arxiv: 2604.15917 · v1 · submitted 2026-04-17 · 💻 cs.CV

Recognition: unknown

Making Image Editing Easier via Adaptive Task Reformulation with Agentic Executions

Bo Zhao , Kairui Guo , Runnan Du , Haiyang Sun , Pengshan Wang , Huan Yang , Kun Gai , Yixin Cao

show 1 more author

Wei Ji

Authors on Pith no claims yet

Pith reviewed 2026-05-10 08:19 UTC · model grok-4.3

classification 💻 cs.CV

keywords image editingtask reformulationmultimodal agentsinstruction followingadaptive frameworksgenerative models

0 comments

The pith

Reformulating vague image editing instructions into adaptive operation sequences with an MLLM agent lifts performance without changing the model.

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

Many instruction-guided image edits fail on seemingly simple requests because the original task is poorly posed, such as when targets are small, spatial relations are implicit, or instructions are underspecified. The paper treats these as formulation problems rather than capacity limits and introduces an agent that analyzes the input, routes and reformulates it into a sequence of simpler operations, then refines the plan through feedback. This adaptive reformulation is executed on top of existing editing models and yields consistent gains on ImgEdit, PICA, and RePlan benchmarks, with the largest improvements on hard cases across different backbones.

Core claim

A large portion of image editing failures stem not from insufficient model capacity, but from poorly formulated editing tasks such as those involving small targets, implicit spatial relations, or under-specified instructions. The proposed adaptive task reformulation framework transforms the original image-instruction pair into a sequence of operations dynamically determined and executed by an MLLM agent through analysis, routing, reformulation, and feedback-driven refinement, producing consistent improvements without modifying the underlying editing model.

What carries the argument

The MLLM agent that performs analysis, routing, reformulation, and feedback-driven refinement to turn an original editing request into a tailored sequence of operations.

If this is right

Editing performance can be raised by matching task formulation to the model's effective operating regime rather than by scaling model size.
Gains appear without any retraining or architectural changes to the base editing models.
The method produces especially large benefits on cases with small targets, implicit relations, or vague instructions.
Task reformulation emerges as a critical but previously underexplored lever for reliable image editing.

Where Pith is reading between the lines

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

The same reformulation approach could be tested on related generative tasks such as text-to-image synthesis or video editing where instruction clarity also matters.
It implies that robustness to varied prompt styles may be more valuable than raw generative power for practical deployment.
Hybrid systems might pair lightweight agents for formulation with specialized executors for pixel-level changes.

Load-bearing premise

That most editing failures are caused by how the task is stated rather than by limits inside the generative model itself.

What would settle it

Running the same benchmarks after applying the reformulation and observing no improvement, or finding that well-reformulated tasks still fail at rates comparable to the original instructions.

Figures

Figures reproduced from arXiv: 2604.15917 by Bo Zhao, Haiyang Sun, Huan Yang, Kairui Guo, Kun Gai, Pengshan Wang, Runnan Du, Wei Ji, Yixin Cao.

**Figure 1.** Figure 1: Overcoming editing failures via task reformulation. We show that success depends heavily on task presentation. [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗

**Figure 2.** Figure 2: Overview of our framework. Each edit query is first profiled by its target, constraints, and scope, and then routed to [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** Figure 3: Qualitative editing results on the ImgEdit benchmark. Compared to direct editing baselines, our ATR framework [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗

**Figure 4.** Figure 4: Qualitative editing results on the PICA benchmark. [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗

**Figure 5.** Figure 5: Additional qualitative results on the PICA bench [PITH_FULL_IMAGE:figures/full_fig_p011_5.png] view at source ↗

**Figure 6.** Figure 6: Additional qualitative results on the Replan bench [PITH_FULL_IMAGE:figures/full_fig_p012_6.png] view at source ↗

**Figure 7.** Figure 7: Additional qualitative results on the ImgEdit benchmark. Our methods (Qwen-Edit-ATR and Nano Banana-ATR) [PITH_FULL_IMAGE:figures/full_fig_p015_7.png] view at source ↗

**Figure 8.** Figure 8: Detailed execution flow for Route A2 (Instruction Rewriting). [PITH_FULL_IMAGE:figures/full_fig_p016_8.png] view at source ↗

**Figure 9.** Figure 9: Detailed execution flow for Route B (Spatial Decoupling). [PITH_FULL_IMAGE:figures/full_fig_p016_9.png] view at source ↗

**Figure 10.** Figure 10: Detailed execution flow for Route C (Localized Editing). [PITH_FULL_IMAGE:figures/full_fig_p017_10.png] view at source ↗

**Figure 11.** Figure 11: Limitations of our framework [PITH_FULL_IMAGE:figures/full_fig_p018_11.png] view at source ↗

**Figure 12.** Figure 12: Ill-posed QA example 1: Moving the glass. A logically correct edit is penalized due to an unreasonable spatial [PITH_FULL_IMAGE:figures/full_fig_p019_12.png] view at source ↗

**Figure 13.** Figure 13: Ill-posed QA example 2: Straightening the rope. A successful structural edit fails the evaluation because the natural [PITH_FULL_IMAGE:figures/full_fig_p019_13.png] view at source ↗

read the original abstract

Instruction guided image editing has advanced substantially with recent generative models, yet it still fails to produce reliable results across many seemingly simple cases. We observe that a large portion of these failures stem not from insufficient model capacity, but from poorly formulated editing tasks, such as those involving small targets, implicit spatial relations, or under-specified instructions. In this work, we frame image editing failures as a task formulation problem and propose an adaptive task reformulation framework that improves editing performance without modifying the underlying model. Our key idea is to transform the original image-instruction pair into a sequence of operations that are dynamically determined and executed by a MLLM agent through analysis, routing, reformulation, and feedback-driven refinement. Experiments on multiple benchmarks, including ImgEdit, PICA, and RePlan, across diverse editing backbones such as Qwen Image Edit and Nano Banana, show consistent improvements, with especially large gains on challenging cases. These results suggest that task reformulation is a critical but underexplored factor, and that substantial gains can be achieved by better matching editing tasks to the effective operating regime of existing models.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The paper shows that an MLLM agent can reformulate editing instructions on the fly to lift results from fixed backbones, with the biggest help on vague or spatially tricky cases.

read the letter

This paper's main claim is that a lot of instruction-guided image editing failures come from how the task is worded rather than from the model being too weak, and that an MLLM agent can fix it by breaking the request into better steps. They build a loop that analyzes the input, routes it, reformulates it into operations, and refines via feedback before handing the sequence to the base editor. The tests cover ImgEdit, PICA, and RePlan across Qwen Image Edit and Nano Banana, with the gains described as consistent and larger on hard examples. That modular setup is the clearest new piece: it treats the editor as a fixed tool and adds an agent layer on top instead of retraining anything. The approach is practical for anyone who already has a working editor and just wants fewer bad outputs on small targets or implicit relations. The experiments are presented as holding across backbones, which gives the idea some weight if the numbers check out. On the downside, the abstract gives no quantitative deltas, no ablation on which agent step matters most, and no direct comparison to simpler prompting fixes, so it is still unclear how much of the lift is from the full pipeline versus just better task phrasing. The added MLLM step also brings latency and potential new failure modes that are not discussed in detail here. This is the sort of incremental but usable idea that could help tool builders. It has enough structure and claimed results to merit peer review, though the referees will need to see the full tables and controls before deciding how far the gains generalize.

Referee Report

2 major / 2 minor

Summary. The paper claims that instruction-guided image editing failures often stem from poorly formulated tasks (e.g., small targets, implicit relations, under-specified instructions) rather than model capacity limits. It introduces an adaptive task reformulation framework in which an MLLM agent performs analysis, routing, reformulation, and feedback-driven refinement to convert the original image-instruction pair into a dynamic sequence of operations executed by a fixed editing backbone. Experiments across ImgEdit, PICA, and RePlan benchmarks with backbones including Qwen Image Edit and Nano Banana are reported to yield consistent improvements, with larger gains on hard cases.

Significance. If the reported gains are reproducible and properly controlled, the work is significant because it reframes editing performance as a task-formulation problem solvable by an additive agent layer rather than by retraining or scaling the base model. This agentic pipeline (analysis-routing-reformulation-feedback) is a concrete, model-agnostic contribution that could be applied to other generative tasks. The emphasis on matching tasks to the effective operating regime of existing models is a useful perspective, and the claim of larger gains on challenging cases, if substantiated, would strengthen the practical value.

major comments (2)

[§4] §4 (Experiments): The central claim of 'consistent improvements' and 'especially large gains on challenging cases' across ImgEdit, PICA, RePlan and multiple backbones is asserted without any quantitative metrics, success rates, baseline tables, ablation results on individual agent components, or statistical controls. This absence prevents verification that the data support the claim that task reformulation, rather than other factors, drives the gains.
[§3.3] §3.3 (Feedback-driven refinement): The description of the iterative refinement loop does not specify termination criteria, maximum iteration limits, or safeguards against non-convergence. Without these details the practical reliability of the agentic execution pipeline cannot be assessed, which is load-bearing for the claim that the method improves editing without modifying the backbone.

minor comments (2)

The abstract and method sections use the term 'Nano Banana' for one of the editing backbones; a brief clarification of the model name or reference would improve readability.
Figure captions and the pipeline diagram (presumably Figure 1 or 2) would benefit from explicit labeling of the four agent stages (analysis, routing, reformulation, refinement) to match the textual description.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments. We address each major point below and will revise the manuscript to incorporate the requested details and clarifications.

read point-by-point responses

Referee: [§4] §4 (Experiments): The central claim of 'consistent improvements' and 'especially large gains on challenging cases' across ImgEdit, PICA, RePlan and multiple backbones is asserted without any quantitative metrics, success rates, baseline tables, ablation results on individual agent components, or statistical controls. This absence prevents verification that the data support the claim that task reformulation, rather than other factors, drives the gains.

Authors: We acknowledge that the current version of Section 4 summarizes the results without providing the full quantitative tables, success rates, baseline comparisons, component ablations, or statistical controls. In the revised manuscript we will expand this section to include success-rate tables for all benchmarks and backbones, ablation studies isolating the contributions of analysis, routing, reformulation, and feedback, and statistical significance tests. These additions will allow direct verification that the observed gains are attributable to task reformulation. revision: yes
Referee: [§3.3] §3.3 (Feedback-driven refinement): The description of the iterative refinement loop does not specify termination criteria, maximum iteration limits, or safeguards against non-convergence. Without these details the practical reliability of the agentic execution pipeline cannot be assessed, which is load-bearing for the claim that the method improves editing without modifying the backbone.

Authors: We agree that the description in Section 3.3 is incomplete regarding the iterative loop. The revised manuscript will specify termination criteria (e.g., feedback quality threshold or no further improvement), a maximum iteration limit of three, and safeguards such as fallback to the original task upon non-convergence. These explicit details will enable assessment of the pipeline's reliability while preserving the model-agnostic nature of the approach. revision: yes

Circularity Check

0 steps flagged

No circularity in derivation chain

full rationale

The paper proposes an empirical agentic framework for adaptive task reformulation in image editing, supported by benchmark experiments across multiple backbones. No equations, derivations, or mathematical predictions are present. The central claims rest on observed performance gains from an additive MLLM-agent layer described as independent of base models, with no self-definitional reductions, fitted inputs renamed as predictions, or load-bearing self-citations that collapse the argument to its inputs.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Framework depends on unverified assumptions about MLLM agent reliability for task reformulation; no free parameters or invented entities are specified in the abstract.

axioms (1)

domain assumption MLLM agents can reliably analyze images, route decisions, reformulate instructions, and refine via feedback for editing tasks
Invoked as the core mechanism enabling the adaptive reformulation without further justification in the abstract.

pith-pipeline@v0.9.0 · 5515 in / 1177 out tokens · 42686 ms · 2026-05-10T08:19:07.102990+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

37 extracted references · 12 canonical work pages · 3 internal anchors

[1]

Samyadeep Basu, Mehrdad Saberi, Shweta Bhardwaj, Atoosa Malemir Chegini, Daniela Massiceti, Maziar Sanjabi, Shell Xu Hu, and Soheil Feizi. 2023. Editval: Benchmarking diffusion based text-guided image editing methods.arXiv preprint arXiv:2310.02426(2023)

work page arXiv 2023
[2]

James Betker, Gabriel Goh, Li Jing, Tim Brooks, Jianfeng Wang, Linjie Li, Long Ouyang, Juntang Zhuang, Joyce Lee, Yufei Guo, et al . 2023. Improving im- age generation with better captions.Computer Science. https://cdn. openai. com/papers/dall-e-3. pdf2, 3 (2023), 8

2023
[3]

Tim Brooks, Aleksander Holynski, and Alexei A Efros. 2023. Instructpix2pix: Learning to follow image editing instructions. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 18392–18402

2023
[4]

Tingfeng Cao, Chengyu Wang, Bingyan Liu, Ziheng Wu, Jinhui Zhu, and Jun Huang. 2023. Beautifulprompt: Towards automatic prompt engineering for text- to-image synthesis. InProceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track. 1–11

2023
[5]

Pengtao Chen, Xianfang Zeng, Maosen Zhao, Mingzhu Shen, Peng Ye, Bangyin Xiang, Zhibo Wang, Wei Cheng, Gang Yu, and Tao Chen. 2025. RegionE: Adaptive Region-Aware Generation for Efficient Image Editing.arXiv preprint arXiv:2510.25590(2025)

work page arXiv 2025
[6]

Guillaume Couairon, Jakob Verbeek, Holger Schwenk, and Matthieu Cord. 2022. Diffedit: Diffusion-based semantic image editing with mask guidance.arXiv preprint arXiv:2210.11427(2022)

work page arXiv 2022
[7]

Patrick Esser, Sumith Kulal, Andreas Blattmann, Rahim Entezari, Jonas Müller, Harry Saini, Yam Levi, Dominik Lorenz, Axel Sauer, Frederic Boesel, et al. 2024. Scaling rectified flow transformers for high-resolution image synthesis. InForty- first international conference on machine learning

2024
[8]

Tsu-Jui Fu, Wenze Hu, Xianzhi Du, William Yang Wang, Yinfei Yang, and Zhe Gan
[9]

Guiding instruction-based image editing via multimodal large language models.arXiv preprint arXiv:2309.17102(2023)

work page arXiv 2023
[10]

Zigang Geng, Binxin Yang, Tiankai Hang, Chen Li, Shuyang Gu, Ting Zhang, Jianmin Bao, Zheng Zhang, Houqiang Li, Han Hu, et al. 2024. Instructdiffusion: A generalist modeling interface for vision tasks. InProceedings of the IEEE/CVF Conference on computer vision and pattern recognition. 12709–12720

2024
[11]

Yaru Hao, Zewen Chi, Li Dong, and Furu Wei. 2023. Optimizing prompts for text-to-image generation.Advances in Neural Information Processing Systems36 (2023), 66923–66939

2023
[12]

Kaiyi Huang, Kaiyue Sun, Enze Xie, Zhenguo Li, and Xihui Liu. 2023. T2i- compbench: A comprehensive benchmark for open-world compositional text-to- image generation.Advances in Neural Information Processing Systems36 (2023), 78723–78747

2023
[13]

Yuzhou Huang, Liangbin Xie, Xintao Wang, Ziyang Yuan, Xiaodong Cun, Yixiao Ge, Jiantao Zhou, Chao Dong, Rui Huang, Ruimao Zhang, et al. 2024. Smartedit: Exploring complex instruction-based image editing with multimodal large lan- guage models. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 8362–8371

2024
[14]

Bahjat Kawar, Shiran Zada, Oran Lang, Omer Tov, Huiwen Chang, Tali Dekel, Inbar Mosseri, and Michal Irani. 2023. Imagic: Text-based real image editing with diffusion models. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition. 6007–6017

2023
[15]

Hongyu Li, Manyuan Zhang, Dian Zheng, Ziyu Guo, Yimeng Jia, Kaituo Feng, Hao Yu, Yexin Liu, Yan Feng, Peng Pei, et al. 2025. Editthinker: Unlocking iterative reasoning for any image editor.arXiv preprint arXiv:2512.05965(2025)

work page arXiv 2025
[16]

Long Lian, Boyi Li, Adam Yala, and Trevor Darrell. 2023. Llm-grounded diffusion: Enhancing prompt understanding of text-to-image diffusion models with large language models.arXiv preprint arXiv:2305.13655(2023)

work page arXiv 2023
[17]

Vivian Liu and Lydia B Chilton. 2022. Design guidelines for prompt engineering text-to-image generative models. InProceedings of the 2022 CHI conference on human factors in computing systems. 1–23

2022
[18]

Xingang Pan, Ayush Tewari, Thomas Leimkühler, Lingjie Liu, Abhimitra Meka, and Christian Theobalt. 2023. Drag your gan: Interactive point-based manip- ulation on the generative image manifold. InACM SIGGRAPH 2023 conference proceedings. 1–11

2023
[19]

Dustin Podell, Zion English, Kyle Lacey, Andreas Blattmann, Tim Dockhorn, Jonas Müller, Joe Penna, and Robin Rombach. 2023. Sdxl: Improving latent diffusion models for high-resolution image synthesis.arXiv preprint arXiv:2307.01952 (2023)

work page internal anchor Pith review arXiv 2023
[20]

Yuandong Pu, Le Zhuo, Songhao Han, Jinbo Xing, Kaiwen Zhu, Shuo Cao, Bin Fu, Si Liu, Hongsheng Li, Yu Qiao, et al. 2025. PICABench: How Far Are We from Physically Realistic Image Editing?arXiv preprint arXiv:2510.17681(2025)

work page arXiv 2025
[21]

Tianyuan Qu, Lei Ke, Xiaohang Zhan, Longxiang Tang, Yuqi Liu, Bohao Peng, Bei Yu, Dong Yu, and Jiaya Jia. 2025. RePlan: Reasoning-guided Region Planning for Complex Instruction-based Image Editing.arXiv preprint arXiv:2512.16864 (2025)

work page arXiv 2025
[22]

Nataniel Ruiz, Yuanzhen Li, Varun Jampani, Yael Pritch, Michael Rubinstein, and Kfir Aberman. 2023. Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition. 22500–22510

2023
[23]

Yongliang Shen, Kaitao Song, Xu Tan, Dongsheng Li, Weiming Lu, and Yueting Zhuang. 2023. Hugginggpt: Solving ai tasks with chatgpt and its friends in hugging face.Advances in Neural Information Processing Systems36 (2023), 38154–38180

2023
[24]

Shelly Sheynin, Adam Polyak, Uriel Singer, Yuval Kirstain, Amit Zohar, Oron Ashual, Devi Parikh, and Yaniv Taigman. 2024. Emu edit: Precise image editing via recognition and generation tasks. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 8871–8879

2024
[25]

Narek Tumanyan, Michal Geyer, Shai Bagon, and Tali Dekel. 2023. Plug-and-play diffusion features for text-driven image-to-image translation. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition. 1921–1930

2023
[26]

Haoran Wang, Bo Zhao, Jinghui Wang, Hanzhang Wang, Huan Yang, Wei Ji, Hao Liu, and Xinyan Xiao. 2025. Sega: A stepwise evolution paradigm for content-aware layout generation with design prior. InProceedings of the IEEE/CVF International Conference on Computer Vision. 19321–19330

2025
[27]

Zhenyu Wang, Aoxue Li, Zhenguo Li, and Xihui Liu. 2024. Genartist: Multimodal llm as an agent for unified image generation and editing.Advances in Neural Information Processing Systems37 (2024), 128374–128395

2024
[28]

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

work page internal anchor Pith review arXiv 2025
[29]

Ling Yang, Zhaochen Yu, Chenlin Meng, Minkai Xu, Stefano Ermon, and Bin Cui
[30]

InIcml, Vol

Mastering Text-to-Image Diffusion: Recaptioning, Planning, and Generating with Multimodal LLMs.. InIcml, Vol. 3. 7
[31]

Yang Ye, Xianyi He, Zongjian Li, Bin Lin, Shenghai Yuan, Zhiyuan Yan, Bohan Hou, and Li Yuan. 2025. Imgedit: A unified image editing dataset and benchmark. arXiv preprint arXiv:2505.20275(2025)

work page internal anchor Pith review arXiv 2025
[32]

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

2023
[33]

Lvmin Zhang, Anyi Rao, and Maneesh Agrawala. 2023. Adding conditional con- trol to text-to-image diffusion models. InProceedings of the IEEE/CVF international conference on computer vision. 3836–3847

2023
[34]

Zhixing Zhang, Ligong Han, Arnab Ghosh, Dimitris N Metaxas, and Jian Ren. 2023. Sine: Single image editing with text-to-image diffusion models. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition. 6027–6037

2023
[35]

Bo Zhao, Yihang Liu, Chenfeng Zhang, Huan Yang, Kun Gai, and Wei Ji. 2026. TexEditor: Structure-Preserving Text-Driven Texture Editing.arXiv preprint arXiv:2603.18488(2026)

work page arXiv 2026
[36]

Bo Zhao, Huan Yang, and Jianlong Fu. 2025. Learning position-aware implicit neural network for real-world face inpainting.Pattern Recognition165 (2025), 111598. Conference acronym ’XX, June 03–05, 2018, Woodstock, NY Trovato et al

2025
[37]

perfect local edit

Jun Zhou, Jiahao Li, Zunnan Xu, Hanhui Li, Yiji Cheng, Fa-Ting Hong, Qin Lin, Qinglin Lu, and Xiaodan Liang. 2025. Fireedit: Fine-grained instruction-based image editing via region-aware vision language model. InProceedings of the Computer Vision and Pattern Recognition Conference. 13093–13103. Making Image Editing Easier via Adaptive Task Reformulation w...

2025