Factuality Matters: When Image Generation and Editing Meet Structured Visuals
Authors: Le Zhuo
Songhao Han
Yuandong Pu
Boxiang Qiu
Sayak Paul
Yue Liao
Yihao Liu
Jie Shao
Xi Chen
Si Liu
Hongsheng Li
Venue: ICLR 2026 Poster (2026)
Keywords: Generative Modeling
Unified Model
Image Editing
Text-to-Image Generation
Benchmark
Primary area: generative models
TL;DR: We present the first comprehensive, systematic investigation of this domain, encompassing data construction, model training, and an evaluation benchmark.

Abstract: While modern visual generation models excel at creating aesthetically pleasing natural images, they struggle with producing or editing structured visuals like charts, diagrams, and mathematical figures, which demand composition planning, text rendering, and multimodal reasoning for factual fidelity. To address this, we present the first comprehensive, systematic investigation of this domain, encompassing data construction, model training, and an evaluation benchmark. First, we construct a large-scale dataset of 1.3 million high-quality structured image pairs derived from executable drawing programs and augmented with chain-of-thought reasoning annotations. Building on it, we train a unified model that integrates a VLM with FLUX.1 Kontext via a lightweight connector for enhanced multimodal understanding. A three-stage training curriculum enables progressive feature alignment, knowledge infusion, and reasoning-augmented generation, further boosted by an external reasoner at inference time. Finally, we introduce StructBench, a novel benchmark for generation and editing with over 1,700 challenging instances, and an accompanying evaluation metric, StructScore, which employs a multi-round Q\&A protocol to assess fine-grained factual accuracy. Evaluations of 15 models reveal that even leading closed-source systems remain far from satisfactory. Our model attains strong editing performance, and inference-time reasoning yields consistent gains across diverse architectures. By releasing the dataset, model, and benchmark, we aim to advance unified multimodal foundations for structured visuals.

Published as a conference paper at ICLR 2026
FACTUALITY MATTERS: WHEN IMAGE GENERA- TION AND EDITING MEET STRUCTURED VISUALS
Le 1.30 Songhao Han Yuandong Pu4.5, Boxiang Qiu², Sayak Paul", Yue Liao?, Yihao Liu Jie Shao", Xi Chen', Si Liu2i, Hongsheng Li CUUK MMLab, 'Beihang University, 'Krea AI, 'Shanghai Jiao Tong University, "Shanghai AI Lab, HHuging Face, "National University of Singapore. *ByteDance, 'The University of Hong Kong
structvisual www.titthuu.....
ABSTRACT
While modern visual generation models excel at creating aesthetically pleasing natural images, they struggle with producing or editing structured visuals like charts, diagrams, and mathematical figures, which demand composition planning, text rendering. and multimodal reasoning for factual fidelity. To address this, we present the first comprehensive, systematic investigation of this domain, encom- passing data construction, model training, and an evaluation benchmark. First, we construct large-scale dataset of 1.3 million high-quality structured image pairs derived from executable drawing programs and augmented with chain-of-thouge reasoning annotations. Building it, train unified model that integrates a VLM with FLUX.1 1 Kontext via a lightweight connector for enhanced multimodal understanding. A three-stage training curriculum enables progressive feature align- ment, knowledge infusion, and reasoning-augree external reasoner at inference time, Finally, we introduce StructBench, novel benchmark for generation and editing with over 1,700 challenging instances, and an accompanying evaluation metric, StruetScore, which employs multi-round Q&A A protocol to assess fine-gra grained factual accuracy. Evaluations of 15 models reveal that even leading closed-source systems remain far from satisfactory. Our model attains strong editing performance, and inference-ting titrenceetimeeeee reasoning yields consistent gains across diverse architectures. By releasing the dataset, model, and benchmark, we aim to advance unified multimodal foundations for structured visuals.
generation, further boosted by
Structured Image Generation Editing
Structured
Natural Banc Composition
Sephutticted Compesition
Moth
Math Editing
Science T21
Math 721
Brish
Editing
Shhrr undernardinna
Strong Trasking Dergand Twedae with woilpdd
Direct Edding
Thart
Puzzle T21
Miz
Chart Edning
Sinclared Kiuals
Base Teke Berdenig
Graph Editing
Graph T21
Tobie T21
Figure Overview of our work. Left: We showcase the diverse text-to-image (T2I) and editing examples from our dataset. In contrast to natural images, modeling structured visual demands sophisticated composition planning, strong multimodal understanding, and precise text rendering, as highlighted by the three key characteristics. Right: Our model demonstrates competitive performance against leading closed-source systems in both structured image generation and editing benchmarks.
Equual Contribution. Corresponding Authors.

---

Published as a conference paper at ICLR 2026
INTRODUCTION
Recent advances in visual generation have enabled models to follow user instructions and produce highly aesthetic images that are often indistinguishable from professional photography (Esser et al..
2024 Zhuo et al.) 2024 Labs 2024, Qin et al.] 2025 Xie et al. 2025a Wu et al. (025a). With the emergence of unified multimodal models, such as GPT-Image (OpenAl 2025b). Nano Ba- nana (Google| 2025), and Bagel (Deng et al. 2025), these systems can better encode multimodal contexts and go beyond text-to-image generation to support more sophisticated visual tasks, including free-form image editing, style transfer, and more.
Despite producing increasingly appealing and intricate images, current state-of ofte-of-the-arn the-art models struggle to accurately generate edit charts, mathematical figures, and diagrams. We argue that modeling such structured visual components requires more than making images look aesthetically appealing: it demands accurate factuality, i.e., encompassing capabilities like composition planning, precise text rendering, and multimodal reasoning as illustrated in Fig. I For instance, effective image editing in this domain necessitates robust understanding and extraction of multimodal representation from the input image and instruction. In contrast, vision-language models (VLMs) have made notable progress in understanding structured images (Fu et al. 2025 Wang et al.] 2024 Zhang et al. 2024), revealing a pronounced gap between visual generation and understanding that hinders unified modeling. Consequently, structured visuals represent a crucial yet underexplored setting for multimodal generation. However, most existing works remain focused on aesthetics (Wu et al. 2023 Ma ct al. 2025) or instruction following (Ghosh et al.) 2024 Huang et al. 2023 Ye et al. (2025b) for natural images, leaving these unique challenges and their evaluation insufiicietly addressed.
In this paper, we present the first systematic investigation into structured image generation and editing, including comprehensive benchmark, large-scale training corpus with chain-of off-thoughttt (CoT) amnotations, and a strong unified model. Observing that many structured images map naturally to executable code, we collect millions of drawing programs and render them into seed images. Our pipeline performs edits at the code level to construct paired code-editing examples, which are then rendered to produce image-editing pairs. Unlike conventional image datasets (Sun al., [023 Weil et al. 2024 Ye et al. (2025b), images in our dataset are strictly aligned with their source codes, and editing instructions are driven by concrete code editing actions, yielding precise and verifiable state transitions. The final dataset comprises 1.3M editing instructions, all annotated and filtered by GPT-5 (OpenAll (202a)). Moreover, each sample is augmented with GPT-S-generated CoT reasoning annotations, further providing explicit reasoning trajectories for both structured image generation and editing.
image pairs with both text prompts and
Building our dataset with rich annotations, we train unified model for image generation and editing across both natural and non-natural domains based on FLUX.1 Kontext (Batifol et al. 2025). Different from approaches such MetaQuery (Pan et al. 2025) that rely on heavy transformer-basel projectors, we employ a lightweight MLP connector to align multimodal features from Qwen- VL (Bai et al. 2225)) with FLUX.1 Kontext backbone, thereby improving the model's 's understanding of multimodal inputs such as charts and mathematical figures. We further design a three-stage training curriculum to progressively achieve feature alignment, knowledge infusion, and reasoning-augmeent generation. After the final stage, we are able to decompose complex generation and editing tasks by leveraging an external reasoner to scale up inference-time compute for analysis and planning to enhance performance (Zhuo et al. 2025 Fang et al. (025a).
Finally, we introduce StructBench, which contains over 1,700 high-quality samples carefully selected from six categories defined by structural characteristics. Unlike natural images, evaluating structured visuals more challenging and demands reliable assessment of fine- -ne-grained details. To this end, we design a novel metric named StructSeore that improves upon the naive "VI.M-ss-a a-dd-ee-- paradigm (Lin et al. 2024) and reduces hallucinations. Concretely, we utilize VL.MS in a multi-round process to generate set of fine-grained question-answe pairs that comprchensively probe all salient visual elements. We then elicit predicted answers from model -ooeelggeeertted images and leverage VLMs to compare them against ground- truth responses, producing overall score.
We comprehensively evaluate 15 models on our text-to-image and editing benchmarks, covering both open- and closed-source systems. Results reveal that, while closed-source models substantially outperform open-source ones, even state-of-ther the-----tttttttte systems are far from satisfactory. consistent with initial observations. Notably, our model achieves the best performance on image editing, enabled
2

---

Published as a conference paper at ICLR 2026
Source Image
Instruction:
Target Image
Gaven solatte riguze and its cozzesponding souzce code, you are requized to identity one salient feature that characterizes the core donain specific property of the imag, Based this salient feature, need to forulate natched pair of code editing instmiction and image editing ins tmuctioon such that beth corrands transforatiinn sirsle elasent the figure.
Eddtd Regise
Editd Region
equivalent semantic
Bale Ful
Nender
GPT-5
Resderr
Source Code
Turget Code
Setteer Feetinc
Esecuse
and pndiater
i paarcbble carse y af sds ahateiiooos sghiysshg dee lnumnal ave a
Inner Guldance
Image Ediing frcomntionn
Narovr the abaded woreed beod aper Ii
Edited Code
Cade Fallng fnmmccioon
de 101 JeneENO
Aeyendpouul4 Pyyloerte shilii
@ Einttruction:
X
Mod
code strictly according provided code editing Instruction.
Figure 2: Data construction pipeline. We prompt GPT-5 to extract salient features, then generate paired editing instructions from the source code and rendered image. The source code is modified according to the code-editing instructions. The target image rendered from modified code is passed through rule-based filters to ensure the overall quality of the constructed dataset.
by adding explicit reasoning trajectories at inference time. We validate that applying the same reasoning procedure to other models yields consistent improvements, indicating that structured image generation and editing particularly benefit from scaling inference-time compute for reasoning, which is the key bottleneck in current unified systems. By releasing our dataset, model, and benchmark, we aim to draw broader attention in the multimodal community to structured visuals and accelerate progress toward truly unified foundation models.
Benchmarks. Existing T2I benchmarks (Wu et al.
2023 Ghosh et al. 2024 Huang et al. 2023)
primarily emphasize visual quality and instruction following, with particular focus on composi- tionality. Similarly, editing benchmarks (Sheynin et al. 2024 Wang et al. et al.) 20256) center on visual consistency and adherence to editing instructions, frequently relying on similarity metrics such as DINO (Oquab et al., (023) or CLIP (Radford et al. 2021) scores. As model capabilities have improved, recent benchmarks (Wu et al.) 2025c Zhao et al., 2025a Sun et al. 2025 Cao et al., 2025) have begun to probe more challenging tasks that require world knowledge and reasoning for generation or editing. Since these evaluations mostly target natural images, their assessment protocols tend to be coarse-graid coarse-grained, using learnable scoring models naive VLM-as-a-judge (Chai et al. 2024: Han et al. 2025b) pipelines, which are ill-suited for non-natur non-natural, structured imagery.
2023: Ma et al., 2024 Ye
STRUCTURED IMAGE DATASET
2.1 DATA CURATION PIPELINE
key challenge to training and evaluating models on structured images is the absence of open-source datasets tailored for generative modeling. Although the visual understanding community offers related resources (Masry et al., 2022 Zhang et al. 2024 Wang et al., 2024). their quality is inconsistent and they rarely support constructing precise image-editing pairs. Reviewing data curation pipelines for natural-image generation and editing, prior work (Sun et al. 2023 Wei et al., 2024 Fang et al.. 2025b) typically collects user or synthetic prompts models as renderers to synthesize images. Given that structured images can be programmatically specified (Fu et al.) 2025). , natural alternative is to collect source code seed prompts, create code-editing operations to obtain target code, and leverage code-bases graphics libraries as the renderer to automatically synthesize high-fidelity structured images and editing pairs.
seed instructions and then employs expert
Code-Aligned Image Synthesis. Concretely, we collect approximately 2M programs for rendering structured images from diverse sources (Belouadi al. 2023 Wang al. 2025b Zhao al. 2025b).
3

---

Published as a conference paper at ICLR 2026
covering mathematics, charts, puzzles, scientific figures, graph diagrams, and tables, primarily in Python and LaTeX. We execute each program and retain only those that render a valid image, yielding source code-image pairs. The next step is to generate code-editing instructions to obtain the corresponding target code-image pairs. However, we observe that directly editing the code often yields overly specific, low-level actions that are not easily discernible at the visual level, whereas image editing is largely guided by perceptible visual elements. As illustrated in Fig. [2 we design multi-step annotation process. Given the source code-image pair, GPT-5 (OpenAl) (025a) first analyzes the visual characteristics of the source image, producing salient features (caption). Guided by these features, it then generates matched pair of amaage-ditinn and code-editing instructions in parallel. This design ensures that the image-editing instruction is constrained to reference only visual elements, while the code-editing instruction specifies the precise program-level changes. Finally, GPT-5 applies the code-editing action to the source program to obtain the target code, which we exccute to render the target image, yiekding a strictly aligned and verifiable state transition.
2.2 POST-PROCESSIN PIPELINE
Filtering. To ensure data quality and annotation richness, we implement a comprehensive post- processing pipeline. First, we discard invalid samples by executing each program and removing those that fail to render or produce corrupted outputs. Second, apply heuristic, rule-based filters to climinate (I) source-target pairs with null edits, i.e., low perceptible visual difference, and (2) low- information images with minimal semantic content (e.g., near-uniform trivially simple renderings). The final dataset contains 1.3M examples spanning diverse categories. Each example comprises a source-target image pair, caption of the source image for text-to-image generation, and an image editing instruction. Fig. 4a) visualizes the percentage and number of each editing category.
Reasoning Trajectory Synthesis. Instructions in prior image generation and editing datasets often overly terse (e.g.. "add tree right," "a photo of soap bar"), offering insufficient semantic guidance. This limitation is especially problematic for structured visuals, where one-to-many mappings between instructions and outputs hinder both learning and evaluation. A distinct feature of our dataset is the inclusion of chain-of-thought (CoT) reasoning annotations. Specifically, each text-to-image example is paired with carefully constructed dense caption with detailed attribute analysis, and each image-editing example accompanied by a three-step reasoning chain (i.e., input image analysis, editing instruction interpretation, target image prediction). all generated by GPT-5, As illustrated in Fig. b.c), these annotations are substantially longer, providing rich and accurate semantic and analytical signals that better support complex structured image generation and editing.
3 STRUCTBENCH
3.1 DATA SELECTION
The dataset introduced in Sec. is generated via code actions and filtered for validity, yielding high- quality samples that are well-suited for benchmark construction. To ensure diversity and balance, we first cluster images by salient visual features and then carefully curate six primary domains, including Math, Graph, Chart, Puzzle, Science, and Table, through manual taxonomy and selection. We apply stratified sampling within each domain, and all samples undergo dual review by GPT-5 and human annotators to verify image quality and instruction correctness.
3.2 EVALUATION PROTOCOT.
Evaluating structured images differs from natural images in three fundamental respects: (1) structured images demand strict prompt following at both the global layout level and in fine-grainee structural elements such as numerical values, dense text, and geometric primitives, whereas prompts for natural images are often short and permissive; (2) aesthetic criteria for structured images are less important and substantial different from those for natural images; and (3) structured-imag domains exhibit pronounced inter-domain differences that call for adaptive, domain-specific evaluation criteria. Consequently, common metrics used in text-to-iine sot-to-immae or editing benchmarks, such as CLIP score, aesthetic score (Wu al.) 2023), and naive VLM-based scores (Lin et al. 2024), not well-suited for this setting. To bridge this gap, we propose StructScore, a metric that leverages a VLM in a controlled multi-turn workflow assess fine-grained, instance-specific attributes.
4

---

Published as a conference paper at ICLR 2026
(a) Benchmark Construction
Constructed with
GPT-5
E-diting T21 Pair
Instruction:
Instruction:
Bescriba visual elements related instruction focusing
Tuzn each sentence of descriptions into Q6A pair, using its exact inforration
aspects.
without
Editing Instruction T21 prompt
Detailed Descriptions of Image
Testset Split
Curated Categories
GT
Evaluation QA Pairs
Q: What kind of chart is shown?
Human Filter
A: 2D bar chart.
Global Q&.A
Math
Chart
Graph
XZ
Human Check
What Max heights il Local Q&A
LiDARSensor category?
Science
Puzzle
Table
Around
(b) Evaluation Protocol
Questioning Checking with
GPT-5
Example Output
What type of chart shown?
GT Image
Omeationing
3D bar chart
bar chart
What are Max heights LiDARSensor category?
Questioning
Check Around 10.9
Generated By
Gemini
Below 4.0
Sample Score: 9/34=26.477 .4%%
Figure 3: Benchmark construction and evaluation workflow. (a) Benchmark construction: We cluster the data into categories, and for each editing and text-to-image (T2I) example, GPT-5 generates detailed image descriptions that transformed into Q&A pairs for evaluating diverse visual aspects. (b) Evaluation protocol: Using the Q&A pairs, GPT-5 is queried on generated images for open-ended responses, which compared with ground-truth answers to yield final score.
Q&A Construction. Our goal is to build, for each data item, Q&A pairs that can comprehen- sively assess generated or edited samples. Starting from the instruction and ground-truth image, first prompt GPT-5 to describe all salient and relevant visual elements with their attributes. GPT-5 then decomposes this detailed description into sentences and converts each into verification Q&A pair targeting specific attributes or relations, as illustrated in Fig. 3(a).
Evaluation Metric. A naive evaluation would binarize these Q& and query VLM on the model- generated image, yielding yes/no answers. However, this yields an unreliable ceiling as random guessing achieve 50% accuracy. Instead, because questions to produce open-ended answers for each question based on the generated image, forming /question, predicted-answe ground-truth/ triplets. We then compare the predicted and ground-truth answers and average the accuracy of similarity scores across all Q&As to obtain the sample-level accuracy.
atomic, we prompt the VLM
Unlike text-to-image generation, image editing cannot be fairly evaluated by simply averaging all Q&A scores. This is because editing simultaneously assesses visual consistency and instruction following, and visual consistency is substantially easier: model can attain high scores via near- identity mappings. To disentangle these dimensions, we compute Q&A accuracy on both the source and ground-trun truth target images. If a Q& is correct for both, it is labeled as cssual-consisteccy related; if it is incorrect on the source but correct on the target, it is labeled as instruction-follovinn related. This binary categorization reveals that lissal-conisttenyy A& are far more frequent than cnrrucctionfoolowinn Q&As, the latter typically focus small but critical changes. To counter this imbalance and prioritize instruction adherence, we report the final editing accuracy as a weighted score, í.e. 0.1x analyzes alternative weightings and their alignment with human preferences, where our chosen setting achieves the best correlation with human evaluations.
accuracy and 0.9x instruction-followw following accuracy. Appendix A..5 A.5
Atomic Q& Refinement Improves Metric Reliability, We conduct strict human audits to regulate the number of Q& per sample, validate their reliability, and ensure metric alignment with
5

---

Published as a conference paper at ICLR 2026
Peir
59318 PRSRATER descrpoon CST Prediction
Rubic Feege
Ad0%o faroed
15 (23
hattis
sabs
Alviuue mestaction
Dete kingit
(a) Distribution of editing calegoriies
(b) Token length distribution for oditing
(c) Token length distribution for T21
Mun
Audd
14.2%
26
10
Havatu ge Fight
Tabe
doler
194, 4h4
baken (11.0%)
Co
Labe
loa
Sclense d4
Math
Pazzl (4.73)
poant
text
shown
(24
(5.5)
Lxxss image
rabelec Leg
30. 0%
appear
F- Legeeged left ts
43. 2%
(f) Word cloud of instruction
(d) distributton benchmark
(e) Q&& pairs per image
evaluation question
Figure 4: Statistical analysis of our dataset (a-c) and benchmark (d-f).
we observe that certain Q&A pairs, while appearing plausible humans, are not clearly verifiable by the VLM evaluator. We attribute this issue non-atomic questions that bundle multiple attributes or relations, thereby introducing answer stochasticity. To mitigate hallucination and enhance determinism, we revise the VLM prompts to split compound descriptions into multiple atomic items and enforce minimal, concise ground-truth answers. This refinement process nearly doubles the number of Q&A pairs, reflecting increased atomicity. side-by-side comparison of the original and revised atomic Q&A pairs is provided in Fig. 15
human judgments. As shown in Fig. 15
Given that our benchmark contains ground bouund-truth images, we leverage the StructScore on these images proxy for metric reliability. An ideal metric should achieve 100% accuracy ground- truth images; however, the initial StructScore is approximately 80%. Therefore, we prompt GPT-5 to rewrite the failing Q&A pairs, re-aligning them with ground-truth images to make them clear and unambiguous. Following this refinement, the updated Q&A set yields over 95% accuracy on ground-truth images, demonstrating substantially improved reliability.
3.3 NUMERICAL STATISTICS
Our StructBench consists of 1,714 items with 32,031 and 37,941 Q&A pairs for editing and generation respectively. Fig. dd summarizes the distribution of StructBench across six domains. Given the importance of charts, we further stratify the chart test set into five categories by editing type. Fig. (e) reports the number of Q&A pairs per sample, where over 87% of samples contain more than ten questions, and 14% include more than twenty-six. Finally, the word clouds for editing instructions and questions in Fig.FTe highlight focus on the key elements and attributes of structured images.
MODEL TRAINING
4.1 MODEL ARCHITECTURE
We build on FLUX.1 Kontext (Batifol et al. 2025). a diffusion transformer with unified image generation and editing capabilities, and adapt to structured visual content. Following its original configuration, we encode textual instructions with T5 (Raffel et al. 2020 [] and encode both the input image and the target image with VAE. The resulting token streams are concatenated into unified sequence and processed with joint attention. While the VAE supplies low-level features of the input image, structured image editing often depends on higher-level semantics. For example, converting a bar plot into pie chart requires recognizing the underlying quantitative relationships to render the correct proportions. To enhance semantic perception, we utilize lightweight MLP connector
We discard CL.PP features due its limited token budget and observed degradation in output quality.

---

Published as a conference paper at ICLR 2026
phar
teil the marter
Generation with Think
Difficully Increase
Hard T21 Editing
Basic T2I & Editing
Stage 1
Stage 2
Stage 3 Thinking Enhancement
Unified Alignment
Hybrid Visual Learning
Figure 5: The three-stage progressive training pipeline. Training difficulty increases across stages, from alignment hybrid visual learning and thinking enhancement.
that aligns Qwen- VL Bai et al. 2025 encoded multimodal features with FLUX. Kontext. This deisgn choice reduces training overhead and, empirically, stabilizes optimization without sacrificing performance compared to learnable-query, transformer-base connectors (Pan et al. 2025)
4.2 TRAINING PIPELINE
To gradually introduce structured-visual knowledge to our model while maintaining its general capabilities, we adopt a three-stage progressive training pipeline, as illustrated in Fig.
Stage 1: Unified Alignment. The goal of this stage is to align Qwen-VL's features with the diffusion backbone rather than injecting new knowledge, Accordingly, we freeze the backbone model and train only the newly added connector, using relatively simple text-to-image and editing data. To prevent the already well-aligned T5 encoder from becoming shorteut that impedes connector alignment (Lin et al. 2025), we remove T5 features during this stage and rely solely on Qwen-Vi WL features. After alignment training, the model can roughly follow instructions for both generation and editing.
Stage 2: Hybrid Visual Learning. We then incorporate our constructed structured text-to-image and editing datasets and jointly fine-tune the diffusion backbone and connector to inject domain knowledge of structured visuals. To preserve general-domain capabilities, we include mixture of high-quality text-to-image and editing datasets. However, structured visuals exhibit markedly different pixel statistics from natural images, which contain large regions of uniform backgrounds and, in editing, small regions change. Therefore, we introduce a mask- based training strategy that adaptively downweights losses background and unchanged regions during training. Leveraging Qwen-VI. multimodal representations and the pretrained T5 encoder, the model improves on both structured and general domains.
Stage 3: Thinking Enhancement. We observe that our Stage-2 model, as well as other baselines, often produce images that appear visually plausible but contain semantic errors on complex tasks. We attribute this to an overreliance on shallow visual patterns driven by text features, coupled with insufficient grounding in the input image or prompt (e.g., layout or quantitative relationships). Because such semantic comprehension is core strength of VLMs, we leverage the CoT annotations from Sec. 2.2 and open-source datasets (Fang et al., 2025b) as long-context inputs to Qwen- VI. to inject explicit reasoning. This endows the model with the ability to follow complex multimodal reasoning instructions. also enables scaling inference- input image-text pair and predicts the ideal target content, and the resulting expanded analysis is then fed to the generator to guide synthesis.
compute: a VLM first analyzes the
5 EXPERIMENTS
5.1 EXPERIMENTAL. SETUP
Training Details We provide comprehensive training details in Appendix A.2 A.2 including model configurations, stage-specific hyperparameters, and the datasets used. Our base model is FL.UX.1
7

---

Published as a conference paper at ICLR 2026
Table 1: Quantitative comparison on StructEditBnchh. The table showcases the results of Accuracy (%) and PSNR (with ground-trun truth target image) for various methods. indicate the first and second Accuracy results, respectively.
Model
Math
Chart
Graph
Puzzl
Science
Table
Overall
Acc
PSNR 1
Acc
PSNR
Acc
PSNR 1
Acc
PSNR
Acc
PSNR
Ace
PSNR
Ace
PSNR
30.46
14,93
52.97
21.22
66.56
22.92
69
22.61
75.71
19:75
51.57
21.09
GPT-Image
17:24
Scedream 4.0
51.06
23.63
46.83
16.54
51.22
24.12
71.13
26.93
09:22
26.45
$5.19
24.75
52.85
24.45
Nano Banona 2.0
64.64
24.88
63.28
16.74
72.58
26.
81.07
28.12
71.19
26.71
91.10
25.
67.05
25.59
UniWorid VI
9.41
8.84
5.99
7.87
8.83
6.16
9.11
7.71
19.91
7.67
16.13
8.24
8.40
8.21
DiMOO
26.79
21.56
16.52
14,96
24.03
21.77
29.52
22.26
26.08
22. 57
21.61
19:47
21.00
21.19
OnniiGen2
15.45
HiDream-EEl 11.1
28.07
18:43
26.36
12.91
29.53
18.26
43.77
18.04
66.06
16.47
48.79
17.12
29.63
18.01
Bagel
21.27
21.38
27.11
16.38
29. 94
22.70
41.59
24.22
47.16
23, 56
47.35
21.54
28.87
22.06
Bagel-Think
37.
23.97
78
16.82
42.51
26.49
36.11
26.75
43.15
25.57
40.46
23.83
33.34
24.70
X-Edit
34.47
23.41
46.47
FLUX. Kontext
19.78
12
30.99
37.56
Qwen-Edit
40:48
23.73
30.17
12.33
44.8
26.11
53.74
27.31
$3.09
23.53
67.76
25.7
38.
24.81
Ours
54.74
23.31
30.58
15.33
60.
24.65
73.0
26.33
73.05
25,80
77.08
23 19
55 98
24.01
Table 2: Quantitative comparison Struc(T2IBench. reporting Accuracy (%).
Model
ad Chart
A Graph
Math
Puzzle
Science
Table
Overall
Seedream 4.0
35.79
54.08
63.33
50.89
62.59
68.94
47.52
Nano Banana
35.55
58.96
64.81
63.87
60.75
67.20
48.45
GPT-Image
37.09
57.00
63.25
59.42
60.94
83.31
49.58
Nano Banana 2.0
93.79
90.51
89.04
88.42
87.69
95.35
92.00
UniWorld-Vi
1.71
5.52
4.72
1.58
8.82
5.25
3.20
Bagel
4.66
3.61
4.02
4,46
8.60
5.74
4.69
Bagel-Think
4.81
15.33
13.89
15.22
19.05
8.97
9.03
HiDream-II-Full
9.47
20,84
19.20
18.00
26.77
27.05
14.77
OmniGen2
10.67
22.51
22.89
18.63
28.00
22.61
16.24
FLUX.1
12.35
20.09
19.86
20.63
25.25
27.00
16.51
FLUX.1 Kontext
17.22
24.64
21.42
24.06
30.97
29.16
20.36
Ovis-UI
24.75
16.08
19.45
21.23
26.03
12.70
22.83
Qwen-Image
32.23
48.05
46.98
48.90
53.51
73.65
41.03
Ours
20.91
33.45
41.70
30.66
41.46
32.26
28.80
Kontext [dev] Batifol et al. 2025 where remove the original CLIP encoder and replace it with Qwen2.5-VL- 2onn2.55VL-77. (Bai et al. 2025) to encode both the input instructions and images. Throughout training, adopt dynamic-resolution sampling, where images resized to lie 51 512x512 while preserving their native aspect ratios, maximizing information retention. During inference, we employ external reasoner to provide reasoning trajectories from inputs.
GPT-5 (OpenAl
2025a)
Evaluation Details We evaluate 15 closed- and open-source models, covering the most recent text- to-image, image-editing, and unified models. Closed-source systems include GPT-Image (OpenAl 2025b), Nano Banana (Google 2025), and Seedream 4.0 (Seedream et al., 2025). Open-source baselines include FLUX.1-dev Labs| (024). FLUX.1 Kontext (Batifol et 2025), StepIX-Edit (Liu et al. 2025). Bagel (Deng et al.) 2025), Bagel-Think (Deng et al. 2025), HiDream-EL.. (Cai ai et al. 2025). WniWorld-Vi (Lin et 2025), ) Ovis-U1 (Wang et al. | OmniGen2 (Wu et al. (025b). Qwen-Image (Wu et al. 2025a), and DiMOO [Tem (025). For aspect ratios, models that support custom output sizes are configured to match the ground-truth image proportions. To ensure fairness and reproducibility. all models generated on H200 GPUs. For the LLMe-a evaluator, we report results using both the current state- of-the-art art closed-source LLM (GPT-5 (OpenAl, 2025a)) in the main text and the leading open-source alternative S-een255-VL-722. (Bai et al.) 2025)) in Appendix A.3 We also release the complete prompts used for data construction and evaluation in Appendix A.7
run with the default settings from their official repositories and
5.2 RESULTS AND ANALYSIS
We report the performance of 15 models 6 subtasks of StructEditBench and StructT2IBench in Tab. [and Tab. respectively. To provide finer-graine analysis, we break down chart-related editing by editing type Tab. Case study demos provided in Appendix A.6 The main findings
8

---

Published as a conference paper at ICLR 2026
Table 3:
Quantitative comparison on StructEditBench (charts only).
indicate the first and
second Accuracy results, respectively.
Category
Color
Num
Auxiliary
Ddd&Del
Overall
Model
Acc 1
PSNR
Acc f
PSNR
Acc f
PSNR
Acc f
PSNR
Aoc
PSNR
Acc
PSNR
GPT-lmage
40.62
9.85
54.57
13.91
33.12
13.48
64.03
13.48
38.02
12.73
45.82
12.56
Nano Banana
39.75
10.33
54.34
17.59
35.64
16,56
67.40
17.39
36.77
13.88
46.42
14.93
Seedream 4.0
38.13
9.67
61.84
21.77
36. 00
19.22
65.92
19.46
36.04
14.28
46.83
16.54
Nano Banana 2.0
53,91
10.38
69.21
21.22
60.11
19.30
73.53
19.09
63.04
15.08
63.28
16.74
UniWorid-VI
5.98
7.60
8.19
8.29
2,58
7.57
10.24
8.62
2.81
7.35
5,99
7.87
DIMOO
11.20
9.82
15.31
16.97
17.39
17.30
21.57
17.59
18.57
14.46
16.52
14.98
OmniGen2
17.30
28.84
11.78
15.48
14.03
22.42
16.59
10.54
12.11
18.55
12.44
Ovis-Ul
18.15
9.57
30.68
15.38
20.79
15.13
25.49
14.25
17.21
13.11
21.94
13.30
HiDream--11.1
22.69
9.29
39.86
14.07
21.49
15.04
32.65
14.34
18.05
12.71
26.36
12.91
Bagel
25.69
9.08
38.20
20.46
26.30
20.29
30.00
21.24
17.79
14.93
27.11
16.82
DeppX--ddit
21.96
19.75
25.46
20.22
34,40
19.46
24.92
15.11
16.68
Bagel-Think
24.55
9.00
45,46
19.35
25.60
20.14
34,54
20.62
18.69
14.58
28.98
16.38
Qwen-Edii
23.53
9.52
41.80
13.63
22.90
13.69
42.39
13.07
23.27
12,46
30.17
12.33
FLUX.1 Kontext
24.67
10.14
44.56
16.53
29.24
16.74
44.02
16.79
23.06
13.95
32.29
14.61
Ours
50.81
10.40
64.10
18.16
33.45
17.04
66.34
17.90
38.10
14.37
50.58
15.33
Closed-source models lead, yet all models remain far from satisfactory. Across both editing and T2I benchmarks, sourcd systems consistently occupy the top positions. For example, on T21, the best closed-source model (GPT-Image) attains a score of 49.58, whereas the strongest open-source baseline (Qwen-Image) achieves 41.03. Notably, our model achieves the highest score (55.98) on the StructEdittench. However, even the best model achieves only about half accuracy on either generation or editing, highlighting substantial room for improvement on structured visuals.
Data, rather than architecture, is the dominant driver of performance. Among open-soure models, varying visual encoders (e.g., VAE (Batifol et al.) 2025), Qwen-VL (Bai et al.) 2025), SigLIP (Zhai et al. ) and modeling paradigms (diffusion transformer, unified autoregressive transformer, discrete diffusion) do not yield a consistent winner. By training on our curated dataset, model delivers strong performance gains over the original FLUX.1 Kontext, underscoring the importance of data scale and quality. Instead, models trained predominantly on natural-image corpora with limited task coverage (e.g., UniWorld-Vi V1) significantly lag behind on structured- image tasks. Besides, the performance gap is larger for T2I than for editing, likely because editing emphasizes transferable operations like add, delete, move, whereas T2I requires synthesizing fine- grained attributes from scratch, a substantially more challenging objective.
Reasoning capability has substantial impact. For relatively simple edits such as modifying colors or adding auxiliary lines (Tab. B). most models approach 50% accuracy. In contrast, performance drops sharply on more complex tasks like chart-type conversion, which require first understanding the input (e.g., proportions and ordering) and then generating the target according to structural specifications. Moreover, both the "thinking" variant Bagel-Thinl and our model with explicit reasoning substantially outperform non-reasoning baselines in both generation and editing, underscoring the benefits of incorporating reasoning before generation.
5.3 SCALING COMPUTE WITH EXPLICIT REASONING
Motivated by the strong performance of our model, we further investigate the impact of adding explicit test-time reasoning for structured image editing. We introduce an external reasoner (GPT-5) during inference that performs a three-step analysis over the instruction and input image: (1) summarize salient visual elements; (2) localize the elements to edited and specify the intended changes; and (3) forecast the post-editing outcome. This design decouples complex visual reasoning and planning from image synthesis, allowing the generator to focus on the generation task itself.
As shown in Fig. and providing explicit reasoning trajectories yields substantial improvements for most models. Notably, Bagel augmented with our carefully designed reasoning trajectories significantly outperforms its native "thinking" variant, Bagel-Think (38.44 vs. 33.34), indicating that the trajectory design and quality are critical. Moreover, the family of unified models such GPT-Image and Bagel benefits more from added reasoning than specialized expert models like

---

Published as a conference paper at ICLR 2026
Prassn Conclazio De
Narsur Condudor Codfliinn
25.0
22.23
W/ Think
22.5
20.0
10.3
3253
10.
1566
1050
GPT.maage
HDrsam
Figure 6: Pearson correlation analysis among Accu- racy, PSNR, and human Elo score for editing models.
Figure 7: Study of explicit reasoning added to different models.
Source
Prompt: Change the circular, pic-chart representation of research aren distribution vertical bar chart format displaying distinct height indicating funding amounts, while maintaining color consistency and adding labels to the horizonial and vertical axes to enhance data accessibilit..
w/ CoT
The overall plot and the x-y layout correct. llowever, the x-axis label 'Medical Research' are missing, the label assignments for the four categories are incorrect, and data is inaccurate.
Error Analysis:
Reasoning Process:
w/ Co'T
Source Description: A clcan 2D vertical bar chart titled Five categories on the x-axis:.. Bars are colored ... with approximate heights in
Editing Explanation: Focus
Preserve the existing color mapping (Space Exploration: red...).
Ensure axis labels are explicit. 3) Vorify bar heights reflect the funding amounts (Technology $5....
Editing Prediction: Final image shows a vertical bar chart with five distinet bars: Technology (cyan)
Economic
(neon green) s45... appear each
Figure 8: Case study: explicit reasoning improves fidelity. When dealing with complex tasks, direct generation often leads to plausible yet incorrect images. Incorporating a reasoner to explicitly describe, analyze, and predict enables the generator to produce faithful, correct outputs.
HiDream-E1..1 Overall, these results again validate that high-quality multimodal reasoning is essential for complex editing. Whether via interleaved multimodal pretraining or reasoning-centric post-training, unified models with native multimodal reasoning appear to be a promising path forward.
5.4 HUMAN ALIGNMENT STUDY
To evaluate how well StructScore aligns with human judgments, we conduct large-scale Elo rating study to elicit human preference rankings. Implementation details and full results are provided in Appendix A.4 We then compute the Pearson correlation coefficient (r) between the human Elo rankings and scores from various metrics across multiple benchmarks. As shown Fig. 6 StructScore achieves strong correlation with human preferences (r 0.9), substantially exceeding traditional metrics like PSNR. This verifies the effectiveness of breaking down complex evaluations into atomic questions and indicates that StructScore is reliable proxy for human assessment.
CONCLUSION
In this work, we aim to address the critical challenge of structured image generation and editing, domain where existing models fall short due to the high demand for factual accuracy. We introduced comprehensive solution, including large-scale, code-aligned dataset with chain-of-thougl! annota- tions, a strong unified model trained with a progressive curriculum, and rigorous benchmark-mett suite for fine-grained, low-hallucination evaluation. Our experiments demonstrate that even leading models struggle with structured visuals, yet our approach sets a new standard for open-source models and shows significant benefits from inference-time reasoning, In the future, hope incorporate more diverse domains of structured visuals that can be rendered via code, such as molecular formulas, musical staff, and even structured videos like educational videos.
10

---

Published as a conference paper at ICLR 2026
7 ACKNOWLEDGMENTS
This study was supported in part by National Key R&D Program of China Project 20222D0161 100. 2022ZD01 15502, in part by the Centre for Perceptual and Interactive Intelligence, a CUHK-led InnoCentre under the InnoHK initiative of the Innovation and Technology Commission of the Hong Kong Special Administrative Region Government, in part by NSFC-RGC Project N. 498/24 Ningbo Science and Technology Innovation 2025 Major Project (20257034). National Natural Science Foundation China (NO. 624661240000 160308, U23B2010). "Pioneer" and "Leading Goose" R&D Program of Zhejiang (No. 2024C01 161). In addition, this research supported in part by NUS Start-up Grant A-0010106-00-00 and in part by Guangdong Basic and Applied Basic Research Foundation (No. 202381515130000, XW).
REFERENCES
Shuai Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, Sibo Song, Kai Dang, Peng Wang, Shijie Wang, Jun Tang. Humen Zhong, Yuanzhi Zhu, Mingkun Yang. Zhaohai Li, Jianqiang Wan, Pengfei Wang, Wei Ding, Zheren Fu, Yiheng Xu, Jiabo Ye, Xi Zhang, Tianbao Xie, Zesen Cheng, Hang Zhang, Zhibo Yang. Haiyang Xu, and Junyang Lin. Qwen2.5-vl technical report. arXiv preprint arXiv:2502. 13923,, 2025.
Stephen Batifol, Andreas Blattmann, Frederic Boesel, Saksham Consul, Cyril Diagne, Tim Dockhorn, Jack English, Zion English, Patrick Esser, Sumith Kulal, et al. Flux. 1 kontext: Flow matching for in-context image generation and editing in latent space. arXiv e-prints, 2025.
Jonas Belouadi, Anne Lauscher, and Steffen Eger. Automatikz: Text-guided synthesis of scientific vector graphics with tikz. arXiv preprint 10000223 2023.
Tim Brooks, Aleksander Holynski. and Alexei A Efros. Instructpix2pix: Learning to follow image editing instructions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 18392-184022 2023.
Qi Cai, Jingwen Chen, Yang Chen, Yehao Li, Fuchen Long, Yingwei Pan, Zhaofan Qiu, Yiheng Zhang, Fengbin Gao, Peihan Xu, et al. Hidream-il: A high-efficient image generative foundation 2025.
model with sparse diffusion transformer. arXiv preprint arXiv:2505.222:
Shuo Cao, Nan Ma, Jiayang Li, Xiaohui Li, Lihao Shao, Kaiwen Zhu, Yu Zhou, Yuandong Pu, Jiarui Wu, Jiaquan Wang, et al. Artimuse: Fine- grained image aesthetics assessment with joint scoring and expert-level understanding. arXiv preprint arXiv:2507. 14533, 2025.
Wenhao Chai, Enxin Song, Yilun Du Chenlin Meng, Vashisht Madhavan, Omer Bar- Bar-Tal, Jenq-Neng Hwang, Saining Xie, and Christopher D Manning. Auroracap: Efficient, performant video detailed captioning and new benchmark. arXiv preprint arXhv:2410.0305/ 2024.
Di Chang, Mingdeng Cao, Yichun Shi, Bo Liu, Shengqu Cai, Shijie Zhou, Weilin Huang, Gordon Wetzstein, Mohammad Soleymani, and Peng Wang. Bytemorph: Benchmarking instruction image editing with non-rigid motions. arXiv preprint 1iX::2506.03100 2025.
Junsong Chen, Jincheng Yu, Chongjian Ge, Lewei Yao, Enze Xie, Yue Wu, Zhongdao Wang, James Kwok, Ping Luo, Huchuan Lu, al. Pixart-o: Fast training of diffusion transformer for photorealistic text-to-image synthesis. Proceedings of the International Conference on Learning Representations (ICLR), 2023.
Junying Chen, Zhenyang Cai, Pengcheng Chen, Shunian Chen, Ke Ji, Xidong Wang, Yunjin Yang, and Benyou Wang. torregpt----imaaaee Aligning multimodal models with gpt-4o-level image generation. arXiv preprint urXiv:25061 18095, 2025a.
Xi Chen, Zhifei Zhang, He Zhang, Yuqian Zhou, Soo Ye Kim, Qing Liu, Yijun Li, Jianming Zhang, Nanxuan Zhao, Yilin Wang, al. Unireal: Universal image generation and editing via learning real-world dynamics. In Proceedings of the IEEE Conference on Computer Vision and Pattrnn Recognition (CVPR), pp. 12501-12511, 2025b.

---

Published as a conference paper at ICLR 2026
Xiangyu Chen, Yihao Liu, Yuandong Pu, Wenlong Zhang, Jiantao Zhou, Yu Qiao, and Chao Dong. Learning a low-level vision generalist via visual task prompt. In Proceedings of the 32nd ACM International Conference Multimedia, pp. 267 16800, 2024.
Guillaume Couairon, Jakob Verbeek, Holger Schwenk, and Matthieu Cord. Diffedit: Diffusion- semantic image editing with mask guidance. arXiv preprint 2rXiv:2210.144222 2022.
Chaorui Deng, Deyao Zhu, Kunchang Li, Chenhui Gou, Feng Li, Zeyu Wang, Shu Zhong, Weihao Yu, Xiaonan Nie, Ziang Song, et al. Emerging properties in unified multimodal pretraining. arXiv preprint arXiv:2505. 14683, 2025.
Patrick Esser, Sumith Kulal, Andreas Blattmann, Rahim Entezari, Jonas Müller, Harry Saini, Yam Levi, Dominik Lorenz, Axel Sauer, Frederic Boesel, et al. Scaling rectified flow transformers for high-resolution image synthesis. In Proceedings of the International Conference on Machine Learning (ICML), 2024.
Rongyao Fang, Chengqi Duan, Kun Wang, Linjiang Huang. Hao Li, Shilin Yan, Hao Tian, Xingyu Zeng, Rui Zhao, Jifeng Dai, Xihui Liu, and Hongsheng Li. Got: Unleashing reasoning capa- bility of multimodal large language model for visual generation and editing. arXiv preprint arXiv:2503. 10639, 2025a.
Rongyao Fang, Aldrich Yu, Chengqi Duan, Linjiang Huang, Shuai Bai, Yuxuan Cai, Kun Wang, Si Liu, Xihui Liu, and Hongsheng Li. Flux-reason-6m & prism-bench: million-scale text- to-image reasoning dataset and comprehensive benchmark. arXiv preprint Siti::25090066688 2025b.
Xingyu Fu, Minqian Liu, Zhengyuan Yang, John Corring, Yijuan Lu, Jianwei Yang, Dan Roth, Dinei Florencio, and Cha Zhang. Refocus: Visual editing as chain of thought for structured image understanding. arXiv preprint arXiv:2501.054555 2025.
Dhruba Ghosh, Hannaneh Hajishirzi, and Ludwig Schmidt. Geneval: An object-focused framework for evaluating eox-tt--imaee alignment. Advances in Neural Information Processing Systems (NeurlPS), 36, 2024.
Google. Nano banana. Amage-generation//
2025.
URL attps://geeaal.ggglll//vveese
Jian Han, Jinlai Liu, Yi Jiang, Bin Yan, Yuqi Zhang, Zehuan Yuan, Bingyue Peng, and Xiaobing Liu. Infinity: Scaling bitwise autoregressive modeling for high-resolution image synthesis. In Advances in Neural Information Processing Systems (NeurIPS), pp. 15733-15744, 2025a.
Songhao Han, Wei Huang, Hairong Shi, Le Zhuo, Xiu Su, Shifeng Zhang, Xu Zhou, Xiaojuan Qi, Yue Liao, and Si Liu. Videoespresso: A large-scale chain-of-thought dataset for fine-grained video reasoning via core frame selection. In Proceedings of the Computer Vision and Pottern Recognition Conference, pp. 26181 11-1999 2025b.
Kaiyi Huang, Kaiyue Sun, Enze Xie, Zhenguo Li, and Xihui Liu. T2i-comphench: A compre- hensive benchmark for open-world compositional text-to-image generation. Advances in Neural Information Processing Systems (NeurIPS), 77777773777777 2023.
Yuzhou Huang, Liangbin Xie, Xintao Wang, Ziyang Yuan, Xiaodong Cun, Yixiao Ge, Jiantao Zhou, Chao Dong, Rui Huang, Ruimao Zhang, et al. Smartedit: Exploring complex cattucction-base image editing with multimodal large language models. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 8362-8371, 2024.
jackyhate. text-to-image-211
text -t- image- 2M. 2024.
Maksim Kuprashevich, Grigorii Alekseenko, Irina Tolstykh, Georgii Fedorov, Bulat Suleimanov, Vladimir Dokholyan, and Aleksandr Gordeev. Nohumansrequired: Autonomous high-quality image editing triplet mining, arXiv preprint 2111 2025.
Black Forest Labs. Flux. https://githhb..e hwtp://blccckkkk 1asii-labssffa 2024.
12

---

Published as a conference paper at ICLR 2026
Bin Lin, Zongjian Li, Xinhua Cheng, Yuwei Niu, Yang Ye, Xianyi He, Shenghai Yuan, Wangbo Yu, Shaodong Wang, Yunyang Ge, et al. Uniworld: High-resolution semantic encoders for unified visual understanding and generation. arXiv preprint arXiv:2506.03147 2025.
Zhiqiu Lin, Deepak Pathak. Baiqi Li, Jiayao Li, Xide Xia, Graham Neubig, Pengchuan Zhang, and Deva Ramanan. Evaluating text-to-visual generation with image-to-tex generation. In Proceedings of the European Conference on Computer Vision (ECCV), pp. 366-384. Springer, 2024.
Dongyang Liu, Shitian Zhao, Le Zhuo, Weifeng Lin, Yu Qiao, Hongsheng Li, and Peng Gao. Lumina- mgpt: Illuminate flexible photorealistic text-to-image generation with multimodal generative pretraining. arXiv preprint arXiv:2408.0266 2024.
Shiyu Liu, Yucheng Han, Peng Xing, Fukun Yin, Rui Wang, Wei Cheng, Jiaqi Liao, Yingming Wang. Honghao Fu, Chunrui Han, et al. Steplx-edis A practical framework for general image editing. arXiv preprint 2r04v:2504. 10004.17700 /7761, 2025.
Yiwei Ma, Jiayi Ji, Ke Ye, Weihuang Lin, Zhibin Wang, Yonghan Zheng, Qiang Zhou, Xiaoshuai Sun, and Rongrong Ji. 12ebench: A comprehensive benchmark for instruction-based image editing. Advances in Neural Information Processing Systems (NeurIPS), 17:41494-41575 2024.
Yuhang Ma, Xiaoshi Wu, Keqiang Sun, and Hongsheng Li. Hpsv3: Towards wide-spectrum human preference score. arXiv preprint arXiv:2508.0375/ 2025.
Ahmed Masry, Do Xuan Long, Jia Qing Tan, Shafiq Joty, and Enamul Hoque. Chartqu: A bench- mark for question answering about charts with visual and logical reasoning. arXiv preprint arXiv:2203. 10244, 2022.
Jordan Meyer, Nick Padgett, Cullen Miller, and Laura Exline. Public domain 12m: A highly aesthetic image-text dataset with novel governance mechanisms. arXiv preprint arX1v:2410.23144, 2024.
OpenAI. Gpt-5. 2025a. URL htt.pt/ss///eeea....cc//////////
OpenAI. Gpt-image-l. Batroddciing-c---mg--e-eeee
2025b.
URL
Maxime Oquab, Timothée Darcet, Théo Moutakanni, Huy Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, et al. Dinov2: Learning robust visual features without supervision. arXiv preprint arXiv:2304.07193, 2023.
Xichen Pan, Satya Narayan Shukla, Aashu Singh, Zhuokai Zhao, Shlok Kumar Mishra, Jialiang Wang, Zhiyang Xu, Jiuhai Chen, Kumpeng Li, Felix Juefei-Xu. Ji Hou, and Saining Xie. Transfer between modalities with metaqueries. arXiv preprint arXiv:2504.06256. 2025.
Zhihong Pan, Riccardo Gherardi, Xiufeng Xie, and Stephen Huang. Effective real image editing with accelerated iterative diffusion inversion. In Proceedings of the IEEE International Conference Computer Vision (ICCV), pp. 15912-15921, 2023.
William Peebles and Saining Xie. Scalable diffusion models with transformers. In Proceedings of the IEEE International Conference on Computer Vision (ICCV), volume 4172, 2022.
Dustin Podell, Zion English, Kyle Lacey, Andreas Blattmann, Tim Dockhorn, Jonas Müller, Joe Penna, and Robin Rombach. Sdxl: Improving latent diffusion models for high-resolution image synthesis. arXiv preprint arXiv:2307.01952. 2023.
Yuandong Pu, Le Zhuo, Kaiwen Zhu, Liangbin Xie, Wenlong Zhang, Xiangyu Chen, Peng Gao, Yu Qiao, Chao Dong, and Yihao Liu. Lumina-omnily A unified multimodal framework for general low-level vision. arXiv preprint arXiv:2504. 04900, 2025.
Qi Qin, Le Zhuo, Yi Xin, Ruoyi Du, Zhen Li, Bin Fu, Yiting Lu, Jiakang Yuan, Xinyue Li, Dongyang Liu, et al. Lumina-image image 2.0: unified and efficient image generative framework. arXiv preprint arXiv:2503.21758 2025.
13

---

Published as a conference paper at ICLR 2026
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In Proceedings of the International Conference Machine Learning (ICML), pp. 8748-8763. PmLR, 2021.
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. Exploring the limits of transfer learning with a unified text-to-text transformer. Journal of machine learning research, 21(140):1-61 2020.
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High- resolution image synthesis with latent diffusion models. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 10684-10695, 2022.
Team Seedream, Yunpeng Chen, Yu Gao, Lixue Gong, Meng Guo, Qiushan Guo, Zhiyao Guo, Xiaoxia Hou, Weilin Huang, Yixuan Huang, al. Seedream 4.0: Toward next-generation multimodal image generation. arXiv preprint arXiv:2509. 20427, 2025.
Shelly Sheynin, Adam Polyak, Uriel Singer, Yuval Kirstain, Amit Zohar, Oron Ashual, Devi Parikh, and Yaniv Taigman. Emu edit: Precise image editing via recognition and generation tasks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 8871-8879 2024.
Kaiyue Sun, Rongyao Fang, Chengqi Duan, Xian Liu, and Xihui Liu. T2irreasonbench: Benchmark- ing reasoning-informee informed text-to-image generation. arXiv preprint (rXii:2508.17472 2025.
Keqiang Sun, Junting Pan, Yuying Ge, Hao Li, Haodong Duan, Xiaoshi Wu, Renrui Zhang, Aojun Zhou, Zipeng Qin, Yi Wang, al. Journeyd:: benchmark for generative image understanding. Advances in Neural Information Processing Systems (NeurIPS), 16:49659-4966 2023.
Peize Sun, Yi Jiang, Shoufa Chen, Shilong Zhang, Bingyue Peng, Ping Luo, and Zehuan Yuan. Autoregressive model beats diffusion: Llama for scalable image generation. arXiv preprint 0625::2406.06525, 2024.
Alpha VL.MM Team. Lumina-dimoo: A unified masked diffusion model for multi-modal generation and understanding, 2025. URL https:://ithhb. www.alphaaaaaaaaaaapaaaca http/AllpaaVLLMMMM Laa.ia
Guo-Hua Wang, Shanshan Zhao, Xinjie Zhang, Liangfu Cao. Pengxin Zhan, Lunhao Duan, Shiyin Lu, Minghao Fu, Xiaohao Chen, Jianshan Zhao, et al. Ovis-ul technical report. arXiv preprint arXiv:2506. 23044, 2025a.
Ke Wang, Junting Pan, Linda Wei, Aojun Zhou, Weikang Shi, Zimu Lu, Han Xiao, Yunqiao Yang, Houxing Ren, Mingjie Zhan, et al. Mathcoder-vl: Bridging vision and code for enhanced multimodal mathematical reasoning. arXiv preprini arXiv:2505. 10557, 2025b.
Su Wang, Chitwan Saharia, Ceslee Montgomery, Jordi Pont-Tuset, Shai Noy, Stefano Pellegrini, Yasumasa Onoe, Sarah Laszlo. David J Fleet, Radu Soricut, et al. Imagen editor and editbench: Advancing and evaluating text-guided image inpainting. Proceedings of the IEEE Conference 2023.
Computer Vision and Pattern Recognition (CVPR), pp.
Yuhan Wang. Siwei Yang, Bingchen Zhao, Letian Zhang, Qing Liu, Yuyin Zhou, and Cihang Xie. Gpt- image-edit-1.5 m: million-scale, gpt-generated image dataset. arXiv preprint arXiv:2507.21033, 2025c.
Zirui Wang, Mengzhou Xia, Luxi He, Howard Chen, Yitao Liu, Richard Zhu, Kaiqu Liang, Xindi Wu, Haotian Liu, Sadhika Malladi, et al. Charxiv: Charting gaps in realistic chart understanding in multimodal llms. Advances in Neural Information Processing Systems, 37: 133569-113697, 2024.
Cong Wei, Zheyang Xiong, Weiming Ren, Xeron Du Ge Zhang, and Wenhu Chen. Omniedit: Building image editing generalist models through specialist supervision. In Proceedings of the International Conference Learning Representations (ICLR), 2024.
Chenfei Wu, Jiahao Li, Jingren Zhou, Junyang Lin, Kaiyuan Gao, Kun Yan, Sheng ming Yin, Shuai Bai, Xiao Xu, Yilei Chen, et al. Qwen-image technical report. arXiv preprint arXiv:2508.00 02324, 2025a.
14

---

Published as a conference paper at ICLR 2026
Chenyuan Wu, Pengfei Zheng. Ruiran Yan, Shitao Xiao, Xin Luo, Yueze Wang, Wanli Li, Xiyan Jiang, Yexin Liu, Junjie Zhou, et al. Omnigen2: Exploration to advanced multimodal generation. arXiv preprint ar arXiv:2506. 18871, 2025b.
Xiaoshi Wu, Yiming Hao, Keqiang Sun, Yixiong Chen, Feng Zhu, Rui Zhao, and Hongsheng Li. Human preference score v2: A solid benchmark for evaluating human preferences of text-to-image synthesis. arXiv preprint arXiv:2306.099 09341, 2023.
Yongliang Wu, Zonghui Li, Xinting Hu, Xinyu Ye, Xianfang Zeng. Gang Yu, Wenbo Zhu, Bernt Schiele, Ming-Hsuan Yang, and Xu Yang. Kris-bench: Benchmarking next-level intelligent image editing models. arXiv preprint arXiv:2505. 16707, 2025c.
Enze Xie, Junsong Chen, Junyu Chen, Han Cai, Haotian Tang. Yujun Lin, Zhekai Zhang, Muyang Li, Ligeng Zhu, Yao Lu, et al. Sana: Efficient high-resolution image synthesis with linear diffusion transformers. Proceedings of the International Conference on Learning Representations (ICLR), 2025a.
Enze Xie, Junsong Chen, Yuyang Zhao, Jincheng Yu, Ligeng Zhu, Yujun Lin, Zhekai Zhang, Muyang Li, Junyu Chen, Han Cai, et al. Sana 1.5: Efficient scaling of training-time and inference-ting compute in linear diffusion transformer. arXiv preprint arXiv:2501. 18427, 2025b.
Yi Xin, Juncheng Yan, Qi Qin, Zhen Li, Dongyang Liu, Shicheng Li, Victor Shea-Jay Huang, Yupeng Zhou, Renrui Zhang, Le Zhuo, et al. Lumina-mgpt 2.0: Stand-alone autoregressive image modeling. 2025.
arXiv preprint ar 2rxiv:2507.17800
Binxin Yang. Shuyang Gu, Bo Zhang, Ting Zhang, Xuejin Chen, Xiaoyan Sun, Dong Chen, and Fang Wen. Paint by example: Exemplar-based image editing with diffusion models. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 18381-18399 2023.
Junyan Ye. Dongzhi Jiang, Zihao Wang, Leqi Zhu, Zhenghao Hu, Zilong Huang, Jun Hc, Zhiyuan Yan, Jinghua Yu, Hongsheng Li, et al. Echo-4o: Harnessing the power of gpt-4o synthetic images for improved image generation. arXiv preprint 2VXiv:2508.09987, 2025a.
Yang Ye, Xianyi He, Zongjian Li, Bin Lin, Shenghai Yuan, Zhiyuan Yan, Bohan Hou, and Li Yuan. Imgedit: unified image editing dataset and benchmark. arXiv preprint arXiv:2505.20275, 2025b.
Xiaohua Zhai, Basil Mustafa, Alexander Kolesnikov, and Lucas Beyer. Sigmoid loss for language image pre-training. In Proceedings of the IEEF/CVF international conference on computer vision, pp. 11975-11986, 2023.
Renrui Zhang, Dongzhi Jiang, Yichi Zhang, Haokun Lin, Ziyu Guo, Pengshuo Qiu, Aojun Zhou, Pan Lu, Kai-Wei Chang, Yu Qiao, al. Mathverse: Does your multi-modal I1m truly see the diagrams in visual math problems? In Proceedings of the European Conference on Computer Vision (ECCV), pp. 169-186. Springer, 2024.
Xiangyu Zhao, Peiyuan Zhang, Kexian Tang, Xiaorong Zhu, Hao Li, Wenhao Chai, Zicheng Zhang, Renqiu Xia, Guangtao Zhai, Junchi Yan, et al. Envisioning beyond the pixels: Benchmarking reasoning-infe inffommed visual editing. arXiv preprint arXiv:2504.0282 02826. 2025a.
Xuanle Zhao, Xianzhen Luo, Qi Shi, Chi Chen, Shuo Wang, Zhiyuan Liu, and Maosong Sun. Chartcoder: Advancing multimodal large language model for chart-to-code generation. arXiv preprint arXiv:2501.06596 2025b.
Le Zhuo, Ruoyi Du, Han Xiao, Yangguang Li, Dongyang Liu, Rongjie Huang, Wenze Liu, Xiangyang Zhu, Fu-Yun Yun Wang. Zhanyu Ma, al. Lumina-next: Making lumina-12x stronger and faster with next-dit. In Advances in Neural Information Processing Systems (NeurIPS), 2024.
Le Zhuo Liangbing Zhao, Sayak Paul, Yue Liao, Renrui Zhang, Yi Xin, Peng Gao, Mohamed Elhoseiny, and Hongsheng Li. From reflection to perfection: Scaling inference-ting optimization for text-to-image diffusion models via reflection tuning, arXiv preprint arXiv:2504. 100004..6080 16080, 2025.
15

---

Published as a conference paper at ICLR 2026
A
APPENDIX
A.1
RELATED WORK
A.1.1 GENERATIVE MODELS FOR VISUAL CONTENT
Text-to-Ine Text-to-Image Generation. The text-to-image (T2I) field has advanced rapidly in recent years, with diffusion models driving much of the progress, Early U-Net-based diffusion models (Rombach et al.
2022 Podell et al. 2023) have been followed by diffusion transformers (Peebles & Xie 2022 Chen
et al. 2023 Esser et al. 2024 Zhuo et al. 2024; Xie et al.) 2025a Labs 2024] Qin et al. 2025 Xie
ctt al. 2025b). which substantially improve scalability and fidelity. More recently, aided by advances
in large language models and visual tokenizers, autoregressive image generation has narrowed the
gap with diffusion-basee methods (Sun et al. 2024 Liu et al.) 20244 Xin et al.] 2025 Han et al.
(025a)). State-of-the-art T2I systems now produce high-resolution, photorealistic, and aesthetically appealing images, largely through scaling data and model capacity.
Image Editing. Instruction- based image editing has evolved in tandem with T2I. Some early approaches (Couairon et al.) 2022 Pan et al., 2023 Yang et al. 2023) achieved training-free edits by manipulating or guiding the denoising trajectory during diffusion sampling, but these methods
often lacked stability and robustness. Subsequent work (Brooks et al. 2023 Sheynin et al. 2024
Huang et al., 2024 Wei al., 2024] Chen al.) 2024; Pu et al.] (025) has increasingly framed editing as controllable generation, constructing large-scale editing datasets to fine-tune pre-trained T2I backbones. Recently, systems such as GPT-Image (OpenAl 2025b), Nano Banana (Google) 2025), and Bagel (Deng et al.] (025)) have begun to unify visual generation, editing, and even visual understanding within a single framework. These unified models inherit world knowledge from language models and, through data-driven multimodal training, exhibit strong instruction following alongside emerging reasoning capabilities.
A.1.2 DATASET AND BENCHMARKS FOR GENERATION AND EDITING
Datasets. Most T2I datasets (Meyer et 2024 Sun al. 2023 Ye et al. 2025a Fang et al.
2025b
consist of natural images, either filtered from real photographs using aesthetic scores or synthesized with open-source models. This bias has encouraged models to overfit aesthetic metrics, often yielding images with a conspicuous "AI look." Image-editing datasets (Wei et al.] 2024 Ye et al.) 2025b) are largely inspired by InstructPix2Pix (Brooks et al. 2023). where synthetic pairs are produced via expert models and carefully designed pipelines, other efforts extract editing pairs from frames in real or synthetic videos (Chen et al. 2025b Chang et al.) 2025). Consequently, these editing datasets can content and video-derived pairs introduce substantial noise. In contrast, our dataset provides strict code-image alignment and enforces precise state transitions through explicit code-level editing operations, ensuring faithful and verifiable supervision for both generation and editing.
be imprecise, as both
A.2 TRAINING DETAILS
The training of our model is organized into three progressive stages as shown in Fig. 9 The hyperparameters for the three-stage training process are provided in Tab. 4 Throughout all stages, we incorporate a diverse mixture of image generation and editing datasets, spanning both natural and structured sources, which helps the model learn generalizable representations and effectively adapt to diverse editing scenarios. Specifically, simple text-to-image datasets (fackyhate 2024 Chen et al. 2025a) and various image editing datasets (Chen et al. 2025al Wang et al. 2025c Ye et al., 2025b Kuprashevich et al., 2025) are used for stage training. For stage training, we further incorporate editing and rext-to-image data from structured dataset, along with recent high-quality text-to-image dataset (Fang et al.) 2025b). For stage 3 training, we use the same data sources stage 2, but replace the short prompt with the detailed thain-of-thoughh instruction.
16

---

Published as a conference paper at ICLR 2026
MMDIT
MMDIT
MMDIT
MLP
VAE
MLP
VAE
T5
MLP
VAE
T5
VLM
VLM
VLM
Stage Unified Alignment
Stage Hybrid Visual Learning
Stage Thinking Enhancement
Figure 9: Progressive training pipeline. For the Unified Alignment stage, only newly added MLP parameters are updated. Both MMDiT and MLP parameters are updated during the Hybrid Visual Learning and Thinking Enhancement stages.
Table 4: Training hyper-parameters for our model.
Hyper-parameters
Stage
Stage
Stage 3
trainable parameters
MLP
MLP DiT
MLP DiT
warmup steps
500
100
learning rate schedule
constant
optimizer
AdamW
optimizer hyper- parameetsssss
31, B2
(0.9,0.95) 0.95)
weight decay
0.1
gradient norm clip
0.5
deepspeed stage
zero1
zero2
zero2
learning rate
Se-4
le-5
le-5
batch size max text length
1024
512
512
256
256
512
A.3 ADDITIONAL EVALUATION RESULTS
Using Qwen2.5-VL-12 VL--2BB as evaluator. With the rapid improvement of open-source models, it has become increasingly feasible to adopt them as cost-effective evaluators. Therefore, we provide full benchmark results using open-source Owen2.5-VL-77. (Bai et al. 2025) our evaluator.
The ranking trends evaluated by GPT-5 OpenAl 2025al and Qwen2.5-Ve L are generally well aligned.
Both evaluators reliably highlight the strongest models (e.g., Nano Banana (Google 2025), Seedream 4.0 (Seedream et al. (025)) and the weakest one (e.g., UniWorld-V (Lin et al. 2025 ). And we observe that slight discrepancies arise only among closely performing models. For example, GPT-5 slightly favors Seedream 4.0 over Nano Banana (shown in Tab [T. whereas Qwen2.5-Ve shows the opposite trend in Tab A similar alignment holds the Struer121Bench, where both evaluations yield nearly identical overall rankings, with only minor variations among the top-performing models (Tab. 2 vs. 7.. This alignment indicates that current open-source models have the potential to promote broader adoption of apen-source evaluation within the community, reducing reliance on proprietary systems while maintaining reliable assessment quality.
Different metrics analysis. As shown in Tab. 5 Tab. and Tab. we observe that previously widely used metries such as PSNR and SSIM are not reliable in structured visuals. since evaluation here emphasizes semantic-level correctness rather than purely pixel-level correspondence. Both GPT-5 and Qwen2.5-VI. evaluators consistently reflect this phenomenon. further validate this finding, we conduct human alignment study, with detailed results presented in Fig.
General editing benchmark evaluation. To assess the effectiveness our proposed multi-stage training pipeline in preserving model capabilities in general domains, we provide the results evaluated on ImgEdit (Ye et al. 2025b) benchmark in Tab. 8. Our model achieves results competitive with ofate-of-the-art systems and outperforms the original FLUX.1 Kontext, validating that the training pipeline improves editing competence without sacrificing general-dowe domain capability. We provide
additional qualitative samples from ImgEdit benchmark in Fig. T0
17

---

Published as a conference paper at ICLR 2026
Table 5: Quantitative comparison on StructEditBench with Qwen2.5-VL-72B evaluator. The table showcases the results of Accuracy (%), PSNR and SSIM (with GT image) for various methods. indicate the first and second Accuracy results, respectively.
Math
Clast
Graph
Pinde
Sekene
Talec
Overali
Aoc
PSSR 1
SSIEE
Acc
PSSEE
AzE1
PINR
553ME
Ace
FSNGL
Acc
PINE.T
SSIVT
AcE
A4C1
FSNRT
SIRM
cerlllaage
12:56
17:34
55.55
14:55
64.91
News Raness
20:17
0.8536
01:31
16:33
0.790
66.73
31.25
0.8805
$3.35
22:03
01902
55.33
22:11
03844
65.33
1375
6.006
18.35
71.09
3,34
0.6354
5.57
0.562
DaMBO
13:35
31:56
03:000
10:48
16:30
33:27
53.36
0955
13.11
22.47
33300
16:33
51.0
13:44
17:43
07183
11:31
16.55
16:50
15:0
03615
37.43
18:60
18.00
15:09
Ri
13:00
19:03
0.330
13.35
17:91
0.795
33.40
18.36
0.6665
18:04
25.0
16-07
15.33
17:1
03118
23.07
16:01
0996
Ragel
ThakkThakk
23.02
23.57
16.53
0/822
26.49
23.22
20.35
33.19
23.57
15.33
0.8331
26.17
00730
27:33
23:41
0.8311
16.55
09155
11
18.56
0.9755
0385
00788
35.09
34.66
09011
33
019697
28.36
36/15
09736
Qvee 12l:
34.00
33.15
0.8764
35.50
12.33
B4600
18.25
36.11
0.884
43.35
27.31
0.776
63.08
25.83
09782
58.33
25.73
0 0.905
31.90
34.91
(0731
13.11
0.315
1531
0.70%
37.30
31.65
0.465
$7.58
76
53.9
69618
33.14
56
Table 6: Quantitative comparison of different methods on StructEditBench (Chart only) with indicate the first and second Accuracy results, respectively.
Qwen2.5-VL-72B evaluator.
Misel
Category
Color
5 Num
Amsillary
Add&DDi
Overall
Acc 7
PSNK
SSIM 1 /662
PSNK.T
SSIM
Ao2
PSNK 7
SSIM
Aue
PSNRR
SSIM
Ace 7
PSNR 1
SSIM
Acc
PSNR.
SS3M
33:65
9.55
0.6755
45.25
13.91
0,5902
31.33
13.48
0.6595
50.63
13.48
0.6315
33.55
12.73
0.6386
39.61
12.55
0.6550
Nano Banuna
10.31
0.6718
17.50
0.8117
15.56
0.3665
55.05
17:19
0.7591
18.72
19:38
(1/680
16.93
0.7992
Sombeam
9.67
0.6668
54475
31.77
0,9152
39.62
19.20
0.3309
$8.69
19:46
0.8490
14:38
0.7457
43.17
16.54
0.7992
Uniword
6.52
7,60
015867
7.53
3:29
0,5542
4.71
7:57
0.4904
8.90
K.62
0.8754
2555
7.35
01409
6.19
7.57
DSMO0
525
9:42
0.6999
8:54
16.97
0
11.40
17.30
0.7750
9:90
17.59
0,2770
4.61
14.45
0.7100
8.76
14.95
017457
OumiGes2
12:41
3,51
0.6579
21.35
11.78
0.7756
13.91
14.03
0.7138
13.05
16.59
0.7539
9.35
12.01
0.6335
13.44
12,44
0.7157
Ova-4:1
11:11
0.57
01867
20.54
15.38
0.7656
13.35
15:19
0.3167
11:27
14:25
0.6541
11:35
15.11
0.6612
13.48
13:90
0.854
Hidromn-E1.1 1
18.14
0:30
0.43%
30.63
14.07
0.7667
15.60
15.04
0.3418
22.30
14.34
0.7135
13.86
12:31
0.678
20.25
12.91
0.7050
Bagel Think
20.19
9.08
0.602
36.04
10.25
0.632
24.15
20.29
0.3645
23005
21.24
018885
16.42
14:53
0.733
23.34
16:83
B12227
Bagel
0.614
40.57
0.9356
30.00
0.5760
36.19
30.52
0.8764
14.38
16:35
0.178
SteplX
10:40
33.09
0.9202
36.38
19:46
0.8992
31.77
0.7712
16.63
0.154
0.7334
Oue
39.55
10.40
0.7075
50.46
18.16
0,8257
30.39
17.04
0.7721
48.99
17.30
0.7685
31.73
14.37
0,7012
40.44
15:33
0.7328
A.4 DETAILED HUMAN ALIGNMENT STUDY
Details of Elo rating. We leverage Rapidate to collect human preference annotations. We compen- sated all anmotators at or above the local minimum wage. After collecting human preferences, we adopt a robust Elo rating scheme to analyze the results of human studies. For match between model A and model B with current ratings (RA, RR), the expected win probability of A is defined as:
E
Rn RA
10
where a = 400 is the scaling factor.
Given the empirical vote proportion E [0, for model A (with 8n = - 8n), the ratings are updated as:
R'' max (amin RA + Kott EA)),
R'B max( max(Rmin, RB + Ket(SB EB)),
where Ken = K V is the effective step size, scaled by the number of votes V, Ruin 700 is the rating floor, and K 24 is the base learning rate.
5
To improve robustness, we shuffle the match order and repeat the Elo computation for T' = 50 rounds. The final Elo score for each model m is reported as the mean and standard deviation across rounds:
T
T
Rm
1 T
R m
Tm
(t) R
Rm
(3)
T
This procedure accounts for vote strength, enforces a minimal rating floor, and provides both robust mean scores and uncertainty estimates. The parameter setting is shown in Tab.
hhtps//wwww.mmpdan..ai
18

---

Published as a conference paper at ICLR 2026
Table Quantitative comparison on StructT2[Bench with Qwen2.5-VL-72B evaluators, report- ing Accuracy (%).
Model
Chart
c Graph
Math
Puzzle
& Science
Table
Overall
Seedream 4.0
32.07
48.38
55.62
42.81
54.43
65.61
42.33
Nano banana
33.26
52.00
54.87
53.26
47.81
65.20
43.16
GPT-Image
34.00
51.47
53.12
$1.05
52.52
79.26
44.08
Bagel
2.33
0.78
1.46
2.51
5.19
2.94
2.21
Bagel-Think
2.83
10.56
8.87
11.94
11.76
8.35
5.93
Uniworld
4.66
11.82
10.45
8.01
14.14
9.85
7.40
Hidream-I1-Full
5.07
16.57
14.29
13.61
22.65
24.69
10.39
OmniGen2
8.62
19.23
16.93
14.51
22.63
19.96
12.87
Flux1.1 Dev
10,28
18.84
17.43
18,64
19.06
24.66
14.19
Fulx Kontext
14.81
20.60
16.73
21.68
24.54
26.44
17.09
Ovis-U1
29.33
11.79
11.74
17.89
18.02
10.21
21.81
Qwen-Image
29.58
42.99
41.74
42.32
45.14
69.00
37.03
Ours
15.90
23.83
31.87
24.87
31.69
27.81
22.12
Table 8: Quantitative comparison on ImgEdit-Full benchmark.
Model
Add
Adjust
Extract
Replace
Remove
Background
Style
1ybrrid
Action
Overall
MagicBrush
2.84
1,58
1.51
1.97
1.58
1.75
2.38
1.62
1.22
1.90
Instruct-Pix2PA
2.45
1.83
1.44
2.01
1,50
1.44
3.55
1.20
1.46
1.88
AnyEdit
2.95
2.24
1.56
2.65
UliraEdit
3,44
2.81
2.13
2.96
1.45
2.83
3.76
1.91
2.98
2.70
OmniGen
3.47
3.04
1.71
2.94
2.43
3.21
4.19
2.24
3.38
2.96
Step1X X-Fdiit
3.88
3.14
1.76
3.40
2.11
3.16
4.63
2.64
2.52
3.06
ICEdit
3.58
3.39
1.73
3.15
3,08
2.04
BAGEL
3.56
3,31
1.70
3.30
2.62
3.24
4.49
2.38
4.17
3.20
UniWorld-V1
3.82
3.64
2.27
3.47
3.24
2.99
4.21
2.96
2.74
3.26
OmniGen2
3.57
3.06
1.77
3.74
3.57
4.81
2.52
4.68
3.44
FLUX.1 Kontext
3.76
3.45
3.78
2,96
3.52
GPT-Imaaee
4.61
4.33
2.90
4.35
3.66
4.57
4,93
3.96
4.89
4.20
Qwen-
4.38
4.16
3.43
4.66
4.14
4,38
4.93
3.82
4.69
4.27
Ours
4.15
4.07
1.62
4.01
3.60
4.01
4.52
3.58
4.22
3.75
Table Elo parameter setting.
Parameter
Number
initial Elo mean
1,000
Elo standard deviation
300
base of logarithm scaling factor
400
K-factor
minimum Elo rating number of simulated matches
700
4,500
Results analysis. depicted in Fig. StructScore Accuracy demonstrates much stronger corre- lation with human evaluation results than conventional metrics such as PSNR and SSIM. Specifically, StructScore achieves Pearson correlation of 0.87 at the overall level and r 0.84 on chart- specific cases, substantially higher than PSNR (r 0.69 /0.64) and SSIM (r 0.70/0.55). These results clearly indicate that StructScore better captures the semantic-level fidelity that aligns with human preference, whereas pixel-level metrics fail to reflect such perceptual correctness. However, compared with GPT-5 evaluators' r = 0.92 (shown Fig. 6). the alignment of Qwen2.5-VL. with human evaluation is still weaker. This is probably because its performance such structured images remains inferior to closed-source models, which could be improved as open-source models continue to advance.
19

---

Published as a conference paper at ICLR 2026
Input
Ours
Input
Ours
Prompt: Change the animal's fur color to a lighter shade of brown.
Prompt: Change the color of the suit to deep blue.
Prompt: Add deer standing the edge of the snow-coveree forest the right side of the image, close to the leaning tree.
Prompt: Replace the red trolley in the image with giant vintage radio, keeping it the same train tracks in the urban environment.
Prompt: Transfer the image into a bold halftone pop-as art comics style.
Prompt: Remove the child wearing the firefighter uniform from the image.
Figure 10: Qualitative results of our model on ImgEdit-Full benchmark.
A.5 ABLATION ON DIFFERENT WEIGHT SETTING
We conducted a comparative study of benchmark ranking calculated with different weight ratios by evaluating their alignment with human preference judgments. As illustrated in Fig. [12 the configuration with a 1:9 weighting achieved the highest correction coefficient, followed by the 2:8 setting. In contrast, the 3:7 and 4:6 ratios yielded substantially lower values. These findings demonstrate that the 1:9 ratio most accurately captures the alignment between the model's evaluation accuracy and human preference consistency.
A.6 CASE STUDY
For qualitative evaluation, we provide text-to-image generation and image editing examples across various models in Fig. 13 and 14 Utilizing Nano Banana 2, we generate collection of structurally complex and more realistic images, on which our model only trained code-rendered images exhibits generalization capability (shown Fig. (6). These results demonstrate that proposed dataset effectively promotes the advancement of unified models within the structured image domain.
20

---

Published as a conference paper at ICLR 2026
Peersnt Cone ation Conffciiet mi0.44
Pecrson Nucoonn Condiatinn Co
Paraan Carre olizn Confiizent re0.3
40
10
30
10
10
0.85
0.60
9:0
1000
1000
1400
150
10000
3050
950
1000
1050
Eie Score
Bo Score
Eo Score
(a) Correlation of overall metrics with human preference
Pearson Corre otion Conffiiont
00.87
Pearsp Corre atton Coefficiet ru0.54
Pearson Coree alton Confiicient n.0.55
50
27.5
0.05
25.0
0.60
40
22.5
20.0
37.5
15.0
0.60
12.5
0.55
10.0
0.50
900
850
1002
1050
7.5
930
850
1000
1050
900
950
1009
1850
Score
Eio Score
Eo Score
(b) Correlation of chart-specifie metrics with human preference
Figure 11: Pearson correlation analysis among Accuracy (Qwen2.5-VL-72B evaluator), PSNR, SSIM and human Elo score for image editing.
Peamen Co
Feasoon Cometaion Cod
Pasran Commiction Corffileetr
Fearson Coeffic
(8) 1:9
(b) 2:8
(c) 3:7
(d) 4:6
Figure 12: Correlation of our metric with human preference under different weighting ratios.
GT
GPT-e
Nano Banana
Seedream 4.0
Flux Kontext
Ours
Quontym Computtnn
Quantum Compating Overvisnr.
3.39
colozed sectors under sectors laheled around circle Quantua Crypfography at the upper right black, Quantur Communication Correction the lower
Pxompt: irrage
circulaz
"Quantue Computing Overvicw."
red, Quanntun Sinulation the lower right light peach, Quantuu left in pusple, and quantun Algorithn at the left in bright green. Percentage texts appear on the sectors as 25.58 on the green sector, 15.68 on the purple sector, 28, 1N on the light peach sector, and 9.4% on the dazk red sector. The black sector labeled Quantun Czystography does not display percentage text. The labels are positioned outside the circle neaz their corresponding sectors a white backgzound.
right
GT
GPT GPT-Image
Nano Banana
Seedream 4.0
Flux Kontext
Ours
Flowors plants
Infortrrrt plactts
number
Disy
Thendyy
To rales
Frnday
0
trides
Grardy
Prompt: Green table titled "Flowers plants" summricing counts of flosers by day. Tao columns: "Day" (left) and "Number of flowers" (right), with bold headezs. Rows list five consocutive days and integer counts: Mediesdda- Thursday=4, Fricay-B, Sorurday-. Sunday-S. The table has black gridlires, light-green cell fill, and centexed black text, The data show nisheek peak on Friday (0), with equal counts on Mednesday, Saturday, and Sunday (5 each), and the vinimus on Thursday (4).
Figure 14: Qualitative comparison for structured image generation. "GT" stands for ground truth.
21

---

Published as a conference paper at ICLR 2026
Input
GPT-Image
Banana
Seedream
Flux Kontext
Ours
Proept: And dashed red harizontal Iime at the overall
sedian and include legend Tabel "Overall Nedian".
Input
GT
GPT -Image
Nano Banana
Seedream 4.0
Flux Kontext
Ours
Prompt: cixcle
Input
GT
GPT -mmage
Nano Banana
Seedream 4.0
Flux Kontext
Ours
Pronpt: Add the label "Now Categozy" kith segeent s1ze 142: and colox 'gold"
Input
GT
GPT-Image
Nano
Seedream 4.0
Flux Kontext
edge
upper right
upper right
Input
6T
GPT-Imag
Nano Banana
Seedream 4.0
Flux Kontext
Ours
Pzoept:
segrent
Figure 13: Qualitative comparison for structured image editing, "GT" stands for ground truth.
Initial Q&A pair
Atomic Q&A pairs
Question: Where the dark red slice labeled "Feature what pereentage is shown inside it?
Question: Where is dark red slice labeled "Feature this figure? GT Answer: Left.
GT Answer: A dark red slice labeled "Feature 1" is en the left.
with the percentage "13 33,2%% shown inside
dark red slice labeled "Feature is chart, Inside percentage shown around 13%. GPT-5 Judgement:
Model Response:
Question: percentage shown
located the middle side
inside the slice labeled " Feature the pie chart?
False
GT Answer: 13.2%
Figure 15: Comparison of the initial and revised atomic Q&A pairs. Initial Q&A pairs sometimes entangle multiple attributes, hindering unambiguous verification and accurate scoring. Enforcing atomicity, i.e. one attribute or relation per Q&A, substantially improves metric reliability.
22

---

Published as a conference paper at ICLR 2026
Input
Ours
Input
Ours
Eng Rocemmaatty
lrea fnveresenon
Prompt:
Change the blue color in the chart to green.
Prompt: Remove all line segments connected to node 3.
Prompt: Remove the red dashed lines from the chart.
Prompt:
Replace the number with 5.
Figure 16: Qualitative results of the generalization capability of our model.
23

---

Published as a conference paper at ICLR 2026
A.7 PROMPTS
In this section, we present the complete set of prompts utilized in the data generation pipeline, alongside those employed for evaluation. Specifically, these include the prompt designed for editing pair construction in Fig. [7 the prompt for generating the T2I prompts in Fig. [8] the prompt used for constructing CoT in Fig. 19 the prompt applied for benchmark description construction Fig. 20 the prompt for benchmark Q&& construction in Fig. 21 the prompt for benchmark Q&A refinement in Fig. the the prompt for GT judgement in Fig. 23
Editing Pair Construction
You will be given: (a) STEM-related figure, and (b) the source code that renders it. Your goal is to analyze the figure and produce one salient feature and matched pair of edit commands by strictly following these steps:
1. Identify Salient Feature:
First, internally determine the image's academic discipline or content domain (e.g., geometry, mathematical chart, puzzle game, physics diagram, photograph, etc.).
Then, based the identified domain, describe its most core and relevant characteristic in concise sentence.
For a geometric figure, describe key "geometric feature."
For a mathematical chart, describe core "mathematical property" (such as a trend or relationship).
For puzzle game, describe a key "game state rule." For other specialized images, identify the domain-related core content.
Produce Edit Commands:
Based on the feature above, provide pair of concise, direct instructions (code edit command, image edit command) that modify one core semantic element to create new (code, image) pair.
Use diverse editing operations (e.g., add, remove, replace, move, rotate, scale, etc.) to modify diverse attributes of the diverse elements in the image.
For code edit command, provide precise and executable instruction to modify the source code to achieve the desired change.
For image edit command, derive only from the visual content in the image. Do not mention specific content in the source code, such as exact numeric values.
The two commands must strictly correspond: executing the code edit should produce the stated image change on exactly one element.
Your oulput must strietly adhere to the format below, containing no additional text explanations.
Salient Feature: [A single sentence describing the most relevant, domain-specife feature] Code Edit Command: [one precise, code-level instruction that implements the image edit] Image Edit Command: [one concise, visual-only instruction for a single element]
Figure 17: Prompt constructing the image editing pairs with code commands.
24

---

Published as a conference paper at ICLR 2026
T21 Prompt Construction
You are an expert describer non-natural STEM visuals (e.g., plots, charts, diagrams, schematics, equations, geometric figures, etc.). Given an image, produce single, self-contained caption that enables reader to understand the content without seeing the image.
Requirements:
1. Identify the type of visual and its main concept or purpose.
Describe all visible elements with their structure and relationships.
For each element, precisely describe its attributes like positions, values, symbols, text labels, colors, scales, and other annotations, if applicable.
4. Be specific and technical where appropriate, using proper STEM terminology.
5. Use concise, well-structured sentences or short paragraphs, not bullet lists.
Do not mention non-existing elements in the image.
Provide ONLY the caption text coherent string within 200 words, no additional commentary,
Figure 18: Prompt to obtain captions for non-natural STEM data.
CoT construction
You are expert image editor analyzing an editing task. Given a source image, target image, and an editing instruction, annotate Chain-of- Chain-of-Thought reasoning step by step for the edit.
Step I: Describe the key elements and their notable attributes source image.
Step 2: Identify the elements areas in the source image that need to be edited and precisely predict the specific changes to make.
Step 3: Describe the final edited image, especially focusing on the modified content and how
integrates with the unchanged parts of the source image to achieve the target result.
Make sure the content in each step is concise but information-dense. Besides, make sure the information in each step is distinct and no overlapping.
Editing Instruction: fedit ciimindcm
Output ONLY in this exact JSON format, with no additional text before or after. Ensure your response a valid JSON object:
"stepl": "Your description for Step 1",
"step2": "Your identification for Step 2", "step3": "Your description for Step 3",
Figure 19: Prompt to obtain reasoning traces for the editing task of non-natural STEM images.
25

---

Published as a conference paper at ICLR 2026
Benchmark deseription construction
You expert describer of non-natural STEM visuals (e.g., plots, charts, diagrams, schematics, equations, geometric figures, etc.). Given an image, produce single, self-contained caption that enables reader to understand the content without seeing the image. Caption the provided target image by following these requirements: Edit instruction: {edit instruction).
Identify the visual type and its primary purpose.
Describe all visible elements, their structure, and relationships.
Include explicit attributes such positions, values, text labels, colors, scales, and annotations.
4. Use concise, well-structured sentences with technical terminology when appropriate.
Do not invent elements that are not visible.
Return only the caption text as a single coherent paragraph within 200 words.
Figure 20: Prompt for evaluation.
Benchmark Q&A construction
Convert following image description into Q&&AA format by breaking down EVERY piece of information inlo the most detailed Q&& pairs possible.
Instructions:
Break down FACH sentence into its smallest meaningful information units
2. compound sentences or lists, create SEPARATE Q&A pairs for EACH individuul item or fact
Keep answers as concise as possible use only essential words/numbers needed
4. Format: "Q: [question]" followed by "A: [answer]" on next line
Examples how to break down:
"The title of the plot 'Distribution of Customs""
Q: What
of this plot?
Distribution Customs
"The numbers Af B, C, are 50, 70, 80, 100, respectively."
What the number
For lists multiple items in sentence, individual pairs EACH item
(like "The". "is", when possible while keeping meaning clear 8. Fach A pair should immediately followed by the next with only single line break between them not skip any information from the original description
7. Remove unnecessary words from
10. add information present the original description
Image description: (caption)
Convert ALI. information above into detailed Q&A pairs:
Figure 21: Prompt for constructing question-answer pairs for our benchmark.
26

---

Published as a conference paper at ICLR 2026
Benchmark Q&A Refinement
Task: Rewrite the question and answer to be clear and unambiguous
The model gave an incorrect answer, likely because the original question was ambiguuus or answer didn't match what's aetually in the imuge. Your lusk is lo rewrite BOTH the question and answer while maintaining their original intent. CRITICAL REQUIREMENTS:
Preserve the original intent - The rewritten Q&& must ask und answer about the SAME aspect/element as the original Make the question unanbiguous Remove any vagueness so there's only ONE possible correct answer Be specific about visual elements Reference specific parts image (e.g. "the blue bar", "the leftmost column", "the value at point
Ensure answer matches question The answer must directly correspond Keep the answer concise and precise Use minimal words while being accurate Both must verifiable Anyone looking at image should arrive same answer
the question asks
Original Question: (question)
Original Answer: (ground truth answer)
Model's Incorrect Response: (model response)
Rewriting Guidelines:
askking value: Specify WHICH value (e.g. "What the value of at year 2020?" not just "What the value?")
asking about colors: Specify WHICH element's color (e.g.. "What color is the bar category A?")
asking about comparisons: Specify WHAT being compared (c.g. "Which month has the highest sales: Jan, Mar?")
askking ahout locations: precise (e.g- which quadrant red point located?")
Avoid pronouns without clear antecedents (it, this, that)
Avoid relative
without reference points (high, low, large, small)
Examples of good rewrites:
Vague: "What is the lighest value?" - Clear: "What the highest valtue the Y-axis?"
Vague: "What color it?" Clear: "What color the line representing sales data?"
Vague: "Where is the peak?" Clear: "Al which X-coordinate does the blue line reach its maximum value?"
Output Format:
Output your response JSON format with exactly these two keys:
("rewritten question": "[elear. unambiguous question about the SAME aspoct originalJ", "rewritten answer": "[precise that matches the rewritten question and the image]")
Remember: Keep the same intent as the original Q&AA, just make clearer and more precise!
Figure 22: Prompt to refine the question-answer pairs.
VLM GT Judgement
Task:
Evaluate if the model's response is correct.
Based on the "Ground Truth Answer" and "Model Response" provided below, please determine if the "Model Response" is acceptable.
Evaluation Criteria:
Textual Part: The model's response should contain key information from the "Ground Truth Answer".
Accept responses that include the correct answer, with additional context minor phrasing differences
Reject responses that provide fundamentally different or incorrect information case-insensitive comparison
Numerical Part: tolerance 10% allowed for any numerical values compared to the "Ground Truth Answer". You are expected to perform this calculation check if the value is within the acceptable range.
Inputs:
Output Requirements:
Please judge strictly according to the rules above and output only the single word "Correct" or "Incorrect". Do not include any other explanations, reasons, or punctuation.
Figure 23: Prompt to check if ground-truth and mdoel response are similar.
27


Reviews:
[{"review_id": "96gLLuwdRD", "reviewer": "Reviewer_HadZ", "summary": "This paper tackles a point in current image generation: models are great at making artistic \"vibes\" but absolutely terrible when you need them to be factually accurate, like drawing a specific chart, a math diagram, or a table.\nTo fix this, the authors didn't scrape the web; they generated a huge dataset (1.3M pairs) by running actual code (like Python/LaTeX), ensuring perfect ground truth. They then trained a FLUX-based model using a three-stage curriculum designed to make the model \"think\" more about structure before generating. They also had StructBench, a benchmark for these tasks. Seems like this benchmark is difficult.", "questions": "See above comments.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "Tackles an under-researched area where standard aesthetic-focused models fail precisely because high factual accuracy (text rendering, exact layout) is required.\n\n\nGood idea: Utilizing programmatically generated images (via code) creates a verifiable and noise-free ground truth, superior to scraping diverse but unreliable web data for this specific task.\n\nStructScore improves upon naive \"VLM-as-a-judge\" approaches by using fine-grained, verified Q&A pairs, showing strong alignment with human evaluators.\n\nIntegration of Reasoning in the paper", "weaknesses": "1. Do we really need image editing to do this task? People can just ask LLM to write code to generate the new image, which is more accurate. With pixel level image generation model, there will be inevitable artifacts. \n\n2. How to make sure this data curation pipeline is accurate? Any error rate statistics?\n\n3. No evaluation in addition to infographics. You should evaluate on the natural image editing task.", "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": "pcFi9J9zZr", "reviewer": "Reviewer_pUs6", "summary": "The paper targets structured image generation/editing (charts, math/graph diagrams, tables, scientific schematics), where current T2I/VL models often produce plausible but factually wrong images. The authors build a 1.3M code-aligned dataset with paired instructions and CoT, plug Qwen-VL into FLUX.1-Kontext via a lightweight connector, train in three stages, and propose StructBench + StructScore to evaluate fine-grained factuality. They report strong gains over open models and show that adding explicit reasoning helps other models too", "questions": "- Have you considered evaluator bias? In StructScore, if the VLM used to ask questions and the VLM used to answer them belong to the same model family as the model being evaluated, how do you avoid evaluation bias or overfitting to the evaluator’s visual priors?\n\n- Have you considered how stable the automatic metric is across different model versions? Could small changes in the judge cause the model rankings to change?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- Well-motivated task: clearly shows why structured visuals are different from “pretty pictures” and why factuality/layout need a dedicated treatment.\n- Clean data pipeline: executable graphics → instruction + code edit - re-render - filtered, so supervision is tight and verifiable.\n- Useful benchmark: StructBench/StructScore gives a more faithful measure than CLIP-style metrics and correlates with human judgment.\n- Clarity and Comprehensiveness: The paper is exceptionally well-written and structured. The problem, contributions, and methodology are articulated with clarity.", "weaknesses": "- Dataset Domain and Generalizability: The reliance on executable programs for data generation may introduce a significant domain gap. It is questionable whether this dataset fully captures the diversity, noise, and \"messiness\" of structured visuals found in real-world sources", "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": "hW2PSZolup", "reviewer": "Reviewer_ZnGr", "summary": "The paper proposes a large-scale dataset tackling the structured visuals editing problem, along with a novel benchmark, StructBench, and metric StructScore to evaluate the quality of edits. The proposed dataset, with the three-stage training paradigm introduced, helps achieve state-of-the-art performance in the StructBench benchmark.", "questions": "I am happy with the paper overall. Some technical details I would like to ask\n\n- In Appendix A.2, only part of the training hyperparameters were provided. It would be helpful to include additional info such as GPU hours, number of iterations, LR scheduling, etc., given that the three-stage training involves fairly large datasets such as FLUX-Reason-6M and the one proposed in this paper.", "limitations": "", "rating": 8, "confidence": 3, "soundness": 4, "presentation": 4, "contribution": 4, "strengths": "1. The proposed large-scale dataset contains image pairs and respective drawing programs and editing instructions, which are useful for future advancements in text-to-structured visual generation or editing. With the drawing program info, it can also be beneficial to text-to-vector-based (such as SVG) diagram generation by simply changing the output format for image rendering.\n\n2. The proposed StructBench, along with the novel metric, StructScore, is superior to traditional editing benchmarks with metrics such as PSNR, as demonstrated by the high correlation with human preference. The addition of Qwen-based evaluator also rules out the reproducibility concerns of closed-source models such as GPT-5.\n\n3. The three-stage training approach in the paper effectively bridges Qwen2.5-VL with FLUX.1, achieving state-of-the-art performance on the proposed benchmark, while maintaining competitive general editing capabilities.", "weaknesses": "- Some minor unclear points in the paper structure. In L365, there are mentions of StructEditBench and StructT2IBench. In section 3, only StructBench was introduced. I assume the Q&A construction and metrics around L244 are applied to both editing and T2I settings, but it should be clearly explained in this section.", "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": "yemqnzxvx7", "reviewer": "Reviewer_vk2E", "summary": "The paper first introduces a dataset for structured image editing, then propose a benchmark built from this dataset. It also trained a model using the dataset and evaluate on the benchmark.", "questions": "Please see weakness", "limitations": "", "rating": 6, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "1. Used programs to generate image, which makes it more robust and controlled.  \n2. The attempt to show alignment between human scores and benchmark metrics helps validate whether automated evaluation aligns with human judgment.\n\nOverall, the data generation process seems reasonable for producing a large-scale benchmark, as the authors demonstrate that models trained from this pipeline show good performance both on this benchmark and on another relevant benchmarks.", "weaknesses": "1. The training pipeline can benefit from more ablation analysis. For example, what is the improvement when training with and without Stage 3 ? The choice of three stages is intuitive, but it requires more empirical evidence to justify the curriculum design. In addition, since the authors incorporate another dataset outside of their own dataset in stage 3, an ablation should be conducted to clearly show the effect of this addition, i.e. whether the improvement comes from the ChatGPT generated CoT or from the dataset itself.\n\n2. The variation in evaluation results remains quite large with a different LLM(when comparing tables in the appendix produced by Qwen versus those in the main paragraph), even though the trends are similar. This is my main concern for the paper. Especially for a benchmark, if the score or model ranking can change depending on which VLM/LLM backend is used for scoring, it becomes difficult to trust this measure of progress. This subjectivity potentially suggests that the evaluation protocol may need further stabilization.", "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)