WorldEdit: Towards Open-World Image Editing with a Knowledge-Informed Benchmark
Authors: Wang Lin
Feng Wang
Majun Zhang
Wentao Hu
Tao Jin
Zhou Zhao
Fei Wu
Jingyuan Chen
Sucheng Ren
Alan Yuille
Venue: ICLR 2026 Poster (2026)
Keywords: Image Editing
Primary area: datasets and benchmarks

Abstract: Recent advances in image editing models have demonstrated remarkable capabilities in executing explicit instructions, such as attribute manipulation, style transfer, and pose synthesis.    However, these models often face challenges when dealing with implicit editing instructions, which describe the cause of a visual change without explicitly detailing the resulting outcome.    These limitations arise because existing models rely on uniform editing strategies that are not equipped to handle the complex world knowledge and reasoning required for implicit instructions. To address this gap, we introduce WorldEdit, a dataset specifically designed to enable world-driven image editing. WorldEdit consists of high-quality editing samples, guided by paraphrased instructions that align with real-world causal logic. 
Furthermore, we provide WorldEdit-Test for evaluating the existing model's performance on causal editing scenarios. With WorldEdit, we use a two-stage training framework for fine-tuning models like Bagel, integrating with a causal verification reward. Our results show that the proposed dataset and methods significantly narrow the gap with GPT-4o and Nano-Banana, demonstrating competitive performance not only in instruction following but also in knowledge plausibility, where many open-source systems typically struggle.

Published as a conference paper at ICLR 2026
WORLDEDIT: TOWARDS OPEN-WORLE IMAGE EDIT- ING WITH A KNOWLEDGE-INFORMBD BENCHMARK
Wang Lin'
Feng Wang"
Majun Zhang
Wentao Hu
Tao Jin'
Zhou Zhao'
Fei Wu'
Alan Yuille
Sucheng Ren"* 2*
Jingyuan Chen 1
'Zhejiang University Johns Hopkins University
è Nanyang Technological University
1 i nwanglwez ju. edu.
ABSTRACT
Recent advances image editing models have demonstrated remarkable capabil- ities in executing explicit instructions, such as attribute manipulation, style trans- fer, and pose synthesis. However, these models often face challenges when deal- ing with implicit editing instructions, which describe the cause of a visual change without explicitly detailing the resulting outcome. These limitations arise because existing models rely uniform editing strategies that are not equipped to handle the complex world knowledge and reasoning required for implicit instructions. To address this gap, introduce WorldEdit, dataset specifically designed to en- able world-driven image editing. WorldEdit consists of high-quality editing sam- ples, guided by paraphrased instructions that align with real-world causal logic. Furthermore, we provide WorldEdit-Tee Test for evaluating the existing model's per- formance causal editing scenarios. With WorldEdit, we use two-stage training framework for fine-tuning models like Bagel, integrating with a causal verification reward. Our results show that the proposed dataset and methods significantly nar- row the gap with GPT-4o and Nano-Banana, demonstrating competitive perfor- mance not only in instruction following but also in knowledge plausibility, where many open-source systems typically struggle. See Project Page.
INTRODUCTION
In recent years, image editing models (Simsar et al., 2024; Zhu et al., 2025; Simsar et al., 2025) have made remarkable progress, demonstrating excellent performance on tasks with explicit in- structions-see such attribute modification (Cao et al., 2023; Xu et al., 2023), style transfer (Chung et al., 2024; Wang et al., 2023), and pose synthesis (Yin et al., 2025; Shen et al., 2023). However, as illustrated in Figure I, when confronted with implicit editing instructions, which only provide the cause of a visual change without explicitly describing the resulting visual outcome, most existing models still exhibit significant limitations in editing quality.
An intuitive workaround is to paraphrase implicit instructions into more explicit editing prompts. Yet, as shown in Figure we observe that even when using paraphrasing to convey editing intent to pre-trained generative models, the editing results of most models are still quite poor. On the one hand, the visual outcomes implied by such instructions are often highly complex and require accurate world knowledge to realize. For instance, the instruction "a water balloon hits a cactus" entails visual effects (e.g., splashing trajectories of water droplets) that must adhere to physical laws and object interaction logic. How to generate high-qualit textual prompts based on such knowledge describe the visual outcome in detail remains an open problem (Deng et al., 2025).
On the other hand, even giving paraphrased instructions, many visual expressions remain challeng- ing for pre-trained generative models to follow and render, like the single-sided structure of a Möbius strip or scattering pattern of collapsed building blocks in Figure 5. This reveals a critical limitation in the generalization ability of existing models, which closely tied to the "input-dependency" edit- ing instructions. For conventional explicit instructions (c.g., remove the object from the image), the
"ooresponningg author.

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Published as a conference paper at ICLR 2026
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Unlike traditional image editing (left), which adopts uniform editing strategy for different editing objects, world editing (right) needs to take into account the nature of the editing objects in the real world and produce editing results that conform to causal logic.
Figure 1:
correlation for instructions with the input image content is relatively low, like remove, which leads to consistent visual change logic across different objects and scenes (i.e., plausible removal of the target region and background completion). Thus, models can maintain good performance even on unseen scenes. In contrast, world knowledge-driven implicit instructions are highly input-contingent. As shown in Figure 1, applying the same action "hit a cactus" to balls of different materials results in vastly different visual outcomes (e.g., degree of deformation, reaction of the cactus) due to variations in physical properties (mass, elasticity, surface material, etc). This strong instruction instruction-input result coupling imposes far greater demands on model generalization than traditional tasks.
Thanks to the development of unified models (Wang et al., 2024; Zhou et al., 2024; Wang et al., 2025a; Deng al., 2025), which are based large-scale pre-trained models capable of handling both comprehension and generation tasks, these models can not only rewrite editing instructions based on input images but, more importantly, they implicitly capture the causal logic and visual re- lationships underlying world editing instructions through exposure to large-scale pre-training data. They introduce promising path toward world image editing: transferring the world knowledge em- bedded in unified models into the editing process. Thus, an image dataset specifically designed for world-driven editing to stimulate unified models to leverage their powerful reasoning capabilities for improving image editing is required. Although recent efforts such as AnyEdit (Yu et al., 2025) have collected large-scale and diverse editing data, the vast majority still consist of traditional explicit in- structions, with truly world knowledge deiienee-rienn editing samples remaining scarce. KRIS-Bench (Wu et al., 2025) and RISEBench (Zhao et al., 2025) have proposed corresponding benchmark sets, but these cannot provide supervision for training and are limited in scale.
To address this gap, this paper proposes a comprehensive dataset WorldEdit. Using images from publicly available real-world segmentation datasets original inputs, we employ instruction decom- position and multi-step editing via GPT-4o (Hurst et al., 2024) to generate edited images that con- form to world knowledge logic. To ensure data quality, two-stage filtering strategy- ctoategy-comprising instruction verification pre-filtering and image assessment post-filtrinng-i designed. The pre- filtering stage eliminates editing instructions with ambiguous causal logic through world knowledge consistency checks, while the post-filtering stage removes generated images that are visually unre- alistic or violate world knowledge through visual plausibility assessment. Through this pipeline, the WorldEdit dataset ultimately contains 11k high-quality editing samples.
Finally, to validate the effectiveness of WorldEdit, we conducted a two-stage training procedure based on Bagel (Deng et al., 2025). In the first stage, we perform supervised fine-tuning with para- phrased instructions, enabling the model to better interpret implicit editing commands and align them with corresponding visual transformations. In the second stage, we further refine the model through reinforcement learning with a composite reward function that explicitly accounts for rea- soning quality, visual fidelity, and causal consistency. This reward structure not only encourages the model to produce visually plausible and instruction- alliged images, but also grounds its gener- ative process in interpretable reasoning traces and causal verification. Our main contributions summarized as follows:
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We introduce WorldEdit, along with a challenging benchmark WorldEdit-Test. specifically de- signed to capture cause-driven visual transformations with world knowledge.
With WorldEdit, we fine-tune Bagel in a two-stage process and introduce an inversion-based
causal verification reward to better align generative behaviors with real-world causal logic.
Our approach achieves state-of-the-art performance among open-source models on the WorldEdit-Test, demonstrating superior instruction generation and following capability.
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RELATED WORK
2.1 TEXT-BASED IMAGE EDITING
Image editing aims modify image content according to given instructions while preserving both the consistency and naturalness of the edited output. Initially, diffusion models (Rombach et al., 2022: Esser et al., 2024), due their remarkable image generation capabilities, were adapted for image editing by altering diffusion trajectories (Hertz et al., 2022; Tumanyan et al., 2023). Subse- quent research incorporating masks (Avrahami al., 2022; 2023), and multi-reference images (Ku- mari et al., 2023; Ruiz et al.. 2023) has significantly enhanced the controllability of image editing. However, strong controllability does not necessarily imply intelligence. Since most diffusion- bassed methods rely on relatively small text encoders, they struggle to handle complex and fine-gre tne-grained editing instructions that require reasoning. Recently, researchers have begun explore the use of unified models for image editing. These models extend the generative capabilities of large lan- guage models (LLMs) to the visual domain, enabling cross-modal generation and understanding. Recent approaches such as Chameleon (Team, 2024), Emu3 (Wang et al., 2024), and Selftok (Wang et al., 2025a) adopt a unified next-token prediction paradigm by discretizing images, while Trans- fusion (Zhou et al., 2024) and Show-o (Xie et al., 2024) integrate image diffusion with autore- gressive text prediction within a single framework. Compared to diffusion- based approaches, these models demonstrate improved performance in instruction following and semantic understanding. Furthermore, several commercial systems, such as GPT-4o (Hurst et al., 2024) and Gemini-2.0- Flash (Comanici al., 2025), have exhibited impressive reasoning-basse image editing capabilities, suggesting that unified LMMs provide a promising direction for image editing. However, they still fall short when confronted with instructions that implicitly require world knowledge. This limitation arises because, although unified models possess strong comprehension and generation abilities, it re- mains an open question whether these abilities can be mutually reinforced, and how their pretrained knowledge and reasoning skills can be effectively leveraged to advance image editing.
2.2 BENCHMARKS FOR TEXT-BASED IMAGE EDITING
Collecting high-quality image editing data is inherently challenging, as it requires strong consistency between pre- and post-edit images. Recent datasets such as InstructPix2Pix (Brooks et al., 2023), ImgEdit (Ye et al., 2025), AnyEdit (Yu et al., 2025), SEED-X (Ge et al., 2024) and UniReal (Chen et al., 2024) employing strategies like synthetic generation and video sampling to curate large-scale, high-quality editing pairs. However, these efforts primarily emphasize task complexity, instruc- tion diversity, or dataset scale, without explicitly modeling the reasoning processes or knowledge structures involved in instruction understanding. More recent benchmark studies have begun rec- ognize this limitation by incorporating instruction understanding into their evaluations. For instance, SmartEdit (Huang et al., 2024) investigates spatial and interactive reasoning in anbiguous editing scenarios. RISEBench (Zhao et al., 2025) and KRII--Bende (Wu et al., 2025) support to evaluates knowledge reasoning in image editing, but they do not provide training data. ReasonPix2Pix (Jin et al., 2024) and EditWorld (Yang et al., 2024) only provided limited unverified knowledge editing data, and were constrained by early image editing techniques, resulting in poor image quality and insufficient knowledge. Therefore, the lack of large-scale, high-quality training data continues to be a critical bottleneck. In this paper, we introduce WorldEdit, a benchmark that addresses these gaps by providing both large-scale training and evaluation datasets. WorldEdit offers high-quality sources for supervised training of editing models, while enabling a more comprehensive assessment of their ability to integrate world knowledge, reasoning, and visual editing.
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The automated construction pipeline of the WorldEdit dataset. Open-worid images are filtered and screened along three dimensions: (1) causal consistency of implicit instructions, (2) richness of the expected visual transformations, and (3) quality of the synthesized edited images.
Figure 2:
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WORLDEDIT
3.1
CONSTRUCTION OF WORLDEDIT
Editing Type Definition. Most existing image editing benchmarks primarily focus on perceptual- level modifications or semantic operations, while often overlooking the rich spectrum of natural changes that occur the real world. Previous efforts, such as RISE-Bence -eSE-Bench (Zhao et al., 2025), which organizes data according to types of cognitive reasoning, and KRIS-Bench (Wu al., 2025), which categorizes tasks based on Bloom's taxonomy of cognition, include a substantial portion of evaluation data centered on semantic manipulations (e.g., quantity, spatial location) and logical rea- soning (e.g., mazes, Sudoku). As a result, data that simulate causal transformations in the physical world are systematically underrepresented in current benchmarks. To address this gap, we introduce WorldEdit, benchmark comprising both training and test data. Our benchmark emphasizes how changes unfold under specific real- sal-world constraints, with the goal of providing a more focused and comprehensive evaluation of models' ability to reason about and simulate cause-driven visual transformations.
We categorize the transformations into two primary classes: Environment-driven Transformations result from changes in ambient conditions. Examples include Time (fruit ripening). Temperature (melting ice), Humidity (a flower wilting), Acidity (metal corrosion), and Light (fading fabric due to sunlight). Mechanics-driven Transformations result from the application of physical forces. Ex- amples include Break (a plate shattering), Inflate (balloon expanding), Squeeze (paper crumpling), Twist (cloth wringing), and Stretch (rubber band elongating). A full definition of all transformation types is provided in the Appendix A.
Editing Instructions, Explanations, and Images Collection. To construct the WorldEdit, we de- sign pipeline, as shown in Figure 2, for collecting high-quality editing instructions, corresponding explanations, and edited images, ensuring coverage of the diverse roal-world transformations.
First, we employ a detection model to extract object names from real-world images. This step aims to obtain the objects names and basic descriptions within the images, like a rusted bollard and a white rope securing a ship to the bollard. Accurate object detection is crucial as it forms the foundation for subsequent editing operations.
Next, we combine the detected objects with the predefined 10 editing types. For each object, we generate questions regarding its changes under different conditions. For example, for ship, we might ask What would happen if the ship broke apart?. Notably, we perform filtering at this stage to remove unreasonable combinations those with weak causal links. For example, the combination of Bollard and Inflate is filtered out because bollards, in reality, rarely undergo expansion, ensuring the collected data adheres to real-world plausibility.
We then utilize pre-trained Large Language Model (LLM) to answer the valid questions obtained from the previous step. The LLM maps the causal changes arising from the questions to detailed visual change descriptions. Following this, we evaluate the generated visual change descriptions. We filter out responses with errors or those where the visual changes are not obvious. For instance,
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Figure 3: Statistics of the WorldEdit dataset. (left) Distribution of word counts in paraphrased instruction, along with a word cloud of frequently edited objects. (right) Distribution of 10 editing instruction categories.
a description of slightly taut rope might be filtered as the resulting visual change is too subtle to be effectively edited and evaluated.
Finally, we leverage GPT-4o (Hurst et al., 2024) to generate edited images based on the paraphrased editing instructions. For more complex editing instructions (failed more than 3 times), we decom- pose them and performs multi-step editing to enhance the quality of the edited images. After image generation, GPT-4o is used again to evaluate the edited images. Images with poor visual consistency or incorrect edits are filtered out. For example, an image where a ship edited to appear crushed but lacks convincing structural damage would be rejected. Through this comprehensive pipeline, collect a large-scale, high-quality dataset that accurately reflects real-world causal visual transfor- mations for the WorldEdit benchmark.
Although collecting data from the open-world images can cover most of the variations, considering the limitations of public data, some data, such "magnetic field lines" and "static effects" not common. Therefore, in order to increase the diversity of the data, we supplemented a portion of the synthetic data. For more details, please refer the Appendix B.
Dataset Characteristics and Statistics. Based on the data construction process, we have collected total of 1 1k high-gee high-quality editing data. Among them, "Break" has the highest count (1,746), while "Twist" has the lowest count with only 304. We analyze it from multiple aspects, as in Figure 3, objects like trees. flowers, and buildings are prominently featured, reflecting the dataset's wide cov- erage of both natural and man- entities. The distribution of instruction explanation lengths is typically between 50 and 60 words, ensuring sufficient detail for understanding causal logic.
3.2 TRAINING WITH WORLDEDIT
To assess the capability of WorldEdit in eliciting cross-modal reasoning and transferring knowledge from pre-trained multimodal models to visual generation tasks, we implemented two-stage fine- tuning strategy using Bagel (Deng et al., 2025) as our baseline model. The initial stage involves supervised fine-tuning (SFT), where instructions are paraphrased into structured Chain-of-Thouug (CoT) sequences. This phase aims to enhance the model's 's capacity to interpret editing commands, leverage its inherent world knowledge, and establish robust mapping from language instructions to visual modifications using a causal language modeling objective where loss is applied both to the text and image tokens.
Then, we employ reinforcement learning via the Flow-GRPO (Liu et al., 2025a) algorithm to fur- ther refine the model's s generative behavior. A composite reward function guides this optimization, integrating three complementary signals designed to ensure high-quality reasoned, and causally consistent outputs.
The CoT reasoning reward is motivated by the need for transparent and logically sound internal reasoning. It evaluates the generated CoT text and outputs a scalar score, Reassnn based on the coherence, relevance, and correctness of the causal logic relative to the instruction. This reward encourages the model to produce rationales that faithfully reflect the transformation process, thereby improving interpretability and grounding the model's 's decisions in its pre-existing knowledge.
The image quality reward ensures the visual output both aligns with the instruction and retains consistency with the input image. Taking input the original image, the generated image, and
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Table 1: The performance of both commercial and open-source models on image editing tasks in WorldEdit- Test,, evaluated across different causes and metrics. For each category, the Top-1, Top-2, and Top-3 scores are highlighted respectively.
the instruction, this reward uses a multimodal model to produce a score, Reaelity, that captures in- struction adherence and visual consistency. By directly optimizing the perceptual alignment and minimal invasiveness of the edit, this reward helps maintain high visual quality while executing precise changes.
The causal verification reward addresses the core objective of fostering genuine causal under- standing. A multimodal model is required infer the cause of transformation between the input and output images, and return similarity score Reausal, between this inferred cause and the original instruction, as the reward. By incentivizing the model produce edits that are not only visually correct but also causally explainable, this mechanism ensures that the model learns the underlying physical and environmental principles.
The overall reward is computed as R = Reesson Radelity + Reausal- This multi-objective reward struc- ture ensures the model is guided toward reasoning-awwe aware, high high-fidelity, and causally grounded image editing, effectively leveraging the rich cause-and-effect relationships embedded in the WorldEdit benchmark to support open-world visual transformation tasks.
EXPERIMENTS
4.1 EXPERIMENTAL SETUP
Baselines. We evaluate our method against broad set of representative baselines, including tra- ditional diffusion- based editing models and recent open-source unified multimodal models. In ad-

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Src
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Temperature: What would happen this Rubik's s cube was subjected to extremely high temperatures?
Light: What happens when the halagen light strikes the prism evenly?
will happen this peach three months?
Stretch: What happen if heavy encyclopedia lifted off the industrial spring?
BOLAO
Humidity: What will happen if this field dries up?
Acidity: What would happen if the lange metal basket pole enveloped in acidic atmosphere?
Squeeze: What would happen if all the air in this packaging bag sucked out?
Break: Imagine chalk cliffs crumbling down abruptly.
Inflate: How would pufferfish appear get angry?
Twist: Imagine little girl's ponytails tied
Other: what happers when static electricity from the comb acts the poper scraps below it?
Figure 4: Qualitative comparison across different causal categories. The figure shows representative examples from ten causal reasoning tasks. Each row corresponds to causal scenario, with the source image on the left followed by results from different models. Our method generates outputs that are both visually plausible and causally coherent, whereas baselines often produce irrelevant or stylistic edits, failing to reflect the causal logic the instruction.

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Bnd
You
the Sroeets
Figure 5: Qualitative results on WorldEdit- Test with paraphrased instructions. Text alone often fails to capture fine-grained causal details (e.g., scattering pattern of collapsed building blocks), and mod- els vary their ability to interpret such prompts. Our model, fine-tuned with WorldEdit, generates the most faithful and visually coherent images, underscoring the importance of high-quality world
knowledge-dmee driven data.
Methods
Time
Temperature
Humidity
Acidity
Light
Break
Inflate
Squeeze
Twist
Stretch
Other
Overall
Banana"
4.25
GPT-4o"
4.27
4.52
4.41
4.30
4.43
4.46
4.52
3.83
4.25
4.53
4.67
4.38
Omnigen2"
2.40
2.92
2.62
Bagel"
3.39
3.25
3.05
2.53
3.24
2.75
3:21
2.46
2.37
3.15
3.71
3.01
Ours
4.10
3,96
3.78
3.97
4.09
4.54
4.08
3.73
4.16
4.15
4.27
4.07
Table 2: Performance of different models on the WorldEdit- Test when using paraphrased instruc- tions. Results are reported across ten causal categories and averaged overall.
dition, we compare against commercial systems such as GPT-4o (Hurst et al., 2024), SeedEdit- 3.0 (Wang et al., 2025b), and Aano-Banana (DeepMind, 2025), which have recently demonstrated impressive reasoning-avwe
editing ability.
Metrics. For evaluation, we follow the design of KRIS-Bench (Wu al., 2025) and adopt a multi- dimensional assessment protocol. Specifically, we employ Qwen-VL.-Man (Yang et al., 2025) as the evaluator to score editing results along four axes: visual consistency, which measures the structural alignment between the original and edited images; visual quality, which captures the perceptual realism of the generated images; instruction following, which quantifies how well the edits satisfy the given commands; and knowledge plausibility, which evaluates whether the edits consistent with commonsense and world knowledge. More details are provided in the Appendix H.
4.2 MAIN RESULTS ON WORLDEDIT- TEST BENCHMARK
Compared with baselines. Table reports the overall performance of different models across all causal categories and evaluation metrics. We observe that commercial models such as GPT- 4o (Hurst al., 2024) and ScedEdit-3.0 (Wang al., 2025b) consistently excel in visual quality and instruction following, reflecting their advantages in large-scale pre-training and extensive instruction fine-tuning. However, despite their strong performance, they still face challenges in categories that require explicit causal reasoning, such as time and acidity. Open-source editing models like Flux- Kontext (Labs al., 2025) are competitive in generating visually reasonable outputs but perform poorly in instruction following and knowledge consistency. This is particularly evident in categories such as "break", "squeeze", and "stretch", where capturing the causal dynamics of physical inter- actions is crucial. Similarly, open-source unified models outperform diffusion-based methods in instruction alignment but still have limitations in handling implicit instructions that require world knowledge. Our method has made significant progress in knowledge consistency and instruction following, achieving state-of-the-ee performance among all open-source models and competitive results compared to commercial systems.
Qualitative evaluation Figure presents the visualization results under different causal categories. We can clearly observe the advantages of our method in terms of instruction following and knowl-
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6PT-4o
Nono-Bonona
SeedEdi-3.0
Ours
BASEL
BAR
EMUZ
BAM
OmniGon
FLUX1 Kontext
Visual Consistency
Visual Quality
Instruction Following
Knowledge Plousibility
Figure 6: Human evaluation results for various models on the WorldEdit-Tess GPT-4o consistently
achieves the highest scores across all metrics. Ours shows solid performance, especially in instruc- tion following and knowledge plausibility.
Tomperature
Humidity
Acidity
Light
Break
Squeeze
Stretch
wio Call ressoning reward
3.85
3.92
3.88
4.01
3.98
4.05
3.95
3.82
3.99
4.00
4.03
3.94
wio image quality reward
3.76
3.81
3.72
3.90
3.85
3.92
3.88
3.70
3.89
3,87
3.93
3.85
causal verification reward
3.82
3.89
3.85
3.98
3.95
4.02
3.91
3.78
3.96
3.97
1,00
3.91
3,66
4.09
4.54
4.07
Table 3: Ablation study on the impact of different reward functions on the WorldEdit-Test.
edge plausibility. For example, in the "temperature" case (Row 1), our model successfully simulates the melting and deformation of the Rubik's cube under high temperatures and reveals the internal bearing structure. In the "light" case, only our model and GPT-4o correctly decompose the light. In categories involving strong physical interactions, such as "stretch", "squeeze", and "break" (Rows 4, 7, and 8), our method demonstrates outstanding capability adhering to causal logic. Removing the heavy encyclopedia leads to a natural rebound of the spring, the vacuum vacuum-sealed bag collapses smoothly, and the cliffs break into visually coherent fragments-all consistent with the physical laws of the real world. In contrast, competing models often generate inconsistent or static results that fail to reflect the implied dynamic changes.
Compared with paraphrased instruction. We further evaluated the model's editing results when directly given paraphrased instructions with clear visual changes. Our results reported Table 2 in- dicate that although generating edited images based on paraphrased instructions results in improved accuracy across a majority of the tested scenarios, the performance gap remains significant when compared to the fine-tuning achieved with WorldEdit. For instance, while Bagel" shows modest improvement of 0.25 over its original output, it still falls considerably short of the 4.07 achieved by our method. Moreover, illustrated in Figure 5, even detailed paraphrased instructions fail to enable the model to generate the lifelike results. In these complex cases, the paraphrased instruc- tions alone are insufficient convey the intricate details-such as the chaotic scattering of bricks in the castle or the abstract structure of the Möbius strip, which underscores the limitations of relying solely on textual adjustments. These finding highlights the critical role played by the specialized training data provided by WorldEdit in ensuring high-quality and accurate edits.
4.3 ABLATION STUDY
To dissect the contributions of key components in our framework, we conducted ablation experi- ments on three reward functions in Table 3. Ablating the image quality reward, the overall score dropped by 0.22. Since image quality is a core requirement for visual editing, the absence this reward to constrain the generation process led the model to produce less visually appealing outputs. When the CoT reasoning reward and the causal verification reward are removed, the overall score decreases by 0.13 and 0.16 respectively. This result demonstrates that these two reward functions effectively constrain the model to maintain causal correctness during the editing process.
4.4 HUMAN EVALUATION
From the human evaluation results in Figure 6, several consistent conclusions emerge with Table 1. Both evaluations highlight that GPT-4o and Nano-Bane -ano-Bannaa lead in most categories, aligning with human judgment that these models exhibit superior capabilities in maintaining visual fidelity and adhering to instructions. Models like Kontext and Emu2 score significantly lower in knowledge plausibility, reflecting their struggles in executing real-world, physically consistent transformations.

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5 CONCLUSION
In this paper, presented WorldEdit, novel large-scale dataset that emphasizes world-knowledge- driven image editing. By introducing diverse set of causal transformations, we have enabled image editing models to go beyond simple attribute manipulation and engage in more complex, implicit reasoning tasks. WorldEdit not only provides a comprehensive collection real-world scenarios for training but also introduces benchmark, WorldEdit-Tess Test, to assess the ability of models to generalize to causal scenarios. Through the use of WorldEdit two-stage fine-tuning and integration with causal verification rewards, significantly improved Bagel's 's performance. The results align image edits with real-world causal logic, surpassing current state-of-the-an approaches. Looking ahead, we aim expand WorldEdit to include wider range causal scenarios, and we hope that WorldEdit can provide a crucial resource for advancing the field of knowledge-awarg image editing, enabling the development of more sophisticated and autonomous systems in the future.
6
ACKNOWLEDGMENTS
This research was partially supported by grants from the "Pioneer" and "Leading Goose" R&D Program of Zhejiang under Grant No. 2025C02022, the National Natural Science Foundation of China (No.62307032). and Shanghai Rising-Ste dising-Star Program (23QA 1499000
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A DETAILED TASKS EXPLANATION
In this section, we provide detailed explanations of the editing tasks used in the WorldEdit benchmark. Each task type corresponds to a specific causal transformation mode, covering both environment-driven and mechanics-driven changes, well "Other" category for phenomena not captured by the ten canonical modes. Together, these modes ensure comprehensive coverage of real-world mnwwedde-innormee editing scenarios.
Time. This mode refers to visual transformations that unfold naturally as time progresses, driven by biological growth, chemical reactions, or material aging. Typical examples include fruit ripening, flowers wilting, food decaying, or seasonal changes in plants. Beyond these, the mode also covers oxidation effects such as a bitten apple surface turning brown, structural aging such as buildings weathering and deteriorating, and life cycle developments such as tadpoles growing into frogs or bamboo shoots maturing into bamboo stalks. Edits under this mode simulate how objects evolve time passes, often involving color fading, structural growth, surface corrosion, progressive degradation consistent with real-world temporal dynamics.
Temperature. This mode captures a wide range of transformations driven by thermal conditions, including heating, ignition, burning, melting, and freezing. Classic examples include ice melting under heat, chocolate softening and dripping, snowmen collapsing as they thaw, or plastic deforming and liquefying at high temperatures. Different materials display distinct visual and textural changes: iron glows red and emits light when exposed to several hundred degrees of heat, while fireworks burst into sparks and colorful trails upon ignition. Cooling or freezing leads to effects such as solidification, frost formation, or stiffness in meat and other organic matter. This mode emphasizes how varying temperature conditions trigger material-specific responses, resulting in visually diverse outcomes across different substances.
Humidity. This mode concerns transformations caused by the absorption or loss of moisture. Rep- resentative cases include dry noodles softening when soaked, food becoming moldy, or plants wilt- ing when deprived of water. Beyond these, it also encompasses material- and environment-driven changes such as sponges swelling and softening after absorbing water, dried fungi like black fungus seaweed shrinking and hardening after dehydration, floors appearing darker and glossier when wet, and moss growing on damp stones. Biological cases are also included, such as animal fur becoming flattened and clumped when soaked. Edits in this mode emphasize how the presence or absence of moisture alters texture, volume, and surface appearance, capturing both swelling and shrinking effects consistent with real- world humidity dynamics.
Acidity. This mode represents transformations driven by acidic environments, where chemical ero- sion gradually degrades materials organisms. Classical cases include metal rusting, paint peeling.
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or surfaces developing corrosion stains. Beyond these, acidity can also affect organic objects such as plants exposed to acid rain, leading to leaf damage and decay, or fabrics and clothing that lose color, develop holes, or weaken under acidic exposure. Edits in this mode emphasize chemical wear-and- tear, manifested through discoloration, surface roughness, structural weakening, and progressive material breakdown consistent with corrosive processes.
Light. This mode includes transformations driven by illumination conditions, where changes in the direction, intensity, or nature of light lead distinct visual outcomes. Common examples are the dispersion of light through prisms, the focusing and diverging of light beams, or shifted shadows cast by different light sources. The mode also covers scenarios such as changes in shadow shapes and lengths due to altered light positions, ultraviolet exposure affecting surfaces, transitions between near-beam and far-beam headlights, or adding a specific spotlight to highlight part of scene. Edits in this mode emphasize optical phenomena such as refraction, concentration, and shadow dynamics, as well as the role of light strength in altering the visual atmosphere of the scene.
Break. This mode describes destructive transformations where an object undergoes structural failure once its internal strength is exceeded. Such changes may be driven by exteraal forces, such as impacts, collisions, or pulling and tearing, leading to effects like shattering glass, collapsing cliffs, or ripping fabric. Alternatively, they may arise from internal stresses accumulated within the object, such as tree branches snapping under their own weight, ceramics cracking from uneven thermal expansion, or water freezing inside a container and causing rupture. Edits in this mode simulate cracks, fractures, splinters, complete disintegration, reflecting weal-world principles of mechanical instability under external pressure or internal stress.
Inflate. This mode characterizes transformations in which an object becomes larger, fuller, or more voluminous due to internal growth, external force, or energy accumulation. This includes classical cases such as balloon being inflated, pufferfish puffing up, or dough rising. Beyond these, the mode also encompasses natural phenomena where entities expand in scale or density, such as trees growing more luxuriant, water flows becoming greater and more turbulent, or clouds swelling and spreading across the sky. Edits in this mode emphasize visual cues of enlargement, increased density, and dynamic expansion, while maintaining coherence with the object's 's inherent structure and properties.
Squeeze. This mode represents deformation caused by external compression, collision, or the loss of structural support. Typical examples include crumpled paper, soda can stomped flat, or air being sucked out of a plastic bag. Beyond these, it also covers larger-scale or organic cases such as a car body being deformed after rear-end collision, or skin forming wrinkles under pressure.. Edits in this mode highlight flattened or distorted shapes, visible creases, dents, and surface irregularities, reflecting how objects lose volume structure when squeezed compressed.
This mode denotes transformations where objects deviate from their original orderly or linear structure into bent, entangled, irregular configurations. Unlike purely torsional forces that produce simple spiral shapes, this mode emphasizes more general distortions involving bending, wrapping, or chaotic growth. Representative examples include folding a paper strip into a Möbius band, tangled earphone wires, tree branches growing in irregular directions, or straight iron rod being arbitrarily twisted multiple times. Edits in this mode highlight irregular curvatures, interwoven structures, and complex spatial rearrangements while preserving continuity with the original object.
Twist.
Stretch. This mode corresponds to transformations where objects are elongated, straightened, or expanded under tensile force or external manipulation. Typical examples include rubber band being pulled, dough stretched thin, or springs extended. Beyond these, the mode also covers cases where initially compact, wrinkled, or bent structures are unfolded or spread out. Representative examples include tangled earphone wires being straightened, plants growing upward and outward, sweater being pulled outward with its knitted patterns extended accordingly, wrinkled clothes being smoothed flat, animals spreading their wings, or a fishing net originally clustered together being stretched open. Edits in this mode emphasize proportional lengthening, surface expansion, or the transition from compactness to openness, while preserving material continuity and structural coherence.
Other. This category encompasses causal transformations not fully captured by the ten predefined modes, while remaining consistent with real-world physical or environmental principles. Repre- sentative cases include electrostatic attraction of objects, magnetic field interactions. It also covers
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material and fluid phenomena like sedimentation in liquids, diffusion and dispersion processes, or sudden events like a balloon bursting. In addition, broader contextual or scene-level changes, such as festive transformations involving decorations, lighting, fireworks, are also included. This category ensures flexibility for capturing uncommon but physically valid effects beyond the above modes.
B MORE DETAILS ABOUT DATA COLLECTION
To construct the WorldEdit dataset, we developed a multi-stage pipeline designed to ensure both instructional diversity and visual plausibility of the edited results. Compared with existing bench- marks such as KRIS-Bench (Wu et al., 2025) and RISE-Bench (Zhao et al., 2025), our construction process emphasizes causal transformations grounded in world knowledge and integrates both au- tomated and human-in-the-loop filtering mechanisms. Below, we provide further details of each stage.
Data Sources. The foundation of our dataset is the DF2K-OST (Lim et al., 2017) collection, which provides high-resolution natural images across diverse domains. To emphasize realistic and visu- ally rich scenarios, we filtered out all low-resolution samples and retained only high-quality images. Each image was then center-cropped into 2:3 and 3:2 aspect ratios to standardize the format. Since cropping may occasionally truncate essential semantic content, we employed GPT-4o (Hurst et al., 2024) to automatically filter out images that lost contextual coherence after cropping. The remaining subset was taken the primary pool of original images. To further enrich causal diversity, we sup- plemented the dataset with additional images curated from the internet that emphasize strong logical and causal relationships. Moreover, synthetic samples generated with GPT-4o were incorporated to cover scenarios underrepresented in natural data. Together, these three sources form a balanced mixture of real and synthetic content that grounds the benchmark in both authenticity and coverage.
Object Detection. Given the curated image pool, we applied GPT-4o (Hurst et al., 2024) as the object detector identify salient entities within each image. For an image pt, the detector produces set of detected objects o;, forming tuples ((11, 01) ) (P2, 02). o2)..... (Pn, 0n)). This step provides the semantic anchors required for subsequent instruction generation and ensures that editing tasks are localized to concrete and identifiable objects.
Instruction Design and Filtering. For each detected object, we paired it with one of transformation modes described in Appendix A. Each pairing was used to generate one to three implicit instructions that emphasize causal or outcome-dependent transformations. For instance, a raw piece of pork under the Temperature mode can be frozen, cooked, or charred, reflecting distinct material states under different conditions. To ensure that the instructions remain implicit and causally grounded, applied GPT-4o (Hurst et al., 2024) to filter out trivial cases (e.g.. "make the mango red""), which correspond to explicit attribute changes rather than worrd--nowledge-riivee transformations. Similarly, we removed unreasonable pairings, such as associating "blue sky" with the Break mode.
Paraphrased Instruction Expansion. Once valid implicit instructions were retained, we expanded each into paraphrased instructions that made the underlying world knowledge and causal dynamics explicit. The expansion was required to specify aiee-grinne visual changes at the lowest perceptual level, including texture, shape, color, glossiness, firmness, fragmentation, deformation, and surface alterations. Abstract or metaphorical descriptions (e.g., emotions, atmosphere) were deliberately avoided. A second round of filtering was performed exclude CoTs that failed to capture world knowledge, lacked significant visual impact, or contradicted real-world causal logic.
Image Editing and Synthesis. The surviving CoT instructions were then used to guide visual editing. Specifically, we employed GPT-4o (Hurst et al., 2024) to transform the original images according to the expanded descriptions, producing edited images paired with their corresponding instructions. For complex transformations, multi-step editing adopted to preserve causal fidelity.
Quality Assurance. To guarantee dataset reliability, we enforced a rigorous evaluation pipeline. Each edited image was scored along four axes: Visual Consistency (VC), Visual Quality (VQ), Instruction Following (IF), and Knowledge Plausibility (KP). Samples with low consistency, poor visual fidelity, weak causal logic were discarded. Finally, it undergoes manual review and screen- ing to ensure that only the editorial content that both complies with the instructions and conforms to world knowledge is retained.
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C SCORE DISTRIBUTION OF MODEL OUTPUTS
GPT-46
SpodEd83.D
Niaoo Banana
Fluud No
Orniggnn2
Omnigon
Emuu
Anyedk
Figure 7: The score distribution of the model being tested.
The score distribution of the evaluated models on the WorldEdit-Test is shown in Figure 7. From the distributions, it is clear that GPT-4o (Hurst et al.. 2024), SeedEdit-3.0 (Wang et al., 2025b), and -amo-Banana (DeepMind, 2025) consistently achieve high proportion favorable scores across the four evaluation dimensions: Visual Consistency, Visual Quality, Instruction Following.
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and Knowledge Plausibility. These models demonstrate strong capabilittes in both generating visu- ally coherent outputs and capturing the implicit world knowledge underlying the editing tasks.
Building upon the Bagel and Bagel-Think basclines, across all four dimensions. In particular, it significantly narrows the gap with GPT-4o and ScedEdit- 3.0, demonstrating competitive performance not only in instruction following but also in knowledge plausibility, where many open-source systems typically struggle. This confirms the effectiveness of our knowledge-informed training framework in strengthening both the reasoning and generative aspects of image editing.
model exhibits substantial improvements
By contrast, models such Flux and OmniGen display notable weaknesses. Although they can sometimes maintain perceptual realism, they frequently fail to adhere to implicit instructions or to generate edits consistent with causal logic, resulting in lower overall scores. These shortcomings underscore the necessity of benchmarks like WorldEdit, which disentangle perceptual quality from reasoning ability and highlight the critical importance of world-knowledge grounding for future progress in image editing.
D HUMAN EVALUATION IMPLEMENTATION DETAILS
We employed 20 undergraduate students for the human evaluation process. These individuals were well-educated and possessed a solid understanding of basic real-world changes and common scien- tific knowledge. Each participant first viewed set of 50 images, each labeled with scores on four dimensions (Visual Consistency, Visual Quality, Instruction Following, and Knowledge Plausibility) 1 to 5. This training phase aimed to establish a consistent and unified scoring standard
ranging from across all evaluators.
Following this, each participant was randomly assigned a set of results from the 8 models shown in Figure 6. The students were asked to evaluate the outputs of these models across the same four dimensions. On average, each student evaluated approximately 500 images. This ensured that every image generated by the models was scored by at least two independent human evaluators, providing robust and reliable assessment of the models' performance.
The evaluation process was designed capture a comprehensive understanding of the models' ca- pabilities, particularly in terms of how well they adhered to the provided instructions and how ac- curately they reflected real-world transformations. By involving multiple evaluators, we ensured a diversity of perspectives, which helped in mitigating potential biases and improving the reliability of the final scores. Through this methodology, we aimed to provide a thorough and balanced human evaluation, offering valuable insights into the performance of each model based both subjective visual judgment and a solid understanding of causal logic.
E MORE VISUALIZATION RESULTS
Figure 8 provides additional qualitative comparisons across different causal transformation cate- gories in the WorldEdit benchmark. As illustrated, our method consistently produces edited images that are both visually coherent and causally faithful. In modes driven by environmental factors, such as material responses to humidity and temperature, our results capture fine-grained cues- cues-including texture decay, color fading, moisture-induced deformation-that better aligns with real-world expectations.
From the figure, can also be observed that GPT-4o (Hurst et al., 2024) and SeedEdit-3.0 (Wang et al., 2025b) are often able to interpret the world knowledge implied in implicit instructions and gen- erate satisfactory results. In contrast, Nano-Banan (DeepMind, 2025) generally lags behind GPT-4o and SeedEdit-3.0. For example, in the case of rice field in autumn, both GPT-4o and SeedEdit-3.0 successfully render the green leaves combined with golden ears of rice, while Nano-Banana tends to simplify the scene into uniformly golden field resembling wheat. Our model also demonstrates strong world-knowledge awareness, achieving results close to GPT-4o and SeedEdit-3.0 across var- ious tasks. It shows notable ability preserve editing rationality and incorporate causal reasoning, representing a substantial improvement editing capability compared to the original Bagel (Deng et al., 2025) model and its Bagel-Think variant.
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These visualizations further confirm that WorldEdit not only poses significant challenges for exist- ing editing systems, but also highlights the unique advantages of our knowledge-infone -effodge--ifomeed frame- work in producing edits that simultaneously realistic, faithful to instructions, and grounded in commonsense reasoning.
Ours
GPT-4o
Seededit3.0 Nano_Banamt Banana
Bagel_thinkk think
Bagel
Flux_Knotexx
Temperature: What happens pork being frozen?
Light: Imagine the scene where sunlight filters through the tree canopy, casting shadows on the ground.
Time: What will hoppen to this rice field in autumn?
Stretch: Imagine slice of pizza being up, melted cheese stretching long strands.
Humidity: What would happen to this instant noodles after adding hot water?
Acidity: Envision how the metal stein or tankard on the left side of the sign reacts a highly acidic exposure.
Squeeze: Imagine the grey heron's outstretched wings being gently squeezed.
Break:
What happens when the shirt with floral pattern unexpectedly tears, disrupting the intricate detailing?
Inflate:
Consider the waterfoll cascading down rock face inflating, altering flow and spread across the layered stones.
Twist: What would it look the belt here were rolled up into bundle?
Ither:If strong gusts of wind accur across the meadow, show the effect on the dondelion seed heads.
Figure 8: Additional visualization results across different causal modes in the WorldEdit- Test.
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F
MORE RESULTS ON OTHER BENCH
Src
Ours
GPT-4o
Deubao
Gemini
Bagel_think think
Bagel
Flux_ Kontext
Humanities:
Change the boat the type used races during the Dragon Boat Festival
Practical Knowledge: Correct the incorrect sitting posture in the image
Biology: Cut the fruit shown the image
Chemistry: Color change after adding phenolphthalein reagent to this bottle of chemical liquid
Geagraphy: Changes after Global Warming
Mathematics: Change the triangle into a square
OAMONNOMAO
ONNOMM
Medicine: Change to image pneumonia
Physics: Place balloon filled with regular air the room.
Figure 9: Visualization of results the Conceptual Knowledge subset of KrnchB WorldEdit shows improved robustness in capturing these cross-domain semantics, generating edits that reflect the intended conceptual transformation more faithfully than other open-source baselines.
Figures 9, 10, 11 and 12 together with Tables and present comprehensive comparison of dif- ferent models on Kris-Bench and RISE-Bench. Across all settings, GPT-4o (Hurst et al., 2024) unsurprisingly remains the strongest overall system, but our WorldEdit model emerges compet- itive and robust open-source alternative, consistently outperforming other open-source editors by large margin.
On Kris-Bench (Table 4), WorldEdit achieves the best average performance among open-source models across factual, conceptual, and procedural knowledge. WorldEdit maintains non-trivial per- formance and even surpasses some closed-source models in certain metrics. In conceptual and factual knowledge (Figures and 10), WorldEdit substantially improves over BAGEL and BAGEL- Think, especially on IF and KP in social and natural science categories, indicating better grounding in real-world causal and commonsense relationships rather than relying only on superficial visual alignment.
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Sre
Ours
GPT-4a
Daubaa
Gemini
Bagel_think think
Bagel
Flux_Kontex
Count Change: Change chairs
Anomaly Correction: Correct the unreasonable part of the person in the image.
Color Change: Change this short sleeve to black.
Size Adjustment: Adjust the small orange: to the size of the large orange.
Viewpaint Change: Draw the front view of the object based on its view.
Position Movement: Position the charging adapter the center of the cable.
Part Completion: Complete the stringing of the badminton racket.
Figure 10: Visualization of results on the Factual Knowledge subset of Kris-Bench. Tasks this split involve objective, visually verifiable transformations such quantity change, color change, size adjustment, geometric viewpoint synthesis, and object object repositioning. The comparison highlights each model's ability to perform accurate, eetiil-preseriing factual edits. WorldEdit reliably produces edits that align with both the instruction and the underlying visual structure, showing stronger factual consistency than other open-source baselines.
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Src
Ours
GPT
Doubao
Gemini
Bogel_think
Bagel
Flux_ Kontext
dark blue tome. The Execution:Set the object the image the main visual focus, place
Multi-instructton
centrolly the frome. The backggroun should have light grudwilly dif d'singgg aillard, creating warn orangenyellow effec front the lamp bose. pages open magazine pair sung scathered se overall style orange. title be placec
vintage
moderv arsthetic, atrong contreats light shodow.
acheme creates renalon between attiitudee. V
upper right image, the fedt: YTlluminccing eat just
Abstract Reasoning: Please draw the correct shope the '?' box at the top right corner according to the pattern.
Figure 11: Results on the Procedural Knowledge subset of Kris-Bench. This subset evaluates models on tasks that require executing complex instructions completing abstract visual patterns.
Table 4: Overall performance comparison on Kris-Bench Closed-source frontier models such as GPT-4o and Gemini achieve the highest scores, while WorldEdit delivers the strongest performance among open- open-source systems across most reasoning categories, particularly in attribute perception, spatial understanding, and knowledge-drive
editing. The performance of open-source and closed- source models is separately marked with best performance bold.
Closed-S Model
Open-Source Model
Dimession
GPT-4s
Geminl 2.0
Deubao
WorldEdit
Omunigen
Ema2
Bagel
Bagel Think
Step Edit
Anyedii
Magicletsk
1p3p
74.50
66.75
76.18
35.75
47.75
$4.75
$3.90
17.50
Anribuue
VQ
94.75
81.75
89.00
85.91
49.50
75.25
67.00
75.00
70.25
67.50
76.25
53.50
Perception
80.25
47.75
57.00
53.73
28.50
31.50
33.25
20.75
32.00
15:00
Ang
K1.17
66.31
71.94
17.93
$1.50
58.08
66.41
47.67
$3.92
VC
60.50
60.50
67.50
65.00
24.00
41.50
53.90
27.25
64.25
55.75
35,00
13.25
Sperial
VQ
04.50
20.00
92.00
33.00
72.00
69.25
Percqption
73.25
46.25
21.00
37.50
10.75
15:25
38:75
44.75
8.00
7.75
11.50
10.50
59.17
71.50
45.17
39.58
VC
54.00
$4.50
26.75
64.36
19.25
12.50
0.00
0.00
0.07
0,00
0.07
0,00"
Yemporal
Proliction
64.50
63.25
17.50
69.26
20,00
16.50
0.00
0.00
0.00
0.00
0.00
0.00
22.17
Average
79,80
65.26
63.30
56.98
33.13
45.40
47.21
55.23
45.53
39.25
41.84
23.35
Social
VQ
96.75
$3.75
86. 90
89.40
46.00
72.75
75.90
32.25
72.50
66.75
70.00
50.00
Conreytoul Knowledge
64.50
30.00
54.75
49.50
22.00
27.25
14.25
Scienue
KP
78.75
33.00
48.75
39.50
16.75
11.25
25.25
38.25
17.50
10.50
20.50
10.25
85.50
68.99
64.35
34.19
80.00
65.00
20.25
$4.48
31.00
33,00
65.75
68.00
21.25
61.75
47.00
15.75
Natural
96.0
83.75
21.25
93.36
75.50
77.75
72.75
Scicuse
76.50
44:25
25.00
27.90
19.00
17.50
KP
67.75
34.25
30:25
44.02
12.50
15.25
26.00
40.25
19.90
14.00
13,50
11.75
56.94
69.39
27.19
35.44
42.94
38.06
Average
$1.37
59.65
62.23
68.17
25.03
31.54
$2.17
59.01
45.01
41.55
39.24
25.59
$1.00
23.50
64.75
23.50
55.50
37.25
Procodene Anowledee
Reasoning
Logical
VQ
95.00
$4.50
33.00
24.75
35.00)
25.50
92.50
26.75
66.25
34.25
$5.00
72.25
72.75
75,50
56.75
KP
$1.00
25.50
16,50
12.00
1.75
2.25
16.25
21.25
12:25
7.75
2.00
2.00
VC
71.00
58.25
31.50
65.53
25.75
31.00
30.35'
$2:351
25.75
20.35
30.351
9.50
96.25
82.50
76.75
25:25'
Decomposttion
18.00
24.25
53.50
47.00
32:25
39:25
32.75
24.50"
16.00"
11.75"
9.25"
7.00"
85.08
60.58
65.06
45.00
26.92"
23.08"
Average
78.32
02.58
54.17
56.98
23.89
34.91
40.21
39.20
31.82
31.74
26.54
17.28
Overall
60.70
Results RISE-Bench (Table 5 and Figure 12) reinforce these observations in more diagnostic reasoning setting. GPT-4o again achieves the best temporal, causal, spatial, and logical accuracy, but WorldEdit is the next strongest model overall and the clearly best-performing open-source editor, substantially outperforming BAGEL, Step1X Ediit, OmniGen, EMU2, and other diffusion-baar baselines, which remain close to chance several dimensions.
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Src
GP
Nano Banana
Seedream4.0
Bagel_think
Flux_Canyy
Causal Reasoning:
Draw what it will look like after being exposed to the sun for one hour.
Draw what it will look like after the knot is untied.
Spatial Reasoning: Draw the top view.
Draw front view of this building as seen distance.
Temporal Reasoning:
Draw what it will look like one hour later
Draw what will look like thres months later.
Figure 12: Visualization of results RISE-Bench. This benchmark examines visual reasoning ability along three dimensions: causal reasoning, spatial reasoning, and temporal reasoning. The examples illustrate the varied behaviors different systems when confronted with these reasoning- intensive tasks. WorldEdit produces comparatively more stable and context-aware outputs across several categories.
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Table 5: Overall performance comparison RISE-Bench. The benchmark evaluates tempo- ral, causal, spatial, and logical reasoning abilities. While GPT-4o remains the strongest system, WorldEdit achieves notably higher scores than existing open-source editors across multiple reason- ing dimensions.
Models
Temporal
Causal
Spatial
Logical
Overall
-ma- (Hurst et al., 2024)
34.1 %
32.2 %
37.0% %
10.6 %
28.9 %
WorldEdit(OUrs)
22.4%
26.7%
13.0%
2.4%
16. %
Somini-2.0-Flashh--e (Team et al., 2023)
8.2%
13.5%
23.0%
4.7%
13.3%
Gemini-2.0-Flassh (Team et al., 2023)
10.6%
13.3%
11%
2.3%
9.4%
Bagel (Deng et al., 2025)
3.5%
4.4%
9.0%
5.9%
5.8%
StepIX-Edit Liu et al. (2025b)
0.0%
2.2%
2%
3.5%
1.9%
Omnigen Xiao et al. (2024)
1.2%
1.0%
0.0%
1.2%
0.8%
Emu2 (Sun al., 2024)
1.2%
1.1%
0.0%
0.0%
0.5%
HiDream-Eoo HiDream.ai (2025)
0.0%
0.0%
0.0%
0.0%
0.0%
FLUX. LLUX.I-Cami I-Canny (Labs, 2024)
0.0%
0.0%
0.0%
0.0%
0.0%
Taken together, these quantitative and visual results show that WorldEdit is not just another high- fidelity editor: it consistently strengthens causal, temporal, and logical reasoning in editing, narrow- ing the gap to top closed-source systems while markedly advancing the state of open-source image editing under knowledge-intensive benchmarks.
G LIMITATIONS
While WorldEdit represents a significant advancement in image editing, it does come with chal- lenges that highlight its strengths and potential for further development. First, the dataset's focus on world-knowledge-drieee
transformations inherently requires more computational resources and
advanced models capable of capturing and processing complex causal relationships. also places additional demands on model training, showcasing the need for more sophisticated fine-tuning ap- proaches to handle the rich and detailed world knowledge embedded in the dataset.
Moreover, while WorldEdit offers extensive range of causal transformations, the diversity of real- world scenarios that can be captured remains a work in progress. As the dataset grows and evolves, it will continue to challenge models generalize across broader spectrum of dynamic, real- world situations. This powerful opportunity for future research to expand upon WorldEdit by adding even more nuanced and complex causal scenarios. further refining the ability of generative models to simulate real-world transformations. In essence, the limitations of WorldEdit serve as testament to its ambitious scope and potential drive future breakthroughs in intelligent image editing, offering a valuable resource for advancing the field of knowledge-awwe models.
H PROMPT FOR JUDGEMENT
This section presents the evaluation prompts designed for WorldEdit. Figures 13, 14, 15, and 16 illustrate the prompts used for Visual Consistency (VC), Visual Quality (VQ). Instruction Follow- ing (IF), and Knowledge Plausibility (KP), respectively. We explicitly decouple the assessment of "visual consistency and perceptual quality" from that of "instructional fidelity and knowledge plau- sibility"". VC emphasizes that all non- non-instructed elements must remain unchanged, while VQ focuses solely on perceptual and structural quality without considering task correctness. IF is concerned with whether the correct target has been modified with the appropriate magnitude, whereas KP anchors the evaluation to the declared editing mode, verifying whether the edited outcome faithfully reflects real-world causal dynamics grounded in physics, chemistry, biology, common in the evaluation of SeedEdit-3.0, due to the presence of watermarks, we added a prompt in VC to ignore the watermarks.
Noted that
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To minimize cross- coss-dimensional "information leakage." each prompt is scored according to its criterion: VC does not penalize instructed changes, VQ does not assess task success, IF is unaffected by pure perceptual quality issues, and KP only decreases when causal logic material laws violated. In cases fundamental instruction failure (e.g., replacing the target entirely instead of editing it), IF is capped its lower bound and consistency penalty is simultaneously applied to KP. This design ensures the separability of the four axes while faithfully capturing the implicit, causal, and knovledge-driven properties of WorldEdit.
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Visual Consistency Template
are professional digital artist and image ovaluation specialist.
will be given:
Image A original image.
2. Image B an edited version of Image
3. Editing Instruction**; a hypothetical prompt describing how Image A should be tramsformed or imagined inlo
Image B.
Your Objective:
Your task is to **vvaluate the visual consistency between the original and edited images, focusing exclusively on cloments that are NOT specificd for change in the instruction** That is, you should only consider whether all non-instructed details remain unchangod. Do **ot** penalize or reward any changes that are explicitly required by the instruction.
Hil Evaluation Scale (1 to 5):
You will assign **consistency scoreft according to the following rules:
**55 Perfect Consistency*** All non-instruction elements are cumpletely unchanged und visually identical.
**4 Minor Inconsistency Only very small, non-instruction detail is different (e.g.. tiny accessory,
subtle shadow, or minor background artifact).
**3 Noticcable Inconsistency **; One clear non-instruction element is changed (e.g., different hairstyle,
shitted object, visible background alteration).
**2 Significant Inconsistency** Two more non-instruction elements have been noticcably altered.
*+1 Severe Inconsisteney** Most all major non-instruction details different (c.g., changed identity,
gender, overall seene layout).
Hill Guidance:
First, **identify all elements that the instruction explicitly allows requires be changed**
Exclude these from your consistency check; then verify that every other pixel and element remains unchanged between the original and edited images.
For all other elements (e.g., facial features, clothing, background, object positions, colors, lighting, composition, especially image color tone, brightness, contrast, elc.). commprre Image B Image A *+ and check if they remain visually identical.
If you observe uny change in mon-instruction element, note and consider impact the If the instruction vague ambiguous, make best-effort factual inference about which elements intended change, and treat others non-instruction elements.
Note:
D* penalize changes that required by instruction.** **
D* not reward penalize the quality correctness the instructed change itself** (that evaluated
separately).
If the edited image introduces new artifacts, objects, or changes non-instruction elements, should lower the consislency score.
Input
*mmage A
Image B
*Editing Instruction** (instruct]
Output Format
First, elearly explain your comparison process: list each major non-instruction element and state whether consistent (unchanged) or inconsistent (changed), with brief reasoning.
Then, provide your evaluation the following JSON fornat:
{I
"reasoning": **omparee to original image** [list of non-instruction elements that changed or remained the same] **in the edited image** "consistecyy score": X
Figure 13: Prompt for Visual Consistency (VC).
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Visual Quality Template
are professional digital artist and image ovaluation specialist.
will be given:
Immaee * single Al-generated image.
Objective:
Your task to evaaluate the pereeptual quality** image, focusing on:
**Structurll semantic coherence*
Natural appearance**
Absence of generation artifacts
You must nol penalize low resolution or moderate sofmess*** unless it introduces sernantic umbiguity or visually degrading effects.
#* Evaluation Scale (1 to 5):
You will assign *qquality score** with the following rule:
**5 Excellent Quality**: All aspects are visually coherent, natural, and free from noticeable artifacts.
Structure, layout, and textures are accurate and consistent.
**4 Minor * * One small imperfoction (c.g., slight texture blending, minor lighting inconsistency).
**3 Noticeable *; One or two clear visual flaws or semantic problems (e.g., extra fingers, minor duplication, slight distortion).
**2 Structural Degradation Multiple distracting errors (e.g., melted hands, warped shapes, unreadable text).
**1 Severe *rrors**; Major structural failures or hallucinations (e.g., bruken anatomy, garbled symbuls).
н4 Guidance:
Check the following visual aspects and mark them as / (satis factory) or X (problematic):
Structural coherence (c.g.. correct anatomy, object shapes, legible text)
Naturalness (lighting. perspective, shadow logic)
Artifact-free (no duplication, ghosting, watermarks)
Texture fidelity (clothing, hair, surfaces not melted comupted)
Optional: Sharpness (only penalize if blur causes semantic loss)
The more checks, the higher the score.
Example
"reasoning": "Structural coherence: v. Natural appearance: J, Artifacts: J, Texture fidelity: X (fabric partially deformed).", "quality_score""" score": 4
д4 Oulput Format:
After evaluation, provide your and concise reasoning using the following JSON format:
"reasoning": : XXX.
quality score":
Figure 14: Prompt for Visual Quality (VQ).
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Instruction Following Template
You professional digital artist and image evalustion specialist. You will have evaluate the effectivenes of the Al generated imagets) based on given roles.
You will be given:
*Image A* the original image.
2, **maag 8**; an edited version of Image A.
**Editing Instruction" " lespotiicicall prompt describing Image A should transformed imagined into Image
Objective:
Your task is to **vvaluate how the edited image faithfllly fulfills the editing instruction"* ** focusing eexclusivelly on the presence und corectness of specified chunges"*.
You must:
*ddentiff detailed visual differences** berween Image A and Image B correctly and faithfully**
Determine those differences match exactly what the editing instruction requests**
**Not assess unintended modifications beyond the instruction" " sach evaluations fall under separate criteria (e.g.. visual consistency).
"BBe curefal an edit may introduce visual change without fulfilling the actual instruction (e.g. replacing the object instead modiiiyng it)
#e Reasoning:
You follow these reusoming steps hefore scoring:
**1, Detect Difference**. W hat has visually changed between Image A and Image B? (egg. size, shape, color, state) In this step, you don't have oditing instruction.
information
**2. Expected Visual Cuption* Write factual description of how the edited image should look if the instruction were perfectly followed.
**3. Instruction Match**
Compare the observed differrncee in *=jee to the expecred change in *egee;
the correct object modified (not replacedy?
Was the requested attribute (e.g, size, color, state) modified as intended??
the degree of modification accurati (e.g. "match size". "an hour lateer"" etc.)?
*4, Decision * Use the 1-5 scale assign final
Evaluntion Scale (1 to 5%;
You will assign *"instrctionn score** with following nale:
**5 Perfect Compliance** The edited image **precisely matches** the intended modification; all required changes are present and accurate.
Minor Omission"*: The care change is made, but minor detail is missing or slightly incorrect.
Partial Compliamcs** main idea present, but more required aspects are wrong or incomplete.
*+2 Major Omissiion** Most of the required changes are missing or poerly implemented.
Non-Compliance"* The instruction **pot followed all** coomplleelyy misinterpretedd*
Example:
Instruction: Imagine what the apple will look like month.
II
"instruction score":
"reasoning""
Detecting differences:
the original image, the apple shows a bright red color and smooth surface. However, in the processed image, some black spots appear on the surface of the apple.
2. Expected visual description:
originally shiny skin the would become and brown, dark spots blemishes appearing on water content
decreases, the fruit will hecome wrinkled and shrink in size, and the flesh will become soft and spongy, and may even collapse in some areas.
Explanation:
Comparison: This instraction requires imagining the appearance of the apple after one month. This editing adds black spots to the apple's surface, which to some extent meets the requirements the instruction, but apple does not undergo dehyydaaiion or wrinkling changes.
conseept altempted, but it not fully achicved.
Decision:
Since only part of apple's appeanunce reached the required level, this should counted us 3 cases that partially the requirements! }}
#e Input
**ImageA *
*"Image B**
**Editing Instraction*: [instruuct]
#e Output Format
Look the input again, provide evaluation score explanation following
II
"instruction score": X,
"reasoning:: Deteet Difference 2. Expected Visual Caption Instruction Match Decision
B)
Figure 15: Prompt for Instruction Following (IF). The red words indicate the modifications made on Kris-Benchh
27

---

Published as a conference paper at ICLR 2026
Knowledge Plausibility Template
You professional digital artist and image evaluation specialist. You will have to evaluate the effectiveness of the Al generated image(s)
given
You he given:
1. ** lmage A the original image.
2. *Image B**; an edited version of Image A.
3. ** Editing Instruction **; hypothetical prompt describing how Image should trunsformed imagined into Image B.
4. **diiiing mode** the fundamental editing regime that must be observed throughout imnage editing process; dictates the governing
condition under which all operations performed g.g, break, time, temperature, stretch, etc.
Objective
You must provide *"corre** for the ** edited image"* **Knowledge Score**; the instruction and original image, the image reflect realistically huppen mode?
## Knowledge Plausibility
Your Objective:
Evaluate whether the edised image, after applying the instruction the original image, accurately reflects the real-world behavior described in the provided mode.
You must:
"Ground your reasoning the Real- World Knowledge Explanation based on the provided mode.
Focus only whether the resulting image makes logical sense based "*physical, chemical, biological, commonsense understanding"" **Not penalize issues unrolated to knowlodge"* (e.g, visual polish or stylistic artifacts)
WWl Reasoning Steps:
**1. Detect Difference**,
visually changed
Image Image (e.g. size, shape, color) this
step. you don't have use information from the editing instruction
=*2. Extract Knowledge Expectation** What visual outcome expected instruction upplied, provided
mode?
ve3, Knowlodge Match"*:
Compare the visual changes identified in to the expected outcome
Do the edits visually and logically match the world behuvior?
the salee-effect relationship shown correctly?
Are key physical/chemical hiologicaa phenomena depicted correclly?
**4, Decision **; Assign knowledge
from to
H48 Evaluation Scale to
**5 Fully Plausible**, *Il All visual elements follow werld logic and match the explanation exactly.
**4 Minor Implausibiiity One deviation expected real-world behavior.
**3 Noticeable Implausibility **; One clear conffict with domain knowledge the explanation.
Major implansihility Multiple serious violations
real-world logic.
**1 Completely Implausible**. The image contradicts fundamental facts ignores the explanation entirely.
not followed (score assign 'knowledge score note: ""Insmucton failure knowledge
##6 Example 1:
What if rese is placed on the table for month? "*Editing Instruction** + What the placed the "*Editing mode**; time.
month?
**Compared original image" the
droopy, and faded.
The ** Expected Caption"*, The rose is dry, droopy, and Cadod.
"knowledge, score":
"reasoning" "v The rose dry, droopy, and faded."
Example glass breaks.
**Editing Instraction** e. The glass breakes under the server pressure.
**Editing mode**
**Compared original image** Tiny cracks spread acmoss the glass surface.
X The glass only slightly breaks instead of completely breaking. contradicting roal-world behavioe under server pressure.
** ""Epecteed Caption**, The glass shattored completely into small sharp picees.
"knowledge score":
"reasoning": " XX The degree breakage small, conrradicting real-world behavior under pressure.""
Input
**Original Image**
**Edited Imagee"*
*"Editing Instractiou**; (instructt
""dditing mode+*; [mode)
## Ourput Format
Provide both seores and clear reasoning the following JSON format:
11
"knowledge score": : X.
"knowledge reasening": Detect Difference 2. Expected Knowledge Expectation Knowledge Match 4. Decision
Figure 16: Prompt for Knowledge Plausibility (KP). The red words indicate the modifications made on Cris-Bench.
28


Reviews:
[{"review_id": "UHLlSOPHsS", "reviewer": "Reviewer_xFyh", "summary": "The paper aims to address the limitations of existing image editing models in processing implicit instructions (which describe the cause of change rather than explicit visual outcomes) and their lack of world knowledge reasoning, introduces the WorldEdit dataset comprising 11,000 high-quality editing samples across 10 causal transformation types, and proposes a two-stage training framework based on the Bagel model (supervised fine-tuning followed by reinforcement learning, integrating triple rewards of CoT reasoning, image fidelity, and causal verification), achieving state-of-the-art performance among open-source models and significantly narrowing the gap with commercial models such as GPT-4o in instruction following and knowledge grounding.", "questions": "1. The paper could benefit from including comparisons and distinctions with existing similar data benchmarks to better highlight its significance, such as ReasonPix2Pix [1] and EditWorld [2].\n\n2. Table 1 demonstrates good quality, but what is the scale of the test set?\n\n3. While the model can now handle implicit instructions, does it maintain editing capabilities for explicit instructions? For example, object removal and style transfer also align with world knowledge.\n\n4. Regarding reproducibility, the paper does not mention whether code or data will be released, nor does it clarify the computational resource requirements for training.\n\n5. Minor Points\n- The paper mentions \"11,000 high-quality editing samples\" but could more explicitly state upfront how these are split between training and test sets.\n\n[1] Reasonpix2pix: instruction reasoning dataset for advanced image editing\n[2] EditWorld: Simulating World Dynamics for Instruction-Following Image Editing", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "### originality\n1. The paper primarily constructs a dataset designed to elicit existing models' capabilities for world knowledge understanding and generation.\n2. It employs supervised fine-tuning (SFT) and reinforcement learning with multiple reward signals to train models, thereby validating the effectiveness of the proposed dataset.\n\n### quality\n1. The data construction pipeline is rigorous and reliable. The collection, rewriting, construction, and generation of instructions undergo mature filtering mechanisms, substantially ensuring data quality, which is critical for model training.\n2. The baseline comparisons are comprehensive. The four evaluation criteria—visual consistency, visual quality, instruction following, and knowledge plausibility—are well-justified and, combined with human evaluation, demonstrate reasonable results.\n3. The progressive framework from supervised fine-tuning with Chain-of-Thought (CoT) reasoning to reinforcement learning with composite rewards is well-motivated, and ablation studies confirm the contribution of each component.\n### clarity\nThe paper is well-structured and clearly written.\n### significance\nThis work demonstrates that high-quality data fine-tuning can effectively leverage existing models' prior knowledge to elicit world knowledge understanding and generation capabilities, holding both research and practical value.", "weaknesses": "1. From a visual inspection perspective, the color tone appears to inherit characteristics from GPT-4o. For instance, in Fig. 8, the pizza and instant noodles exhibit noticeably intensified color saturation. The model may learn to mimic GPT-4o's output distribution, raising concerns about the actual physical accuracy.\n\n2. A discussion of how dataset scale affects model performance and generalization would add significant value to the work.\n\n3. The paper lacks object-level metrics to verify whether the target object was actually modified (rather than background elements), whether non-target objects remained unchanged, and whether the transformation was appropriately spatially localized.\n\n4. The paper does not discuss whether baselines were fine-tuned on WorldEdit or tested in a zero-shot setting, which may affect the fairness of comparisons and could enhance the demonstration of the dataset's impact.\n\n5. While the paper mentions extensive filtering operations, it provides limited quantitative details, such as what percentage of generated pairs were rejected during filtering.", "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": "uRfEQHBwIQ", "reviewer": "Reviewer_ReWH", "summary": "This paper introduces WorldEdit, a dataset and benchmark targeting implicit, cause-driven image edits that require world knowledge and physical reasoning. The authors (i) curate 11k editing pairs via an automated pipeline (1. object detection, 2. instruction generation/paraphrase, 3. multi-step editing, 4. pre/post filtering for causal plausibility and visual quality; (ii) define WorldEdit-Test with ten causal categories (environment- and mechanics-driven); and (iii) finetune a base editor (based on BAGEL) using a two-stage scheme: SFT on paraphrased CoT instructions and RL (Flow-GRPO) with three rewards (reasoning, image quality, causal verification). Evaluations (automatic and human) show competitive results vs. commercial systems (GPT-4o, Nano-Banana) and SOTA among open-source models on knowledge plausibility and instruction following.", "questions": "See weaknesses above", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "1. The problem of implicit, world-knowledge image editing is a timely research problem and opens new possibilities for image editing models.\n2. The proposed dataset design contains comprehensive editing categories (Time, Temperature, Humidity, Acidity, Light, Break, Inflate, Squeeze, Twist, Stretch).", "weaknesses": "1. Both the dataset construction pipeline and the benchmark evaluation pipeline rely on proprietary models: Data synthesis and filtering are based on GPT-4o, and model evaluation is based on Qwen-VL-Max. This makes the method difficult to scale up, especially when constructing training datasets, where all the training samples are generated from GPT-4o.\n2. Following weakness 1, since the evaluation pipeline is based on Qwen-VL-Max, there could be potential bias from the model during evaluation. Although there are some human evaluation results, these results only cover a limited set of models. I would recommend conducting a more detailed human-VLM agreement and inter-rater agreement study to validate the effectiveness of the automatic evaluation pipeline.\n3. The method is only validated on WorldEdit-Test, which is an in-domain benchmark with respect to the training set (both training data and evaluation data are generated by GPT-4o). Therefore, it is naturally expected that WorldEdit performs the best among open-source models. There should be more evaluation results on out-of-domain evaluation benchmarks (such as KRIS-Bench and RISEBench as mentioned in the paper) to further validate the effectiveness of WorldEdit.\n4. The proposed method (dataset + two-stage training pipeline) has only been experimented on one model (BAGEL), without further validation on more candidate models.", "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": "4NIzlrw8sH", "reviewer": "Reviewer_yu5k", "summary": "This paper addresses the challenge of implicit editing instructions in image manipulation by introducing WorldEdit, a dataset specifically designed for world-driven image editing. The authors identify that existing models struggle with instructions that describe the cause of visual changes without specifying the outcome, due to their reliance on uniform editing strategies. The proposed solution includes a curated dataset of 11k high-quality samples generated through instruction decomposition and multi-step editing, rigorously filtered for world knowledge consistency. Using this dataset, a two-stage training framework fine-tunes models like Bagel: first through supervised fine-tuning with paraphrased instructions, then via reinforcement learning with a causal verification reward. The approach demonstrates state-of-the-art performance among open-source models, significantly bridging the gap with leading commercial systems like GPT-4o in knowledge plausibility and causal reasoning.", "questions": "From the perspectives of dataset collection methodology, training strategy, the base models used, and the generated results, I find it difficult to identify significant flaws in the proposed approach. I believe the paper warrants acceptance, but I acknowledge that my view might be partial. Therefore, I am providing a conservatively initial score and am willing to adjust it based on subsequent discussions.\n\nAdditionally, several prior works have explored world knowledge editing. The authors may consider citing the following relevant studies:\n\n  [1] Unireal: Universal image generation and editing via learning real-world dynamics \\\n  [2] Editworld: Simulating world dynamics for instruction-following image editing \\\n  [3] Reasonpix2pix: instruction reasoning dataset for advanced image editing", "limitations": "", "rating": 6, "confidence": 3, "soundness": 4, "presentation": 4, "contribution": 4, "strengths": "- The paper presents a novel world knowledge editing method and demonstrates state-of-the-art results with convincing showcased outcomes.\n- The authors provide a new dataset along with a corresponding test set, and employ Flow-GRPO to train the Bagel model, reflecting substantial research effort.\n- The paper is logically structured and clearly written, with effective comparative results.", "weaknesses": "See the \"Question\" part.", "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": "X0sdR5GETu", "reviewer": "Reviewer_JkQY", "summary": "This paper designs a dataset called WorldEdit, which is designed to enable world-driven image editing. It boasts high-quality images and more realistic text. They also provide WorldEdit-Test as an evaluation benchmark. When it comes to the training framework, they use a fine-tune framework with a causal verification reward. Experiment results prove the effectiveness of the proposed method.", "questions": "Since this is majorly a dataset work, I think the authors can have more discussions about the problems I mention in the weakness section.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. This paper is clearly organized and easy to understand. \n2. Constructing a dataset, along with a benchmark, is beneficial to the community and future research. \n3. The experiment results are abundant, with clear analysis. The baseline methods cover the mainstream methods. All the results are listed in a very clear organization. So I think the experiment section is good, and the results are convincing. \n4. Some figures in this paper helps a lot, for example, Figure 4 shows comparison results of a lot of methods in one figure very clearly.", "weaknesses": "1. As it is a dataset work, I think the authors can have more discussions about the limitation or future work, so that the research community can further expand the current dataset. \n2. When it comes to expanding the current dataset, the authors can have discussions about the budget or computational costs of data generation and collection.", "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)