EditAgent: Rethinking Agentic Image Editing

Haiyang Xu; Tianyu Wang; Jialu Li; Jiteng Mu; Yuanjun Xiong; Jianming Zhang; Zhuowen Tu; Mingze Xu

cs.CVOct 29, 2026

EditAgent: Rethinking Agentic Image Editing

Authors: Haiyang Xu, Tianyu Wang, Jialu Li, Jiteng Mu, Yuanjun Xiong, Jianming Zhang, Zhuowen Tu, Mingze Xu

Organizations: UC San Diego · Adobe Research

Abstract

Agentic image editors decompose complex requests into steps, but when a step fails, they mostly retry with the same narrow set of tools and a judge that scores each output by eye directly. We argue that such agents are bounded by two spaces: the action space of what the agent can do, and the observation space of what its judge can see. On our expert-annotated benchmark, a general-purpose editor yields no usable result in over 40% of the cases, and no judge is reliable across edit types. We present EditAgent, which expands both spaces. It routes each step to general editors and specialized methods, and attaches to each candidate a verification record. With these records, off-the-shelf VLM judges outperform finetuned reward models. Candidates are generated in parallel and judged in one listwise call: experts prefer this to sequential refinement in 78.5% of decided comparisons, at no more than half the judge calls. EditAgent improves two open-weight editors on three benchmarks and serves as a data engine for training stronger editors.

Figures & tables

Figure 2: Where the action space and the observation space fall short, by edit group. (a) Action space: share of the expert-labeled sets in which only general-editor candidates, only specialized candidates, both kinds, or no candidate are usable. (b) Observation space: Winner-Set Hit@1 on the expert-labeled sets with at least one usable candidate. Random is the expected Hit@1 of a uniform random pick.

Figure 3: Overview of EditAgent. Bottom: a request runs as dependent steps, and each accepted result becomes the source of the next step. Top: step 1, enlarged. (A) The planner orders the steps; the analyzer and router select the applicable methods for the current step. (B) The selected methods generate candidates in parallel from the same source image, each with a verification record (evidence). (C) An off-the-shelf VLM compares the candidates with their records and accepts one, or returns feedback for another attempt.

Judge	Pointwise	Listwise (Ours)	$\boldsymbol{\Delta}$
EditScore-7B	65.9	–	–
EditScore-32B	68.0	–	–
EditScore-72B	70.3	–	–
EditScore-7B, Ensemble@4	72.7	–	–
EditScore-32B, Ensemble@4	73.3	–	–
EditScore-72B, Ensemble@4	76.3	–	–

Table 1: Comparative judging is better. Overall accuracy (%) on EditReward-Bench [ 35 ] . Pointwise scores each candidate alone; listwise (ours) compares them together in one call.

Figure 4: Examples of the verification record (evidence). (a) A close-up of the edited region reveals the blurred checks left by the removal specialist. (b) Measured brightness, clipping and color shift accurately reveal the performance of the photometric operator.

Listwise	Evidence	Style	Content	Scene	Photo.	Geom.	Enh.	All
		84.4	81.7	67.5	54.2	66.4	65.0	71.6
✓		87.1	87.1	72.0	47.2	78.6	36.8	73.6
✓	✓	87.6	86.4	81.9	64.4	81.4	80.3	81.3

Table 2: Per-edit-group Winner-Set Hit@1 (%) of Qwen3.8-27B on our diagnostic benchmark.

Figure 5: Verification record (evidence) further lifts off-the-shelf VLM judge. Winner-Set Hit@1 on our diagnostic benchmark.

Model	EdiVal-IF $\uparrow$	EdiVal-CC $\uparrow$	EdiVal-O $\uparrow$
Closed-source
FLUX.1-Kontext-max † [ 23 ]	69.49 / 46.89 / 31.83	93.93 / 90.90 / 88.40	80.79 / 65.29 / 53.04
GPT-Image-1 † [ 41 ]	73.12 / 54.89 / 38.35	81.00 / 77.78 / 75.50	76.96 / 65.34 / 53.81
Nano Banana † [ 14 ]	70.70 / 50.66 / 35.35	93.91 / 90.48 / 89.48	81.48 / 67.70 / 56.24
FLUX.2-max [ 25 ]	75.55 / 55.27 / 39.36	92.91 / 88.30 / 85.78	83.78 / 69.86 / 58.10
Nano Banana 2 † [ 10 ]	73.89 / 54.17 / 38.61	93.54 / 90.52 / 88.61	83.14 / 70.02 / 58.49

Table 3: Comparison of different editing models on EdiVal. Closed-source model numbers from EdiVal. Each cell reports turn 1 / turn 2 / turn 3 scores (0-100). † Generates at 1024 px, while all outputs are downsampled to 512

\times

512 for evaluation.

Figure 6: The history check extends what the judge observes to earlier turns. Letting in more daylight in turn 2 clears the frosted glass from turn 1. The history check detects this and repairs.

Model	ComplexBench-Edit						Complex-Edit
Model	Obj. $\uparrow$	Obj.-At. $\uparrow$	Obj.-At.-G. $\uparrow$	Two-chain $\uparrow$	Three-chain $\uparrow$	Avg. $\uparrow$	IF $\uparrow$	IP $\uparrow$	PQ $\uparrow$	O $\uparrow$
Qwen-Image-Edit-2511	56.98	60.81	67.97	57.59	28.67	54.40	8.721	6.259	7.132	7.371
+ EditAgent	68.92	70.74	73.17	60.21	31.11	60.83	8.736	6.391	7.209	7.445
FLUX.2-klein-9B	67.52	70.56	73.87	61.67	33.61	61.45	8.763	7.304	6.964	7.677
+ EditAgent	70.05	74.69	76.80	64.48	36.53	64.51	8.783	7.131	7.224	7.713

Table 4: Editing performance on ComplexBench-Edit (scores 0-100) and Complex-Edit (scores 0-10).

Model	Qwen-Image-Edit-2511 [ 63 ]				FLUX.2-klein-9B [ 27 ]
Model	Turn 1 $\uparrow$	Turn 2 $\uparrow$	Turn 3 $\uparrow$	Inference Cost $\downarrow$	Turn 1 $\uparrow$	Turn 2 $\uparrow$	Turn 3 $\uparrow$	Inference Cost $\downarrow$
Standalone Editor	75.97	58.58	47.64	1.1	81.37	66.30	53.20	0.5
MIRA † [ 75 ]	75.79	57.59	45.59	11.3	82.57	67.08	53.76	5.6
IMAGAgent [ 49 ]	76.83	60.15	49.78	10.9	82.63	68.00	55.21	4.6
EditThinker [ 28 ]	76.58	60.47	49.23	9.4	82.62	67.20	55.97	4.6
GenArtist † [ 61 ]	79.51	62.94	50.54	9.5	82.94	67.48	54.74	5.2

Table 5: Comparison with agentic editors on EdiVal. We report EdiVal-O at turns 1/2/3 and inference cost (H100 GPU hour). All agents use the same base editors and Qwen3.8-27B [ 56 ] as the VLM. Best bold , second underlined . † Re-implemented from the paper.

Figure 7: Multi-turn editing comparison between FLUX.2-klein-9B and EditAgent.

Baseline	Training Data Source	EdiVal-O Turn 3 $\uparrow$	Complex-Edit O $\uparrow$	ComplexBench-Edit Avg $\uparrow$
Qwen-Image-Edit-2511	—	49.66	7.371	54.40
	FLUX.2-klein-9B	48.51	7.334	55.84
	EditAgent	51.61	7.435	57.57
FLUX.2-klein-base-9B	—	54.53	7.517	57.84
	FLUX.2-klein-9B	52.83	7.351	57.47
	EditAgent	56.53	7.615	59.98

Table 6: Training data from EditAgent versus data generated directly by FLUX.2-klein-9B. Each baseline model is fully finetuned for one epoch; “—” denotes no finetuning. To bridge the train-eval gap of prompting, we rewrite the input prompts for EdiVal.

Vanilla & Agent	Action Space	Observation Space		EdiVal-O (T3)
Vanilla & Agent	Action Space	Parallel Comparison	Verification Record	EdiVal-O (T3)
$\times$	$\times$	$\times$	$\times$	47.64
$\checkmark$	$\times$	$\times$	$\times$	49.43
$\checkmark$	$\checkmark$	$\times$	$\times$	52.37
$\checkmark$	$\checkmark$	$\checkmark$	$\times$	53.18
$\checkmark$	$K{=}2$	$\checkmark$	$\times$	47.53

Table 7: Component ablation on EdiVal with Qwen-Image-Edit-2511. Rows with

K{=}n

replace the action space with

n

identical copies of the general editor. The last row shows the full EditAgent.

Figure S1 : Diagnostic sets in which only the specialized candidate is usable (✓). The general editor turns the bear around, puts the snow on the TV screen, and changes the saturation.

Group	Edit Type	Definition	General	Hit@1
Content	Add Text	add readable text/writing/signage somewhere it fits naturally	19	19
	Remove Text	remove existing text/logo/signage (only if some is visible)	15	15
	Replace Text	replace visible text with different text (only if text is visible)	14	15
	Change Text Style	change color/font/size/style of visible text	9	12
	Add Object	add a new object/animal/person that fits the scene	18	18
	Remove Object	remove an existing object/person/animal	15	17

Table S1 : The 30 edit types of the diagnostic benchmark and the definitions given to the instruction writer. Each type has 20 sets. General counts the sets with at least one usable general-editor candidate from either run, and Hit@1 counts the sets used for Winner-Set Hit@1.

Role	Model	Input	Output	Prompt
Planner	VLM	Request of the turn, the version graph and the carried requirements	Goal and ordered steps, each with its operation, target, regions to keep, parameters and new requirements
Analyzer	VLM	One step, source image of the step, carried requirements, feedback on earlier attempts, reports of earlier steps, parameter documentation of the applicable methods	Explicit instruction stating what changes and what stays, operation, target, regions to keep, parameters, acceptance criteria, candidate count
Router	Code	Analyzed step	Methods, each contributing at least one candidate ( Tab. S4 )	None
Grounder	VLM and SAM 2.1	Image and a referring expression	One box per referred object in coordinates normalized to $[0,1000]$ , refined into a mask by SAM 2.1 and reviewed by the VLM	and
Editing Methods	Tab. S5	Source image of the step, instruction, target, parameters	Candidate image and its execution artifacts, such as a mask	None
Judge	VLM	Request of the step with its scope, acceptance criteria and regions to keep, source image, and all candidates with their verification records in a shuffled order	A note, a pass/fail verdict and a score from 1 to 10 for every candidate, then the best candidate and a ranking

Table S2 : Roles in EditAgent. In the benchmark experiments and in the runs that produced the diagnostic candidates, every VLM role uses Qwen3.8-27B with the settings of Tab. S3 . The data engine uses Qwen3.8-27B as the judge ( Sec. H ).

Setting	Value
Candidates per Attempt	at most 3
Attempts per Step	3
Re-Plans per Turn	2
Repairs per Turn	1
Repair Confidence	at least 0.4
Seed of Candidate $j$ in Attempt $a$	$32+7a+j$

Table S3 : Default settings of EditAgent, used in all benchmark experiments and in the data engine (EdiVal images at

512^{2}

px).

Step	Operation	Methods
Remove a target	remove	General Editor Removal Specialist
Recolor a target	recolor	General Editor Masked Recolor
Restyle a target	restyle	General Editor Masked Local Editor
Replace or add an object	replace, add	General Editor
Move a target	move	General Editor Move Specialist
Resize a subject	scale_subject	General Editor Subject Scaling

Table S4 : Methods raced for each kind of step, with the operation that the planner and analyzer assign to it.

Method	Backend	Established by Code or Construction	Left to the Judge
General Editor	The backbone (FLUX.2-klein-9B or Qwen-Image-Edit-2511) edits the whole frame.	None	Requested change, preservation, realism.
Masked Local Editor	Target mask from grounding, SAM 2.1 [ 46 ] and VLM review. The general editor edits the frame, and only its pixels inside the dilated and feathered mask are kept.	Unchanged outside the feathered mask (code).	Requested look in the mask, seam realism.
Masked Recolor	As above, with a tighter mask.	Unchanged outside the feathered mask (code).	Requested color, coverage, seam.
Removal Specialist	OSOR [ 80 ] with the reviewed target mask, whose output is used directly.	Canvas size unchanged (code).	Target and attached parts removed, background reconstructed, neighbors and supports kept.
Move Specialist	A diffusion transformer trained for object movement, conditioned on the source mask and a destination box designed by the VLM. Pixels outside the source and destination regions are restored from the source.	None	Object gone from its origin, same object at the destination, size consistent with depth, blending.
Subject Scaling	Reviewed mask, ViTMatte [ 69 ] alpha matte, uniform rescaling anchored at the feet, inpainting of the old footprint, contact shadow and color harmonization. A second candidate refines the composite with the general editor.	Every pixel outside the old and new subject and its shadow equals the source (code). No candidate whose scaled subject would cross a frame edge (construction).	Size change in the requested direction, recognizable subject, no halo.

Table S5 : Methods of the capability pool, each with its backend, the claims established by code or by construction, and what is left to the judge. A candidate that breaks its guarantee is removed before judging, and the record of each remaining one carries both columns ( Sec. F ).

Measurement	Used For	Definition	Shown to the Judge
Visible Change	Every step	Share of pixels whose CIE76 color difference $\Delta E$ to the source exceeds 8, after a Gaussian blur with $\sigma=1$ px of both images (no blur for detail edits).	One value per candidate, e.g . “visible change 4.2% of the frame” for a small local edit.
Change Map	Every step	Per-pixel $\Delta E$ to the source, shown as brightness on one scale shared by all candidates.	An image per candidate, black where a pixel is unchanged and brighter where it differs more.
Close-Up	Localized steps	The region that the request names, localized once in the source. The crop pads its box by 30% of its size and covers at least 30% of the frame on each axis.	A BEFORE \| AFTER image per candidate, at the same place and scale for all.
Photometric Statistics	Tone, color and lighting	Brightness is the mean CIELAB $L^{\ast}/100$ and contrast the standard deviation of $L^{\ast}/100$ . Saturation is the mean chroma $\sqrt{a^{\ast 2}+b^{\ast 2}}$ . Clipped highlights and crushed shadows are the shares of pixels whose brightest channel is at least 250 or at most 5, and color shift is the change of mean $a^{\ast}$ and $b^{\ast}$ .	One line for the source and one per candidate, e.g . “brightness 0.62, contrast 0.24, saturation 18.3, clipped 2.1%, color shift $a^{\ast}$ $+0.3$ $b^{\ast}$ $+6.8$ ”.
Sharpness and Noise	Sharpening, denoising, blur	Variance of the Laplacian and the noise level of the luminance, with both images downsampled to a common size, and for a localized step also the sharpness inside the target relative to the source.	Ratios to the source, e.g . “sharpness $\times$ 1.84, noise $\times$ 0.71” for a sharpened candidate.
Structure Kept	Global edits that keep the layout	Mean luminance SSIM between the source and the candidate over the frame.	One value per candidate, where 1 means identical structure and lower values mean more change.

Table S6 : Measurements of the verification record, chosen by the operation of the step. Code computes them from the pixels in the same way for the source and every candidate. The change map and the close-up are images, and every other measurement is one line of text per image.

Agent	How It Is Run
GenArtist †	Official prompts. The unreleased editing loop is re-implemented, and its tools become backbone edits, masked for local tools.
MIRA †	Trained policy model not released. The planning loop of the paper runs zero-shot, with Qwen3.8-27B serving as its policy model.
IMAGAgent	Released code and prompts, with closed models replaced by Qwen3.8-27B and the backbone, and inpainting by masked edits.
EditThinker-8B	Released code and 8B thinker.
VisionDirector-TF †	Trained planner not released. A training-free loop uses the released verifier prompt and runs local goals as masked edits.
RISE-Agent	Released code, with Qwen3.8-27B as planner and verifier and without web search.

Table S7 : How each agentic baseline of Tab. 5 of the main paper is run. Every agent edits with the same backbone and uses Qwen3.8-27B wherever it calls a multimodal LLM. † Re-implemented.

Model	EdiVal-O	CBE	CE-O
Qwen-Image-Edit-2511	75.97 / 58.58 / 47.64	54.40	7.371
+ EditAgent (Qwen3.8-27B)	80.64 / 67.02 / 55.46	60.83	7.445
+ EditAgent (Gemma-4-31B)	80.34 / 66.28 / 54.17	60.53	7.457
FLUX.2-klein-9B	81.37 / 66.30 / 53.20	61.45	7.677
+ EditAgent (Qwen3.8-27B)	84.46 / 71.10 / 59.45	64.51	7.713
+ EditAgent (Gemma-4-31B)	85.17 / 71.64 / 60.25	63.44	7.715

Table S8 : EditAgent with Qwen3.8-27B or Gemma-4-31B as its internal judge, under unchanged external scoring. The columns are EdiVal-O at turns 1 / 2 / 3, the ComplexBench-Edit average (CBE) and the Complex-Edit overall score (CE-O).

Benchmark	Backbone	Masked Local Editor	Masked Recolor	Removal Specialist	Photometric Operators	Style Filter	Zoom and Other	General Editor	Accepted Steps
EdiVal	Qwen-Image-Edit-2511	9.2	6.7	8.1	0.0	0.0	0.6	75.4	1,253
EdiVal	FLUX.2-klein-9B	7.8	7.7	3.3	0.0	0.0	0.0	81.2	1,628
ComplexBench-Edit	Qwen-Image-Edit-2511	10.1	5.2	16.0	0.0	0.1	0.0	68.6	1,891
ComplexBench-Edit	FLUX.2-klein-9B	8.3	5.3	7.2	0.0	0.0	0.0	79.2	2,246
Complex-Edit	Qwen-Image-Edit-2511	6.0	4.6	3.7	5.4	4.4	1.2	74.8	28,712
Complex-Edit	FLUX.2-klein-9B	4.8	4.7	1.8	4.5	3.6	1.1	79.5	33,224

Table S9 : Accepted steps by method for each backbone and benchmark (% of the accepted steps on the final edit chain of each request). Masked Local Editor and Masked Recolor run the backbone inside a mask. Removal Specialist, Photometric Operators (whole-frame and local) and Style Filter do not use the backbone. Zoom and Other covers Zoom-In Crop, Canvas Extension, Flip and Exact Rotation, Rotation Tool and the two style-transfer methods.

Group	Turns	Accepted (%)	First (%)	Attempts	Candi- dates
Content	46,675	94.1	79.6	1.58	4.06
Style & Effects	22,864	94.6	82.0	1.58	4.03
Scene	28,595	92.2	70.4	2.23	5.15
Photometric	22,841	93.8	65.5	2.13	3.35
Geometry	31,390	83.3	69.6	2.56	5.29
Enhancement	11,441	89.9	63.8	2.59	4.63

Table S10 : Data engine statistics by edit group over all 163,806 executed turns. Accepted is the share of turns accepted by the judge, and First the share whose steps are all accepted at the first attempt, without re-planning. Groups follow Tab. S1 . Attempts and candidates are means per turn.

Figure S3 : Multi-turn editing comparison with FLUX.2-klein-9B.

Panel of Figure 26 — Figure S4 : Multi-turn editing comparison with GPT-Image-2.

Figure S6 : Single-turn editing comparison with Qwen-Image-Edit-2511.

Figure S7 : Single-turn editing comparison with FLUX.2-klein-9B.

Term in the Paper	Name in the Prompts
Candidate	TRIAL n
Source Image of a Step	BEFORE
Change Map	CHANGE-MAP
Close-Up	CLOSE-UP , THEN \| NOW close-up
General Editor	edit_model
Masked Methods	masked_composite

Table S11 : Terms of the paper and their names in the prompts.

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