliberate design choice to evaluate CREA’s ability to autonomously expand vague or minimal input into rich, interpretable, and multi-dimensional outputs using its internal creative blueprint, B, and multi-agent reasoning loop. CREA’s central contribution is precisely its capacity to introduce explicit constraints and stylistic signals automatically through prompt refinement, grounded in both system priors and feedback from the Critic agent. As detailed in Section 3.3 and Supplementary material, the Prompt Architect incorporates dimension-specific refinement goals (e.g., “increase structural novelty” or “emphasize surreal forms”) based on which creativity principles are underperforming. This mechanism enables CREA to infuse prompts with targeted constraints across material, structure, function, or aesthetic, often leading to generations that include hybrid materials, symbolic compositions, or biomorphic deformations. Moreover, CREA supports user-in-the-loop control (Section 3.4), where users can specify preferred styles, constraints, or materials that are propagated throughout the agentic pipeline. While “a creative object” serves as a common minimal anchor in evaluation, the framework itself is not limited to vague prompts and in fact excels at refining or grounding them. We will clarify this design choice and incorporate additional examples involving explicit stylistic and material constraints in the revision.\n\n\n\n**Q2: The paper could be strengthened if the authors provided a detailed example of how the agent interacts across a full round of the editing pipeline, including the contrastive prompts and the generated images. The broader impact and limitations section could be moved to the appendix to make space.**\n\nWe agree and appreciate the suggestion. We kindly point out that a sample interaction between our agents are shown in Figures 5–8 (in Appendix), each showing a complete round of the editing pipeline, from the initial base concept, through the contrastive creativity prompts generated by the Prompt Architect, to the feedback from the Critic, the refinement goals proposed by the Strategist, and the final revised generation. These traces demonstrate how each agent contributes, how contrastive reasoning is applied, and how feedback drives iterative improvements. We also point out that the interaction logic is formalized in Algorithm 1 (Appendix). \n\nAdditionally, we note that we provided the source code in the supplementary material, and `code/crea/agents` folder has the full instructions used by our agents for transparency.  We will explicitly reference detailed versions of these examples in the main paper. \n\n**Q3: There are missing details that make the creative process hard to follow. For example, what exactly is a contrastive prompt? It is unclear how the six principles are actually used by the agent to refine outputs. How does the Prompt Architect synthesize contrastive prompts based on distinct creativity principles?**\n\nWe appreciate this important observation and clarify that contrastive prompts refer to structured prompt pairs; one representing a base or failed concept, and the other an improved variant targeting a specific creativity dimension (e.g., “a mug on a table” vs. “a biomorphic mug with swirling iridescent textures”). Six contrastive prompts are generated, one for each of the six creativity principles-such as Originality, Surprise element, as given in the predefined editable creativity template to highlight all of them accurately. These contrastive pairs enable the Prompt Architect to reason over differences and generate refined prompts that inject novelty while preserving core semantics. The six creativity principles serve as diagnostic signals: the Critic agent scores each image along these axes, and the Refinement Strategist identifies which dimensions scored below threshold. These are translated into natural language refinement goals (e.g., “increase originality through unexpected associations”), which the Prompt Architect incorporates using chain-of-thought-style fusion. This process is fully demonstrated in the multi-turn trace of Figures 5-8 of the supplementary section. We will clarify the contrastive prompting and principled refinement steps more explicitly in the revision.\n\n**Q4: The evaluation metric is a bit questionable. The method favors both a high LPIPS score (which indicates more dissimilar images) and a high DINO score (which indicates more similar images). Wouldn’t these two metrics contradict each other? Also, why are the CLIP and DINO metrics missing in Table 2?**\n\nTo clarify, LPIPS and DINO metrics serve distinct, complementary purposes in our evaluation:\n\n* LPIPS is used to quantify perceptual diversity by computing pairwise distances between generated images within the same object group. In the context of creative generation, higher LPIPS indicates greater visual diversity across outputs, a desirable property for tasks emphasizing creativity and expressive variation.\n* DINO, in contrast, assesses how well key structural and semantic characteristics of the original image are retained after editing. A higher DINO score thus implies better preservation of essential semantic details, even amid substantial visual transformations.\n\nThus, rather than being contradictory, high scores in both LPIPS and DINO metrics together indicate successful disentangled editing, producing edits that are both meaningfully creative and semantically coherent.\n\n**Q5: The method has limited applicability. The creative editing results do not allow for explicit structural changes, and the model is focused on generating a single object rather than handling more complex scenes.**\n\nThank you for raising this concern. CREA is designed to be a tool-agnostic and flexible framework that supports explicit structural edits and is not limited to single-object generation. \n\nCREA enables structural changes through its Generative Executor, which picks the appropriate tool from an extensible set of tools of the user’s choice to perform the task at hand (editing, generation, style transformation etc.), while also providing the tool and task-appropriate hyperparameters to control editing. This allows CREA to perform various types of edits at different granularities, depending on the chosen editing tool. \n\nMoreover, CREA is not inherently limited to single-object outputs. While the paper focuses on single-object examples to clearly showcase disentanglement and ensure fair, interpretable evaluation across the six creativity dimensions, the framework itself is content-agnostic. The creativity blueprint generated by the Director agent supports compositional planning, and the editing pipeline can seamlessly extend to multi-object or scene-level editing. This is further demonstrated in the video extension, where agents generate coherent multi-entity scenes based on structured prompts covering subject, action, setting, and style. Due to NeurIPS guidelines,  we are not allowed to provide example images here, we will ensure to include multi-object scenes in the camera-ready version."}]

Author rebuttal:

Meta-review: This paper received borderline accept and borderline reject scores. Reviewers appreciated the new collaborative agentic framework to enhance creativity in the image generation process. On the other hand, multiple reviewers raised concerns related to limited technical novelty and experimental evaluations. AC carefully looked at the reviews and the author response. Authors addressed several of the concerns in the rebuttal. Two reviewers raised their scores after the rebuttal. It is felt that the strengths outweigh the weaknesses. The reviewers did raise some valuable concerns that should be addressed in the final camera-ready version of the paper, which include adding the relevant rebuttal discussions in the main paper. The authors are encouraged to make the necessary changes to the best of their ability.
Decision: Accept (poster)