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arxiv: 2604.15917 · v2 · pith:ZJSJDOQXnew · submitted 2026-04-17 · 💻 cs.CV

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
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Wei Ji
This is my paper

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

classification 💻 cs.CV
keywords image editingtask reformulationmultimodal agentsinstruction followingadaptive frameworksgenerative models
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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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. Figure 11: Limitations of our framework [PITH_FULL_IMAGE:figures/full_fig_p018_11.png] view at source ↗
Figure 12
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. 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.

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)
  1. [§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.
  2. [§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)
  1. 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.
  2. 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

  1. 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

  2. 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)

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