Draw-In-Mind: Rebalancing Designer-Painter Roles in Unified Multimodal Models Benefits Image Editing
Authors: Ziyun Zeng
David Junhao Zhang
Wei Li
Mike Zheng Shou
Venue: ICLR 2026 Poster (2026)
Keywords: Image Editing
Image Generation
Unified Multimodal Model
Multimodal
Primary area: datasets and benchmarks
TL;DR: We facilitate image editing via rebalancing designer-painter roles in UMMs.

Abstract: In recent years, integrating multimodal understanding and generation into a single unified model has emerged as a promising paradigm. While this approach achieves strong results in text-to-image (T2I) generation, it still struggles with precise image editing. We attribute this limitation to an imbalanced division of responsibilities. The understanding module primarily functions as a translator that encodes user instructions into semantic conditions, while the generation module must simultaneously act as designer and painter, inferring the original layout, identifying the target editing region, and rendering the new content. This imbalance is counterintuitive because the understanding module is typically trained with several times more data on complex reasoning tasks than the generation module. To address this issue, we introduce *Draw-In-Mind* (DIM), a dataset comprising two complementary subsets: (**i**) DIM-T2I, containing 14M long-context image-text pairs to enhance complex instruction comprehension; and (**ii**) DIM-Edit, consisting of 233K chain-of-thought imaginations generated by GPT-4o, serving as explicit design blueprints for image edits. We connect a frozen Qwen2.5-VL-3B with a trainable SANA1.5-1.6B via a lightweight two-layer MLP, and train it on the proposed DIM dataset, resulting in DIM-4.6B-T2I/Edit. Despite its modest parameter scale, DIM-4.6B-Edit achieves SOTA or competitive performance on the ImgEdit and GEdit-Bench benchmarks, outperforming much larger models such as UniWorld-V1 and Step1X-Edit. These findings demonstrate that explicitly assigning the design responsibility to the understanding module provides significant benefits for image editing. Our dataset and models are available at https://github.com/showlab/DIM.

Published as a conference paper at ICLR 2026
DRAW-IN-MIND: REBALANCING DESIGNER-PAINTER ROLES IN UNIFIED MULTIMODAL MODELS BENEFITS
IMAGE EDITING
Ziyun Zengt
David Junhao Zhang'
Wei Li
Mike Zheng Shou
Show Lab, National University of Singapore
TikTook
ABSTRACT
In recent years, integrating multimodal understanding and generation into sin- gle unified model has emerged as a promising paradigm. While this approach achieves strong results in text-to-image (T2I) generation, it still struggles with precise image editing. We attribute this limitation to an imbalanced division translator
responsibilities. The understanding module primarily functions that encodes user instructions into semantic conditions, while the generation mod- ule must simultaneously act as designer and painter, inferring the original layout, identifying the target editing region, and rendering the new content. This imbal- ance counterintuitive because the understanding module is typically trained with several times more data on complex reasoning tasks than the generation module. To address this issue, we introduce Draw-In-Mind (DIM), dataset comprising two complementary subsets: (i) DIM-T21, containing 14M long-context image- text pairs to enhance complex instruction comprehension; and (ii) DIM-Edit, con- sisting of 233K chain-of-
imaginations generated by GPT-4o, serving explicit design blueprints for image edits. We connect a frozen Qwen2.5-VL- 3B (Bai et al. 2025) with trainable SANAI.5-1.6B (Xie al., 2025a) via lightweight two-layer MLP, and train on the proposed DIM dataset, result- ing
Despite its modest parameter scale, DIM-4.6B-Edit achieves SOTA or competitive performance on the ImgEdit and GEdit-Bench benchmarks, outperforming much larger models such as UniWorld-VI (Lin et al., (025) and Step -d-iitt (Liu et al., (025). These findings demonstrate that ex- plicitly assigning the design responsibility to the understanding module provides significant benefits for image editing. Our dataset and models are available at httpss:///qthu.. com showlab/DwI
INTRODUCTION
Over the past few years, considerable effort has been devoted to developing unified models capa- ble of both multimodal understanding and generation. Many such trials, e.g., Show-o (Xie et al.) 2024] and MetaQuery (Pan al. (2025), have achieved impressive results T2I generation, yet this paradigm falters when extended to instruction-guidee guided image editing. Even recent methods such BAGEL (Deng al. 2025), UniWorld-V (Lin et al. 2025). and Step1X-Edit (Liu et al. 2025) struggle, as evidenced by the substantial performance gap with proprietary models like 4P Image (OpenAl 2025) the ImgEdit and GEEiit-Benel benchmarks. While much concurrent search focuses on scaling parameters and data or on architectural modifications, in this paper we identify a novel challenge underlying current image editing models: fundamental imbalance divi- sion of responsibiliries between the understanding and generation modules.
Specifically, we observe that current image editing models often translate user instructions into semantic conditions through a semantic encoder, typically multimodal large language model, yet this process lacks intermediate reasoning or refinement. The resulting conditions are then forwarded to the generation module, which is responsible for completing the editing process. At this stage, the
Corresponding Author.

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Published as a conference paper at ICLR 2026
Source Image
Noise
Concaterate
GUM-4.1V
Oowen2 S-V--88
InternMEs.5-
MLLM
acce ocon Native Tokens
Di
Instruction
Imagenatorn
User
GPI-4c
Blueprint
Edited Image
External Designer
DIM 6 B EDIT
(Training-freet
ImgEdit Score
Overall Parameter
(Max: 5)
StepiX-Edit
7B
12:58
BAGEL
dftieent 25
UniWorld-v1
7B
125
+0.3 20
DIM-4.68-Edit
Trainable Paramettrr
DIM--4.60
w/ different designers
Figure 1: Upper: We employ a lightweight MLP connector to bridge a frozen MLLM, i.e., Qwen2.5- VL.-BB (Bai al., 2025), with a trainable DIT, i.e., SANAI.5-1 (Xie al.
2025a), forming DIM-4.6B-Edit. In the editing process, we first leverage an external designer to produce a textual blueprint in a thain-of-thought style, which then provided DIM-4.6B- to earry out precise image editing. Lower: DIM-4.6B-Edit establishes new state-of-the-art results the challenging ImgEdit benchmark across diverse designers, while requiring 5x fewer parameters than existing frontier models. These results highlight both the effectiveness of the proposed DIM dataset and the generalizability of our approach.
generation module must simultancously infer the original layout, determine the editing region, and render the new content. In this paradigm, the understanding module functions merely as a translator, while the generation module burdened with the demanding tasks of both design and painting. This arrangement contrasts with natural human workflows, where planning and refinement typically precede the act of drawing. A more intuitive strategy is therefore assign design-oriente reasoning to the understanding module while allowing the generation module to focus exclusively painting.
Motivated by this observation, we introduce Draw-In-Mond (DIM), a dataset consisting of two com- plementary subsets: (i) DIM-T2I that contains 14M long-context image-text pairs annotated across 21 dimensions by in-house models to lay the groundwork for complex of-hhuo comprehen- sion; and (ii) DIM-Edit that comprises 233K high-quality chain-of-though imagination generated by GPT-4o from existing image editing data, enabling the model to interpret explicit design plans from external designer. We then establish simple baseline by concatenating a frozen MLLM, ie., Qwen2.5-VL-3B, with a trainable DIT, i.e., SANAI 1,5-1.66,,, via a two-layer MLP and train end-to-end on both public data and the proposed DIM dataset, resulting in DIM-4.6B-T2//EAA During edit inference, we employ an arbitrary external designer, feeding its chain-of-thought imag- ination into the model to guide precise image edits. The framework and performance overview are illustrated in Figure [ Despite its simplicity. DIM-4.6B -.--4. matches or outperforms 5x larger models such as StepTX-Fad benchmark. These results validate the effectiveness of the proposed DIM dataset and confirm the benefit of shifting the design responsibility from the generation module to the understanding module.
(Liu et al.) (025) and UniWorld-Vi VI (Lin et al., (025) on the ImgEdit
To summarize, we make the following contributions in this paper:
We pinpoint a fundamental imbalanced division of responsibilities in current image editing mod- els, which overburdens the generation module with both design and painting tasks.
We introduce Draw-In-Mind (DIM), a unified dataset with two complementary subsets: DIM-T2I and DIM-Edit. This dataset explicitly frees the generation module from design responsibility and enables it to concentrate on painting, leading to substantial improvements in editing performance.
We establish simple baseline by connecting frozen Qwen2.5-VL-3B with trainable SANAI.5-1.6B via two-layer MLP and train on DIM. Despite its modest size and simple architecture, DIM-4.6B- 4.---BB-diit
Edit outperforms 5x larger competitors, validating the efficacy of DIM.
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Published as a conference paper at ICLR 2026
2 RELATED WORK
2.1 EXISTING IMAGE GENERATION DATASETS
T21 Datasets. Existing T2I datasets have provided many high-quality image-text pairs. They can be roughly grouped into three categories: (i) parely Al-generated data, egg, JourneyDB (Sun et al.) 2023) and MidJourney-V [CortexLM 2025). which collect images from the MidJourney API, and HQ-Edit (Fui al. 2024), which generates images via DALL--E 3; (ii) real-world data, e-g-, COCO (in et al.] 2014). which harvests images from Flickr and annotates them by human workers; and (iii) mixed data, e.g. InstructP2P (Brooks et al. 2023). which sources real images from LAION- Aesthetics and produces edited variants via Prompt2Prompp (Hertz et al.) 2022). Although these datasets deliver high perceptual quality, their prompts are typically short, limiting their utility for complex chain-of-thought reasoning in the editing stage. To ensure broad concept coverage, we opt for harvesting real-world images and annotate them with our in-house models from 21 dimensions, yielding 14M long-context image-text pairs, namely DIM-T2I, that form robust foundation for complex CoT-guided editing.
Image Editing Datasets. Most large-scale image editing datasets either employ Al editors for end- to-end modification, e.g., InstructP2P (Brooks et al. 2023) and HQ-Edit (Hui et al. (024). or adopt two-stage pipeline that first localizes the edit region via grounding models and then applies in- painting to alter the target objects, e.g., UltraEdit (Zhao et al., 2024). A few efforts enlist human experts annotate small-scale but high-quality edit pairs, e.g., MagicBrush (Zhang et al. 2023 and BBED--ata-Edit-Paae (Ge et al.) 2024). However, their instructions are typically brief and occa- sionally misaligned with the corresponding image pairs. In contrast, our DIM-Edit comprises 233K deliberately designed chain-of- -ffthhugttt imaginations derived from these existing editing datasets. These rich and detailed CoT instructions act as explicit design blueprints, lighten the cognitive load on the generation module, and significantly improve editing performance.
2.2 UNIFIED MODELS FOR IMAGE GENERATION
T21 Models. In recent years, numerous successful attempts have been made integrate understand- ing and generation modules into a unified architecture. These approaches can be broadly categorized into two technical routes: (i) Integrative approaches, e.g. Show-o (Xie et al. 2024) and Janus (Wul et al. 2025a), which typically adopt an autoregressive generation paradigm to produce both image and text tokens; and (ii) Connector-
approaches. e.g. MetaQuery (Pan et al., 2025). which use a connector to bridge an understanding module and a generation module. Since the understanding and generative capabilities are tightly coupled in the former architecture and sometimes lead to con- flicts that degrade both, we adopt the connector-base based design to preserve state-of-the-an cognitive ability by freezing the understanding module while enhancing generation performance.
Image Editing Models. When it comes to image editing, the challenge becomes significantly harder, as neither the latest integrative models (Lin et al., (025) nor connector-based ones (Liu et al.) mainstream benchmarks such ImgEdit and GEdit- 2025) achieve satisfactory performance GPT-44-Image, even when employing large-scale under- Bench compared to proprictary models like standing and generation models such as Qwwn2.55VL-77 (Bai al. 2025) and FLUX.I-dev (Labs) 2024a). This suggests that simply scaling model size is not an effective strategy for improving im- age editing capability. In this work, take different approach by addressing the problem from a perspective of imbalanced division of responsibilities, We propose DIM-4.6B-Edi,, which lever- ages an external designer create blueprints in CoT manner in the textual space before editing. Despite having only 1.6B generative parameters, our model achieves SOTA editing performance, highlighting the effectiveness of shifting the design responsibility to the understanding module.
3
METHODOL.OGY
3.1 THE PRAW-IN-MIND (DIM) DATASET
3.1.1
DIM-T2I
There are typically two strategies to train an editing model, i.e., (i) learning drawing first (T2I), fol- lowed by adaptation for editing, and (ii) directly learning editing. We observe that the vast majority of image editing models are built upon established T2I foundations (Brooks et al. 2023 Zhao et al.)
2024 Liu al. 2025). This aligns with the first strategy and represents robust technical route
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Published as a conference paper at ICLR 2026
that benefits from curriculum learning. Consequently, we elected to achieve basic T21 ability and subsequently tine-tune the base model for the more challenging editing task.
However, we observed that despite the current T21 datasets performing well in terms of prompt- image aligmment and image perceptual quality, the prompts in existing datasets are typically short and simple, shown in [ While these prompts accurately capture the semantics of the target image, they fall short in fostering long-context comprehension, which is an essential foundation for complex CoT-guided image editing. To bridge this gap, collect 14M images with resolutions higher than 512 x 512 from the web. We believe that the aspects emphasized in widely recognized understanding datasets and benchmarks effectively capture the most frequent interactions between humans and objects in the real world. Therefore, we conduct a thorough literature review and an empirical analysis of existing understanding datasets and benchmarks, and finally derive 21 diverse dimensions and use internal models to generate long and detailed annotations, thoroughly covering all dimensions, resulting in DIM-T2I. As shown in Table [ its average prompt length is at least twice that of existing corpora, effectively establishing strong basis for complex CoT-guided image editing. The dimension-specifie prompts and referred datasets/benchmarks are listed in Appendix
3.1.2 DIM-EDIT
As for image editing, the short- prompt issue is even more pro- nounced in current datasets. As shown in Table | prompts in main- stream datasets are generally overly simplistic, often consisting of only a few descriptive words. Such data is not conducive to effective image editing learning, as the prompts may fail to accurately reflect the actual changes between the source and tar- get images. This phenomenon can be attributed to two main reasons: (i) Inaccurate Al editing or human misoperation. We observe that even SOTA proprictary models like GPT- 4o-Image frequently over-edit images, e.g., removing objects not mentioned in the prompts. Such cases exist widely in Al-generated datasets like ShareGPT-4o-Image and UltraEdit. While in human- controlled datasets, operators may misunderstand or misapply the edits, resulting in unaligned data. (ii) Ambiguous semantics. Even if the prompt correctly describes the intended change, overly sim- ple prompts can still result in multiple equally valid edits. For example, in SEED-Data-Ede Partt,, a common prompt is "change the background", yet the definition of "background" varies across im- ages, while in practice the change almost always occurs in the sky, thereby reducing the effectiveness of the resulting edit data.
Table 1: The statistics of existing high-quality datasets and our proposed DIM dataset. APL is short for Average Prompt Length, counted by word numbers.
Dataset Name
Source
APL
Text-to-Imog
Midlournyy V6 (Cortexl M. 2025
1.2M
Gen.
9.59
COCO (Lin et al 2014
0.4M
Real
10.46
InstructP2P Brook clll 2025
0.6M
Real & Al Gen.
11.37
JouneyDB (un F 2014
4.2M
Al Gen.
29.27
HQ Edit (Hii et
0.2M
Al Gen.
38.08
Dimba Fa ct a1. 2024 DIM
0.3M
Real
78.29
Real
146.76
Image Editing
MagicBrush Zhang et al. 2023
8K
Real
6.50
SEED Data Ean Part ala 2024
82K
Real
7.39
UtraEdit (Zhao et al 2024
4M
AT Gen.
8.32
ShorGGP1+0-migg Chen et al. 2025b
46K
Al Gen. 34.75
FdM.
233K
Real & Al Gen.
252.64
In addition, existing models typically use the understanding module merely translator, directly converting natural language instructions into semantic conditions. The generator must then rely on these conditions to simultaneously organize the layout of the edited image, recognize existing objects, localize the edit area, render new content, and preserve unchanged regions. In other words, the generator is forced to act both designer and painter, which is a challenging and counterintuitive setup. By contrast, humans naturally prepare mental blueprint before editing and then simply let their hands follow complete the changes.
Motivated by the above issues, we propose DIM-Edit, which first optimizes prompts and then imi- tates human thinking to complete the edits. The DIM-Edit creation pipeline illustrated in Figure 2 We construct it from 233K high-quality image pairs collected from three sources: (i) 160K highly consistent edit pairs from UltraEdit, referred to as UltraEdit- 160K-CoT, selected using a joint SSIM, DINOv2 similarity, and CLIP similarity- filtering; (ii) 46K semantically rich samples from the editing subset of ShareGPT-4o Image, referred to as ShareGPT-4o Imaoo- CoT; and (iii) 8K human- edited images from the MagicBrush training set and 19K numan-edited images from SEED-Dos Edit-Part3, specifically targeting remove operations, referred to as HumanEdit-CC CoT. A detailed data collection pipeline can be found in Appendix C
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Published as a conference paper at ICLR 2026
Source Edit Pair
Quality Judge
Prompt Optimization
Source
Torget
Row Prompe
Audgo
Action Optimized Prompe
Refine?
mork embiee
the ond book
Dosard
Target
pccus the bodigno und
Remove the eccden barrer runningg rg across badiground
Partial
vidiides bandeer
normingg POTTEN
CoT Imagination
ginn lell cowiiiind park holdde brr singt tall acoee the mape with condoo
Pecocttos The trags
letthe bear be
deliingt Alacing dhin mut fir ameud seddnga assedd
Yorr ter read
standes dndeeche but
batind will renoove,
DIM-Edit Creation Pipeline
Akkul enoee bull bel sajo Rulus tra siiit an and in yay thdigpenn cwn iraeddy
wooden
The creation pipeline of DIM-Edit begins with a quality assessment of existing image editing data, followed by prompt optimization using GPT-4o. Finally, the optimized prompts to- gether with the corresponding image pairs are fed into GPT-4o, which generates four-step chain- of-thought imagination in the textual space.
Figure 2:
After collecting raw data, we first sent the raw edit pairs to GPT-4o for prompt quality evaluation, as shown in Figure 2 The results are categorized into three groups: (i) Misaligned. The prompt does not reflect the actual edit at all, possibly due to misannotation or misoperation. (ii) Partially aligned. The target image exhibits over-editing, i.e., redundant objects are added to removed from the source image. (iii) Aligned. The prompt fully corresponds to the edits.
Next, take different actions to optimize the prompts based on the judgment: (i) For misaligned prompts, they are discarded outright. (ii) For partially aligned prompts, we ask GPT-4o to add details about unmentioned changes, e.g., including objects that were incorrectly removed in the prompt. (iii) For aligned prompts, we instruct GPT-4o to remove ambiguity and refine the prompt, for example, by specifying the exact objects to be edited to avoid confusion with visually similar objects.
Finally, we provide the optimized prompts, along with the source image to GPT-4o and instruct it produce a four-step CoT imagination that emulates human editing behavior. For the sake of accuracy, we also provide the target image to it for reference. The target of each CoT step is as follows: (i) Global Layout Perception: identify and describe all key objects and their positions in the source image. (ii) Local Object Perception: describe the appearance of each object or background element in the source image, including shape, color, texture, and state. (iii) Edit Area Localization: specifyy which objects or regions will be modified, based on the refined instruction. (iv) Edited Image Imagination: describe the expected appearance of the edited image, with an emphasis on the modified areas. As shown in Table I and Figure the resulting CoT imagination is not only ultra- long but also highly accurate, effectively removing the design responsibility from the generation module and thereby significantly enhancing the efficiency of image editing learning. A quality assessment of the CoT's involving both MI.LMs and human verification can be found in Appendix
3.2 DIM-4.6B-T22/EDDT
Leveraging MLLMs to provide multimodal conditions for image generation has become common practice recently. In this work, we first build a base T2I model and then adapt it to the editing task.
For the base T2I model, we start by establishing simple baseline, similar to MetaQuery (Pan al. 2025), to preserve state-of-the-att understanding capability. We select Qwen2.5-VL-33 (Bai et al. 2025) as the MILMM and SANA 1.561.BB666 (Xie et al. 2025a) as the diffusion decoder for their mod- est size. Unlike MetaQuery, which employs a large 24-lr transformer with 1.6B parameters as a connector between the MLLM and the diffusion decoder, we adopt a much simpler design, i.e. two-layer MLP, to directly project multimodal tokens into the generation space. We refer to this model as DIM-4.6B -TIII, illustrated in Figure T We train DIM-4.6B-TT --IIII on a mixture of the pro- posed DIM-T2I dataset and additional 6.9M image-text pairs from MidJourney-V V6 (Cortex
2025), COCO (Lin et al. 2014), InstructP2P (Brooks et al. 2023). JourneyDB (Sun et al.) 2023),
HQ-Edit (Hui et al. 2024). and Dimba (Fei et al. 2024). During training, Qwen2.5-VI.-3B remains
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Published as a conference paper at ICLR 2026
Table The text-to-image generation performance on GenEval and MJHQ-30K. and indicate that higher and lower values are better, respectively; i denotes using an LLM rewriter, * and denote frozen and trainable parameters, respectively.
GenEvall
MJHQ-30K
Model
Params
Single Ohj. Two Ohj. Counting Colors Position Color Attr. Overall
FID
Gen. Only
PixArt-n Chen et al 2023
0.6B
0.98
0.50
0.44
0.80
0.08
0.07
0.48
6.14
SDXL PPdell et al. 2022
2.6B
0,98
0.74
0.39
0.85
0.15
0.23
0.55
8.76
Baaker
2023
0.96
0.67
SDS-Medium Esser ot
2.0B
0.99
0.94
0.72
0.89
0.33
0.60
0.74
11.92
Unified
Janus etu al
2025a
1.3B
0.97
0.68
0.30
01.4
0.46
0.42
0.61
10.10
Ema3-Gen Waan et
2024h
8.0B
0.99
0.81
0.42
0.80
0.49
0.45
0.66
Show-o Xie ct al. 2024
1.3B 6
0,98
0.80
0.66
0.84
0.31
0.50
0.68
15.18
Show-o2-7B
Xie
20256
7,0B
1,00
0.87
0.58
0.92
0.52
0.62
0,76
Manus-Pro-71
Chen et
2025c
7.0B 6
0,99
0.89
0.59
0.90
0.79
0.66
0.80
13.48
BAGEL Deng et
2025
14.0B
0.99
0.94
0.81
0.88
0.64
0.82
MetaQuery-Li PPn al.
2025
3.0B
|3.2BB
[3.2B
0.78
6.35
DIM-1668-T1
3.0B
0.99
0.77
frozen, and we finetune only the parameters of the connector and 1.-NA1.5-1.66 Notably, distilla- tion datasets like BLIP3-o-60K (Chen et al., (222a)) explicitly curate data to align with the structural patterns of benchmarks like GenEval, we exclude them avoid any risk of data leakage (Wu al.) 2025b) or benchmark hacking in the evaluation to justify the contribution of our DIM data. We adopt vanilla flow matching as the sole objective, avoiding parameter-tuning tricks highlight data effectiveness and maintain simplicity.
Thanks to the rich world knowledge and DIM-4.6B-T21 model provides strong foundation for complex CoT comprehension. We then adopt a two-stage training strategy to adapt it for the editing task. In the first stage, we initialize the editing model from DIM-4.6B-T2 -BI and fine-tune it on the UltraEdit (Zhao al. (204) dataset to develop basic editing capability. Following InstructP2P (Brooks et al.] (203)). we concatenate the source image with noise along the channel dimension, illustrated in Figure In the second stage, fine-tune the stage-one model exclusively on the proposed DIM-Edit dataset, resulting in DIM-4.6B- Edit. During inference, we employ external designer to prepare a blueprint in the same format as DIM-Edit, except without access to the target image, ensuring alignment with real usage scenarios.
long-contexs prompts in DIM-T2I, the trained
EXPERIMENTS
4.1 EXPERIMENTAL SETUP
During training, we use AdamW as the optimizer and keep most hyperparameters unchanged for simplicity. For DIM-4.6B-T21, we first warm up by training only the connector for one epoch with learning rate of 2 x 10-5, then jointly train the connector and BANAI.5-1.68 for eight epochs with the same rate and a batch size of 256. For DIM--.6B-Edit we the batch size to 32, training on UltraEdit for 10 epochs at 1 10-4 learning rate, then finetuning DIM-Edit for 50 epochs at 1 x 10-5 learning rate. During inference, GPT-4o serves the designer unless otherwise specified.
Although the primary focus of this paper is image editing, we evaluate DIM-4.6B. -B-T2I on T2I bench- marks verify the effectiveness of DIM-T2I. We report the GenEval (Ghosh al. 2023) scores and MJHQ-30K (Li et al.) 2024b) FID. Following MetaQuery (Pan et al.) 2025) and Emu3 (Pan jet al. 2025), we test L.e--tewitteen prompts for GenEval evaluation. For image editing, we report scores on the recently proposed ImgEdit (Lin et al., (025) and
EN (Liu et al., 2025) benchmarks, using GPT-4.1 for evaluation to ensure fair comparison with existing results. We also report results MagicBrush (Zhang et al. 2023) show the performance automated metrics.
4.2
MAIN RESULTS
4.2.1
TEXT-TO-IMAGE GENERATION
We first report T2I performance GenEval and MJHQ-30K in Table Our DIM-4.6B-T2) adopts a simple architecture with very few trainable parameters yet achieves SOTA or competitive per- formance, demonstrating the high data quality of DIM-T2I, For semantic alignment, DIM-4.6B-

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Published as a conference paper at ICLR 2026
Table 3: The image editing performance on ImgEdit. We use GPT-4.1 for evaluation ensure consistency with the existing results reported in UniWorld-VI. * indicates results evaluated by us using the official weights; * and denote frozen and trainable parameters, respectively.
Model
Params
Add Adjust Extract Replace Remove Background Style Hybrid Action Overall
MagicBrush Zhang et
2023
0.9B 6
2.84
1.58
1.51
1.97
1.58
1.75
2.38
1.62
1.22
1.83
Instruct- P2P [Brooks
2023
0.9B
2.45
1.83
1.44
2.01
1.50
1.44
3.55
1.46
AnyEdit Yu et 2025
1.3B
3.18
2.95
1.88
2.47
2.23
2.24
2.85
1.56
2.65
2.45
UltraFdit Zhao et al 2024
2.0B
3.44
2.81
2.13
2.96
1.45
2.83
3.76
1.91
2.98
2,70
Step1X-Edit (Liu et al 2025
7.0B |12.5B
3.88
3.14
1.76
3:40
2.41
3.16
4.63
2.64
2.52
3.06
BAGEL Deng et al_ 2025
14.08 &
3.56
3.31
1,70
3.30
2.62
3.24
4.49
2.38
4.17
3,20
UniWorld-VI Lin al
2025
7,0B 7.00* 11.0BB
3.82
3.64
2.27
3,47
3,24
2.99
4.21
2.96
2.74
3.26
Janus-4o" et al.
2025b
7.0B 6
3.35
3.35
2.25
3.01
2.18
3.32
4.71
2.49
4.04
3.19
GPP--4--maage (OpenXT]
2025
4.61
4.33
2,90
4.35
3.66
4.57
4.93
3.96
4.89
4.20
DIM-1.6B-Edi
3.0B 1 6
4.09
3.47
2.30
4,00
3.43
3.87
4.92
2.85
4.08
3.67
Table 4: The overall wase performance on GEdit-Bench-EN Full set. iindicates results evalu- ated by us. Task abbreviations: Background Change (BC), Color Alter (CA), Material Alter (MA), Motion Change (MC), PS Human (PH), Style Change (SC), Subject-Add (SA), Subject-Remove
(SRM), Subject-Replace (SRP), Text Change (TC). Tone Transfer (TT), and Average (AVG).
Model
BC
CA
MA
MC
PH
SC
SA
SRM
SRP
TC
TT
AVG
AVG w/o TC
UniWorld-VI (Lin et al. 2025
4.92
6,37
4.79
1.85
4.03
5.64
7.23
6.17
5.70
1.15
5,54
4.85
5.22
Janus-4o* (Chen ct al.
2025b
4.31
5.02
4.41
2.71
4.09
5.80
4.07
1.69
3.69
2.35
3.96
3.83
3,97
StepIX-Edit Liu et al DIM-4.6--Edh
2025
7.03
6.26
6.46
3.66
5.23
7.24
7.17
6.42
7.39
7.40
6.62
6.44
6.35
7.02
6.81
6.00
4.67
5.88
7.16
7.48
6.67
6.76
2.99
6.55
6.18
6.50
T2I shows only small gap compared to much larger models like BAGEL (Deng al.] 2025) on
GenEval. Compared with MetaQuery (Pan et al.] (2025), which employs large L6B--praameter con- nector for query learning, our model achieves nearly the same performance using only two-layer MLP connector and naive multimodal tokens. In addition. it attains optimal perceptual quality, as evidenced by the lowest FID on the dicate that even without complex aesthetic filtering, carefully crafted long-context prompls enable robust text-to-image generation, offering a practical approach for rapid large-scale dataset creation by directly harvesting images from the web.
MJHQ-30K benchmark. These results in-
4.2.2 IMAGE EDITING
The image editing performance on ImgEdit is reported Table 3 Our DIM-4.6B-Edi shows a significant improvement over previously available open source models. In comparison with other connector-based architectures such as StepIX-Eal IX-Edit and UniWorld-VL. which rely on a 12B FLUX backend for generation together with a 7B multimodal large language model for condition transla- tion, DIM-4.6B- achieves superior results while maintaining both a much smaller total parameter count and very limited number of trainable parameters.
Since DIM-Edit includes high-quality images from ShareGPT-4o-Image (Chen et al. 2025b), we also evaluate Janus-4o, which is trained the same dataset, for reference. Janus-4o achieves only suboptimal results. indicating that the improvement comes from DIM-Edit itself, whose natural and precise edit blueprints substantially enhance editing performance. These encouraging results val- idate our assumption that imbalanced division of responsibilities degrades image editing, confirm the soundness of our data creation pipeline, and highlight the effectiveness of the Draw-In-Mint paradigm: assigning the design responsibility to the understanding module while allowing the gen- eration module to focus on actual editing exclusively.
We further demonstrate the capability of DIM-4.6B-Fad through intuitive visual comparisons of editing results on four Al-generated out-of-domain images in Figure 3 Janus-4o exhibits severe distortions despite being trained on GPT-4o-gon -e--er-geeeetted edit pairs, while Step1X-Ech produces less natural edits (rows 2-4) and fails in complex scenarios such as row 1, which involves manipulating multiple objects. In contrast, DIM-4.6B-EAA successfully follows the instructions produce natural and consistent edited images. Please refer to Appendix A for more visualizations.
We also include overall task- wise performance on GEdit- Betth-Bnnch-N in Table The results reveal similar pattern as reported in UniWorld-Vi V1 (Lin et al. 2025): StepIX-Edi Edit achieves notable gains in the Text Change task, whereas other models, including ours, perform less effectively due to the
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Published as a conference paper at ICLR 2026
Prompt
Romove the lemons the sable.
betch.
Change bacoground
sunglasses
Figure 3: Green and Blue the edits of Janus-4o and Step1 X-Edit; Red the edits of our models trained different data corpora. All variants tuned from the base checkpoint & in Table g
Table 5:
The MagicBrush test set performance. Metrics are calculated between human-edited
groundtruth and Al-generated edits.
and
denote the 1st and 2nd best model, respectively.
Method
Gen Params
L14
CLIP-I1
DINO
InstructP2P Brooks ct al.
2023
0.9B
0.114
0.851
0.744
MagicBrus (Zhang et al. 2023
0.9B
0.074
0.908
0.847
UltraEdit (Zhao
2024
2.0B
0.066
0.904
0.852
FluxEdit [Paul] 2025
12.0B
0.114
0.779
0.663
FLUX.1 Fill (Labs 2024b
12.0B
0.192
0.795
0.669
RF-Solver Edit [Wang ci al.
2024a
12.0B
0.112
0.766
0.675
ACE+ MMa et al. 2025
12.0B
0.195
0.741
0.591
ICEdi (Zhang al. 2025
12.0B
0.060
0.928
0.853
DIM-4.6B-Edit
1.6B
0.065
0.928
0.882
absence of such data in DIM-Edit. Excluding the Text Change task, DIM-4.6B-EdO beats Step1X- XX- Edit while maintaining a compact size, underscoring the high efficacy our CoT data. Please refer Appendix A for full GFddit-Bench-NN results.
We further conduct evaluation on the MagicBrush to test automated pixel-to-pixel metrics computed between human-edits and Al-edits. The results presented in Table DIM-4.6B- achieves SOTA performance. Notably, ICEdit employs 12B FLUX. 1 Fill backbone, with MagicBrush sam- ples constituting approximately 20% of its total training set. contrast, DIM-4.6 4.....6BB... 6Bit utilizes compact 1.6B generation backbone, where MagicBrush data accounts for less than 3% of our DIM- Edit dataset. These comparable results validate the effectiveness of the Draw-In-Man the generalizability of our DIM-Edit COT. Despite our training distribution being significantly less driven by MagicBrush data, our model matches the performance of 5x larger competitors.
paradigm and
4.3 ABLATION STUDY
Generalizability to External Designers. Although our proposed DIM-Edit is annotated with GPT- 4o, we show that the resulting DIM-4.6B-Edi compatible with various external designers, as reported in Table 6 In the first row, remove the designer and directly use the raw prompt from ImgEdit. Even under this setting, D1M-4.6B-BB achieves performance comparable to frontier models such BAGEL, demonstrating that high-quality CoT annotations help strengthen basic editing by mitigating prompt-edit misalignment. We then replace GPT-4o with four mainstream MLLMs as externl designers, i.e., Qwen2.5-VI--7B (Bai et al. 2025 MiMo-VL--7 (Xiaomi
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Published as a conference paper at ICLR 2026
Table 6: The ImgEdit performance w.E. different external designers.
External Designer
Add
Adjust
Extruct
Replace
Remove
Background
Style
Hybrid
Action
Overall
3.53
3.23
2.01
3.49
1.47
3.42
4.79
2.35
3.64
3.10
Qwen2.5-Vi Wwe-2.5-VL-7B Bai ct al. 2025
3.95
3.35
2.25
3.85
3.31
3.57
4,88
2.81
4.02
3.55
MiMo-VL- Xtaomi 2025
3,95
3.32
2.20
2,46
3.82
2.52
3,93
3.43
Intem 815 Wang et 2025
3,98
3.40
2.05
4.14
3.30
3.84
4.94
2.77
3.89
3.59
GLM-4.1V-98 Hong et 2025
3,95
3.27
2.23
3,90
2.64
3.81
4.92
2.23
4.02
3.44
GPT-4o (urst ct 2024
4.09
3,47
2.30
4,00
3,43
3.87
4.92
2.85
4.08
3,67
Table 7: The ImgEdit performance w.t.. the internal Qwen2.5- --LLBBBBBB designer.
Internal Designer
Add
Adjust
Extract
Replace
Remove
Background
Style
Hybrid
Action
Overall
3.53
3.23
2.01
3.49
1.47
3.42
4.79
2.35
3.64
3.10
Qwen2.5-VL-33 *
3.80
3.24
2.03
3.89
3.21
3.52
4.92
2.71
4.05
3.49
Qwen2.5-VD VL-3BB
3,96
3.36
2.25
3.98
3,31
3,81
4.95
2.83
4.02
3.61
GPT-4o
4.09
3.47
2.30
4.00
3,43
3,87
4.92
2.85
4.08
3.67
2025 Intern VL.3.5-BB (Bai et 2025), and GLM-4.1 (Hong et al. 2025
All of them deliver strong results compared to previous state-of-the-an models Table highlighting the robustness of DIM-4.6B-EA and the generalizability of our DIM framework. Furthermore, models equipped with external designers significantly outperform the raw raw-prompt setting, confirming that CoT imagination effectively reduces the burden the generation modules and enhances overall editing quality.
Integrated End-to-End Evaluation. To exclude potential influence from external designers, we es- tablish "self-play" configuration. In this setup, CoT embeddings generated by the internal MLLM (Qwen2.5-VL-3) VL-3B) are directly fed into the painter to exccute edits, effectively eliminating the need for the external inference round. The result (Table 2nd row) shows that this "self-play" model achieves SOTA performance, validating the effectiveness of the Draw-In-Mind paradigm and the high quality of the DIM-Edit data. We further investigate whether the CoT blueprints DIM-Edit can serve as a corpus to bridge the gap between open-source and closed-source designers. To this end, we perform lightweight fine-tuning on Qwen2.5-VI.
and subsequently feed its blueprints into DIM-4.6B Edit. The results (Table / 3rd row) demonstrate that fine-tuning from DIM-Edin CoTs can effectively mitigate the performance disparity with the proprietary models like GPT-4o.
Data Composition. In Table g we present rigorous data composition analysis for the editing task to identify the sources of performance improvements. In the first stage, we observe that train- ing solely on ShareGPT-44oImman
already yields satisfactory ImgEdit score, indicating strong semantic alignment, which consistent with the behavior Janus-4o. However, models trained ex- clusively GPT-4o-generated data tend to alter the overall layout noticeably, which is undesirable. In contrast, training on UltraEdit produces slightly lower scores but preserves better consistency between the source and target images. When combining the two datasets, performance improves significantly, as the model benefits from the semantic richness while retaining the edit consistency.
In the second stage, finetune the checkpoint trained solely UltraEdit. The effectiveness of our CoT data is demonstrated by comparing row 4 with row in Table where using the CoT version of ShareGPT-4o-Imaae yields a significant improvement in overall scores compared with its non- CoT counterpart. We also observe that using UltraEdit- MtrraEdit-11OK-Cot alone provides only marginal gains, while the HumanEdit-CoT portion has a more notable impact due its high edit quality, though still less pronounced than the semantically rich all three CoT components, i.e., the proposed DIM-Edit, performance improves substantially once again, indicating that MirraEdii-160K-CC and HumanFdit-Coo are crucial for maintaining edit consistency, which consistent with the pattern of row
CoT. When combining
The visualization of three variants finetuned from the base checkpoint @ in Table X is shown in Fig- ure 3 for intuitive analysis. The variant tuned on ShareGPT-4o-Image significantly alters the layout despite following the edit prompt, while its counterpart tuned on ChareGPTT-4o-magge preeserre more details, indicating that CoT imagination helps maintain editing consistency. However, using ShareGPT-4o. CeTT-----mage--CC image-CoT alone still produces unstable edits. In contrast, the model tuned on the full DIM-Edit dataset, i.e., DIM-4.6B-EBB Ed-tt,, achieves the best results in both semantic alignment and edit consistency, demonstrating the effectiveness of all three data components in DIM-Edit.
preserves

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Published as a conference paper at ICLR 2026
Table Impact of data compositions during the two training stages of DIM-4.6B-Edi on ImgEdit. Stage 2 models are tuned from checkpoint W.
Data Composition
Add Adjust Extract Replace Remove Background Style Hybrid Action Overall
Stagel Non-CoT Data
ShareGPT-44-Immn UIr &
3.35
2.74
1.93
3.05
1.95
3.16
4.91
2.00
3.70
2.98
3.41
3.03
1.91
2.94
1.07
3,09
3.77
2.64
2.97
2.76
ShareGPT-4o-Imaae
3.85
3.09
1.84
3.71
2.26
3.51
4.88
2.17
3.67
3.22
Stage2 CoT Data
ShareGPT-4o- -ew.GG--------a------
4.01
3.19
2.19
3.74
2.53
3.57
4.93
2.25
3.66
3.34
UUtraddit-160K
3.69
3.21
1.90
2.50
1.22
3.20
3.53
2.71
3.14
2.79
+ HumanEdit-CoT 3.63
2.99
2.01
3.01
2.64
3.11
3.73
3.03
3.01
3.02
+DM-EEdit
4.09
3.47
2.30
4.00
3.43
3.87
4.92
2.85
4.08
3.67
Table 9: Impact of CoT compositions on ImgEdit. GLP refers to Global Layout Perception, LOP Local Object Perception, EAL to Edit Area Localization, and EII Edited Image Imagination.
COT Compusition Add
Adjust
Extract
Replace
Remove
Background
Style
Hybrid
Action
Overall
DIM-Edit
4.09
3.47
2.30
4.00
3,43
3.87
4.92
2.85
4.08
3.67
w/o GLP
3.85
3.29
2.06
3.91
3.24
3.55
4.80
2.79
3.92
3.49
w/o LOP
3.80
3.15
1.92
3.83
3.07
3.60
4.79
2.44
3.92
3.39
EAL
3.79
3.25
1.96
3.73
2.96
3.65
2.82
3.82
3.42
w/o EII
3.77
3.22
1.82
3.88
2.96
3.61
4.78
2.55
3.58
3.35
CoT Composition. We also analyze the effect of each CoT component by individually removing it, as shown in Table 8 All components contribute positively to the performance, though their impor- tance varies. The GLP has only minor impact. likely because is an easy task for the generator. In contrast, the other three CoT components, i.e., LOP, EAL., and EII, have significant effect. LOP and EAL. require the model to focus on specific regions, while Ell demands complex reasoning; none of these trivial for the generator. These findings further validate the Draw-In-Min paradigm, which reduces the cognitive burden on the generator and thereby improves performance.
Table 10: The ImgEdit performance of models initialized from scratch/DIM-4.6B
Initialization Add
Adjust
Extract
Replace
Remove Background Style
Hybrid
Action
Overall
Scratch
2.70
2.56
1.93
2.23
2.47
2.82
4.68
2.38
2.15
2.66
DIM-4.6B-T21
4.09
3.47
2.30
4.00
3.43
3.87
4.92
2.85
4.08
3.67
Necessity of DIM-T2I, Our CoT-guided editing requires robust comprehension capabilities. We posit that T2I generation simpler than editing and is better suited for fostering this capability. Rather than simultaneously tackling two challenging objectives, i.e., complex instruction compre- hension and image editing, we chose to establish strong instruction comprehension first in the T21 stage. To justify our assumption, we trained model exclusively on DIM-Edit to test the feasibility of simultaneously achieving modality alignment, complex instruction comprehension, and editing capabilities in a single stage. As evident from the Table 10 the model trained from scratch signifi- cantly underperforms the version initialized with DIM-4.6B-T22 This performance gap empirically validates the necessity of DIM-T2I foundational cornerstone for the Draw-In- In-Mind paradigm.
CONCLUSION
In this paper, we identify crucial issue in existing image editing models, i.e., imbalanced division of responsibilities, where the generator burdened with complex reasoning, leading to reduced performance. To address this, we propose the Draw-In-Mind (DIM) dataset, consisting of two parts: (i) DIM-T2I, 14M web-crawled image-text pairs with carefully crafted long-context prompts that provide a foundation for complex CoT comprehension in editing; and (fi) DIM-Edit, 233K high- quality image editing pairs with detailed and precise CoT imagination. By training on the DIM dataset and incorporating
external designer during editing, we present DIM-4.6B-Edi, which achieves SOTA or competitive performance on ImgEdit and tiny overall and trainable parameter size. These results validate our motivation to shift the design responsibility from the generation module to the understanding module, as well as the high efficiency of our proposed CoT-guided DIM dataset.
while maintaining a
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Published as a conference paper at ICLR 2026
ETHICS STATEMENT
All authors of this paper strictly adhere to the ICLR Code of Ethics. The proposed image-text pairs in DIM-T2I have undergone a rigorous safety check to filter harmful content, e.g, NSFW images. The image pairs in DIM-Edit are collected from publicly available datasets, i.e., UltraEdit (Zhao et al., 2024). MagicBrush (Zhag et al. 2023). SEED Data- Edit-Part3 (Ge et al. 2024), and ShareGPT- 4o- Mo-Image (Chen et al. 2025b), without introducing new content that may raise ethical concern.. The CoT's generated by GPT-4o were subjected to both OpenAl's internal safety mechanisms and additional safety check by the authors, confirming that no harmful content is present. Therefore, the training process and the trained models do not introduce ethical issues.
REPRODUCIBILITY STATEMENT
The authors take full responsibility for the reproducibility of this work. For the proposed DIM dataset, we provide a detailed data creation pipeline in Section 3.1 Appendix C including data sourcing and processing. The prompts used for image annotation are presented in Appendix For the DIM-4.6B-T2//EAE
models, describe their architectures in detail in Section 3.2 In addition, we specify our training configurations and evaluation setup in Section 4.1 To facilitate reproducibility, we have released the DIM dataset, the DIM-4.6B-T2//Edl models, and the related code to the public at httpp::: //github///tbbb../ com/ wwwwwah/wwaaacc
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A ADDITIONAL EXPERIMENTS
Table 11: The overall image editing performance GEdit-Bench-EN. We GPT-4.1 for eval- uation to ensure consistency with the existing results reported in StepIX-Edit. * indicates results evaluated by us. SC and PQ denote Semantic Consistency and Perceptual Quality, respectively.
Intersection subset
Full
Model
SC
PQ
Overall
SC
PQ
Overall
Proprictary Models
Gemini (Comanici al.
2025
6.82
7.41
6.87
7.44
6.51
GPT-4o OpenAL
7.40
7.90
7.22
7.89
6.98
Doubao Gong 2025
7.87
8.10
7.59
7.74
8.13
7.49
Open-Source Source Models
Instruct-P2P (Brooks et al.
2023
3.34
6.21
3.23
3.30
6.19
3.22
MagicBrush Zhang ct al 2025
4.56
6.34
4.24
4.52
6.37
4.19
AnyEdit (Yu et al. 2025
3.12
5.87
2.92
3.05
5.88
2.85
OmniGen (X1a el al. 2025
6.04
5,86
5.15
5.88
5,87
5.01
UniWorld VI (Lin al.
2025
4.93
7.43
4.85
Janus-4o* (Chen ef al.. 2025h
4.69
4.68
3.91
4.64
4.57
3.83
StepIX-FAn Edit (Liu et al.
2025
7.29
6.96
6.62
7.13
7.00
6.44
DIM-4.6B-EL
6.91
6.90
6.46
6.65
6.71
6.18
Table 12: The detailed task-wise performance on GEdit-Bench-EN Full indiicates results eval-
uated by us. Task abbreviations: Background Change (BC), Color Alter (CA), Material Alter (MA), Motion Change (MC), PS Human (PH), Style Change (SC), Subject-Add (SA), Subject-Remove
(SRM), Subject-Replace (SRP), Text Change (TC), Tone Transfer (TT), and Average (AVG).
Model
BC CA
MA
MC
PH
SC
SA SRM SRP
TC
TT
AVG AVG w/o TC
Semantic Consistency
UniWorld-VI
5.17
7.21
4.71
1.14
3.49
5.98
7.42
6.50
6.04
1.07
5.52
4.93
5.32
Janus-4o
5.48
6.68
6.00
2.75
4.04
8.03
5.10
1.74
4.27
2.11
4.88
4.64
4.90
Step ReplX-Edii Edit
8.40
7.68
7.95
3.40
5.06
8.13
7.92
6.88
8.27
7.72
7.05
7.13
7.07
DIM-4.6B-Ed
7.68
7.65
7.48
4.78
5.64
8.22
8.10
7.05
7.45
2.34
6.73
6.65
7.08
Perceptual Quality
WniWord-
7.59
6.82
6.86
8.68
8.61
6.58
7.61
7.28
6.78
7.44
7.48
7.43
7.43
J
4.00
4.20
4.08
5.73
6.07
4.40
4.77
4.07
4.72
4.44
3.78
4.57
4.58
X-Eiix
6.40
6.10
5.60
7.63
8.31
6.75
7.27
7.49
6.85
7.86
6.73
7.00
6.91
DIM-4.6B-Fad
6.73
6.55
5.13
7.15
7.43
6.53
7.28
6.83
6.65
6.61
6.88
6.71
6.71
Overall
Uni Un
4.92
6.37
4.79
1.85
4.03
5.64
7.23
6.17
5.70
1.15
5.54
4.85
5.22
J
4.31
5.02
4.41
2.71
4.09
5,80
4.07
1.69
3.69
2.35
3.96
3.83
3.97
Step1X-Edt
7.03
6.26
6.46
3.66
5.23
7.24
7.17
6.42
7.39
7.40
6.62
6.44
6.35
DIM-4.6B-E00
7.02
6.81
6.00
4.67
5.88
7.16
7.48
6.67
6.76
2.99
6.55
6.18
6.50
Table 13: The generation configuration and inference speed of SteplX-Edit and DIM-4.6B-Edi.
Model
Gen. Resolution
Gen. Steps
Und. Params
Gen. Params
VAE
Prompt
Speed
Step1X-Edit 1024x 1024
7B
12.5B
8x
Raw 28.19s
30
DIM-4.6B-Edh
3B
1.6B
32x
CoT
6.23s
Detailed Performance on GEdit-Bench-EN. Table II and I overall and detailed task-
wise performance of different models on GEdit-Bench- just behind the in-domain tester, i.e., Mtep1X-Edin while surpassing all other oft-of-domain com- petitors. Moreover, among out-of-domain testers, DIM-4.6B-B is the only model that consistently preserves both semantic consistency and perceptual quality. This demonstrates the effectiveness. of
Bench-ENN EN, respectively. Our DIM-4.6B-EAA ranks
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Table 14: The ImgEdit performance of different models with/without using DIM CoT as instruction.
Model
Params
CoT
Add
Adjust
Extract
Replace
Remove
Background
Style
Hybrid
Action
Overall
DIM-4.6B-Edi Und
Gen A
x
3.53
3.23
2,01
3.49
1,47
3.42
4.79
2.35
3.64
3.10
4.09
3.47
2.30
4.00
3.43
3.87
4.92
2.85
4.08
3.67
Janus-4o
Und Gen 6
3.35
3.35
2.25
3.32
3.19
3,95
2.74
2.49
3.59
2.28
3.31
4.72
2.62
4.02
3.30
StepIX-Edit
Und
Gen
x
3.56
3.88
2.47
3.14
1.81
1.76
3.13
3.40
2.41
2.84
3.16
4.63
4.18
2.64
1.80
2.46
2.52
2.70
3.06
Table 15: The performance of DIM-4.6B-T2//Edit understanding benchmarks.
Model
Params
MME-P
MMB
SEED
MMMU
MM-Vet
Wu
2025a
1.3B
1338.0
69.4
63.7
30.5
34.3
Emu3-Gen Wang et al.
2024b
8.0B
58.5
68.2
31.6
37.2
Show-o Xie et al. 2024
1.3B
1097.2
26.7
Show-o2-7B Xie
2025b
7.0B A
1620.5
79.3
69.8
48.9
Janus-Pros ProssB (Chen et al
2025c
7.0B
1567.1
79.2
72.1
41.0
50.0
BAGEL (Deng et al. 2025
14.0B
1687.0
85.0
55.3
67.2
MetaQuery-LL [Pan 2025
3.0B
3.2B I
1574.3
78.6
73.8
53.1
63.2
DIM-4.6B-22//Fdit
3
1.6B
1574.3
78.6
73.8
53.1
63.2
DIM-Edit, where edits with high perceptual fidelity are precisely aligned with CoT-style imagina- tion, thereby ensuring semantic correctness.
Inference Efficiency. Beyond precise image editing, our DIM-4.6B-Edi, also maintains highly efficient inference inherited from the SANA architecture. To verify this, we compare the average editing time over 100 samples between StepIX-Edit and DIM-4.6B-Edie as reported in Table Specifically, StepIX-Edit X-Edii is provided with short raw prompts, while DIM-4.6B-Edit is evaluated with longer CoT prompts. Even under this more demanding setting, our model achieves a 4.5x speedup while preserving high editing quality, highlighting the effectiveness of the proposed DIM dataset and the Draw-In-Ming paradigm.
Impact of DIM CoT for Different Models. To investigate the impact of DIM-style CoT on different models, we evaluated the performance Janus-4o and Step when directly provided with the same CoT blueprints imput instructions ImgEdit. The results presented in Table 14 Based on these results, we have the following observations and analysis:
DIM-4.6B-Edit explicitly trained on complex CoT-style blueprints from the DIM-Edit dataset, it achieves superior CoT comprehension. Consequently, it demonstrates substantial performance gains when DIM-style CoTs are applied during inference.
Janus-4o employs an end-to-end fine-tuning approach, which minimizes the gap between instruc- tion understanding and generation. This makes more robust to input distribution shifts. While possesses mild CoT comprehension capabilities and benefits slightly from DIM-style CoTs, the performance gain is less pronounced compared to DIM-4.6B-EAA
Step1X X-Edit adopts training recipe similar to (using frozen understanding core), this design makes it susceptible to input distribution shifts when facing unseen instruction formats. struggles process CoT inputs effectively, leading to performance degradation when DIM-style Co''s are applied.
Based on these findings, we conclude that superior CoT comprehension is pivotal for enhancing editing performance. This finding validates strategy of fostering CoT comprehension by structing DIM-T2I and utilizing DIM-4.6B- as the initialization for the editing task.
Understanding Performance. Since the MLLM component is frozen during DIM training, its un- derstanding performance remains unaffected and is identical to the results reported in the original paper. To ensure clarity regarding the model's capabilities, report the corresponding understand- ing performance in Table 15) Our experiments demonstrate that DIM-4.6B- -B-Edit achieves superior editing performance when utilizing a relatively small MI.LM under frozen setting. This find-
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ing highlights the fiexibility of our approach: users seamlessly upgrade advanced MLLMs to unlock even greater understanding and editing performance. Such integration is straightforward as streamlined architecture and training recipe avoid the need for intricate parameter tuning.
B ADDITIONAL VISUALIZATIONS
B.1 VISUALIZATION OF DIFFERENT EDITING OPERATIONS.
Beyond Figure B in the manuscript, we further visualize the edits of Janus-4o, Step1 XtepiX-Edit, and our DIM-4,6 4.6B Edit under the operations of add, change, remove, replace, and style transfer in and 8 respectively. As shown, DIM-4.6B-Edit consistently preserves the overall Janus-4o fails to generate details of the wooden cabin, while StepIX-Edit places the chimney on the river, which is counterfactual. contrast, our DIM-4.6B-Edit carefully adds the wooden cabin while ensuring naturalness. In Figure [] Janus-4o fails to follow the color change instruction. Step changes the singer's shirt to blue but also alters fine details such as the collar shape. By comparison, our DIM-4.6B- Edit changes the shirt red while preserving all details, including the shadow cast by the hand. In and Step 1X- 1--Edit perform successful removals, whereas Janus-4o fails remove the seaplane. In Figure 7 only 3IM-4.6B-Ec. captures the semanties of "majestically" and generates roaring lion. Finally, Figure & although all three models succeed in style transfer, only DIM--66BEEit captures subtle visual cues, such as the green grass the last row, and repaints them faithfully in the edits.
Figure 4 S
layout while performing natural edits. For instance, Figure 4
F both DIM-4.6B-
Prompt
Source
Janus-4o
Step1X-Edit
DIM-4.6B-Edit
Add person walking along the dirt path, facing toward ocean, wearing a backpack and casual hiking clothes.
Add small wooden cabin with chimney near edge the forest on the right side of the image.
Add a small wooden cabin to the left side the image, near the tree, blending naturally with the landscape.
Figure 4: The edits of Janus-4o
Step1X-Edit and DIM-4.6B-Edit for the add operation.
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Prompt
Source
Janus-4o
Step1X-Edit
DIM-4.6B-Edit
Change the person's shirt color to blue.
Change the animal's fur color to solid shade of brown.
Change the background from the snow beach setting.
Figure 5: The edits of Janus-4o
Step1X-Edit and DIM-4.6B-Edit for the change operation.
Prompt
Source
Janus-4o
Step1X-Edit
DIM-4.6B-Edio
Remove the child standing near the edge of the water.
Remove the sheep foreground.
Remove the seaplane on the shoreline.
Figure 6: The edits of Janus-4o
Step1X-Edit and 3IM-4.6B-Edii for the remove operation.
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Prompt
Source
Janus-4o
Step1X-Edit
DIM-4.6B-Edit
Replace the deer in the image with a lion standing majestically in the same forest setting, under the glowing golden light and light snowflakes.
Replace the mountain goat in the image with rabbit.
Replace the horse in the image with cat.
Figure 7: The edits of Janus-4o, Step1X-Edi and DIM-4.6B-Edit for the replace operation.
Prompt
Source
Janus-4o
Step1X-Edit
DIM-4.6B-Edi
Transfer the image into colourful ceramic mosaic-tile style.
Transfer the image into traditional ukiyo-e woodblock-print style.
Transfer the image into folded-paper origami art style.
Figure 8: The edits of Janus-4o
Step1X-Edit Edit and DIM-4.6B-Edit for style transfer.
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B.2 VISUALIZATION OF THE DRAW-IN-MIND WORKFLOW'S IMPACT ON IMAGE EDITING.
Relying solely on numerical metrics may not intuitively convey the practical impact of the Draw- In-Mind workflow on image generation. To address this, we present Figure 10 12 and [3 to showcase several advanced usage scenarios. These examples demonstrate complex cases that are successfully handled by DIM-Edit-4.6B, highlighting capabilities that remain beyond the reach of current baseline methods.
Instruction Disambiguation. In Figure 9 the user instruction presents an inherent ambiguity due to the presence of three lemons on the table. This task necessitates precise muult--bbecc localization and removal, which is a challenge that proves difficult without the models often struggle with the required multi-object reasoning. Consequently, both the 7B Janus-4o and 12B Step1 X-Edit fail to execute the edit correctly. Similarly, when CoT disabled, our DIM- 4.6B-Edit also fails to remove all targets. However, with DIM CoT enabled, the generated design blueprints effectively disambiguate the instruction. They accurately localize the three lemons to the right of the vase and ensure their complete removal, while perfectly preserving the integrity of the unedited regions.
paradigm, as standard
Edit Navigation and Structural Planning. In Figure 10 the user instruction presents two distinet challenges: (i) determining the optimal placement for a wooden cabin, and (ii) identifying the ap- propriate structural integration for a chimney. These dual requirements impose significant burden on the generation model. Consequently, in Janus-4o's output, the chimney is nearly invisible, while StepIX-Edis places the cabin counterintuitively close to the river. Similarly, DIM without CoT fails to simultancously resolve the cabin placement and chimney addition. In contrast, DIM powered by CoT effectively navigates these challenges. It observes that "the trees thin out on the right side" (GLP) and selects this area the optimal location (EAL). It then explicitly envisions the cabin 's appearance, including a chimney emitting smoke (EII), ultimately yielding the most plausible and high-quality edit among all competitors.
Commonsense-guardes Editing. In Figure IT the editing task presents a subtle complexity:
requires commonsense reasoning regarding scale. From the same viewpoint, cat should appear significantly smaller than a horse. All baseline models, including our own DIM w/o CoT, overlook this physical constraint, simply replacing the horse with a cat of identical dimensions. In contrast, DIM with CoT successfully leverages commonsense reasoning. It recognizes the size discrepancy and executes a "commonsense-guute" guardedd" edit, placing naturally scaled cat at the target location, thereby preserving scene realism.
we present an advanced causal editing scenario where the instruction implies the target quantity (referencing "the second prime number") rather than stating explicitly. Unsurprisingly, all baseline models fail resolve this implicit requirement. In con- trast, DIM with CoT swiftly infers the correct number of cherries and executes a successful edit, demonstrating its ability to handle knowledge-intensive instructions.
Advanced Causal Editing. In Figure 12
Advanced Temporal Editing. Figure 13 illustrates the most complex temporal editing scenario, which necessitates deep understanding of chemical reaction dynamics. Similar to the previous example, none of the baseline models succeed in this task. In contrast, DIM with CoT accurately characterizes the reaction process and executes physically plausible edits, demonstrating its capabil- ity to handle sophisticated temporal reasoning.
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Prompt
Source
Janus-4o
Step1X-Edit
DIM w/o CoT
Remove the lemons on the table.
How many lemons are on the table?
DIM CoT: Instruction Disambiguation
DIM-4.6B-Edit
2. Global layout Perception: The image features uase with pink liles placed on table nearss windoww the right of the vour, there are theee lemons and glass yellow liuud. the foreground, there is plate of dlams, The ound shows blurned grewn outdoor scene visitle through the window.
Local Obteect Perception:
Vasee Transparent glass
water holding pint with gr leawes
Pink Lifes: Large, wibrant pink flowers with dark stamens and green leaves.
sightly Glass of Hellow Liqui:: clear glass filld with a pale yellow drink, positioned to the right of the lemons.
Plate of Elams: white plate containing opened clams with brown and shalls.
Tablecloth:
tableciots with
patierns.
Background: Blurred green outdoor sconery visible through the window.
Edit Localiaatio::
5. Edited image Imagination: The editd image no longer have the three lemons the table, The spoce where the lemons were will now show the white lace tablesicth uninter rupted. maintaining the overall composttio bon of the scene. The vase, pink llles, gloss of yellow louis and plate dame will remain unchanged, and the focus shirt sightly more these elements.
Figure 9: The edits of Janus-4o StepIX-Fai
DIM w/o CoT and DIM-4.6B-Edit when the user instruction is ambiguous. DIM CoT is capable of instruction disambiguation under this case.
Prompt
Source
Janus-4o
Step1X-Edit
DIM w/o CoT
Add a small wooden cabin with chimney near the edge of the forest on the right side of the image.
Where is the most proper place to add the wooden cabin? How should integrate a chimney into it?
DIM CoT: Edit Navigation & Structural Planning
DIM-4.6B-Edit
instructionn
Global Layout Perception: mmge fectures a flowing river foreground with white rapids. Behind the river is dense forest evergreen trees, extending scroes the middle the image. in background, large mountain rises arominently, partally absoured the trees. right this out siihhtl..
image shows edge the forest, where
Local Object Perception:
rapids
protruding
tursulent
Forest: Tall evergreen trees with gi swn needles, some showing signs of brown discolrattion. The trees are densely packed, with dight thinning on the right edge.
Mountain: Grayish
parttally covered dorker
potches
overcast,
lighting.
Edit Arca Locolization: The right sice the mage, near the edge of the forest where the trees thin out siighll, will be modifted to includ the wooden cabin with chimney.
Edited Image Imagination: small
planks chimney
edge
cabin posiionned realicte
behind
by remain unchanged.
surrounding
Figure 10: The edits of Janus-4o Step1X-Edit DIM w/o CoT and DIM-4.6B-Edit when the user instruction requires localization and involves fine- fine-grained structure modification. DIM CoT capable edit navigation and structural planning under this case.
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Prompt
Source
Janus-4o
Step1X-Edit
DIM w/o CoT
Replace the horse in the image with a cat.
From the same viewpoint, the cat should be much smaller than the horse...
DIM COT: Commonsense-guarded Editing
DIM-4.6B-Edit
Deplace
image
2. Global Layout Percaption: The image features a horse standing in grassy field. The field covered with frost and scattared laaves the background. there is line of tall leafless tr and the sun shining through the trees, creating long shadows across the ground.
Local Object Percaption:
slightly
light beige
visible harness. standing facing slighll
Reid: The ground covered with frosty grass and scattered brown leaves, giving textured appesrance.
leafless bright sunlight.
intricato branches form background.
sihouctted
-unllghh: the sun is the sky casting warm golden light and long shadows across the
4. Edit Area Local lacaioatio: The hore will be replaced with car. The area of change the space occupied by the horse, inclucing immediate surrounding: ensure the cat seamlessly integrated into the scone.
Edited imagirations:
smaller
postsoned same area
sitting stancing, lightng casting image frasty tranqual atmosphere.
beige
sunlight unchanged,
Figure II:
The edits of Janus-4o, StepIX-Edit DIM w/o CoT and DIM-4.6B-Edh
when the user instruction involves implicit commonsense constraint. commonsense-guarde editing under this case.
DIM CoT is capable of
Prompt
Source
Janus-4o
Step1X-Edit
DIM w/o CoT
Generate this still-life scene with cherries visible totaling the second prime number.
Which number is the second prime number?
DIM CoT: Advanced Causal Editing
DIM-4.6B-Edit
ratt rssuetic meneeattio come wlll chemris tetiling secend arm
iecoppiee
Teo
rially
i lang yilloe siliiii at juiitiil v- prliuaiio celeeiiccte upen centrr
d couser chemias locret in lover rigt ausdren.
l grer dinpustoon
fore the canapnatior.
weeden
right
"haa gobler":
rnflcctie, and
aggina hylliuu and langing Pornts
mighia ptopert nd, tass scurt, gherg with zal precidr-ett vetit hus.
theeshch gie snteiiann and
oo ioveceadin para arrias
"uarp tuloo Smouth and giih sestoret vibrant 2alss
trowuaee sali"*
enCrree wiagt" Grean tndter
texture
L.B uocal catiee: The duase cheisinn lower cuaceuat will ind Theraabee numberr of charlessd will seuuutedd
fastue
charlesinde
contioa moce must me deattos dhavet seran goaay atacddd the oriee sostien
will
sproited
Figure 12: The edits of Janus-4o Step1X-Edit the user instruction requires causal reasoning. DIM CoT is capable advanced causal editing under this case.
DIM w/o CoT, and DIM-4.6B-Edi when
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Prompt
Source
Janus-4o
Step1X-Edit
DIM w/o CoT
What will these iron nails look like after soaking in 3% saltwater for 4 hours?
Is there any chemical reaction that will change the appearance of the iron nail?
DIM CoT: Advanced Temporal Editing
DIM-4.6B-Edit
sooking
2. Global Layout Percepton: the imag shows transpanent plashe cup filled with clear water Invide the rup, thene a ungle inon nall submerged in the water The is placed on black fabris bac mockgrourd, and the green wall sisille in the tackground.
Local Object Parception:
metalli,, rright,
shiny olvengray
appears
smooth and uncorroded.
""Water"" The water clear and transparent, filing most of the
cup
plastic, cylinerical shape.
visibiiity
"Sackground" The black fabric is textured and contrasts with the gr ean wall behind it. Localization: The
the scaking
likely signs cormssoon surface appear dulles possible reddish-bmmen spats forming, empecially near v.bmerged partion. water also appear slightly cloudy particles The cup and background unchanged."
imagination. soaking saltwater
Figure 13: The edits of Janus-4o Step1X-Edit.
DIM w/o CoT and DIM-4.6B-Edit when
the user instruction requires temporal reasoning. DIM CoT is capable of advanced temporal editing under this case.
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B.3 VISUALIZATION OF FAILURE CASES
We are also open to discuss the limitations of our work, and provide three failure types with six specific cases in Figure 14 15 and T6 intuitively show the boundaries of our DIM-4.6B-Edit
Large-scale All-in-One Editing. In Figure 14 the instructions involve simultaneous multi-step edits, task that remains essentially challenging for almost all editing models, and one where DIM- Edit also encounters difficulties.
For the first case, Janus-4o and SteplX-Edit completely fail to follow the physical laws dictating that the wooden tower should collapse. Our DIM-4.6B-Edi successfully imitates scene of imminent collapse; however, it fails to preserve the exact appearance of the individual wooden blocks, as too many objects are involved in the manipulation.
For the second case, Janus-4o and StepIX-Edis X-Edit fail to change the view at all. While our DIM- 4.6B-Edit completes the primary editing task, some fine-grained details are distorted (e.g., the window of the shoreside house is missing).
Text and Logic Editing. In Figure [5] where instructions involve complex text rendering and logical editing, DIM-4.6B-EBE struggles due to a combination of data scarcity and inherent VAE compression issues.
For the first case, the use of SANAL.5's VAE with a 32x downsampling rate makes complex text rendering particularly challenging, difficulty exacerbated by the lack of targeted training data. contrast, Step1X X-Edit employs an 8x downsampling VAE and trained on proprietary, text-specific in-house data, allowing it to perform relatively well. We regard this as necessary trade-off between efficiency and rendering quality: as shown in Table 10, DIM-4.6B 14-.66.-Ediit requires only 6 seconds to complete edit with 200+ word CoT, whereas Step X-Edit takes 28 seconds with a short raw prompt.
For the second case, all editing models fail. This is fundamentally because none of the models, including DIM-4.6B-Edit, are specifically trained on geometric data. The underlying painter struggles to even draw these shapes, let alone edit them. We believe crafting such datasets remains a valuable and under-explored topic for future research.
Reference-free Editing (in Pixel Space). In Figure 16. the reference image does not provide strong pixel constraint for the target image. Consequently, this task resembles multimodal generation rather than strict editing. All models fail here because existing editing architectures typically enforce strong pixel alignment with the source image.
For the first case, which requests a view of the Golden Gate Bridge, Janus-4o and StepIX-Edi IX-Edit are completely ineffective. DIM-4.6B-Edit struggles break free from the structural constraints of the reference image, resulting in a "scratchy" and distorted view that fails to meet the objective.
For the second case, where the task involves a re-imagination of the source scene. Janus-4o pro- duces a black-and most plausible result, successfully covering the scene with white snow. However, because the transformation fundamentally alters the source structure, specific details such the castle inevitably distorted.
edit, and X-epiX-Edit fails completely. DIM-4.6B-Edit generates the
In summary, the majority of failure cases arise when the task necessitates either generating an im- age that diverges drastically from the source or rendering complex text and geometric shapes. Even in these challenging scenarios. DIM-4.6B 6B-Edit demonstrates superior cnttructionfolllwwie capa- bilities compared to baseline models. These limitations highlight persistent challenges within the current landscape of open-source editing models. We suggest that future research directions, such as intelligent routing that dynamically selects between T2I generation and editing pipelines based on instruction intent, offer promising avenues for resolving these issues, though significant progress is still required in the field.
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Failure Type: Large-scale Oll-in-One Editing
Prompt
Source
Janus-4o
Step1X-Edit
DIM-4.6B-EEd Edii
Predict what happens when one wooden block is removed from the third layer (counting from bottom) of this tower
Failure Case: Spatial Reasoning + Massive Object Manipulation
Prompt
Source
Janus-4o
Step1X-Edit
DIM-4.6B-Edii
Generate the view from boat position 3 meters forward showing the building on the right bank in painting style.
Failure Case: Viewpoint Change Style Transfer
Figure 14: The edits of Janus-4o
StepIX-Edit and DIM-4.6B-Edit for the failure type large-
scale all all-in-one editing.
Failure Type: Text & Logic Editing
Prompt
Source
Janus-4o
Step1X-Edit
DIM-4.6B Edit
Change the text 'ESTATE TACHEN' to 'Timeless Fashion'
BEXEL TACHEN
Tur
Tarbe
TIMESS TAFFEN
Failure Case: Complex Text Rendering
Prompt
Source
Janus-4o
StepiX-Edit
DIM-4.6B-Edit
28
Find x. Please annotate your answer directly the image.
Failure Case: Geometry Understanding
Figure 15: The edits of Janus-4o and logic editing.
Step1X-Edit and DIM-4.6B-Edit for the failure type text
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Failure Type: Reference-free Editing (in Pixel Space)
Prompt
Source
Janus-4o
Step1X-Edit
DIM-4.6B-EdE
Generate the scenic landmark view from this red map pin location.
Failure Case: Map Pin to Real Photo
Prompt
Source
Janus-4o
Step1X-Eds
DIM-4.6B-Edit
Generate what this mountainous landscape would look like during heavy winter snowstorm.
Failure Case: Temporal Imagination for Whole Image
Figure 16: The edits of Janus-4o, reference-free editing (in pixel space).
StepIX-Edit and DIM-4.6B-Edit for the failure type
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C DIM-EDIT DATA COLLECTION PIPELINE
As stated in Section 3.1.2. we collect raw edit data from four publicly available datasets:
UltraEdit (Zhao et al. 2024). In addition to the prompt quality evaluation and optimization the DIM-Edit creation pipeline (Figure[), which aligns textual prompts with actual editing behaviors, metrics on the UltraEdit dataset to improve visual consistency and stabilize training: (i) CLIP image similarity, (ii) DINOv2 similarity, and (iit) SSIM. These metrics are used to select edit pairs that maintain consistent visual appearances. We retain only those edit pairs that satisfy the following conditions: (i) the CLIP similarity between the source and edited images is greater than 0.9; (ii) the DINOv2 similarity is greater than 0,9; (iii) the SSIM score is greater than 0.8; and (iv) the prompt does not contain "rainbow", since many edit pairs meeting (i)-(ii)) are associated with low-quality "rainbow" edits. After filtering, we obtain roughly 160K edit pairs.
we employ three
MagicBrush (Zhang al. 2023). We include only 8K images from the training set to avoid potential information leakage during evaluation.
-aee--DtttE-it----- (Ge et al. 2024). Since the "remove" operation is absent in UltraEdit, additionally select 19K edit pairs from SHED--aat--FditPPart by filtering prompts that explicitly contain "remove."
(Chen et al. 2025b). We include only its 46K image-to-image subset.
By combining these collected datasets, we obtain a total of 233K raw edit pairs for the proposed DIM-Edit.
D DIM-EDIT QUALITY ASSESSMENT
We further assess the quality of the CoTs in DIM-Edit through MLLM- --owerdd validation. Specif- ically, due to API quota limitations, we randomly sample 30K edit pairs from DIM-Edit and use GPT-4.1 to evaluate the quality of the GPT-4o-annotated CoTs, categorizing them into four levels:
Low: The optimized edit instruction does not flect the change between the source and edited im- ages all.
4.5%
12.1%
The optimized edit instruction captures the major change between the source and edited images, but the th-iughf contains some factual errors.
Medium:
Quality Level
Medium
High Ultra-high
The optimized edit instruction captures the major change between the source and edited im- ages, and the chain-of-thought contains only mi- nor factual errors.
High:
83.4%
Ultra-High:
The optimized edit instruction accu- rately captures all changes between the source and edited images, and the chain-of-thour contains no factual errors.
The percentage distribution of each quality level is shown in Figure 17 Notably, no data is categorized "Low", while the majority falls under the "Ultra- High" level, demonstrating the strong overall quality of DIM-Edit.
Figure 17: The percentage distribution of each quality level in DIM-Edit judged by GPT-4.1.
We further conducted a human verification study. Specifically, we randomly sampled 25 instances from each of the data sources listed in Appendix C resulting in comprehensive evaluation set of 100 samples. Three human annotators were then recruited to assess the quality of the CoTs from two distinct perspectives:
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DIM-Edit CoT User Study
DIM-Edit
89%
Perfect Match
81%
Raw 5%
Minor Error 15%
Tied 6%
Major Error 4%
(a) Instruction Win Rate
(b) Ell Quality
Figure 18: (a) The win rate of the optimized DIM-Edit instruction and the raw instruction. (b) The quality the Edited Image Imagination (EII).
Evaluation of Optimized Instructions (Start of CoT). We presented annotators with both the raw instructions and the optimized instructions from DIM-Edit, alongside the corresponding source- edit image pairs. Annotators were tasked with selecting the instruction that best reflected the actual editing operations. A "Tied" option was included for cases where neither instruction was sufficiently accurate. The metric reported is the average win rate for each instruction type.
Evaluation Edited Image Imagination (End of CoT), We asked annotators to assess the align- ment between the Edited Image Imagination (EIl) and the actual edited image. The quality was categorized into three levels: (i) Perfect Match, (ii) Minor Errors, and (iii) Major Errors. The metric reported is the percentage distribution across these error levels.
This efficient evaluation protocol enables a rapid yet robust assessment of the overall CoT quality within DIM-Edit. The results for both the instruction optimization (Win Rate) and the Edited Im- age Imagination (Error Distribution) are summarized in Figure 18] in which we have the following analysis:
Consistency with MLLM Assessment. These results align closely with the MLLM-based quality assessment presented in Appendix D, where over 80% of DIM-Edit CoT's were judged clearer than the raw instructions, with factual emrrr detected. Even in "Tied" cases where the optimiza- tion was not deemed strictly superior, the semantics the raw instruction were fully preserved, ensuring that the optimization process introduces regression.
Analysis of Minor Errors. We observed that minor errors typically relate to subtle environmental inconsistencies, such slight shifts in brightness (e.g.. "the image should be bit lighter")). These artifacts usually stem from the VAE's inability to perfectly reconstruct raw images AI- generated pairs (e.g., from UltraEdit), leading slight loss of nigh-ffrequncy features. As these discrepancies are barely perceptible to the human eye, they have a negligible impact on overall training efficiency.
Analysis of Major Errors. Instances classified as having major errors generally correspond to extremely challenging scenarios where the edits are minute (e.g., the removed object occupies less than 2% of the pixels). These cases are difficult even for human annotators and advanced MLLMs like GPT-4o. Given their extreme rarity, these outliers do not adversely affect the stability of the training procedure.
Overall, the CoTs produced by our DIM-Edit pipeline maintain high quality and serve as effec- tive design blueprints. This high data quality directly translates to better editing capabilities, as evidenced by the superior performance of the DIM-4.6B. 4.6B-Edit model trained on this dataset.
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E DIM-T2I ANALYSIS DIMENSIONS
Figure 19 and 20 illustrate the 21 analysis dimensions and their corresponding prompts used in the DIM-T2I annotation process. The 21 dimensions were derived from thorough literature review and an empirical analysis of existing understanding datasets and benchmarks. They are listed as follows:
MME (Fu al. 2025), MMMU (Yue al., 2024).
MMMU-Pro Yuue al. 2025
MMLU (Hendrycks et al. 2020), MMStar (Chen et al. (024b), MMT-Bench (Ying et al.
2024).
MM-Vet (Yu et al. 2023). Met V2 (Yu et al.) 2024). LLaVa ----Bench-Will (Liu et al. (Li et al.) 2024a), WildVision (Lu et al.)
2023a).
LLaVA-Bench- Wilder
(024), COCO (Lin et al. 2017). 7). OK-VQA (Marino et al. 2019), TexiCaps (Sidorov et al, 2019).
2014)
VQAv2 (Goyal et al.]
2020
TextVQA (Singh et al.
A12D (Kembhavi et al.
2016). ChartQA (Masry et al.
2022
VQA (Mathew et al.
2021), Math (Lu et al.
2023). MIA-Bench (Qian et al,
2024
MegaBench (Chen et al.,
2024at.
RWQA (XAI. 2024).
OCRBench (Liu al. 2023b
GSM8K (Cobbe et al. 2021). GPQA (Rein et al. 2024). IFEval
Zhou et al. 2023).
We believe that the aspects emphasized in widely recognized understanding datasets and bench- marks effectively capture the most frequent interactions between humans and objects in the real world. This makes them an ideal foundation for learning text-to-image generation tasks involving long and complex instructions. By constructing prompts that span these diverse fields, DIM-4.66 T2I not only masters long-form instruction processing but also acquires the broad world knowledge necessary to facilitate sophisticated CoT comprehension and precise editing, thereby achieving high GenEval scores and low FID on MJHQ-30K.
F THE USE OF LARGE LANGUAGE MODELS
This paper uses OpenAl ChatGPT solely for polishing the writing. The authors provided raw text to ChatGPT to correct grammatical errors and refine the statements into a more formal academic style. All polished text was manually reviewed and verified by the authors, who affirm that the paper contains no fabricated content. No statistical data were provided to ChatGPT, All numerical values tables and figures were originally written by the authors.
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Dimension
Prompt
Please describe images details, including but not limited user pre-defined dimensions. Please make sure your description visually grounded for user provided image, namely the user can visual cues image for your generated image caption. The user pre-defined are: [DIMENSIONS] **something" just give overall image caption including the dimensions.
System Message
don't generate your response
dimension, e-g-
character shown in the image, you must describe his/her names. The character include but not limited to the characters shown in Movies, TV Shows, Anime, Comics, Literature, Games, Virtual idols/Characters.
Character Name
Provide overview the image, identifying key objects, people, any interactions. Clearly classify and describe each object (e-g., people, animals, buildings, plants). Specify their attributes, such as size, color, material, and texture.
Scene Description
Describe actions taking place in the image. Who performing them, and how are they interacting with other objects or people? If there dynamic elements (e.g., movement), detail their (e.g, running, jumping, flying, waving).
Actions and Interactions
Describe the setting of the image, including the location (indoor outdoor), time of day, weather, and any background elements (e-g., sky, buildings, roads). does environment contribute overall scene? setting enhance the mood or theme?
Context and Environment
people present, describe their emotional based body language, facial expressions, other visual cues. What mood or tone does the image convey (e-g,, happiness, sadness, tension, peace]? How do these emotions connect to the scene?
Emotion and Sentiment
Explain how objects, people, and other elements are positioned in relation another (e.g, "next to," "above," "to the right af"). Consider foreground, background, overall spatial composition. How does the positioning influence the overall visual balance or narrative?
Relationships and Spatial Arrangement
Describe the color palette the image (e.g., colors of objects, background), and note any texture details (e.g., smooth, rough, soft). do these color and texture choices contribute the atmosphere or style of the image?
Color and Texture
Symbolism Abstract Interpretation
If relevant, interpret any symbolic or abstract elements within the image. What deeper meanings metaphors can be inferred from the visual elements? How do these symbols tie into the image's broader themes message?
Observe the lighting conditions in the image (e.g., sunlight, artificial light) and how shadows reflections influence the objects' appearance. Note the intensity of the light and any patterns created by it. How do these lighting effects contribute to the mood or focal points of image?
Lighting and Shadows
Focus smaller, intricate details the image (e.g., wrinkles clothing, textures surfaces, distinct features). These elements may carry significant meaning help provide a more vivid, precise description.
Details and Fine Elements
Figure 19: The 21 analysis dimensions and corresponding prompts for DIM-T2I.
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Dimension
Prompt
Describe the viewpoint the image (e.g., aerial, eye-level, side view) and composition (e.g., symmetry, balance, focal point). How does the choice of perspective and composition affect the viewer's perception or interpretation of the scene?
Perspective and Composition
possible, infer the time of day or season based on visual cues (e.g., light quality, weather, clothing style). For example, winter snow scene, summer beach setting, autumn forest could suggest the specific season.
Time and Season
Consider there's specific target audience for the analysis. For instance, an analysis for an art historian might use more technical terms, while one for general audience may keep the description simpler. Does the complexity of the image suggest it's meant particular demographic or purpose?
Target Audience
text appears in the image, you must describe the text in its original language and provide English translation parentheses. example: # (book). Additionally, explain the meaning the text within its context.
OCR
there are people in the image, describe their physical features (e.g. age, gender, hairstyle, clothing, etc.), their surrounding environment. there single person, use 'he' 'she' reference instead 'they'.
Person Description
expressions, their relationship
Analyze the image and describe the mathematical concepts it represents. Include specific details like geometric shapes, equations, numeric values, or relationships between elements. If the image includes graph, describe its axes, scales, and key points. applicable, explain how mathematical operations visualized.
Mathematics
Examine the image and extract textual and contextual information. If the image contains document, transcribe content accurately. GUI structured data, describe its layout, labels, and functionality. Summarize the core message or purpose of the content.
Information Extraction
Identify the sequence logical arrangement the image. depicts process, explain the steps their correct order. puzzles games, provide the rules possible solutions.
Planning
Explain the scientific content or phenomenon depicted in the image. Provide details experiments, natural phenomena, or theoretical concepts, including relevant terminology.
Science
Provide a detailed perception- description of the image. Identify objects, their attributes (color, shape, size), and spatial relationships. For specific tasks like facial analysis or pose estimation, include characteristics like expressions, poses, or physical traits.
Perception
Evaluate the image based predefined metrics. Assess quality, authenticity, and adherence to caption content. For paper review or comparative tasks, provide constructive feedback or preference reasoning.
Metrics
Figure 20: The 21 analysis dimensions and corresponding prompts for DIM-T2I. (Continue)
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Reviews:
[{"review_id": "YzEIOJtVRD", "reviewer": "Reviewer_ui71", "summary": "This paper introduces the Draw-In-Mind (DIM) dataset and corresponding framework, which rebalances the roles of designer and painter in text-guided image editing. The authors identify an important issue in current editing models, that is, an imbalanced division of responsibilities, where the generator is overloaded with reasoning and design tasks. To address this, they propose the DIM dataset, consisting of DIM-T2I (14M long-context image–text pairs with CoT blueprints) and DIM-Edit (233K image editing pairs with CoT blueprints). Based on DIM-Edit, the proposed DIM-4.6B-Edit model achieves SOTA performance on image editing benchmarks with a simple architecture.\n\nThe paper’s main strengths lie in its explicit introduction of CoT reasoning into image editing, its high-value datasets and clear annotation taxonomy. The main finding of this paper is that comprehension, rather than model size, is key to editing performance. The experiments are thorough and convincing.\n\nHowever, the narrative focus could be more consistent, as T2I experiments slightly distract from the editing theme. The relationship between DIM-T2I and DIM-4.6B-Edit is unclear, and there are fairness concerns about using GPT-4o as an external reasoning module.", "questions": "1. The title \"DRAW-IN-MIND: REBALANCING DESIGNER–PAINTER ROLES IN UNIFIED MULTIMODAL MODELS BENEFITS IMAGE EDITING\" emphasizes image editing as the main goal. However, the main experiments also include extensive T2I results (Table 2), which slightly dilute the paper’s thematic focus. The paper appears to have initially targeted both T2I and image editing but was later revised toward editing, leaving traces of both directions throughout the text. While this does not affect the experimental validity, it affects narrative coherence.\nFuture revisions could streamline the storyline to emphasize editing as the main task, framing T2I results as auxiliary evidence that supports DIM-Edit rather than a co-primary objective.\n\n2. Line 479-480 states\n\n>  \"DIM-T2I, 14M web-crawled image–text pairs with carefully crafted long-context prompts that\nprovide a foundation for complex CoT comprehension in editing.\"\n\nHowever, it remains unclear whether DIM-4.6B-Edit is trained based on DIM-4.6B-T2I (i.e., fine-tuned from its checkpoint) or whether it is simply trained with the same architecture on different datasets. If the latter is true, mentioning T2I results in the main narrative feels somewhat off-topic. Clarifying this relationship would make the methodology more coherent, that is, whether DIM-4.6B-Edit is initialized from DIM-4.6B-T2I. \n\n3. Line 297-299 states\n\n> \"During inference, we employ an external designer, e.g., GPT-4o, to prepare a blueprint in the same format as DIM-Edit, except without access to the target image, ensuring alignment with real usage scenarios.\"\n\nAlthough Table 2 compares DIM-4.6B-T2I with unified base models such as Show-o and BAGEL, DIM adopts a modular design that includes an external reasoning interface (GPT-4o). Strictly speaking, this introduces an auxiliary capability that is unavailable to other baselines.\nA more rigorous comparison would involve providing the same GPT-4o CoT blueprints to all models, to confirm whether the observed improvements stem from DIM’s superior CoT comprehension or from GPT-4o’s reasoning power itself.\n\n4. In Figure 2 (third example on the right), the optimized prompt reads \n\n> \"Remove the lower sign and the sticker on the ‘No Parking’ sign.\"\n\nHowever, the sticker mentioned in the text is not clearly visible in the original image. This inconsistency should be corrected or clarified.\n\n5. The paper uses an LLM to automatically evaluate CoT quality with a fine-grained scoring scheme (Line 254). However, the paper would be stronger if a small manually verified subset were included to cross-validate the LLM’s automatic scoring results. Such a human validation experiment could be briefly mentioned in the main text, with full results moved to the appendix.\n\n6. In Figure 1 (lower part), the meanings of the blue and red colors on the left and right sides are not explained. A short explanation clarifying what each color represents would improve readability.\n\n7. Line119-122 states \n\n> \"To ensure broad concept coverage, we opt for harvesting real-world images and annotate them with our in-house models from 21 dimensions, yielding 14M long-context image–text pairs, namely DIM-T2I, that form a robust foundation for complex CoT-guided editing.\"\n\nIt is unclear whether these 21 dimensions were derived from empirical analysis of existing datasets, theoretical considerations, or prior taxonomies. Providing the rationale or references for these dimensions would enhance transparency and reproducibility.\n\n8. The editing dataset, UltraEdit-160K-CoT, is filtered using SSIM, DINOv2, and CLIP similarity metrics before CoT annotation (Line 209).\nHowever, later in the pipeline, the data are again classified into misaligned, partially aligned, and aligned categories. Is the filtering in the first stage necessary? Why? how does it differ from the later alignment classification?\n\n9. Line 862-863\n\n> \"We include only 8K images from the training set of MagicBrush to avoid potential information leakage during evaluation. \"\n\nLine 269\n\n> \"we exclude distillation datasets such as BLIP3-o-60K (Chen et al., 2025a) to avoid any risk of data leakage\"\n\nIt is not explicitly explained why such leakage might occur. A short clarification of the specific risk would make this precaution clearer and more accessible to less familiar readers.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "1. This paper explicitly introduces CoT reasoning in image editing and is the first to leverage an external designer to produce textual\nblueprints in a chain-of-thought style, which enables precise and stable image editing.\n2. This paper constructs two high value datasets for T2I and image editing, where DIM-Edit comprises 233K carefully designed CoT imaginations derived from existing editing datasets. If released publicly, this dataset would be extremely valuable for community-based MLLM image editing research.  Moreover, the paper also provides explicit annotation dimensions for both T2I and image editing,\nwhich would be highly helpful references for future multimodal dataset construction.\n3. The paper demonstrates that, for image editing, model size is not the dominant performance factor. Instead, improving the model’s instruction comprehension and text–image alignment capability is far more important. This offers an insightful and meaningful perspective for the field.\n4. The proposed DIM-4.6B-Edit model adopts an extremely simple architecture, which is just a two-layer MLP connecting an MLLM and a diffusion decoder, but it achieves SOTA performance on image editing tasks. This indicates that the paper’s data annotation paradigm has great potential for more sophisticated architectures.\n5. To ensure strict independence between training and evaluation data, the paper takes great care to avoid any data leakage. The experiments and analyses are sufficient, convincing, and logically coherent.", "weaknesses": "Seen in Questions", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "e5VK7yukdT", "reviewer": "Reviewer_RiTv", "summary": "The paper avoids complex architecture, using only a “two-layer MLP + small DiT”, thereby making it clear that the performance gains stem from data and the division of responsibilities, which keeps the causal narrative coherent. However, systematic evaluation on understanding tasks and on broader generative tasks remains insufficient, and the reliance on LLM-as-Judge and on GPT-4o as the data/designer limits the external validity of the conclusions.", "questions": "1. Is there no way for a unified model to unify both thinking/reasoning and generation? If not, what is the significance of unifying understanding and generation? (Just for discussion)\n2. If this paper splits generation and understanding into two components, how does it differ from prior encoder-side work in models that use an MLLM to unify generation tasks? Is the main difference simply the addition of CoT?\n3. Why claim a unified understanding–generation model without evaluating understanding tasks or other generation tasks?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. Emphasizes the importance and contribution of data.\n2. Demonstrates the effectiveness of \"think before drawing\" (plan-then-generate).", "weaknesses": "1. Evaluation relies on LLM-as-Judge; adding quantifiable, automated metrics is recommended.\n2. Claims to be a unified understanding–generation model, but lacks evaluation on understanding tasks.\n3. Insufficient exploration of how the new workflow impacts image generation.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "wu8tzZFnDz", "reviewer": "Reviewer_PX1Q", "summary": "This paper identifies a key issue in current image editing models: an imbalanced division of labor where the generator bears both design and painting responsibilities. To address this, the authors propose the Draw-In-Mind (DIM) dataset, which includes detailed, CoT blueprints for image edits. They also introduce a simple model, DIM-4.6B-Edit, which leverages an external designer to generate these blueprints. This approach shifts the design burden from the generator to the understanding module. Extensive experiments show that their method achieves state-of-the-art performance on multiple image editing benchmarks, outperforming much larger models.", "questions": "- The paper shows many successful editing examples. Could the authors provide some analysis or examples of cases where their method fails? Understanding the limitations and failure modes (e.g., types of instructions or image complexities that are challenging) would help the readers to better understand the boundaries of this approach.", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "*   **Well-Motivated Idea:** The paper is built on a clear and insightful observation about the fundamental limitations of current image editing models. The concept of rebalancing the \"design\" and \"painting\" responsibilities between modules is novel, well-justified, and intuitively appealing.\n*   **High-Quality Dataset:** The introduction of the DIM dataset, particularly DIM-Edit with its detailed CoT annotations, is a significant contribution. The ablation studies effectively demonstrate the value of this data, and the dataset is likely to be a valuable asset to the research community.\n*   **Comprehensive Evaluation:** The paper provides thorough experimental comparisons, convincingly demonstrating the advantages of the proposed DIM-4.6B-Edit model over other strong open-source baselines across multiple benchmarks and editing tasks.", "weaknesses": "*   **Potentially Unfair Parameter Comparison:** The parameter count comparisons in Figure 1 and the main tables do not include the parameters of the External Designer (e.g., GPT-4o), which is a critical component of the proposed pipeline during inference. A significant portion of the performance gain likely stems from the powerful text refinement and CoT generation capability of this external model. Therefore, comparing the 4.6B parameter core model against the end-to-end parameters of other models may present an incomplete picture of the total \"cost\" of the method.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "KxEK7KOzNh", "reviewer": "Reviewer_B3js", "summary": "This paper identifies a key limitation in current image editing models, which it frames as a \"responsibility imbalance\" between understanding and generation modules. The authors propose a new paradigm where the understanding module acts as a \"designer,\" generating a detailed Chain-of-Thought (CoT) blueprint, allowing the generation module to focus solely on \"painting.\" To support this, they introduce the Draw-In-Mind (DIM) dataset, which contains high-quality CoT \"design blueprints.\" Their model, DIM-4.6B, connects a frozen MLLM to a trainable DiT and achieves competitive or state-of-the-art results on benchmarks like ImgEdit and GEdit-Bench, validating the proposed approach.", "questions": "The current framework relies on an explicit, two-stage pipeline at inference time: first, calling a \"Designer\" (which can be an external model like GPT-4o or a separate open-source model) to generate the CoT blueprint, and second, feeding this blueprint into the \"Painter\" module. While effective, this explicitly modular design increases pipeline complexity.\n\nIt is worth discussing whether this \"designing\" capability could be internalized into the main understanding module itself, thereby streamlining the inference process. For instance, could the MLLM component of DIM-4.6B be trained to autoregressively generate the CoT blueprint before generating the control signals for the DiT, effectively merging the two stages into a unified, single-model call (perhaps via a multi-turn dialogue format or purely end-to-end training with CoT data)?\n\nDid you explore standard unified training approaches where the model is taught to implicitly \"reason\" (generate the blueprint internally) before \"acting\" (generating the image)?\n\nWhat are the key trade-offs that led to favoring the current explicit two-stage design over a more integrated, end-to-end approach? Is it primarily for the flexibility of swapping \"designers,\" or did you find that integrated models inherently struggle to balance these two responsibilities even with high-quality CoT training data?\n\nIn summary, this is a good paper that presents an inspiring and effective solution to a key challenge in image editing. The methodology is sound and the experiments are thorough. The questions raised are not intended as criticisms but rather to encourage a deeper discussion on the important trade-offs inherent in this design.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "**Significant Dataset Contribution**: The DIM dataset is a valuable contribution. The authors have developed a systematic pipeline to generate high-quality \"design blueprints,\" creating a useful resource for the community to explore more fine-grained control in image generation.\n\n**Effective Architecture and Strong Results**: The model's simple design effectively isolates and demonstrates the power of the core paradigm and data. The fact that a 4.6B parameter model performs competitively against models over twice its size provides strong validation for the approach.\n\n**Thorough Experimental Validation**: The experiments are well-structured. The main results are clearly presented, the generalizability study (Table 5) effectively demonstrates adaptability to different \"designer\" models, and the ablation studies (Tables 6 & 7) offer valuable insights into the sources of performance gain.", "weaknesses": "A key detail in the data generation process is that the \"designer\" model (GPT-4o) is provided with the target image for reference when creating the CoT blueprints for the training set. This creates a potential distribution mismatch between training and inference:\n\n**During Training Data Generation**: The CoT blueprints are \"oracle-guided,\" created with perfect knowledge of the desired outcome. They are essentially 100% accurate descriptions of the transformation.\n\n**During Inference**: The \"designer\" model must generate the CoT blueprint without seeing the target image, relying solely on prediction. These blueprints are therefore susceptible to errors, omissions, or misinterpretations.\n\nThis discrepancy raises a question about how well the model, trained on \"perfect\" instructions, can handle the inevitably noisier and sometimes incorrect instructions it will receive in a real-world setting. While the strong results in Table 5 (showing generalizability across different designers) are encouraging and suggest a degree of robustness, a direct discussion of this gap would strengthen the paper.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)