JarvisArt: Liberating Human Artistic Creativity via an Intelligent Photo Retouching Agent
Authors: Yunlong Lin
ZiXu Lin
Kunjie Lin
Jinbin Bai
Panwang Pan
Chenxin Li
Haoyu Chen
Zhongdao Wang
Xinghao Ding
Wenbo Li
Shuicheng YAN
Venue: NeurIPS 2025 poster ()
Keywords: AI agent; Photo retouching; Vision-foundation model
Primary area: applications

Abstract: Photo retouching has become integral to contemporary visual storytelling, enabling users to capture aesthetics and express creativity. While professional tools such as Adobe Lightroom offer powerful capabilities, they demand substantial expertise and manual effort. In contrast, existing AI-based solutions provide automation but often suffer from limited adjustability and poor generalization, failing to meet diverse and personalized editing needs. To bridge this gap, we introduce JarvisArt, a multi-modal large language model (MLLM)-driven agent that understands user intent, mimics the reasoning process of professional artists, and intelligently coordinates over 200 retouching tools within Lightroom. JarvisArt undergoes a two-stage training process: an initial Chain-of-Thought supervised fine-tuning to establish basic reasoning and tool-use skills, followed by Group Relative Policy Optimization for Retouching (GRPO-R) to further enhance its decision-making and tool proficiency. We also propose the Agent-to-Lightroom Protocol to facilitate seamless integration with Lightroom. To evaluate performance, we develop MMArt-Bench, a novel benchmark constructed from real-world user edits. JarvisArt demonstrates user-friendly interaction, superior generalization, and fine-grained control over both global and local adjustments, paving a new avenue for intelligent photo retouching. Notably, it outperforms GPT-4o with a 60\% improvement in average pixel-level metrics on MMArt-Bench for content fidelity, while maintaining comparable instruction-following capabilities.

JarvisArt: Liberating Human Artistic Creativity via an Intelligent Photo Retouching Agent
Yunlong Lin
Zixu Lin'
Kunjie Lin' Jinbin Bai Panwang Pan Chenxin Li
Haoyu Chen?
Zhongdao Wang Xinghao Ding't Wenbo Li Shuicheng Yan
Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University, Xiamen, Fujian, China
2 The Hong Kong University of Science and Technology (Guangzhou) The Chinese University of Hong Kong Bytedance
National University of Singapore Tsinghua University
Project Page: https: vttp://jaaviaatt.e.........
Abstract
Photo retouching has become integral contemporary visual storytelling, enabling users to capture aesthetics and express creativity. While professional tools such as Adobe Lightroom offer powerful capabilities. they demand substantial expertise and manual effort. In contrast, existing Al-based solutions provide automation but often suffer from limited adjustability and poor generalization, failing meet diverse and personalized editing needs. To bridge this gap, we introduce Jarvis- Art, multi-modal large language model (MLLM)-driver agent that understands user intent, mimics the reasoning process of professional artists, and intelligently coordinates over 200 retouching tools within Lightroom. JarvisArt undergoes a two-stage training process: initial Chain-of-Thought supervised fine-tuning establish basic reasoning and tool-use skills, followed by Group Relative Policy Optimization for Retouching (GRPO-R R) to further enhance its decision-male makiing and tool proficiency. We also propose the agent-to-Lightroon tate seamless integration with Lightroom. To evaluate performance, we develop MMArt-Bench a novel benchmark constructed from real-world user edits. Jarvis- Art demonstrates user-frient fee-friendly interaction, superior generalization, and fine-grained control over both global and local adjustments, paving a new avenue for intelligent photo retouching. Notably, it outperforms GPT-4o with 60% improvement in erage pixel-level metrics on MMArt-Bende for content fidelity, while maintaining comparable fnslluction-followiing capabilities.
Protocol to facili-
1 Introduction
Photo retouching is fundamental to modern photography, enabling users manipulate exposure, color, contrast, and tone for expressive, high-quality images. Commercial tools such as Adobe Light- room and PicsArt offer extensive manual controls but demand specialized expertise and significant time investment, creating barriers for non-experts. Existing automated methods gettods-including inclluding zero- and first-order optimization [15] 44 54 65), reinforcement learning [55] 29 28). and diffusion-based edit- ing [68] and scene generalization. More recently, instruction-guit guideed multimodal models such as GPT-4o 24 and Gemini-2-Flash [52] have enabled natural- natural-language driven editing but frequently compromise content fidelity, intricate attribute control, and high-resolution support.
automation yet remain limited in stylistic diversity, fine-grained adjustment,
Equal Contributions.
Project Leader
f Corresponding Author.
Supervised the rescarch.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Figure 1: JarvisArt supports multi-graaullrrty retouching goals, ranging from scene-level adjustments to region-specific refinements. Users can perform intuitive, free-form edits through natural inputs such as text prompts, bounding boxes, or brushstrokes. Furthermore, users can edit any anyrresolution images with JarvisArt. Purple: multi-modal context understanding. Green: retouching strategy reasoning. Orange: recision-making tool orchestration.
powered agents have driven breakthroughs in autonomous task execution and problem solving, inspiring us to explore novel reoto-rettuccing paradigm: intelligent, user- friendly artist agent that interprets the user's intent and delivers professional-level edits. To this end, we introduce JarvisArt, which (1) accurately parses visual inputs and natural-language instructions, (2) embeds professional retouching expertise to emulate an artist's reasoning, (3) efficiently manages over 200 Lightroom operations, and (4) supports both global and local adjustments through intuitive interface. All planning and tool invocations are fully transparent, allowing users to interactively refine the retouching workflow to suit their individual preferences.
LLM 13 4 60 33
To translate this vision into practice, we must tackle three core challenges:the scarcity of high quality data (source/target images, textual instructions, and editing configurations). the need for expert-level reasoning strategies, and the absence of a standardized Agent-to- seent-to-Lightron integration protocol. To overcome these, we first design data-generation pipeline that yields the MMArt-55K dataset, comprising 5K standard and 50K Chain-of- Saaii--f-TTouuuh-eehanceeee we employ two-stage post-training regime: (1) supervised fine-tuning (SFT) to instill workflow of "understanding - reasoning + decision-
gaaliigganularity samples. Next,
(recording Lightroom operations into a ROC file)", as illustrated in Figure [ and (2) Group Relative Policy Optimization for Retouching (GRPO- R) augmented with multi-dimen -dimensional tool-use howards-namelyy retouching-operatee (evaluating both global and region-specific parameter prediction) and perceptual quality (assessing the visual fidelity of retouched outputs)- to refine decision-man maaiing and generalization. Finally, introduce the agent-to-Lightron editing with bidirectional feedback. Consequently, JarvisArt deeply understands the intent of the user, generates diverse stylistic renditions, and seamlessly executes global and region-specific adjustments to produce visually compelling results (see Figure
accuracy
(A2L) protocol to enable seamless, automated Lightroom
Our contributions can be summarized as follows:

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We introduce JarvisArt, an intelligent artist agent powered by an MLLM and linked to over 200 Lightroom operations, capable of producing diverse. user-driven stylistic edits that surpass current automated methods and rival professional human retouchers.
We design scalable data-synthesis pipeline to construct the MMArt dataset, comprising 5K standard instruction-based and 50K Mlain-of-Thought--20 for detailed retouching tasks.
multi-granularity samples
We develop a two-stage tool-use rewards to enhance the agent's 's reasoning, tool proficiency, and generalization.
regime: SFT followed by GRPO-R with tailored
We establish an sgent-to-Lightrood communication protocol that enables seamless collabo- ration between JarvisArt and Lightroom, facilitating fully automated editing workflows.
2 Related Work
Photo Retouching.
Existing automated pipelines have been proposed to streamline manual re- were early attempts, but they are constrained by limited parameter prediction and reliance on pre-trained proxies. RL-based methods [55] [2] 28 [9] 45 attempt to mimic human workflows and offer some transparency but fail
touching. Zeroth- and First-order optimizations [13
44 43 54
65 53
to capture artistic vision and lack deeper user interaction. Diffusion models [68] 2 57
75
7 59
[0]
dominate high-fidelity image synthesis but rely static prompts and lack multi-turn reasoning or flexible language alignment, limiting open-ended editing. Additionally, recent unified image editing models have achieved dual breakthroughs in comprehension and generation. Notable examples include flosed-source models like GPT-4o [24] and Demini-2-Flashi (52), as well as open-source models such as Bagel (9) Janus-Pro 88 UniTok [41,, QLIP ( and VARGPT-v1.1 (76). Despite these breakthroughs, three key limitations remain: (1) destructive editing by regenerating all pixels, compromising content preservation; (2) lack of interactive and interpretable local attribute control (e.g., softening or brightening skin); and (3) the absence of arbitrary-resolution editing due to genera- tive model architectural constraints. Conversely, our study presents an interactive and interpretable retouching paradigm that integrates multimodal understanding with expert-level editing tools for non-destructive photo retouching. JarvisArt empowers users through a human- aggett collaboration loop, enabling scene-level edits alongside precise region-specific tweaks-blende creative flexibility with the rigor of a professional workflow.
Rule-based reinforcement fine-tuning, as demonstrated by OpenAl's ol 25 and Deepseck-R1 [13), has shown impressive performance in tasks such mathematical reasoning [4 25 61 64), and code generation [22 26 67 (7). Subsequent research has extended this
Reinforcement Timn-Tuning...
approach to multimodal models, designing task-specific reward functions for visual perception tasks. These include correct class prediction in image classification [6
42), Intersection-owe Prrrrectioonover-Uniee Union
(IoU) metries in image localization and detection [39 21 62 49, accurate click position prediction in GUI grounding tasks (56), and effective interaction with search engines to leverage up-to-date external information (27). However, unlike these tasks with a single correct answer, our task involves tool-integrated retouching, which requires predicting multiple tools and their parameters. Designing effective reward signals to support learning this setting remains an open and underexplored challenge. In this paper, we propose customized tool-use rewards, enabling JarvisArt to equip advanced artistic reasoning and tool invocation capabilities.
L.LM-Empowen Agent. LLM-ppwered agents have revolutionized Al systems due to three key developments: 1) unprecedented reasoning capabilities of LLMS
13
4
60
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17;
2) advancements
in tool manipulation and environmental interaction 31 [1
18
20
38
34
35
11 and 3) sophisticated
memory architectures that support longitudinal experience accumulation 14
7 58 63 Despite
these advancements, three fundamental limitations persist when applying LLM agents to professional photo retouching: 1) the absence of a domain-speeific retouching knowledge base, which hinders accurate interpretation of user intent, 2) limited decision- making abilities in selecting suitable tools and determining precise parameter values, and 3) absence of standardized protocols to ensure compatibility with professional retouching software integrations. To address these limitations, we propose JarvisArt, a powerful artistic agent that integrates three core capabilities: (1) professional retouching expertise for precise understanding of user instructions, (2) proficiency with commercial retouching tools in Lightroom, and (3) standardized communication protocols for seamless Lightroom integration.
3

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The data generation pipeline comprises three main stages: 1) Curation of diverse target examples covering varied scenes and styles with corresponding Lightroom configura- tions; 2) Generation of diverse user instrcutions that reflects different creative intents; 3) Production of Chain-of- ofoouhtt traces that simulate human artist reasoning process.
Figure 2:
3 Method
We begin by outlining the overall workflow of JarvisArt (Sec. 3.1 Next, we introduce comprehen- sive data generation pipeline that constructs MMArt, a high-quality dataset comprising instruction and reasoning samples for agentic photo retouching tasks (Sec. (.2).. Finally, we investigate the core components JarvisArt (Sec.3..) including a two-stage post-training pipeline and the Agent-to- Lightroom (A2L) protocol, which allows scamless collaboration between JarvisArt and Lightroom.
3.1 Overview
JarvisArt an interactive, MLLLM-based photo-retouching system that supports both scene-level and region-level edits. In addition to textual instructions, can specify local areas via free-form brushstrokes or draggable bounding boxes. In Figure JarvisArt's pipeline comprises three stages: Multi-modal context understanding parse user directives, image content, and regions of interest; 2) Strategic reasoning grounded in photographic principles to formulate retouching plan; and 3) Tool orchestration to select appropriate Lightroom operations and parameters. These operations are executed automatically through the A2L protocol. Formally, JarvisArt implements a function:
f(Q, lae) + T = (t1, (t.,
where Q is the user query, Isc the source image, and each 4 denotes a specific Lightroom edit (e.g., exposure-+O. +0.03). The final output is obtained by Todit s T), with g(.) representing Lightroom's execution environment.
3.2 Data Generation Pipeline
We design a three-stage data-generation pipeline (Figure 2 to construct MMArt with explicit Chain- of-Thought (CoT) annotations. Each sample is a five-tuple (Jac: Tig, Q. C, O), where Iuc and Tgt are the beforee images, Q the user's instruction, the COT reasoning wrapped in <think> tags, and O the retouching operation configuration (ROC) file of tool invocations and parameters

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within <answer> tags. The pipeline proceeds as follows: 1) Curation of diverse source-targe examples covering varied scenes and styles, and the corresponding Lightroom configurations: 2) Generation of natural-language instructions that reflect user intents; 3) Production of step-by-step reasoning traces. Further statistics and examples of MMArt can be found Appendix A
Stage I: Generation of image pairs and Lightroom configuration. We source raw images from PPRIOK (32, the Adobe Lightroom community, and licensed open-source collections, then curate diverse library of global and local artistic preset Leveraging Qwen2.5-VL-72B [60] for multimodal role-play aale-plaiig and Grounding DINO [37] for precise region localization, we simulate expert-level edits in four steps: 1) Region detection, in which Grounding DINO (37] identifies regions of interest (confidence 0.8); 2) Preset recommendation, where Qwen2.5-V VVL-22B [60] proposes global and local presets based on image aesthetics; 3) Preset application, applying each recommendation in Lightroom to generate five candidate retouched images; and 4) fluman-in-the--ole selecting the most artistically pleasing outputs. Each finalized sample comprises (T Jug. O), denoting the source image, the retouched image, and the detailed record of Lightroom operations. The role-playing prompts are detailed in Appendix A.4
validation,
Stage II: Generation of user instructions. To simulate diverse editing intents, we employ Qwen2.55 VL-72B [60] with a role-playing prompt (Appendix A.44 to translate each (! lug. O) triplet into both scene-level and region-level instructions Q. We generate descriptions for two user types-casua casual users and professional editors with advanced aesthetic sensibilities, ensuring coverage of simple global edits as well as precise, localized adjustments.
Stage III: Generation of reasoning processes. For each sample quadruple (Tare Tg.. O), we first apply QVQ-max's 's (60) advanced visual reasoning generate initial CoT annotations. To remove redundancy and enforce human-like coherence, we subsequently refine these traces using Qwen2.5- VL-72B (60) through iterative multimodal prompts, producing concise, context-rich reasoning processes C. Full prompt templates are provided in Appendix A.4
3.3
JarvisArt Framework
3.3.1 CoT Supervised Fine-tuning
Drawing on Deepseek-RI 13 we initialize JarvisArt via supervised fine-tuning on CoT annotations to bootstrap its subsequent reinforcement learning. This phase 1) enforces a consistent, structured output format, 2) instills foundational reasoning skills spanning user-intent interpretation and aesthetic judgment, and 3) establishes preliminary proficiency in selecting Lightroom tools and configuring their parameters.
3.3.2 Reasoning-oriented Reinforcement Learning
Building on the SFT-initialized model, as shown in Figure 3 we apply group relative policy opti- mization for retouching(GRPOC and tool-use proficiency. GRPO-R trains the agent with three interpretable, task-specific rewards: a format reward R, that enforces structured output, a retouching operation accuracy reward Rroe that measures the correctness of selected tools and their parameter settings, and a perceptual quality reward Rgg that assesses the visual fidelity of the retouched image. The overall objective is thus R= = RR + Rro + Rpg E ().
[8 (Appendix B.I to further refine JarvisArt's artistic reasoning
Format reward. Following prior work [3
62 49
56], we include format reward Ry E [0, 1] to
enforce structured outputs: reasoning must appear within <think> tags and tool invocations within <answer> tags, ensuring consistent and reliable parsing.
Retouching operation accuracy reward. Inspired by existing explorations of reward designs [47] 27 40 [56] in the fields of GUI and web searching. We consider over 200 retouching tools in Lightroom, containing both global adjustments such as exposure, highlights, and tone curve and local refinements using six types of masks: 1) linear masks for directional gradients, 2) radial masks for circular elliptical regions, 3) object masks for isolating subjects (e.g., people objects), 4) color masks for hue-specific adjustments, 5) luminance masks for brightness-based selections, and 6)
In Lightroom, a preset refers to pre-saved retouching operation configuration (ROC) file, typically created by photographers artists. It encapsulates stylistic parameters tailored for specific visual aesthetics.
5

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Supervised Fine-tuning
Group Relative Policy Optimization for Retouching
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Figure 3: Overview of the two-stage post-training framework. Initially, JarvisArt undergoes super- vised fine-tuning (SFT) on CoT-annotated data to develop foundational artistic reasoning and tool-use skills. Following this, we apply the Group Relative Policy Optimization for Retouching (GRPO-R) algorithm to further enhance the JarvisArt's reasoning, tool proficiency, and generalization.
portrait masks for fine-tuning facial features such as skin and eyes. Further details are provided in Appendix E To assess the accuracy of predicted tools and their parameters, T {TT ..... T
against the ground truth g {11 ..... Tdg6), J. define the ROA reward based on three evaluation criteria:
Tool name matching:
Nrooe O Ny
E [0, 1],
Nyprr V Ny
where Nypre and Nrit are the sets of tool names in the predicted and target sequences, respectively. Parameter name matching:
(eys((T!"r)) n (eys( keys( U keys(T)
Param
Σ
E
[0, [T
(2)
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where keys(-) denotes the set of parameter names associated with predicted or ground-truth tool. is noted that an overlap in parameter names occurs only when the predicted and ground-truth tool names match.
Parameter value matching:
Tvalue
Σ Σ
Σ
S& (K)"
E [0,
Σ TH
keys(T)) (T ( I], (3)
ET TH" ETere (T)
where S& (.) E [0, 1] quantifies correspondence between predicted and ground-truth parameter values, with value of I indicating exact match. Specifically, if the key k is absent in 7, then [k]" is undefined and S₃ = The computation of S₃ depends on the parameter type: scalar differences for standard numerical values, intersection-over-umee
(IoU) for object masks, endpoint distance for linear masks, geometric similarity for radial masks, color distance between sampled points for color masks, luminance range differences for luminance masks, and category-speciff criteria for portrait masks. Refer to Appendix for further details. Finally, the retouching operation accuracy reward is computed by measuring the matching degree between Tire and T
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Perception quality reward. While parameter- based rewards offer critical guidance, they may not fully capture the perceptual quality of the final image, as different parameter settings can produce visually similar results. To address this limitation, we introduce the PQ reward, which evaluates two

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key aspects: 1) global tone consistency via color distribution matching, and 2) pixel-wise fidelity. The reward is defined as:
pg 7 CIedidi Ing:) +1 (1 L(Toin Iua)) [0, 1]
(5)
where Jdir the retouched image and Tn is the target image. CD(.) measures color distribution simi- larity in CIELAB space [69] and L(.) denotes the pixel-wise distance. Both metrics are normalized to the range [0, 1J. with higher values indicating better similarity. The weighting factor is empirically set to y 0.4 to balance both terms.
Agent-to-Lightroom
3.3.3 Agent-to-Lightroom Protocol
A2I. Protocal
User
Figure presents the Agent-to-Lightroomm (A2L) pro- tocol, a standardized client-server interface that inte- grates JarvisArt with Lightroom. The workflow com- prises five stages: 1) handshake, 2) file verification, 3) sandboxed execution, 4) async processing, and 5) result return. A2L features dual-transport commu- nication, structured message format, and resource management. Messages use bar-delimited commands for processing, status, and error handling, enhancing clarity and efficiency. It manages source images and retouching operation configuration (ROC) files, sup- porting ROC-to-I lua translation, and integrity checks. The source image by directly retouched by I Lua file in Lightroom. The Lua file can be directly applied Lightroom to retouch the source image. Additional details are provided in the supplementary materials.
JarvisArt
Client
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Call Calor_Misen
Lightroom-Serrer
Lr
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4
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5
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Figure 4: Agent-to-Lightroom protooo
4 Experiment
4.1 Experimental Setup
Implementation details. We adopt Qwenn25--VL-7B-Instrrc [1 as the base model for JarvisArt. The CoT supervised fine-tuning phase is performed 50K CoT-annotated instances from MMArt, with a batch size of 2, a learning rate of 1e-5, and training for 2 epochs using the Llama- Llama-Factory framework [74] on 8 A100 (80G) GPUs. The reinforcement learning phase, employing the GRPO-R algorithm, is conducted on 5K standard instruction samples from MMArt, using the veRL framework [5). For each training step, sample batch of 2, a learning rate of 1e-6, and generate responses per query, training for 2 epochs on 16 A 1100 (80G) GPUs
MMArt-Bench. To provide comprehensive evaluation of JarvisArt's performance, we introduce the MMArt-Bench, which is sampled from the MMArt dataset. It includes four main scenarios: portrait, landscape, street scenes, and still life, with 50 instances per category, totaling 200 instances. Each primary category contains multiple subcategories (Appendix [. For region-level evaluation, we utilize a portrait subset comprising 50 human-centered images with mask annotations.
Evaluation metrics. Following previous works [68] 30 six assessment metrics are use for evaluation: L1. L2, SC, PQ. and O. LI and L2 to measure the average pixel-level absolute difference between the retouched image and reference image. SC evaluates the alignment between the instruction text and the image (0-10 scale). PQ evaluates contextual coherence and artifact presence (0-10 scale). The overall score O is calculated as /SC PQ. For region-speeiff evaluation, we apply these six metrics to a specified mask region. Further details are provided in Appendix C.I
Baselines. For fair comparison, we evaluate Jarvis against leading open-source photo retouching methods, including 3DLUT [66] and RSFNet [45), as well as instruction-driven editing models
such as InstructPix2Pix (2] MagicBrush (68) OmniGen [57]. VARGPT-vL.1 (76) and SteplX-
Edit [36]. Proprictary solutions such as GPT-4c
(4 and Gemini-2- 2- Flash [52 are also included
'The results are obtained based on ChatGPT APP in May 2025.
'The results are obtained based on Gemini API in May 2025.
7

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Table 1: Quantitative evaluation on MMArt-Bent We highlight the best and second-best instruction- based results. SC, PQ, and refer the metrics evaluated by Gemini-2- 2-FFlshh. The RC means the metric calculated on specific mask region.
Seene-level
Region-level
Method
Instruction
k10
L2x10 4 SC PQT
L18e
L2Rc
SCRC PQ O
RSFNet
45
11.61
26,38
8,80
13.69
3DLUT
66
11.50
25.99
8.33
12.39
InstructPix2Pix
15.67
47.51
6.54
7.79
7.10
12.62
33.39
4.70
5.36
MagieBrush
68
18.39
65.25
3.93
4.09
3.85
12.37
32.81
3.04
3.41
3.13
OmniGen
28.49
133.45
4.42
4.13
25.16
109.10
7.56
VARGPT-vL.
27.05
126.47
1.83
1.38
1.48
23.71
107.32
1.38
1.15
1.08
Step1X Edit
24.28
105.91
7.52
8.67
8.01
15.43
45.85
8.32
9.04
8.66
Gemini-2-
23.07
8.08
16.52
52.88
8.04
9.25
GPT-4o 24
22.84
92.23
8.73
9.66
9.18
15.71
47.87
8.59
9.48
9.03
JarvisArt
12.44
30.56
8.52
12.14
InstructPis1Pis
MagicBrush
Gemini-2-Fias F-Flash
GPT-4o
Twnnt nostalgic, fantasy style with faded tomes and gruiny texture for timeless feel.
Make look like a serene sunsel scene with enhanced golden hues and calm, dreamy atmosphert.
meat juicier more appetizing warm glow, enhancing Inviting appeal.
want dreamy style, softer focus, and gentle tones to evoke serene, romantic mood.
Make the objects in the <box=-0.11,, 0.088, 0.841, 1.000-/ox>> promineat and the background more lightful.
Figure 5: Visual comparison of different methods on MMArt-Bende
for comparison. Notably, all test images are cropped to 512 x 512 resolution, some baselines are incapable of processing high- righ-resolution arbitrarily sized inputs.
4.2
Experimental Results
4.2.1
Evaluation on MMArt-Bench
As shown in Table [ JarvisArt outperforms most open-source instruction-bast baselines, achieving state-of-the-art performance across all evaluation metries. Compared to closed-source models such GPT-4o [25] and Gemini-2-Flaan F-asshh (52), JarvisArt achieves superior content preservation- for

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03.91
(075)
Reward
Reward
Rowad
0.65
600
0.55
Training steps (8) reward
300
150
Training steps
103
200
300 Training
(b) Perception quality roward
(0) Retouching operation accuracy reward
Figure Visualization of the reward trends across training steps for JarvisArt.
instance, an L1 x 102 score of 12.44, which is 45.6% lower (and thus better) than GPT-4o's score of 22.84. JarvisArt also demonstrates competitive instmiction-followingg capability (0= 8.52), closely matching GPT-4o (O = 9.18) and outperforming Gemini-2 Flash (O = 8.08). Notably, in the local editing setting-whert content fidelity is especially critical-ths advantage of our method over GPT-4o and -emini-2-FFil is significantly amplified. As illustrated in Figure especially in portrait scenarios, competing methods often exhibit noticeable uncanny valley effects, producing significant visual artifacts that diverge from users' creative intent. In contrast, JarvisArt mitigates these issues through its cattrroom-integratee. workflow, enabling high-quality, non-destructive editing. More results in Appendix D
4.2.2 User Preference Study
Compurion Conionncy
Campatinn Acethriic Quality
Evaluating instruction-die driven photo retouching remains inher- ently subjective, even expert evaluators often disagree on the "optimal" outcome. To quantify preferences, we conducted a user preference study the MMArt-Bench, recruiting 80 participants evaluate four advanced algorithms: StepIX-Edit 36.. Gemini-2-Flash [52], GPT-4o 24 and JarvisArt. Evaluations focus
offe kett
Sxylx File
Supex Lide
64
Figure 6: User preference study.
criteria: (1) image consistency (preservation of source image content) and
(2) aesthetic quality (visual appeal of retouched results). A five-point ordinal scale (worst = 2, poor 4, fair good 8, and excellent 10) for quantitative metries. Results in Figure 6 show JarvisArt achieves best subjective quality, producing edits favored by users.
4.2.3 Visualization of Reward Trends for GRPO-R
Figure 7 shows additional visualizations of GRPO-R training. The format reward converges quickly early on. While the PQ reward initially fluctuates and grows gradually, the ROA reward rises more rapidly-likely because the model inherits "parameter preferences" " from the SFT phase. As result, it first focuses on the more easily optimized ROA, then gradually shifts attention to the PQ reward, which requires longer exploration due to the broader search space, where different edit operations may yield similar visual outcomes. Moreover, unlike Deepseck-RI [13. JarvisArt does not display clear "aha moment". This absence may stem from the lack of intermediate visual feedback during the artistic reasoning process. For example, when the model makes hypothetical retouching adjustment like highlight+5, it cannot obtain the corresponding visual result, preventing the model from validating this step's 's correctness within the decision-making chain. Unlike mathematical problem-solving where each step can be validated immediately, our artistic reasoning involves numerous retouching parameters. If we perform step-wise validation for each parameter, would require high concurrency in calling Lightroom. This is impractical due to the high computational cost and the slow training speed. Investigating step-wise visual rewards within proxy validation environments may offer promising approach to cliciting the "aha moment". We intend to explore in future work.

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Table 2: Ablation studies different training strategies and reward design.
Configurations
L1 x100 4
L2x10= 4
PQT
0
Training stralegy
only SFT
14.42
44.38
7.32
8.67
7.94
only RI.
17.55
58.19
6.88
8.13
7.38
SFT RL (Ours)
12.44
30.56
7.53
9.82
8.52
Reward design
Format ROA
14.09
40.36
7.45
8.77
8.04
Format PQ
13.78
35.41
7.48
8.92
8.15
Format ROA PQ (Ours)
12.44
30.56
7.53
9.82
8.52
Ablation Study
Training strategy. We assess the impact of different post-training strategies by comparing model performance under three settings: 1) SFT on 50K CoT-enhanced samples, 2) GRPO-R training on 5K standard samples from scratch; and 3) GRPO-R fine-tuning basd on SFT-initial model. Rows 2-4 in Table
2 show that SFT yields better results than GRPO-R trained from scratch. This is likely because, without SFT instill the basic reasoning and tool-use abilities, the GRPO-R training process must explore significantly larger search space, thereby hindering optimization. Our combined SFT+GRPO-R strategy achieves the best results, suggesting that GRPO-R can effectively enhance the SFT-initialized model's reasoning. tool proficiency, and generalization by expanding its exploration capacity.
Reward design. As shown in Rows 6-8 of Table individual reward combinations (Format+ROA or Format+PQ) result in suboptimal performance, with Format+PQ performing slightly better-possibly because PQ aligns more closely with the ultimate objective of enhancing visual quality and offers broader optimization space escape local optima. The full combination (Format+PQ+ROAN achieves the highest performance. This result aligns with our intuition that orrientte-orieeeed (ROA) and perception-die
(PQ) rewards are complementary: ROA ensures parameter accuracy, while PQ maintains visual fidelity. The multi-dimensional reward system provides balanced optimization signal, guiding the model to predict accurate edit operations while preserving high visual quality.
Conclusion
This report introduces JarvisArt, an interactive and interpretable MLLM-guided agent that integrates with 200+ Lightroom editing tools, enabling non-destructive editing on images of anyrresolution. To develop this artist agent, propose new data generation pipeline that curates the MMArt- 55K dataset, comprising 5K standard and 50K CoT-enhanced samples. Based on this dataset, we train JarvisArt using two-stage post-training regimen: 1) CoT SFT instill basic reasoning and tool-use abilities, and 2) GRPO-R to improve the agent's reasoning, tool proficiency, and generalization through customized tool-use rewards: retouching operation accuracy reward for assessing the predicted editing operations, and the perceptual quality reward to evaluate the visual fidelity of the edited outputs. Furthermore, enable seamless, automated Lightroom editing, introduce the ggent-to-Lighhroon protocol. Evaluation results from our MMArt-Bent demonstrate that our proposed algorithm significantly outperforms existing advanced image editing algorithms.
7 Acknowledgments
This work was supported in part by the Xiamen science and technology major project under Grant 3502720241029; in part by the Dreams Foundation of Jianghuai Advance Technology Center project under Grant 2023-ZM01 1D000 in part by the National Natural Science Foundation of China under Grant 82172073, Grant 82272071, and Grant 1430; and part by the Open Fund of the National Key Laboratory of Infrared Detection Technologies.
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References
[1] S. Bai, K. Chen, X. Liu, J. Wang. W. Ge, S. Song. K. Dang, P. Wang. S. Wang. J. Tang, al. Qwen2. 5-vl technical report. arXiv preprint arXiv:2502.13922. 2025.
[2] T. Brooks, A. Holynski, and A. A. Efros. Instructpix2pix: Learning to follow image editing instructions. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 18392-18408) 2023.
[3] V. Bychkovsky, S. Paris, E. Chan, and F. Durand. Learning photographic global tonal adjustment with a database of input/ / output image pairs. In The Twenty-Fourth IEEE Conference Computer Vision and Pattern Recognition, 2011.
[4] Z. Cai, M. Cao, H. Chen, K. Chen, K. Chen, X. Chen, X. Chen, Z. Chen, Z. Chen, Chu, al. Internlm2 technical report. arXiv preprint arXiv:2403. 17297, 2024.
[5] L. Chen, L. Li, H. Zhao, Song, and Vinci. RI-v: Reinforcing super generalization ability vision- language models with less than $3, GitHub repository: httpps///ithhb.coo///
-V, 2025.
[6] P.-Y. Chen, H. Zhang, Y. Sharma, J. Yi, and C.-J. Hsieh. Zoo: Zeroth order optimization based black-box attacks to deep neural networks without training substitute models. In Proceedings of the 10th ACM workshop artificial intelligence and security, pages 15-26, 2017.
[7] S. Chen, J. Bai, Z. Zhao, T. Ye, Q. Shi, D. Zhou, W. Chai, X. Lin. J. Wu, C. Tang, et al. An empirical study 250::255005999 2025.
of gpt-4o image generation capabilities. arXiv preprint
[8] X. Chen, Z. Wu, X. Liu, Z. Pan, W. Liu, Z. Xie, X. Yu, and C. Ruan. Janus-pro: Unified multimodal understanding and generation with data and model scaling. arXiv preprint arXiv:2501.17111 2025.
[9] C. Deng, Zhu, Li, C. Gou, F. Li, Z. Wang, S. Zhong, Yu, X. Nie, Song. Shi, and Fan. Emerging properties in unified multimodal pretraining. arXiv preprint ar ArXiv:2505. 14683, 2025.
[10] K. Feng, Y. Ma, B. Wang, C. Qi. H. Chen, Q. Chen, and Z. Wang. Dit4edit: Diffusion transformer for image editing. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 39, pages 2969-2977, 2025.
[11] K. Feng, Y. Ma, X. Zhang, B. Liu, Y. Yuluo, Y. Zhang, R. Liu, H. Liu, Z. Qin. S. Mo, et al. Follow-your- instruction: A comprehensive milm agent for world data synthesis. arXiv preprint w:Xiv:2508.05580 2025.
[12] T.J. Fu, W. Hu, X. Du, W. Wang. Y. Yang, and Gan. Guiding instruction- image editing via multimodal large language models. In International Conference on Learning Representations (ICLR). 2024.
[13] D. Guo, D. Yang, H. Zhang, J. Song, R. Zhang, R. Xu, Q Zhu, S. Ma, P. Wang, X. Bi, et al. Deepseck-r1: Incentivizing reasoning capability in llms via reinforcement learning. arXiv preprint a2X4922501.12948, 2025.
[14] Z. Guo, L. Xia, Y. Yu, T. Ao, and C. Huang. Lightrag: Simple and fast retricval-augmenned generation. 2024.
[15] N. Hansen, The ema evolution strategy: a comparing review. Towards a new evolutionary computation: Advances in the estimation of distribution algorithms, pages 102, 2006.
[16] J.-Y. He. Z.QQ. Cheng, C. Li. J. Sun, Q. He, W. Xiang. H. Chen, J.P.. Lan, X. Lin, K. Zhu, et al. Metadesigner: Advancing artistic typography through ai-driven, user-centric, and multilingual wordart synthesis. arXiv preprint arXiv:2406. 19859, 2024.
[17] J.-Y. He, Z.Q. Cheng, C. Li, J. Sun, W. Xiang, X. Lin, X. Kang, Z. Jin, Y. Hu, B. Luo, et al. Wor- dart designer: User-driven artistic typography synthesis using large language models. arXiv preprint ar0iv:2310. 18332, 2023.
[18] S. Hong, X. Zheng. Chen, Y. Cheng. J. Wang, C. Zhang, Wang. S. S. Yau. Z. Lin, Zhou, el Metagpt: Meta programming for multi-agent collaborative framework. arXiv preprint trXiv:2308.00352. 3(4):6, 2023.
[19] Y. Hu. H. He. C. Xu. B. Wang, and S. Lin. Exposure: A white-box photo post- proccesocs framework. ACM Transactions Graphics (TOG), 37(2)11-77.7 2018.

---

[20] R. Huang, M. Li, D. Yang, J. Shi, X. Chang, Z. Ye, Y. Wu, Z. Hong, J. Huang, J. Liu, et al. Audiogpt: Understanding and generating speech, music, sound, and talking head. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 23802-23804, 2024.
[21] W. Huang, B. Jia, 7. Zhai, S. Cao, 7. Ye, F. Zhao, Y. Hu, and S. Lin. Vision-rl: Incentivizing reasoning capability in multimodal large language models. arXiv preprint 1XXi:2503306774: 2025.
[22] B. Hui, J. Yang. Z. Cui, J. Yang, D. Liu, L. Zhang. T. Liu, J. Zhang, B. Yu, K. Lu, et al. Qwen2.5-coder technical report. arXiv preprint arXiv:2409. 12186, 2024.
[23] M. Hui, S. Yang. B. Zhao, Y. Shi, H. Wang, P. Wang, Y. Zhou, and C. Xie. Hq-edit: A high-quality dataset for instruction-basee image editing. arXiv preprint urXiv:2404.09990, 2024.
[24] A. Hurst, A. Lerer, A. P. Goucher, A. Perelman, A. Ramesh, A. Clark, A. Ostrow, A. Welihinda, A. Hayes, A. Radford, et al. Gpt-4o system card. arXiv preprint arXiv:2410.21227, 2024.
[25] A. Jaech, A. Kalai, A. Lerer, A. Richardson, A. El-Kishky, A. Low, A. Helyar, A. Madry, A. Beutel, A. Carney, et al. Openai ol system card. arXiv preprint arXiv:2412.16720, 2024.
[26] F. Jiao, G. Guo, X. Zhang, N. F. Chen, S. Joty, and F. Wei. Preference optimization for reasoning with pseudo foedback. arXiv preprint arXiv:2411.1635,, 2024.
[27] B. Jin. H. Zeng, Z. Yue, D. Wang, H. Zamani, and J. Han. Search-rl: Training Ilms to reason and leverage search engines with reinforcement learning, arXiv preprint arXiv:2503. 009566,,, 2025.
[28] 7. Ke, C. Sun, I.. Zhu, K. Xu, and R. W. Lau. Harmonizer: Learning to perform white-box image and video harmonization. In European conference on computer vision, pages 690-706. Springer, 2022.
[29] S. Kosugi and T. Yamasaki. Unpaired image enhancement featuring reinforcement seaareemmnt--earnnnge controlled image editing software. In Proceedings of the AAAI conference on artificial intelligence, pages 11296- 11303, 2020.
[30] M. Ku, D. Jiang, C. Wei, X. image synthesis evaluation. arXiv preprint 2rX11 2023.
Yue, and W. Chen. Viescore: Towards explainable metrics for conditional
[31] LangChain. Langchain: Build context-aware reasoning applications.
langchain-al/langehen ai/langchsin| 2023.
[32] J. Liang. H. Zeng. M. Cui, X. Xie, and L. Zhang. Pprl0k: A large-scale portrait photo retouching dataset with human region mask and group-level consistency. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 653-661, 2021.
[33] Y. Lin, Z. Lin, H. Chen, P. Pan, C. Li, S. Chen, W. Kairun, Y. Jin, W. Li, and X. Ding. Jarvisir; Elevating autonomous driving perception with intelligent image restoration. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2025.
[34] Y. Lin, Z. Lin, H. Chen, P. Pan, C. Li, S. Chen, K. Wen, Y. Jin, W. Li, and X. Ding, Jarvisir: Elevating autonomous driving perception with intelligent image restoration. In Proceedings of the Computer Vision and Pattern Recognition Conference, pages 22369 22380, 2025.
[35] P. Liu, C. Li, Z. Li, Y. Wu, W. Li. Z. Yang. Z. Zhang. Y. Lin. S. Han, and B. Y. Feng. 1r3d-bench: Evaluating vision-language model scene understanding agentic inverse rendering, arXiv preprint arXiv:2506.2332 2025.
[36] S. Liu, Y. Han, P. Xing, F. Yin, R. Wang, W. Cheng, J. Liao, Y. Wang, H. Fu, C. Han, et al. Step1x-edit: A practical framework for general image editing. arXiv preprint arXiv:2504. 17761, 2025.
[37] S. Liu, Z. Zeng, T. Ren, F. Li, H. Zhang, J. Yang, Q. Jiang, C. Li, J. Yang, H. Su, et al. Grounding dino: Marrying dino with grounded pre-training for open-set object detection. In European Conference on Computer Vision, pages 38-55. Springer, 2024.
[38] Z. Liu, T. Hoang. J. Zhang, M. Zhu, T. Lan, J. Tan, W. Yao, Z. Liu, Y. Feng, R. RN, et al. Apigen: Automated pipeline for generating verifiable and diverse function-calling datasets. Advances in Neural Information Processing Systems, 37:54463-54482, 54482, 2024.
[39] Z. Liu, Z. Sun, Y. Zang, X. Dong, Y. Cao, H. Duan, D. Lin, and Wang, Visual-rft: Visual reinforcement fine-tuning. arXiv preprint arXiv:2503.0178 2025.
[40] Z. Lu, Y. Chai, Y. Guo, X. Yin, L. Liu, H. Wang, G. Xiong, and H. Li. Ui-rl: Enhancing action prediction of gui agents by reinforcement learning. arXiv preprint arXh::5003.21620 2025.
12

---

[41] C. Ma. Y. Jiang, J. Wu, J. Yang, X. Yu, Z. Yuan, B. Peng, and X. Qi Unitok: A unified tokenizer for visual generation and understanding. arXiv preprint urXiv:2502.20321 2025.
[42] F. Meng, L. Du, Z. Liu, Z. Zhou, Q. Lu, D. Fu. B. Shi, W. Wang. J. He, K. Zhang, et al. Mm-eureka: Explor- ing visual aha moment with rule-basee large- scale reinforcement learning, arXiv preprint arXiv:2503.07365. 2025.
[43] A. Moslch, A. Sharma, E. Onzon, F. Mannan, N. Robidoux, and F. Heide. Hardware-in thooop end-to-end optimization of camera image processing pipelines. In Proceedings of the IEEE/CVF Conference on Computer Vision and Puttern Recognition, pages 7529-7538, 2020.
[44] J. Nishimura, T. Gerasimow, R. Sushma, A. Sutic, C.-T. Wu, and G. Michael. Automatic isp image quality tuning using nonlinear optimization. In 2018 25th IEEE International Conference on Image Processing (ICIP), pages 2471-2477 IEEE, 2018.
[45] W. Ouyang, Y. Dong, X. Kang, P. Ren, X. Xu, and X. Xic. Rsfnet: A white-box image retouching approach using region-specific color filters, In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 12160-12169, 2023.
[46] 7. Pan and H. Liu. Mctaspatial: Reinforcing 3d spatial reasoning in vlms for the metaverse. arXiv preprint arXiv:2503. 180.20000.18700.. 2025.
[47] C, Qian, E. C. Acikgoz, Q He, H. Wang, X. Chen, D. Hakkani-Tün G. Tur, and H. Ji. Toolrl: Reward is all tool learning needs. arXiv preprint arXiv:2504. 13958, 2025.
[48] Z. Shao, P. Wang, Q Zhu, R. Xu, J. Song, X. Bi, H. Zhang, M. Zhang, Li,, Y. Wu, et al. Deepseekmath: Pushing the limits of mathematical reasoning in open language models. arXiv preprint arX1v:2402.03300 2024.
[49] H. Shen, P. Liu, J. Li, C. Fang, Y. Ma, J. Liao, Q. Shen, Z. Zhang, K. Zhao, Q. Zhang, et al. Vim-rl: stable and generalizable rl-style large vision-language model. arXiv preprint arXiv:554.00665, 2025.
[50] G. Sheng, C. Zhang, Z. Ye, X. Wu, W. Zhang, R. Zhang, Y. Peng. H. Lin, and C. Wu. Hybridflow: A flexible and efficient rlhf framework. arXiv preprint arXiv: 2409. 19256, 2024.
[51] Significant- Gravitts. Autogpt. https:// www. http//iinnnfficaca htt.ainiiiiiatttttraaaaaaaaeeeee
2023,
[52] G. Team, R. Anil, S. Borgeaud, J.-B. Alayrae, J. Yu, R. Soricut, J. Schalkwyk, A. M. Dai, A. Hauth, K. Millican, et al. Gemini: a family of highly capable multimodal models. arXiv preprint 2311: 1805, 2023.
[53] E. Tseng, F. Yu, Y. Yang, F. Mannan, K. S. Amaud, D. Nowrouzezahrai, J.-F. Lalonde, and F. Heide. Hyperparameter optimization in black-box image processing using differentiable proxies. ACM Trans. Graph., 38(4):27-1, 2019.
[54] E. Tseng, Y. Zhang, L. Jebe, X. Zhang, Z. Xia, Y. Fan, F. Heide, and J. Chen. Neural photo- photo-finishing ACM Trans. Graph., 41 (1(6))238-1, 2022.
[55] J. Wu, Y. Wang, I.. Li, F. Zhang, and T. Xue. Goal conditioned reinforcement learning for photo finishing tuning. Advances in Neural Information Processing Systems, 37:46294-46318, 2024.
[56] X. Xia and R. Luo. Gui-rl: A generalist rl-style vision-language action model for gui agents. arXiv preprint arXiv:2504. 10458, 2025.
[57] S. Xiao, Y. Wang, J. Zhou, H. Yuan, X. Xing, R. Yan, S. Wang. T. Huang, and Z. Liu. Omnigen: Unified image generation. arXiv preprint arXiv:2409.1 2024.
[58] T. Xie, Zhou, Z. Cheng, Shi, L. Weng, Liu, T. Hua, J. Zhao, Q. Liu, C. Liu, Openagents: An open platform for language agents in the wild. arXiv preprint ar arxiv:2300 10634, 2023.
[59] Z. Yan, Y. Ma. C. Zou, W. Chen, Q. Chen, and Zhang. Fedit: Rethinking the spatial and temporal redundancy for efficient image editing. arXiv preprint 250iv 10270, 2025.
[60] A. Yang, B. Yang, B. Zhang, B. Hui, B. Zheng, B. Yu, C. Li, D. Liu, F. Huang, H. Wei, et al. Qwen2.5 technical report. arXiv preprint 2412 2024.
[61] A. Yang. B. Zhang, B. Hui, B. Gao, B. Yu, Ci D. Liu, Tu, J. Zhou, J. Lin, et Qwen2.5 math technical report: Toward mathematical expert model via self- seff-improvement. arXiv preprint arXiv:2409. 2024.
13

---

[62] Y. Yang, X. He. H. Pan. X. Jiang, Y. Deng, X. Yang, H. Lu, D. Yin, F. Rao, M. Zhu, et al. RI- onevision: Advancing generalized multimodal reasoning through cross-modal formalization. arXiv preprint arXiv:2503. 10615, 2025.
[63] S. Yao. J. Zhao, D. Yu, N. Du, Shafran, K. Narasimhan, and Y. Cao. React: Synergizing reasoning and acting in language models. In International Conference on Learning Representations (ICLR), 2023.
[64] H. Ying. Zhang, L. Li, Z. Zhou, Y. Shao, Z. Fei, Y. Ma, J. Hong. K. Liu, Z. Wang. et al. Internim-math Open math large language models toward verifiable reasoning. arXiv preprint arXiv:2402.00 06332, 2024.
[65] K. Yu, Z. Li, Y. Peng, C. C. Loy, and J. Gu. Reconfigisp: Reconfigurable camera image processing pipeline. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 4248-4444448 4258-4257.10 2021.
[66] H. Zeng, J. Cai, L. Li, Z. Cao, and L. Zhang. Learning image-adaptive 3d lookup tables for high performance photo enhancement real-time. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(4):2058-200) 2073, 2020.
[67] K. Zhang, Li, Y. Dong, Xu, J. Zhang, J. Su, Liu, and Z. Jin. Codedpo: Aligning code models with self generated and verified source code. arXiv preprint arXiv:2410.0560) 2024.
[68] K. Zhang, L. Mo. Chen, Sun, and Y. Magicbrush: A manually annotated dataset for instruction- guided image editing. Advances in Neural Information Processing Systems, 36:3 1428-31449, 2023.
[69] X. Zhang, A. Wandell, et A spatial extension of cielab for digital color image reproduction. In SID international symposium digest of technical papers, volume 27, pages 731-734. Citeseer, 1996.
[70] Y. Zhang, S. Wu, Y. Yang. Shu, J. Xiao, C. Kong, and J. Sang. ol-coder: an replication for coding. arXiv preprint arXiv:2412.00154 2024.
[71] 7. Zhang, X. Bo, C. Ma, R. Li, X. Chen, Q. Dai, Zhu, Z. Dong, and J.-R. Wen, A survey on the memory mechanism of large language model hased agents. arXiv preprint arXiv:2404. 13501, 2024.
[72] Zhao, X. Ma, Chen, S. Si, Wu, K. An, P. Yu, M. Zhang, Q. Li, and B. Chang. Ultraedit: lnstre fine-grained image editing at scale. Advances in Neural Information Processing Syxtems, 37:3058-3093, 2024.
[73] Y. Zhao, F. Xue, S. Reed, L. Fan, Y. Zhu, J. Kautz, Z. Yu, P. Krähcnbühl, and D.-A. Huang. Qlip: Text-aligned visual tokenization unifies auto-regressive multimodal understanding and generation. arXiv preprini arXiv:2502.05178. 2025.
[74] Y. Zheng, R. Zhang. J. Zhang, Y. Ye, 7. Luo, Z. Feng, and Y. Ma. Llamafactory: Unified efficient fine-tuning of 100+ language models. In Proceedings the 62nd Annual Meeting the Association for Computational Linguistics (Volume 3: System Demonstrations), Bangkok, Thailand, 2024. Association for Computational Linguistics.
[75] D. Zhou, J. Huang. Bai, J. Wang, H. Chen, G. Chen, X. Hu, and P.A. Heng. Magictailor; Component- controllable personalization in text-to-imape diffusion models. arXiv preprint arXiv:2410.13370, 2024.
[76] X. Zhuang, Y. Xie. Y. Deng, D. Yang, L. Liang, J. Ru, Y. Yin, and Y. Zou. Vargpt-v1. 1: Improve visual autoregressive large unified model via iterative instruction tuning and reinforcement learning. arXiv preprint Xix::2004.0944 2025.
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Appendices
Our Appendices includes the following sections:
Sec A Details of the MMArt Dataset.
Statistics of the MMArt Dataset.
Comparison of Existing Datasets,
Data Samples MMArt.
Prompt Templates.
Sec Additional Method Details.
Group Relative Policy Optimization.
Details Reward Calculation.
Sec Additional Experimental Details.
Calculation of Local Metrics.
Prompts for MLLM-based Metrics.
See D
Additional Experimental Results.
Additional Quantitative Evaluation by Owen-2.5-VLL772
Examples of Intricate Retouching Tasks with JarvisArt.
More Visual Comparisons.
Comparison on MIT-FiveK.
Sec Details of Retouching Tools in Lightroom.
A Details of the MMArt dataset.
A.1 Statistics of the MMArt dataset
Figure g a) illustrates the composition and distribution scenarios in our MMArt datasct. The dataset structured into four major scene categories that reflect common real-world photo retouching contexts: portrait (40.8%, including shooting purposes, shooting time/lights, subjects, and indoor/outdoor scenes). landscape (33.3%, comprising nature, city, aerial photography.travel, underwater, night scene and architecture), street scenes (5.71%, including sports, life, event and documentary), still life (20.2%, encompassing food, close-up scenes, black/white photography, art and animals). Each major category contains a diverse set subcategories, ensuring comprehensive coverage and representativeness. Furthermore, Figure () displays word cloud of user instructions, highlighting the linguistic diversity of the instructions.
A.2 Comparison Existing Datasets
Table 3 presents a comparison between our MMArt dataset and existing image editing datasets. MMArt designed with the following key properties to facilitate advanced research in image retouching:
Real Images: All samples MMArt are real photographs, ensuring the dataset's authenticity and practical value for real-world applications.
Diverse User Instructions: Each image is paired with detailed instructions, capturing wide variety of editing intentions and reflecting the diversity natural language expressions.
Flexible Resolution: MMArt supports images of any resolution, including high-resolution samples, making it suitable for both research and practical deployment scenarios.
Chain-of-Thouue (CoT) Annotations: The dataset provides CoT reasoning annotations, which help to reveal underlying logic and step-by-step process user intent understanding and image editing.
Lightroom Retouching Configuration: For every sample, MMArt includes comprehensive Light- room parameter configurations allowing for non-destructive, reproducible, and transparent image editing.
These properties make MMArt high-quality, flexible, and richly annotated resource for the development and evaluation advanced image retouching techniques.
A.3 Data Samples of MMArt
The diversity of collected photos is shown in Figure 9 Moreover, Figure demonstrates MMArt samples with shows standard examples without CoT annotations.
Ch (CoT) reasoning, while Figure []
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Table 3: Comparison of MMArt and existing retouching datasets in terms of data properties.
Property
InstructP2P 2
MagicBrush
UltraFdit
MGIE 12
HQRdit 22
Fivek
MMArt
Real Image?
X
User Instractions?
Any Resolation?
High Resolution?
K
K
CoT Annotations?
Lightrom Configuration?
K
500
-0%
5.07%
SOFTER
SOFT
SKAN
FANTAS
GLOW
AIM
VIBE
10%0
SCENE
200h
MMArt
LEATSS
Uscomake
VINT
AGE
S
COZY
1900
V
EVOKKS
MOODY
BNNTTNNG
DRE
DREAMY
WARMM
Band
LOOK
Setacc
FADED
DESONE
HAZE HER
SHADOWS
ALGGA COLORS FILM
WSS
SPENA
pheapere
SEREMI
100h
Day
(b)
Figure 8: Statistics of the MMArt dataset. (a) The dataset is divided into four primary scenarios: portrait, landscape, street scenes, and still life, each containing a variety of subcategories. (b) A word cloud illustrates the rich linguistic diversity found user instructions.
A.4 Prompt Templates
prompt templates utilized throughout the various stages MMArt are summarized here-Acsthetic Preset Recommendation (Figure 21
User Instruction Simulation (Figures 22 and 23). and Chain-of-Thoughe Data
Construction (Figure 24 and 25
B
Additional Method Details
B.1 Group Relative Policy Optimization
GRPO, given task question, model generates of N potential responses {O1, O2, On J. Each response is evaluated by taking the corresponding actions and computing its reward {R,, R2...., Rx }. Unlike PPO, which relies on single reward signal and a critic to estimate the value function, GRPO normalizes these rewards to calculate the relative advantage of each response. The relative quality A; of the i-th response is computed
A Y: Mean (vi, r2...
(2,((11,2,...,1111 rN))
where Mean and Std represent the mean and standard deviation of rewards. respectively. This normalization step ensures that responses are compared within the context of the group, allowing GRPO to better capture nuanced differences between candidates. Policy updates are further constrained hy minimizing the KI. divergence between the updated and reference models, ensuring stable RI. lcarning. Refer to 13 48
for more details.
B.2 Details of Reward Calculation
The parameter value matching function Sx(-) for each parameter is determined hased on its specific type. Let vi"re and Ve denote the predicted and ground truth values for the k-th parameter, respectively. For notational simplicity, we omit the subscript in the following formulas. The calculation proceeds follows:
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Portrait
Landscape
Still life
Street scenes
Figure 9: Visual examples demonstrate the diversity of the proposed dataset.
Scalar Parameters. For scalar parameters such as exposure or contrast, the matching function defined as:
S max 0, V
V
E
[0. 1],
Vmar Vnnin
where represents absolute error between the predicted and ground truth values.
Linear Gradient Masks. We assess the similarity between predicted and target linear gradient masks by measuring the distances between their start points ps (æt, Uo) and end points Pe (Ze, Ue). with coordinates normalized [0, 1] for resolution invariance. The similarity score computed as:
S max (0, 1 - !
1| -> =1 [0,
where || denotes Euclidean distance.
Radial Gradient Masks. We measure similarity between predicted and target radial gradient masks using three geometric parameters: center position (ar, y). scale factors (W, H), and rotation angle
0. Center point similarity is given by:
Scenter max(0. 1 2 ||c d E [0.
where pre and ee are normalized to [0.1]. Further, scaling similarity compares width/height ratios:
Sacale = max(0, 1 |Were / - 1| - HHe H 1D E 1).
The angle numerical value similarity is defined by:
1 0500
Sangie max 0,
E [0. 1],
Omar 0min
The final similarity score combines these components as follows:
= 0.4 Scenter + 0.4 Secale + 0.2 Sangie [0, 1).
Object Masks. object masks, similarity score is defined Intersection-over-UUe (IoU) hetween the predicted ~0e and ground truth Bl bounding boxes. Fach box is parameterized as [21,, 72, yz). The similarity score is computed as:
S loU( BB'() 10, 1].
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Table 4: Hyper-pane ppper-parameter settings for SFT and GRPO-R
Hyper-parameter
SFT
GRPO-R
Batch size
2
2
Learning rate
10-5
10-6
Weight decay
0
0
Optimizer
AdamW W cosinc 50K
AdamW
Warmup ratio
LR scheduler
cosine
Training samples
5K
Training epochs
Precision
6
bfloat16
KL coefficient
0.04
Reward coefficients
Ry 1, Bros 1, Rp (7 = 0.4)
Number of generations
GPU resources
8 A100 (A:384 GPU hours)
16 x A100 (=:2076 GPU hours)
where higher values indicate better alignment, with S denoting perfect overlap and S indicating no intersection.
Portrait Masks. In portrait masks, the model predicts different special category IDs to denote distinct regions, such as ID=0 for face, ID=] for hair, ID=2 for eycs, ID=3 for skin, etc. The matching score S is defined follows:
if the predicted and target category IDs coincide,
S s= 0, otherwise.
Color Range Masks. To evaluate color range mask similarity, sample N representative points from both predicted and target color distributions and compute the CIEDE2000 color difference A F1od in LAB color space. The similarity score is given by:
N
S max 0,1
Σ AEioo(C
pre dat)
10.
N
where e and e denote the nith sampled colors from the predicted and ground-truth distributions, respectively.
Luminance Range Masks. evaluate luminance range mask similarity, we compare the predicted and target luminance extremes by computing their absolute differences. The similarity score is defined
Mpre
S = max
0,
min min
maax
E [0, 1],
2(1
Aagh
where the denominator normalizes by the target luminance range to ensure scale invariance.
B.3 Hyper-parameter Setting
ensure reprodueibility, we provide the complete hyperparameter settings the SFT GRPO-R
phases in Table
C
Additional Experimental Details
C.1 Calculation of Local Metrics
To evaluate the model's effectiveness in localized regions, we compute six metrics LC
RC L22C SC PQRC and 8CC using numan--cntie masks from the portrait subset of MMArt-Bench. For L1 and L2 RC inspired by PPR10K (32), given image with resolution H W. we define weighting matrix Wi wi,s] E R W where Wi, for human regions and wt, a(a S 1) for background regions, with a empirically set to 0.5. For instance, human-centric L. difference metric is expressed as:
Li &C |W, pred W, 1
where pped and 1 are the predicted and target images, respectively, and o denotes element- wise multiplication. The RC metric is defined in similar manner. For SC PQ and RC with empirically set to 0, we focus solely the mask region of the edited image and prompt the MLLM emphasize local adjustments.
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Table 5: Quantitative evaluation on MMArt-Bench. We highlight the best and second-best instruction-baas based results. SC, PQ, and O refer to the metrics evaluated by Qwen2.5-VL-72B [ The RC means the metric calculated on specific mask region.
Scene-level
Region-level
Method
Instruction
L1 x10
L2x10
4
SCT PQ1 O Li
X100
x1ot
SC 1 PQQRc 1
RSENet 45
11.62
26.38
8.80
13.70
3DLUT
66
11.51
26.00
8.34
12.26
InstructPx2PPix
15.62
47.26
5.47
12.48
32.69
3.85
MagicBrush 68
18.31
64.76
3.93
2.25
2.44
12.44
32.93
2.80
2.15
2.01
OnniGen 577
28.40
132.82
4.14
2.16
2.70
24.85
106.81
3.80
3.85
3.67
VARGPT-v1.1
27.04
126.26
1.27
0.17
0.29
23.59
105.86
0.09
0.02
SteplX-Edi
24.17
105.14
7.03
4.94
5.71
15.27
44.93
7.50
6.89
7.11
Gemini-2-Flast 52
23.06
90.96
7.65
6.77
7.00
16.74
53.76
7.33
7.17
7.19
GPT-4o 24
22.77
91.79
8.52
7.37
7.85
15.67
47.60
8.07
7.87
7.95
JarvisArt
12.66
31.88
6.19
8.51
6.67
7.75
12.38
7.54
8.46
7.91
Table 6: Quantitative evaluation on MIT-FiveK [3]
We highlight the best and second-best instruction-based results. SC, PQ, and O refer the metrics evaluated by Gemini-2-Flash.
Method
Instruction
LI x10
L2x10 4
SC1
PQ 1
O f
InstructPix2Pix 2
16.23
49.54
6.36
8.34
7.15
MagicBrush 68
17.29
53.45
4.92
5.50
4.95
OmniGen [7
28.53
128.59
3.12
2.48
2.57
VARGPT-vI. 776
26.96
117.16
2.94
2.00
2.29
StepIX-Edit [36
22.08
91.72
7.20
8.48
7.69
Gemini-2-Flash 152
18.69
61.27
7.86
9.22
8.47
GPT-4o 24
21.49
78.11
8.72
9.76
9.22
JarvisArt
12.98
30.05
7.36
9.82
8.48
C.2 Prompt for MLLM-based Metrics
As shown in Figure 20 we present the evaluation prompts utilized for both scene-level and region-level assessments of the Semantic Consistency (SC) and Perceptual Quality (PQ) metries. Notably, the overall score calculated O VSC PQ.
D
Additional Experimental Results
D.1 Additional Quantitative Evaluation by Qwen2.5-VL-72B
As shon in Table 5 further evaluate MLL.-bassed metrics, conducted additional quantitative analysis using Qwen2.5-VL-77 VL-22B M. Our findings suggest that such metrics may be unreliable, struggle effectively reflect model's mustruction-followinn capability. Despite this, our model demonstrates instraction-followiin performance comparable to that of contemporary SOTA closed-source model GPT-4o, while achieving a significant improvement in content fidclity.
D.2 Examples of Intricate Retouching Tasks with JarvisArt
Figures 12 present the challenging retouching examples, which involve both global and local editing demands. well vague instructions. JarvisArt excels in understanding these ambiguous intentions. applying modifications at both scene and region levels, and delivering visually effective results the final images.
D.3 More Visual Comparisons
Figures present additional photo retouching results the MMArt Bench and MIT-FiveK, highlighting the superiority of JarvisArt in terms instruction adherence, content fidelity, and visual appeal, while also achieving the style most similar to the target image. Notably, we also include a visual comparison with two commercial editing tools: Adobe Lightroom and Google Photo' auto-retouching modes.
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D.4 Comparison on MIT-FiveK
To assess the generalization ability of our system, conduct comprehensive qualitative and visual comparisons the MIT-FiveK B benchmark dataset. Specifically, we randomly select 50 data samples from MIT-FiveK [ and generate corresponding user instructions based on the source images, expert C's reference images, and associated retouching parameter configurations (see Section 3.2 for implementation details). As shown in Table and Figures 18 and 19 our system achieves state-or satte-of-theaaee art performance in both instruction-folloe following and content preservation metrics. These results confirm the robustness and effectiveness of our method in faithfully executing user instructions while maintaining original image content. Furthermore, our approach consistently outperforms existing baselines on multiple real-world benchmarks, highlighting its strong generalization ability and practical applicability in interactive photo retouching.
Input:
Output:
User Instruction:
pop more, making cathedral stand enhanced details. water needed cooler tone, bridge and path should appear darker for depth. Overall, I aimed for balanced look that emphosizes the architecture and adds cinematic feel.
<Image> wanted
Answer with CoT annotation:
<think>-Alright got this image showing bridge, cathedral, river. goal? Enhance cathedril's detuils, water,
the bridge path, and schieve cinematic foel. The image appears muted, with vibrant water needed. For the cathedral, I'll boost clarity to 10 and use sharpening. To cool the water, I'll adjust blue suturation to 400 and apply split toming. Darkening the bridge/puth involves local exposure reductions
to adjusting HSL, and using vignetie. Highlights 100 may be too much: I'll tweak that. Yellow hue shift (-88) might alter the cathedral unnuturally. I'll check these and refine. Composition- wise, the vignctic and local darkening will focus on the cathedral. Final tweaks ensure the image moots vision stinne<
cinemstic look comes
incressing
Sanswer> SewpwerrCCmppiileeeeeeeoo
251658240, Process ProcesVVersion *11.0", WhiteRalance "Custom", Incremental Contras12122
Highlights2012 -100, Shadows2012 100, Whites2012 -96, Blacks2012 - -30, Texture - 0, Clarity2012 - Dehaze -0, Vibrance - 21,, Saturation 100, ParametricShaoous -0, ParametricDarks: 0.
IncrementalTiin -3, Exposurc2012
PuamennicHighiiggnSoli 75, Shurpoess 40, SharpenRudius 0.5. ShurpenDetail 100, ShuppenldgeMaskingg 60. LuminanceSmoolhing 27,
Parametricl
ParametrieHiggMighss ParametrichaaoowSpeee 25, ParamerriMMidooneSe
100, VemiinnneeiieeRecaaiooCooCoo Controon 0, ColorNoiseRehuction = 0, HucAdjustmentRRd
IlseAddustmennlllee 11, HuueAdjsstmennPPrpl 0, Hue AujeAdjstteetMaagee = AdjutmeonA = 100, SalumtionnA AdjistteenOOnnggee 79,
Aluemmjjusmennnnnneee IlueAdjsstmentVellle
-100 HlueAdjjsmmmntinn -100 Adue/djuumennAAuu
Saturation SuturattionAAjjusseeennn Nellow 12, Saturation SauuanionnAijsstteennneeee 100. Mjjutteinn 34, SuturationAAjjustmennnt AdjustmenDDueeee
AmuuattionAdjuustmenni -100, SsturaiionAujustmmentt
AuminunceAdjjsttmemnt
0, Luminance Aijaammennniisttttttttnnnng
0, Luminance Wajueeeeeeeesimmeellllllly - 0, LuminancceAdjustteennnne
LuminunceAdjsstmennt
0. aeriiiamceedjjsssmentttn
-28,
Luminance LuminanceAdjustteenntt PuupsteennnPrrlle Luminance uuminannee-uuccmmennaaagnn SpliTTaningShadowlHeee
SplirToning Highhghhhhhgh =
highiingggggggghhhhttnnittoo = SpliiToningBalane 0, ColonGradeMfidooneei
ColerGmuleMidueet 0, ColorGmdeGloobule
0, ColarGrudeMichond um LolorGrudeligghiiaee
0, ColorGmaieBlendingg
Color GrdeGleballa
ColarGradeGloball um Autol ateralCA TensProfileFabble =
LensManual AistontionAAmmm 0, VignetteAmounn 0. DefringePurllemmmnn 0. Definggeuuplellell.. - 30, DefringePumlleleelti
70, Defringe Green mounnt 0, DefringeGreenHuell.. 40, DrfringeGreee PersecriveUppight Perspective
prrppetivellooizznaall 0, PerspectiveRotate
PerspectivAsppet PerspectiveScale 100, PerspectiveX - 0, PerspectiveY
Grunnmmmnt 0, PostCrog PostCroVignnttee Vignettemmomun -17, wignrrrrppinnneeeMiiiiiiioo 50, PostCrop Vignetteeeuutter 50,
PostCrup' PostCropVigneteR VignetieRoundness
PostCrop VignettopieeetteSlllee
PostCrop NignetellighigghhCoottss Shadow
RedHHue
RedSaturation 0, GreenIlue 0, GreemSaturatinn - 0, Bluellue 0, BlueSaturation 0, Overridel.ook Vignette - true,
Tone unoCuureNam2221222 "Cusom"", CameraProfile- "Embedded", CumeraProfileDigest
HasSettings trse, AlpiidyAppliied fälse, ToneCurvePV2013
[[]] - 11, [Name "Modem Amount
106, [5] 192, [6] 165, [7] 255,
255,), ToneCurvePV200 22 2Red
[[I] 0, [2]= [3] 1 255, [4] 255,], ToneCunnePV20022eeee = ([1) 0, [2] [3] 255, [4]
255.j. TommCCnnePP220122leee [[]] -2] 255, [4] 255.),
"Adobe"
UUID - (C777//66000AAA559R5CCCCCCCC
SupportsMomcchome false, Copyright - "6 201 Adobe Systems, Inc." Group
[["x-default]] "Modem", J. Parameters (Vension - "15.2", Process Vension "11.0", ConvertooGryyccll - false. LookTable
(T0.:/1999DD00077777722002080000 0.75.J.
RGBTable (113199BIBAA2272255555900
RGBBTableAmoun
Kanswer> ('assers
Figure 10: Examples MMArt data annotated with Chain-of- Thoughh (CoT) reasoning.
26

---

Input:
Target:
User Instruction:
<Images| I want the image to have vibrant colors, highlighting the feather details and overall outline of the the engle, while keeping the cool- loned utmosphere the groudd
Answer:
<answer> PresetType "Normal", Cluster UUID WWA42BBBS6FFBB22AADDD000SSSSSSL AupportsAmoun22 trae.
SupportsAmmunt true. SupponsColar true, SupportsMomchhrenc
true, SupportslighDDyamiisaaua
true.
SupportsoomalDDyaamideee
truc. SupportsSensRoffree truc, SupponsOuputkceeed true, RoquisesRGBTablls false.
CameraModellestticiio
"", Copyright Contactinfo "2 Computible Version - 234881024, ProcessVersion- "11.0", Exposure2012
00.25, Contrast2012 - Highlights2012 -100, Shadows2012
Whites2012 20, Blacks2012 -10, Texture Clarity2012 10,
Dehaze = -5, Vibrance 20, Satumtion = 5, ParamericShadows = 0, ParametricDarkss 0, iattm =0, PammetnicHighlighs D
ParamotrcShadowSlli - 25, PanamcrieMidtoosSplit 50, ParamctrifHighigghSSpt = 75, Sharpness 45, Sharpen/aadius I.
SetrpenDetail 80, Shorpen
30, Luminnceemmoohing 0, Color Roiseeeciii = 25, ColorNoiceRcdsccionneen
75,
ColarViisReductionnnnuutt
25. Hoe/Aijsteeemeed 20, HueAAjusttmeetOrmmg - -15, MlluAAdjuttmenteellle
-88,
Ajjustmeeeennnnnne 25, FlueAdjustmenttqqu
HueeNdjaseennBBle -10, Hue Aijutmmenntee Hieee-dasssemeenaaee
Ajjutmenndd 0, Saturation SiturationAdjistmmnnrrrngg 12, AdturationnAjjjssmmmMtoa
15, SulurationAAjjsstmennnoo -20.
Saturation SsturationAAjssmmeen-ppp AdjustmentAAqu 0, Saturation Adjaaonnnnjaaaa Adjusmmnniieeee -40, Suturation SuturaaionnAjjssmmenPauppp 0, AdturaationAdjisssmemmmeen
Luminance AumitanceedjustmmelRedd 45, LuminnnccA&jjsstenninnnggg
LuminanceAdusstmemntte
Luminanceedjjssteennttean
30, aomiinanceAdjustmeuutae -0, LaminanseA
-20, LuminancoAdjusie AutiiinnncAAdussmmnnnPupeee
Luninnnce-Adjsstteunntt
0, ShiiTonnngShaooHHue - 219, SaaiiTooinngaadowSsuuttttt - 11. SplicToningHighhighthe - 45, SpliiTTniingiiggigghSsaaaaa
14,
MoiiTTonnnBaannn ColeernddMMidooooHuee = ColorGradeMidteeeeSr = 0, ColorGradeShadowLnn 0, ColorGnadeMiidhonelo.m
0. ColeeGradetihhhgghh..m - 0, ColorGindeBlanddnn
100, ColorGradeGlebellHoo - 0. ColorGnndeGloballSet
0.
ColorGradelobaall 0, Autolate I, LensProfileEaable I, waaaaaliissorronmmmmuu
VignerteAmount
0
DefringePurplemmouue 40, GreenngeGeennHeltt = 60, PerppeciveUJprihtt 0, PenspectiveVerrial 0, PerspeciiveHorioontll 0 PerspectiveRoiate
DefingePupllllull.. - DefringePuplllleell 70, DefringeGrrennmmmun
DeffingeGreenllllla Iuelo
0
PespectiveAspes 0, PesspcctivScalle 100, Perspective Penspective GruinAmmmt PosCrop Vignette/Amount
-25,
PosCCoop SooooooViiiin VignenteMidpoint 0, SeuCrropignetteeeeee
100, Vignttopiinntteeouunness 100, Post Viggetttlee
PostCrop VignetteligllighCConttte
0, ShudowTint 0, RedHlue 0, RedSaturation 0, Greenllue 15, GremSStuuration -15, Bluellue
-8, BlucSaturation 45, OveriddLookk Vignette
truc. ToneCurveNms22012
"Custom", LensProfilcSetup
"LensDefaults"
HasSettings - true, ToneCurveP22012 - [[J] - [2] - 16, [3] - 43, [4] - 40. [5] 69. [6] - 60, [7] 127. [8] - 126, [9] [
255), ToneCureeV20122ee - [[1]] - [2] -0, [3] 255, [4] 255), TonsCurveV20112Gnca
[[1]- [2] (3)-0. [3]]332255 [4] 255).
TomeCurveV22022Bue [1] - 0.
[3] 255. [4] 255). MaskKrrouBBaeedCeeeeeee
[WWhat "Correctonn",
CorrectioAAmoutt = 1, CorrectionAAtive - true, CorrectionName "mask 1", LocalExposure 0, LoaalSaturation 0, LocalContrast 0.
LocalClarity 0, LocalShurnees 0, LocalBrightness 0, LocTToningg 240, Local Senn/ToiinSSattraiionn
Lacallxxosure20122 0, LocalWhits20112 0, LocalBlacks2012
0,049964, LocalContrae2012 0, LocalHHighlighs2012
0, LocalShadows2012
LocalClarity0012 -0 0, LocalDehaze 0, Locall moniaaccnnn 0, LozalMoire - 0, LocalDefringe 0, Local realeeempeaauuee 0, Local Tint = 0, Locaffexture LoealCuroRefiinSSuuuttte "lineur gradient"" MuskBlendMode - 00, MaskInverted - false, MaskValue 1, ZeroX - 0.499395, ZeroY - 0,291666, FullX - 0.458988, Fully -0.020506311. (What "Correction,, ComectionAmount 1. ConrectionAetive
100, Comeetion ([Whar= "Mass/Grdienn", MaskActive
MaskName
true, ConectionName LocalEsposure 0, LocalSaturationn LocalContruse 0, LocalClarity LocalShurpness- 0. LiculDrighmesss 0, LocalToningllue
"mask 2",
240, LocalToningSsttration - LocalExposun20022 -0.036337. LecalContras2012 - LocalHiighiihss2212 - 0, LocalStadows2013 0. LocalWhite2012 LocalBlacks2012 0, LocalClarit2012 0, LocalDehuze 0, Locall.umimuneeNoiee
LocalMoire
LocalDefringe 0. LocalTemperstuee 0, LocalTint - 0. LocalTexture 0, LacalCarveRdfinesauaaloo
100, CorroctionMaaks
[[What = "Mask/Gmiien"', MaskActive = true, MaskName "Finear gradeen 1", MaskBlendModee = MaskInverted false, Mask
ZeroX 0.502379, ZeroY 0.91026, FolIX 0,502579, Fully 1.00171]]}, (What- "Correetion', ConrectionAmount CorrectionActive tnae Correction Name = "Highlight subjear", LocalExposure = 0, LocalHue = 0, Locallatumation = 0.029372,
LocalContrast LocalClarity = LocalSharpness 0,201033, LocalBrighhttess =0, LocalToningHHo 240, LonalToningSaatrationn LocalExposine2022 0.019615, LocalComms22012 12- LacalHighhighss2012 0.0625, LocalDlack22012 - -0.012252, LocalClarity2012 0.014814, LocalDehaze 0.805763, LocalLemiinnceNoise 0, LocalMoire
LocalShnoow2213 0.047515, LocalWhite22012
0, LocalDefringe 0, LocalTemperatuee
LocalTint 0, LocalTexture 0, LocalCureeReiiiSttuuuii
100, MainCurve [[I]
"0.0", [2] - "64.60", [3] "201,210". [4] "255.255"), ConrctionMasss [[What "Mask/maaee, MaskActive Mas&Name
"Motiv 1", MuskBlendMade 0, Muskinverted false, MaskValue I, Mask MaskSubType 1, ReferencePoint "0.500000
0.500000", ErrorReason [f]]]-answer
Figure 11: Data samples from MMArt with standard instructions.
27

---

Imput:
Oulput:
Instruction:
<Image>| I want cyberpunk vibe with neon lights and sharp contrests for a futuristic city feel.
JarvisAr::
<think>A Iright, the user wants cyberpunk vibe with neon lights and sharp contrasts. image features illuminated signs and light truils at night perfect for this theme. I'll start by adjusting the white belancce 3200K for cooler tone. Next, 11l recover highlights neon lights and open shadows slightly to keep details. Increasing contrast will enhance light dark differences without harshness. Vibrance and seturation boosts will make colors pop, especially neons. Clarity and debaze ad usfuunttntss will sharpen details and cut through haze. Using the "A dobe Color" look with amount of 1 will complement these changes, Local masks will refine specific amos, ensuring consistency, Checking sharpness and noise reduction will maintain quality. Finally, applying the provided tone curve values will shape brightness and contrast perfoctly. This approach captures futuristic city feel the user desires, <think-
<anserr> W WhiteBalanne "Custom", Temperature 3200, Tint 10, Exposure2012 = 0.96, Contrast2012 4, Highllihhs2201 = -58
Shadows2012 58, Whites2012 = 37, Blacks2012 = -13, Clarity2012 20, D Debaze = 25, Vihrance 15, Saturation 2
25, ParamelricMidlooeSpph SharpenDetall 25. ColorVoiseRahuction = 25,
ParemedricShsddowpp
50, Parometicciighigghlppll... aeeeeKoleeeeeuuttoaaaa.
Sharnness 40, SharpenRadius 1,
= 50, Colarloiseleluctionnnooneee
50,
ColorGrade = 50, DelinggePupleHHuLo - 30, DefriogePupleHtutii 70, DefiingeCreenHuuel.. - 40, D DefringeCreeaFuali - 60,
PensextiveScale 100, TomoCurvNamm22122 "Lineur', HasSellings tnue, MaskG cospGrrupBaoss mopp-asolCCrmstiiionn
[[Wbat bat "Correctonn"
ComectionA mount 1, cooe thee, CorectionName "Mask 1", Locallxppssue2200 = -0.25, LocalHHighlighs2012 0,217391, LocalShadows2012 = -0.25, LocalWhites002 = -0.25, LocalBlack:2012
-0.114906, CorrectionMssks = ((What "Mask/Gradient" MaskA clive true, MaskName "Linoer Grudient 1", MaskBlendMode = 0, Maskinvertod false, MaskValue ZeroX
= =0.630042, ZeroY = 0.5002113, Fullx 00799436,, Fully 0.387017))), (What= "Comection", ComrecionA mount 1, ComeccionA.coo clive
tuue ComectionName = "Mask 2", LocalExposure2112 = -0.1, LocalShadows2012 = -0.153061, hocalWWitts2202 = -0.530661 0.082163, JocalDehaze 0.082163, LocalTexture 0.102164, ComsetionMasks {(W hot = "MaisoGrddienn", MaskA clive true, MaskName "Linear Gradient 1", MaskBlendMode = 0, MaskInverted false, MaskValue
LecalBlacks2012 -0.124449 LocalClarit2012
=1, ZeroX = =.5995519, ZemY = 0.53203, PullX = 0.799826, FullY = 0.682565)))). Look = (Name- = dAdobe Color", Amount =1, UUID "B82C21111CD8EEECFFLA. AGBAA SupportsA mount false, SuppottsMonnecmmee Group = (['x-defaull") = "Proflees), ) Parumelers [Version =15.0" ProcesVersion "11.0", ConvertooG rayscalle false, CameraProfile ([1) 0, (2) = 0, [3] 22, [4] = 16, [5] 40, 35, 127, [8] 127. 224, [10) 230, [1I] 240, (12) 246, [13) 255, [14) 255))) <anser>>
false, SuppotsOutpuRReeemee
false
dobe Standard', LookTable = "E1095149FD 00DD55490DDBB00 39D 7A 05w.055BABBB B.B.BBB88777220 00001 TuueCurve 2012
Figure 12: An example of Jar empowering users to achieve interactive and interpretable editing, transforming their ambiguous intentions into artistic visual outcomes.
28

---

Input:
Oulput:
User Instruction:
<Image> Make image evoke a sense of loneliness and introspection, with soft lighting highlighting the solitary figure within the <box50.446, 0712, 0.540, 0.841-/boxe
JarvisArt:
<thinlo A Aright, focusing on evoking lonelinesss and introspection in the image of solitary figure in fonss. The person is centered at <box>0.446, 0.712, 0.540, 0.841 </bors, The dense forest sets quiet mood. To enhance solitude, I'll adjust lighting amund this slight warning with W hite B Balance settings (Incremental Temperature 15, Tint 10) adds melancholy. Exposure2012 at 1.25 brightens the scone, while Contras22022 at 17 enhanoes details. Shadows2012 set to 47 reveals more arvund the person. Vibrunce incrussed by 10 boosts seturated colors subtly. HueA djustment values (-100 for Yellow, Green, Purple:
for Aqua) hammnnize the palette. PostrmpVignetaa mount at -21 darkens edges, drawing focus the center. These adjustments create poigmant, introspective atmosphere, <thinle-
<answer> PresetType- = "Nomaa", ahiteHalance "Custom", Incremmentllleempensuuee
Incremental Exposune2012 1.25,
Contras:2012 17, Highlighhs2012 = -27, Shadows2012 = 47, W hiites2212 20, Blacks2012 = 18, Clanty2012 = 15, Debaze 10,
Vibrance = 10, Saturation = PanmetricSaadoowppit = 25, ParaneticMichomSSpli = 50, Parametic 50, ColozNoiseRleurrtoo
75, Sharpoess 55,
SharpenRadius Sharpen etail 25, LuminanceSmoothing 25, ietiiiannneooseeeacutttotoo
25,
ColorNoisuReductionDDee etail = 50, Saloo/oiseedaacooommoooootoeoooo
= 50, HosA djustmeofY allow = -100, HunA Cjsstmentrren =100,
HueA djustmentA qua -36, HueA djustmentB lue -22, SaturationA DenistmeeDDaaggg -10, SaturationA djustmenty ellow = -10,
SaturationA djustmentt
SaturationA Ssnurationdjusmmmentt qua -00 SaturationA dinonnAduuuuuuuuu djustmentBlue =-10, SaturationA djustmentPupple Seturatonkjjsmmeedfen
=-100,
SaturationA djusttnnntMagenta
-100, Luminanoad djustmenttOrunge
20. L LuminanceA djustmentt wesstmmnteello ellow
20.
LuminanceA djustmennG room 20, Luminanced djustmenlA qua = 10, 10,,,,nmm LuminanceA djustmenlBBur 10, LuminanueA djustmentPurple
20,
Luminances djustmentMagent
20, SplitToninShadowHHee -25, ColotGradeBledding 100, A utolateralCA 1, DefrngeeuupleHauu. 30,
222, SpiitToningShadove Seturation 13, SpiifToonggHihhhghhhuu 10,
SplifToningBalan Balance 22, ColorOndeehadowllum
DefringeP upple =70, Definggrreenluull... = 40, refrine@oenntulll 60, PurspediveScale = 100, DosiCropVignntlee mount =-
21, Posti:ppViigee MeViinneeMMMdponn
PostCropVignetin Ventroopignnetteeeehher
50, PostCrooVigneeteStty
RedHue
RedSaturation -8,
GreenHue = 45, GreemSaturation = -15, BlueHue 15, BlueSaturation 40, CurvaRefieSaturation = 100, OveridaLookWignaa
troe,
TooeCurveName200
= CCstom"" HasSettings true, ToneCurvePP 2012 = [[1] =0, [2] = 28, [3] = 54, [4] = 62, [5] = 176, [6] = 164. (7)
2555, [8] 224), ToneCuvvPP 2012Red [[1]
[3] 40,
12, [5] 84,
58, [7] 123, [8]
186,
196, [11] = 255, [12) = 255), ToneC UV 2012Green ([1) = [2) = [3] = 40, [4) = 14, [5] = 83, [6] =9,, [7] = 123, [0] = 126, [9] =
172, [10] 185, [11] 255, [12] =0==2555 2553. ToneCurveP220 28lue ([1) =0, [2] =0, [3] =39, [4] = 13, [5] 87, [6] = 65, [7] 123, [8]
124. [9] 183, [10] 199, [11] 255, [12] 255), Mask www.rrrruaaaaeereeeeeeeeeee
[[W "Correction, CorrectionA mount
CorrecionA = true, ComectioName = "Mask 1", LocalSuturatoon = 0.23, LocalExposur20122 = 0.15, LocalSbsdow:2012 =0.60648.
LocalBlackk2012 -0:::0122000330000 00333333,
reaperttee 0.32, LocalTing = 0.25, LocalCCaeeReeeeSSaaene
= 100, CorretionMekss = ({W
="Mask/mmage', Maskc
true, MaskName Person MaskBlendMode 0, Masklnverted false, MaskValue 1, MaskVersion
*1, Mask = ReferencePiint = 0,0000000 0,500000', ErrorResson 0 Gesture ([Wbat 'Mask/Polygon', MaskActive true.
MaskBlendModee =0, MaskInverted = false, VaskValue = 1, Points = ((X = 0,44622, Y 0.712727) (X = 0.5408944 Y 0.712727), (X <nswer>
0.44002 =0.40727)) 0.540894, 0.840727)))))))
Figure 13: An example of JarvisArt empowering users to achieve interactive and interpretable editing, transforming their ambiguous intentions into artistic visual outcomes.
29

---

Lightroom Workspace
Input:
Arlore
Affre
Retouched
peicce Se box
Guld
Caoi
User Instruction:
<Image> I want this image have soft, misty style. In <hoxc-0033, 0,25, 0.6r= which is the face and upper body figure, wanted the skin color to be smoother and more natural. The everall colors should blend softly. with warm, green feeling that evokes but not strong, creating a serene atmospheree.
nostulgia. foliage the background should be
Answer with CoT annotation:
<thinke-Alright, I've got this image to edit for a soft, misty style. The user wants smoother skin tones and gentle color blending with a warm, person's face and upper body in <hoxc003 3 0.25 0.7 0.65/oox>. The background foliage should stay vibrant but not overpowering. First, I'll adjust the white balance for wammth. Then, apply slight blur or glow effeet for the misty style. For the skin tones, I'll use masks reduce suturation
vintage feel, focusing
specified area. Next, tweak tone curve to soften contrasts warm highlights and midtones. Background colors will be balanced to complement the foreground without being too intense. Adding subtle film grain can enhance the vintage look. Finully, I'll ensure all elements harmonize for a serenz, nostalgic atmosphere. These steps align with user's vision while preserving the original image's quality-
smooth textures
Sunswers-PresefTyy "Normal", Cluster SupportsAmount2 trac, SupportsAmount trae, SupportsColor = true, SupportsMenochrume true, liggooosliDDyyammiceBee CupportsOutputtReee efeeree
true, SaaaporrNormaalDymmmiieee eferred = tne, VrocessVVrssion = "15.4" WhiteRalance *As Shot", ParamerieShaaooSSlli 25, ParmmericMiidoneSllit 50, lighhhhhSit 75, ColanGrudeBBeeddnng 50, DefringePurplalleeee 30, Deffingeeurplellleli 70,
true, SuppontsScenRRefereel true.
ParspectiveScale- 100, ToncCurveNamc2202 "Lincar", HasSettings truc, Look - Name - "Adobe Color", Amount - Stubbed - true, Parameters - Compatible - 251920384, ProcessVerriion - "15.4",
DefringeGrrenHHeeoo -40, DefiingeGrrenHuutti
CurvoRefinSSauration 50, ConverToorayscall false, RGBlable "F4B$250DI
ToncCuvePV2011
I [[]] - [2]- 0. 16, - 24, [5] 64, [6] [2] 128, [8] 128, - 192, 176, [1]] 244, [12] 234. [13] 255, [14]
255 ). ToneCCrroPP22112Ra - [1] 0, [2] 0, [ 255, [4] = 255 ToooCoCnePV20 - [1] -0) [2] 0, [3]= 255, [4] 255 ).
RoneCurePV20122Buc
[[]] [2] 0, 255. [4]
3 LensBlur - Version Active true, ImageOrientation
FocalRange = 0 100 100", BlurAmount 50, BokehShape = 0, BokehSSaapeDetail - Hightight - 50, HighlightsBoos- 50,
CatEyeAmount 00 CatEyeSeals 100. BokchAspeet - 0, BokehRottaionn - 0, SphericalAbeerration - MasskGrrop
What "Corretton",, ComectionAmmont CorreccionAetive true, CoreetionName "Color Tone Reduce RED", LocalToningHoo
- 119, LocalToningSaurrriin - 0.1, LocalTint -0.05, LocalCureeReiinSmmmmiiin 100, CorrectioMMusss [ What
"Mask Manic/RangeMask', MaskAcrive true, MaskName "Color Range 1", MaskBlenddMode I, MaskInverted tree Mask
Correction Musk- unggeeionnnngeMuuse
Vension - 3, Type ColorAmount 0,494949, Invert
SumpleType 0, PointModels ("0.595958
0. 0.89660 0.597516 0848823 0.955119 0*] 1, What = "Mask/Image", MaskActive true, MaskName = "person object box",
MaskBBenddMnde MuskInverted true, Mask Value Musk Version 1, MuskSubType 0, ReferencePPint "0.500000 0.500000" ErroRReason 0, Gesture =| [[ What - "Mask Polyyon', MaskActive true, MaskBlendMMode =0 MaskInverted false, Mask Value =1
Points - [ 0.3, Y 0.25 J. X 0.7, y 0.25 J. X-00 0.3. 006.6,
0.6 X 0.7, 006 0
<unswer>
Figure 14: Editing results with JarvisArt are generated under complex prompts, with all retouching operations performed in a Lightroom environment, allowing for iterative adjustments.
30

---

Lightroom Workspace
Input:
Retouched
Virk
Cadlens
Locour
Freddss
User Instruction:
<Image> My goal was to create atmospherii, nostalgic look. For the area <box=(0.33 0.25, 0.7. 0.6]=/box>> around her face and shoulders, wanted smoother textures and a subtic haze effect. The catire image should evoke a scuse of timelessness with muted colors and enhanced contrast, making feel like classic portrait.
Answer with CoT annotation:
<think=Alright, let's dive into this image. The user wants a nostalgic foel smooth textures and a subtle haze around the person's face and shoulders in =box--0.3 0.25 0.7 0.6-//oxx.. Welll warm the white balance to 5185K for that vintage vibe. A slight overexposure of 0.23 brightens the subject, while contrast at adds depth. Highlights reduced by -71 and shadows boosted by 50 preserve detail. Texture at 25 and clarity at cahance details globally, but local dehaze softens specified area. Vibrance up by 20 cariches colors subtly. Split toning with shadow hue at 229 and highlight hue ar 48 adds vistage touch. A dark vignette draws focus to the center. These tweaks balance global adjustments with targeted corrections, achieving the classic portrait look the user desiros
<answer> Masserr-WhitBellen WhitsBllace-- "Custom", Temperature - 5185, Tint 10, Exposurc202 - 0.23, Contras1201 - 7, Highlights0122 -71, Shadow<201 50, Whites2012 12, Black<2012 -18, Texture 25, Claricy2012 = Dehaze 11, Vibrance 20, Saturation 3, ParametieShadowSglii 25, ParanstiiMiioonnSSllt 50, ParamerrifHighliihhSpii - 75, Sharpoess 40, SharpenRadius -
SharpenDetail 25, aminanceSmoolhing 13, aaminancNaasseechcciinn
50, ColorVaiseReduution 25,
ColorNoiseRetuccionDDdeoo 50, wew.oooiseecueeiinnnnoooooooo
50, HucAddjstmentOrrage -11, HucAdjustnenttllloor =3,
Saturation Addiionnnijia djjustmentOrangee -20, Suturation AaaaatioonAjjaaaaa djjusteenYYlloow -50, AnauurationAdjsstmeeeeeeee - -31, Luminance AdjustmentOrange
30, LuminanceAdjusttmenntl
12, Lamiinnneedjpssmenntaa
-12, Lamissneee&usssmsemilee -30, SplicToningghaadwwiii 229.
13, DogiiToonngllighgghhhh - SplitToning highighttthhhttttnttttnnnn 10, ColorCinuleBlendinng 100,
Autol ateralCA = DefringePupplHHull..o o 30, DufringePupleeHeeHii 70, DefringeGreenneell..oo
DefringeGreenHnufii 60,
PerspectiveCcale 100, VesnCropVignetteAmmee
Poctrrop Viineeeegineneeiiiooiois
PostCnogiiinetteeeeeeee
50,
PostCrop VignetieStyle - I, RodHuee 30, RedSaturation 110 GreenHue - 40, GreenSaturation - -10, BlueHue -25, BlucSsturation -10,
VorrideL.ookVignette - true, ToneCureName22112 "Custam", HasSettings true, TomeCurvePV2012 - { []] 0, 29, [3]- (3)-48, [4]-
48. - 127. 126, [7]
[8] 192, 255. [10] 255 ). TomeeCurvoPV20122Rdd
[1] 01)-0,00 [2] [3] 27.
115. [6] - 125, [7] - 159, [8] 173, [9] 255, [10] - 255 TomeCurrePV0 - [1] [2] 0. [3] - 43, [4] 25, [5] 113.
121. - 160,
174, [9] 255, [10] 250002251] TomcCureeV220122Bl - [1] [2] 0, [3] 39, [4] 25, [3] 113. [6] 124, [7]=
ConrecionAcctvve true, ConsetionName- = "Haze", LocalToningHto - 240, LocalBlacks2012 0.0675, LocalClaiiy0112 - 10.198222, LocalDehaze
18]. 174. 255, [10] 225 M.M.kKirougBBseeCreeeeee
What - "Comection", ConrectionAmount
0.208022, LocalTemperature -0.1024, LocalTint -0.09511, LocalTexture -0.250785, ConvecionMasks- [ What "Mask/Image".
MaskActive true, MaskName = "Subjoct I", MaskBlendMMode = Masklnverted true, MaskVahue = MaskVersion MaskSubType
RefeerencePointt "0.3066411 0.521994", Whoelmagerrraarrrr---- "0/1, 707/1,6001,,551 Origin "0,531", ModelVersion-
CorrectionName "Haze", LocalToningHoo 240, LocalBlacks2012-21 0.11575, LocalClarity2012- 0..245554 LocalDehuze -0,364241, LocaTempenaturee - -0.163366, LocalTint 0.1 88755, LocalTexture -0.288385, CorrectionMasss { What "Mass/Gradienn', MaskAetive trie, MaskName "Linear Gradient 1", MaskBleniMMde - 0, MaskInveried - false, Musk 1, ZeroX 0. 0.426926, ZenoY - 0.47269, FullX 0.44185, Fully 0,872828 What = "Mask/mmage, AaskActive = mee, Made = 'Subject I", MaskRlendMode = MaskInverted = false, Mask 0,
234881976 J, What = "Correction,, CorrectionAmounts CorrectionAActivee
MuskVersion Mask Type- ReferenceePint "0,306641 0.521994", WholelmageAres 10111,001,17777/125660 Origin "0,531", VedeiVersson 234881976] 1
<amswer>
Figure 15: Editing results with JarvisArt are generated under complex prompts, with all retouching operations performed in Lightroom environment, allowing for iterative adjustments.
31

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PistruciPix2Piv
MagicRrush
Step
Gemini-2-Flash
RSFNet
GPT4o
Input
Google Photo
VARGPT-1L.1
Lightroom((Att)
OmniGen
3DLUT
Ours
Making the carousel glow softly with a magical, ethereal feel.
InstructPix2Pis
MagieBruch
StepIX-Edit
Gemini-2-Flash
RSENet
GPT4e
Input
Google Photo
VARGPE-1L.
Lightroom(Auto)
OmaiGen
30LUT
Ours
desire dramatic, high-contrast style emphasizing the sky's depth and the structure's
intricate detailk for striking
InstructPis2Pix
MagieBrush
StepIX-Edit
Gemini-2-Flash
RSENet
GPT4e
Inpat
Google Photo
VARGPT-1.1
Lightreom(Anto
OmniGen
Ours
Adding nostalgic,
bustling market
InstructPiclPis
StepIX-Edir
Gemini-2 F-aasb
GPT4e
Input
Gongle Photo
VARGPT~1L.1
Lightroom/AAut)
OmniGen
3DLUT
Ours
Make the scene more dramatic with enhanced contrasis and colors for striking, cinematic look.
Figure 16: Visual comparisons of all state-of-the-art editing methods alongside two automatic retouching modes from commercial software.
32

---

InstructPiv2Pix
MagicBrush
X-epiX-Edir
Gemini-2-Flash
RSENNt
GPT4n
Input
Google Photo
VARGPT-1L1
Lightroom(AAto)
OmaiGen
Ours
aiim for fantasy vibe, enlinncing the colors and textures evoke nostalgia and charm the
InstructPix2Pix
MagicBrush
StepIX-Edit
Gemini-2-Flnsh
RSPNet
GPT4e
Input
Google Photo
VARGPTx111
Lightroom(AAui
OmniGen
3DLUT
Ours
Add a yellowish tint to capture the essence of old memories and highlight the 0,719</box>
InstructPis.PPi
MagicBrush
StepIX-Edil Edit
Gemini-2-Flash
RSFNet
GPT4o
Input
Google Photo
VARGPT=1.1
Lightroom(Auto)
OmniGen
3DLUT
Ours
wanted moody, blue-toned look with soft highlights, Keep the character in the <box>-0.22,, 0.278, 0,821. 0.989</box> prominent.
InstrnetPis2Pis
MagicBrush
StepIX-Edit
Gemini-2-Fladh
GPT40
Input
Google Photo
VARGPT-vL.1
Lightroom((Ato)
OmniGen
3DLUT
Ours
wauted moody, film-like feel with cold shadows, Lit the objects inside <box>0.101, 0.980-/box> property.
Figure 17: Visual comparisons of all state-of-the-arr editing methods alongside two automatic retouching modes from commercial software.
33

---

InstructPix2Pix
MagicBrush
StepIX-Edit
Gemini-2-Flash
Input
OmniGen
VARGPT-v1.1
GPT4o
Ours
Make her look more vibrant lively, a pop art style.
InstructPic2Pix
MagieBrush
StepIX-Edit
Gemini-2-Flash
Input
OmniGen
VARGPT-v1.1
GPT40
Qurs
want moody, atmospheric look with deeper shadows and cooler
InstructPix2PPix
MagicBrush
StepIX-Edit
Gemini-2-Hian
Input
OmniGen
VARGPT-1.11
GPT4o
Ours
aim for a melancholy blues style to evoke sense of nostalgia and depth.
InstructPix2Pix
MagicBrush
Step1X-Fdit
Gemini-2-Finn
Input
OmniGen
VARGPT-v1.1
GPT4o
Ours
want dreamy, soft look with cooler tones for nostalgic feel. Figure 18: Visual comparisons of all instruction-bart editing methods on MIT-FiveK
34

---

InstructPix2Pix
MagicBrush
StepIX-Edit
Gemini-2-Flash
Input
OmniGen
VARGPT-vI.1
GPT4o
Ours
Make bread look fresher, inviting. Enhance texture, warmth for cozy bakery vibe.
InstructPix2Pix
MagicBrush
StepIX-Edit
Gemini-2-Flash
Input
OmniGen
VARGPT-vL.1
GPT4o
Ours
want vintage fantasy style to evoke nostalgia and history.
InstrucdPix2Pix
MagicBrush
StepIX-Edit
Gemini-2-Flnsh
Input
OmniGen
VARGPT-1111
GPT4o
Ours
Make it look warmer and cozier, like vintage photo.
InstructPix2Pix
MagicBrush
Step1X-Edit
Gemini-2-Flash
Input
OmniGen
VARGPT-vI.1
GPT4o
Ours
Dreamy Haze Style image, making feel more romantic ethereal.
Figure 19: Visual comparisons of all instruction based editing methods MIT-FiveK 3 35

---

Prompt for ML M--baeed SC. PO. and O
pumppp scene
You ae postprrduccton speziolist with exportis enhancing photographc imagery thruuh advereed digiel cahinng techniquos. We need your help evaluste the performanee Algowsstod inage posteallinng phologmppyy.
INPUTS:
1. Two images will be provided: The first being the oriainal photogmohic image and the second betng at edited version of the first.
2. The cilling intrasian will be provided The pos-ctliing neeth of photugrpphi images expecsedd by waans with no image procoesing kanckklge..
METRICS (Frmm scale ó 10):
User instruction Satisaation Score: score from to will be given based on how well edits fellow users instructions.
rypicelly
global
woeking photogmiphi images. Theretore, scoee ectting requirements equal importance given each.
holistically, taking
sccoust user's seeds foe both local and global adusteems
indicatee that the olited image does fullow the alting instmation alll
10 indiaate list the edital image fullow the alitng instuctiinn scut petlscily.
Content Consinency Soore: A sccond score from to will moe the concisency of image content before and after edring
Nood
before afher imagre sease content conastency. elemenss
The edred stould maintain consisency
the of landscapes, human (including posture, gender, appearances. buildigg
ofler
detil, such us on clothe, testunes of buildings, indicates that the content the image hefore and alting is completely inconvistnnt. 10 indiaaee list the content the alited imtage exacily the same as the origital imtage
The edhed image needs
the consinency
You will have give output this way (Keep reasoning concise and short.):
"scuee"s [--]
"reasoning"
Pui the scure in lie uuch that culput scoec [scurel, stune),, where Scurel' evalionos the User Enstruciion Saiisfction Score and Sacorel? ovalusics the Contenn Cursistenay Scor.
Editig instruttion: <The olitng instruction of user-
pruspp, sq regins
You and post-prsuuctoon spezislist expertise cnhancing photographc imagery thruuh advareed digiol adhing techniques, now need your help to evaluste performance an Al powered image post editnng sool for photography.
INPUTS:
1. Twu will he providest The find being origitel photegmphic image and aaoond being edital veriion of the first will provided pos-ccliing need photugaphic images expeosed by with inage prososing ksnwledge.
ciling
METRICS (Fiuzs scale 101:
User Instruction Satisfattion Score: A score from to 10 will be given based on how well the edits fellow the user' instructions Uner typisuily lave buth global and losi cliingg regiinments whan wirking with platiogaphic inages. Thereione, this scuec should be evaluned holiatisslly, taking ino account the user's needs fir both local and global adusimenns with equal inportance given to each.
indicates that the olited image loss follow the oliing instrctionn alal indicates that edited image follow editing instruction text perfectis.
consisency of image Consisteccy A images asies content consistaccy.
soore
ofter edring.
Nood compare before
The edned image should maintain consisency in key viual elemeuss such the shape of landscapes, humaz figures (including posture, gender, and appezrances, building structures, and other important fistunc.
The ediod image needs to maintain the consinency of cClocal detuls, such as letten on clothes, testures of buildings,
indicates that the contant the before and after oliingg completely incomeistnt. the edited
indicates that
the
original
IMPORTANT NOTEs
1. The locus of chis assesmmntt the performance of enabled post peocessing tools for photogmatii images response to usees' local modification Instructious.
2. The two image are the sone lucalived srces interst sekectal die user the original and olicad intgge.
3. User oditing instuctions involve both global adjustmennt the entire image and localized editu to specific regints inttrra.
You will give output this way (Keep your reasoning concise short):
"scoce" ff..
"reasoning":
Put the score in lie such that output scoce [scurel, sconc2), where Scorel' evaluates the User Enstruction Saiisfation Scure and 'sore2' evalunte ae Content Cursistency Score
Editing Instruction: TThe editing instruction
and Fdltting Fvaluation Procusingg
Sumne-lovel zdiing evaliationn prompl st. stue Region-levee editing evaliation: prompe P____iigiinnn
Figure 20: Prompt for MLLM-based metrics (SC, PQ) from scene-level and region-level.
36

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Role-Playing Promut for Preset recommculation
Austetiic Prestt Recommendation Expert
protessional sesthelic preset recommenddiion expert doilled analyzing content and providing the best preset combitation sugpesions, You stemiity sulyects images and on their categoriss and charactnistics precisely match suisable global and presets the preset libeary, otfiring
diverse and ligh quality insgge optimizzion solations. au Workflow
You will astecuaically mutch applicable image opomizatioo peesets based on the provided image file, desected subject box information, and preset library, and ourpur the
rcooaneraaaioos.
net Eapat:
Source
2. Derected subject information (category confidencel:
Examppee
"category" : "person".
"confidence": 1995 box" (0.2, 0.3, 0.6. DJ8)
3. Presee library (divided lato four main categories: portralt, Inndscape, street, and food, each containing global and local presets): "slibal":
("er: "PIESON--11
"Purtnn-B8 Buckgramkk Subgaat"
"Ynntion": "Preserves onange subrect, desaniroees baskground and white, high contrast highlight people. Surtable weddings, portait close ups, not suitable
colufil scees plotos where landseape seeds be emplasised!) "
incudes soui global presets acress four categorios
d Portrak (PERSON G scriesk 24 presess covering Japanese style film simdltion. fresh dreamy effects, and various poctrit styles
#'2 Landscape (SC SNERRY G seriest presess including glacier waserfalls, golden sunser, cinematic city views, and other natural landscape styles
2. Street (STREET-G seried) 12 presets including urben day secnss night scenes, cyberpunk, and various urban street platoogupply styles
2/4 Food (STILL - ife-O seriec): prosets inchuding bright transprerrs cineratic warn templing, and varoue food photogrphy styles function description detailing effect, suttable scetaries, unsuitbll scenarios
unique
Tocal":
("Htr: "PEILSON--L11
"amm"" "Skin Bnightening Facial Conturr Unhzncement",
"Manciin" "This a profossional por iait lozal aljisimect prcect dhit achicves dinenioorallt Brough fie-ttuiing of tycs (shar +0.21), 21), eycbnows (exposare -0. 13, and skin tosposure -0, 14coontnes 4:23 Specifically enhoneos irs sharpnese and brightons eye whites (oxporure -6, ..04,, while sofhriigg skin sesture (testure -0 I1) and reducing aoise (-I), Suhable for commercial poctraits and close up portaiis, hightighting facial features dimensionnllty
includes saul 53 local presets
sevend cugganies:
Portmin (PERSONA wriesy proses covering boatifictioon eye
various pertsiti sdjustments
Sky (ALL- senes SKYO: groupt presets including dky brighsening daraennng, drumatic effects,
J. Subjus amphasis (AlL-l sarics SURIECT group): 13 proeele incluling sibjast brightening, yrck workening, dic. 3 Lighting efferts (ALL-- senes LIGIT group': 18 presets inchiding centrr sags, foregrund derkoning, fig effects, etc.
A Each preset las unigue ID, same, and fanction descripiion with some preets having group information
[..1-]
Add Outpat Format:
"scoomeesdatioss
("tnget choo": "persan", "oofidence" 0.85
"gleall prosess:
("k.: "GI", "mmne: "Nsum! Skin Tone Optiniooiin") )
Tocal presuts:
('l:: "LI". "mune". "FFmmll Sullening"),
("is":
"uame" "Light Souree Echanesment").
Toral, apply nane"" [s1g)xx),]]
"ceasening" "Dessted hight confadene partsai (KFA)) nommmending glotel skin lonz optimization with lncal skin soflening and light sounce unfentement"), ", (.
As Working Roles
Special Instructions
slenificatios Only when deweeed subject confidence greater equal 0.85 recorimesdatiou
be resognized
image's main type,
generated accoodiggyy
Proect maiching primiple: All resameniaal prouts musl strislyy murch dotestal subjus box infomution. For comple, when the subjoct bux only containss pretnit category smpets. presets for landscape, street food catepones should not be recommended to eneure precision of procesing effects.
3. Preset combinaiiio statagy: Recommendrion plats should priuiitiee the combined use of global and local preaets to achieve the best overall effect.
4 In's roming convention:
Globul preati: "Juiegury--G (sumberr) (c PERSON-G1)
Local
'catogony) (number) PERSON-LL2)
codes
PERSON
SCENERY (lansccape), STREET FOOD (Bood), (univesal)
All conpgyy indicatos proset aplicablic soone types Application range definiton:
Global presess Applicd to eatire image
Local preses, Use detection box preeis cocedinates (xl.y1a22221 to defice the application area
6. Mudt-sarjjan procoscing simayy: Regenilees of whither there is single or mulliplc wibjects in the inngs, the systen mnd generate multilee diffizent nesemmenition plans When there are nultiple subjects, sen by confidence from high low; witen there is only single sibject, diverse processing plans should be getenneed around that subject All escemenaaiion plans must croure divenitty and non- non-repetiion to provide rich oditing options.
7. Overall offect piirity When exalusting proset comiinations, cunsider the merail effer nefter than the effeect of single stop. For example for photos with insufficient light. even global presentation needed.
effective, may pooduce ideal effect combited with brighteting LI, compoehessive consideration the final
5. Recommendation plan naming convertion: Use "reoommendatioos- cocaaaatiiossssuuaaaa [sumber]" Rommat (c.g, reommecddaiioss.. ().
Proect comfination limitatios::
Each recormeenztionn plan can only include one global preset
TTy to sclass diverse accommendaiioss for cample scmomnenaaiins I has a yellowish purtnil tone; recomennaaionnd should choose a coul lone
Cln include multipls different local presets, but all local presets zus applied to the same zrea (sante apply range', if you think the global preset aufficient, local presets not sustal
presets not used repeatedly
Swiitly svad conflieting
combinntiions ccommmendatio plans, for cxample:
Should simaltnnoucly
prosess females presets
Should not simultancously include presets munually canceling effects
using saturation enhzncemest and saturation reduction preses))
Ensure presets are logically compatibbe with each otter, jomnly serving to enhence the overall effect of the image
10. System ouiput requieeenta The sy viem shuid directly generatc reoommcdation pluss in standadd ISON fonnal, without any additiona test explanations or interpective content, ensaring the ostput noults on he diveatly pansd and used by prograns
11. Expostre balance pracippe: What recommending pessets, the system should filly consider the current image's exposare value, inteligently selecting pesset combinations lust can balunce image hrightnes, meering the fiud abbecns system
roull is ncilher vvnnaguuaa underssuossi, funncy gummtteinng image deail oetastion and vieual comfortt preset conbiattios
Portnitt expo proectios principle: alting images avoited poevent facial zreas from becoming black losng detil. evaluating recommnddation plans, the
special anettio
overesposure excessive
global
ahendy hright, proses signifiaalyy
hrightress shuld avnidal; images alrody
high contrast yresets that further ethance contasi ovcull impact of global and local presets on purinal subjects must conpechanisely dy comollare to everre details are preservall avoiding highlight overflow, facial ovrrxposing excewive shadows, thendhy grarsmoring natura textuno, balanced tones, and detail expression portaits
13. Recemmesdatlon plan quanity pracippe: The systm must generuse 5 diffrrent recommendatio plans.
Figure 21: Role-playing prompt for preset recommendation.
37

---

Playing Promut Retouching Instructions ISimulating Professioal Userl
base, cocedination
an original image,
edring configraation parmmttes, coondirates region imoretloptional, atalyre changes
adjusments made, Infer users original edting goal desetibe their vague imention simple, everyday language direct stutemen: sechnical user's perspeatioe.
Special Notes:
Simulate real client' voice to artiulatte service needs cassal business English. Write if you're the octual decision maker explaining requiremenss a trusted pattner. just fillnng lamplie
2. Focus tnaslattng voclnical
brigliness skappennng +15) velatable visual
withou mentioning nanerical
ouppae pangriph
regiun of imteral and includee the counlinatos enclosed <bot-sbboe- rags for annotation The resporse stould explicity state the identiied category and clearly mark the coondmates within the specified format.
If war prssidos cscedinates for negion of imerral, meenc
curput idlutiifis exiegrry
5. The system should adaptively distibute infored local aljustment imcetions Broughou the dossiptins tzst.
5. Noed to mimic the uset's intentioss for global image modification, not just local modificatios insentions.
Exomple the original edving
want to look like it's stright of
depth, atmoophere, that filmic texture regiin <bosc (0 4419, 0,5507, 0,9967)-hos,, wanted
pareen closer and uuiifi details pap Make emoather and heightan the colons, espacially the juckett sumouraled seca.
2. ln the regiin showe100 10.1222, 0,699, 0,7453, thooe,, wast my face look brighter and my eyes to stand out more. Can you make the skin unoother and Be hair dorker' I ztss ethanee the cntrr image whi making ure the colors don't don' vibrato too much.
Octpat roquicemant::
is possble
possibl intention
modity
indeperatent follwwing onigut Sermat MUST be stricily followed:
'serr want The fins intentions roser>
user want 2": to The second incentins of user
cocndiratos of region configuntion detsil follows Summoquniing hus fike
stegjon,
prompt base wo coondination
Givan original in odited vansion, and the ciliing configuration punneier, Relative commlinatess of the region of inseraaloptieaa), analyze the vieua change und penperative
made. Infer orginal
destibbe their vugue intentio everyday language
aos-technica
Special Noize:
1. Simulate real cliends voice to unicolate service needs in cassall busince English. Write as you the accual decision maker explaining requirenens a inuted pattne, not poet fillnng a tampbre
2. Foar inanslating sellmiesl permecee (ogg brightnes -20, slarpnnig +15) isto relstable visuad ontermas wilhoui mentioning nameriaa valuos icclnical lemms
3. Answer the sysen should adapeively distriute
pangriph of 40 weeds
adjustment
througheut desuripionn
Outpal roquiements:
It accessary provide TWD posible istention of uset modity the image.
Two posoble user inteations and indeperdentt of ruch other.
The Solloing ouigut Soraa MUST be striclly followal:
intentions inentions us
want 2":
The configumtion detsile see as follows "oomospunding hot file-
Type Processing:
level instuction generation: peompt base cocodination
Regian-lovel intruction genention: pamupi hase coonditation
Figure 22: Prompt for simulating the professional user instructions
38

---

Plasin Frompt Recouching Instructions (Stmolating Cresal User)
Style caprosions
The style of odriing the image that cusiomers might express:
Thromy tooe Style Soff focms, low contast, soft tons, hery halo Suivelle for working phanos, portnits, emotinal expeession, etc. Melancholy Blues Style: Blue geyy wises, cold light high shadows, emeional composition > Suttbll for sweet photogaphyy portrits, suryiellisg, etc. Buuming Emotion Style: Highly saturted
interiaced light stadow, and
restesnnes Suitable stage performances, misic vision, dynamic
nomenti
Mininal Fiheraal Style Misimaliis composition, blank speas, soft smee inmquiilty Suitshie mediteiion, Hoylle content, ee
Faturitii Amiidy Style Cold metallic lencs, sloep lincs, high contras digitl miia Suiuble sciene ficiion Indtnoauy prsducte, syhapurk syli,
Vintage Fantasy Style Yellowed faded motes, graminess, old film teature i Suitable nostalgle advertiing, histoncal narraivee illustaion style, etc.
-Glitch Art Style: Digiaal glüch offert, color distrtion, pioel distertion o Suitable for olectmnic misic maan-gmle faction, Inferet culterz etc.
Liguid Abstaction Style: Strong sense of Buides, color fision, similar vatercokoriol paint - Suitable for poster, pockaging desigs, visual import, etc. film effect,
Negative trim Look Style Similrr Euplasize light bur, donamic intancemens, frree distensonall sculpture, and outzanding materal Sultable sculptures, induszial design, fabric display, eoc. -inin Nose Style Simdlate fi suise inggtte textmee and hindnned (se) to Suitable for ducammntrr siyle, ndro photogmphyy dacumentary ostten,
color. high contrast Suitable for documentryy photography, street photography alternative portraiis,
Light Painting Abstraction
Suitable right photognphy, light
installatins,
Textue Stele Piscl level
Transluent Overlay Style Maltilayer image overlay semi-tmmppen chard, decamy interlaiin - Saiubl: fie concsptual plutogappby digita paining, mixed media, otc. Improsterint Styler Siring brievstuke,, vihram oolors, and sof afges o Sunable for lancucaps photogaphhy naturl themes, articti re-crestion etc
Exprosiienist Coler Style High conirast coggerated cokers emotioeal randeing. i Suiablee for cloccopp drantric photogmahyy poycholigical exprcsiion etc. does repested pattems Suttable for celeteity portrais, pop culture, commercalss
Pop Art Style: Highly satrratdd color blocks, lesshatrkes, spaching feeling, coational caplaion Saitulle Wash Style: Black and while gadeett white space, and Duidlt of bruh and lik Sultable oriental cultue, Inditional elements, calligruphy etc -Oil Phinting Elfect Style Bradherd simussion thick puinting fren bonke Suiabll an eddiiition, hightnd portzalis mıdom prining etc.
Abbutnet Expecsooiim
photggaphy, dightl paiming, emotioaal expecminn,
Wattroolee Soyle Fresh and turapeenn olge diflission paper iesture > Saiuble for childrat's photography, iluscationn handicrall beands, etc.
Penenl Skerch Style ine watlnne, gryaaale hand-paimot testure o Sunable for reachnng monmaal, chanateer sterches, skotch diglays etc.
NNeun (llw Style Noon letuur, plowing
dark background c Suitabie for nightché prometion, tranly clothing, whhr photograhhy. etc. metallic
Suiable luurry goods, electioncc products Eishion blockbusters, Motion style: Movement troectory. speed sence, direttior pridence Suiable spors photograhhy advertaing, action soenes, etc.
Metallic Texture Style: High reflectivits. cold
Auron Valon Stste: Flowing light bandi. gracient changes of cold and warn, mysteriou atmosphere- Sultabic for sutuni sonnery, stary sky phosography, meditation spplications, dic
HHarods Hidchi Style: Fresh, sufl, astural and enstioaal > Suiubl: for photos with a girlish or youhful fiel, cic.
i'uifflm Style: Makk pontrais, bindcappe or daily photos present satura, delicato and emetional oolor expression > Suitable for style requieements euch as "movic feel". "Jepanese fresh style" or "retro atmosphere"
Japanese Stste: has visual chamcoerimts such freshness, naturalness emototality, saturtion, and soft tones Suitable for portmits life records, tavel photugaphy,
Kodak Portu Style: nutural totes, warn tanes, delicate tarsitions and rich layering, it can make digital photos look like "oll-school but outhentic" Suluble those who pursic rstuminces, warmth, steytclling etc.
prompt base w coocdination
Given origital image, alital vension, and the eili configuration peraecters audlee the visuad clengex and sljuntments made: Infer the user's uriginal cliting goul deserbee sague inteatio simple, everyday language dieest statemant nen-technical peseppotive.
Special Notes:
1. Simulate real clients voice to imiculate service needs in cassal business Lingtssh Write as if you're the actual decision maker explining requirements to a trusted partner.
not just filingg a templase
2. Feeus on imssltinng schnical permmttens (e.g. brightess -20, shorpening 1155 into velatblle visnad ounormes without montioning mimerizal values or technical temms
3. Answer the octput in pangreh of 20 weeds loa.
-bop-c sys aunotation. The resporse should explicilly that ALL output itemifies the cotegory of the of interest and inchides the coordinates enclosed in
If the provides coordmaates for region of interest,
idestified category and dleslly the coodiaates within die speciffed fomnat. adjustment imeettons throughout description text.
5. system should adaptively distibbte infored
& userks edliiig inieatius for the OVERALL STYLE of the image naal, be given.
7. Expres the inforeed user's intutionn edit the image as vaguely and riclly as possible.
R. Must ose highly conciso wordh to simmarie the ourall sylr of the image is the werks adiing intention, suchas XXX style, etc:
Example of the user's original editnn imeetion: wast
look like ats strightt pesson clearer and the outfit details pop. Make smoother and heighten the coloes, especially the jolket and surounded area.
depth, amsoglhere, and that filmic testure
<bose 0.1982, 0,8507, 0,9967)-bux>, wanted
2. in the regiun shoce(0 1242, 0,609, 0.7453, Jobus,, hope my face lock brighter and my eyos to stand out mome Can you make the skin emuother and the hair darker? Lat's enhance the entire image while making sure the colors don't sibmate too much.
Ousput requirements:
It nocessrry provle TWD puoibll intaniins of toar mudiffy the inaaee the user provides cocedinates
Two possible intenions independent mediffccation imeetians The folluwing ouigui formst MUST be strictly followad:
istavent, posible inteations include
regien intoned.
user want 17 the The first inceuions user >*, user want 2", the The second imeetioss of user
Relutive coonditates of region intsee of interest condinates>> configumtion detsils fsllowe scomequnding files
prompt base wo coondiaation
Given an ociginal image, is edited version, and the editing configuration parameters, zoalyse the visual changes and odjusments made. Inder the users original editing goal and doserin be their inteation simple, everyday language direct etaimmani from nen-keelnical usziss per pusiuiitive.
Special
clicets vouce to articulate service needs cassal business English. Write if you'te actual decision maker explaining re requirements trused partner, aut jusi filing a temphte
1. Simulate
2. Fecus on trasslating sochnical parameters (c.s. brighmess -20, starpening +15) isto relaubll visual outeomes without mentioning numerical values or tecluical terms.
3. Anewer the ouruu in pangresh of 20 werds or las.
4. The system sholl adaptively distiibte infaral locd aijusiment incentins throuheui the docniption text.
5. TTee user's edring intenton for the OVERALL STYLE of the image needs given.
Expres
vdi image as vaguely richly possible
7. Must use highly coacise words to summarize overall atyle the image in user's editing intention, such as XXX style, etc.
It is nocessrry provide TWD posoble intations of mee modify the image Output requiements::
Two pussible inteations are indepersiett of cach other. the following ourgut formet MUST be stricty followed:
user want
intentions user is"
"ser want The second intentons of user
The configunzion details are as follows, sconesponding lua, file>
Image Type Processing:
Scase level instrutioon generation: Styte expresiioss - prompt base we cocodiation Region level instruction peneration: Style, expressions+ + prompt base coootimation
Figure 23: Prompt for simulating the casual user instructions.
39

---

Promut Generating CoT Amnotation
A Prompt fix Geserating Inital Long Thaun-ofThhught with Coonditate information
"* "lease analyze provided beforeiffte images, requiremetss Relative coarditates regint untresiopiioaall and configuration file detailed
aijuement workflon. Alloough the confoumniun file raaugrizedl sopurac shurld wood explially staing that aijuesii prrsicio derivod convey sence espert juigment
ressertd analyss
1. Technical Breakdows: Specify the sool method utlized (c.e., global adjustmentt sone curve adjustmeui HSL adjustment, masking, testure, gain coopoing, dot) akong with the spexifie value or rane of values für the aljistment assccioaal with that tool michod (e.g. "Startion -1 1855, "Highque Filter Radiue Spe') All detsils sqpanling the ioule'nclledd and their cenopanding luiing válues must be derival excluively fun the cmmigraaiin file.
2. Seep-by-SSn Step ixplanation: Deserite adjustments a lopicl sequece, prioritring critical mocification first Use layman frinndyydddyy temns but include profescional jargot
wieree necessary (c.g. 'resovered highlight via luninance mask'.
3. Insiorake: Faplain how cush clange aligne the swa's intere (c lowaling tome applidd to mutch resaccedd Véncmaaii meedf))
Output Vornar: treeform pangrapts with bullet poins numbered steps no mariddwnn Phontize clarity technical precision.
Special Notes:
1. Reqpoud UCCLLSIVVLYY Frglish all culputs Never inclad: sun-Enplich cloeacters or immolations
languagss
2. When mentating cocedinates bounding boxes, you mus enclose themr within <box-ssbox- sags, For example: II might consider adding a subile glow or halo around the person (-box--0 1242. a 699, 0,7453, 1.0000//basry
3 <bustoobsc oigs mus be in the form of chocrrel yl x2 ybohooc to npraset hounding hoses, It importent to noto ther the ferm of -bourss yl yl-//oss> is inrommct
configuration file: scoresponddngg. file> Robeive cuondiratos of region
1t Promptt for Geserating Inical Long Chain of Thought without Coordiaute Information
Pizss undlye the provited beffnnhsller imaga, seer requinmmenss, Raheive connliraacs of the nagiun of interottopiioaal and configuration file to peserate a dettilaa aqjusmentt workftow, Although the configuation file is recognized, the resporse should avaid explicityy statng that the adjuned parameters were denved from it. The tone should entvey sense capert judgmentt and ressented undlyis.
utiizal ( glvial adjastment, tone curve adjeetment, HSI adjmstment marking. textune grain, cropping, dut) rungge values the odjustmnt associazed with that toll method (e.g. "Saturztion -15%s, "High pass Filter Radius 3po"), All details reganding tools'methods their cocresponding tining values must derived escluevely
1. "Tachniaal Heaakdown: Specify the rool mathed along with specitic
configumtion
2. Step-by- Step Explasation: Deserie adjustments a logical sequesee, prictitiziig cittical mndiffiations fiss,, Use laymun laymun-fleolll terms but include profontional jaggos. where nemsaary (eg. hesowenal highlight vis liminence moek)
3. Ratinnae: Explain how each change alians with the sser's intent (eg. locoling tone applled to match requested 'iinematic mood"). Outpat Foruat Freefiem pungrapla with bullet puints numberrdd steps markdown. Prioriitee cheilyy and technisaal protisio.
Special Noiee
Respond EXCLUSIVELY in English for outputs. Never include English charactees tmankations other languages.
configiration scomeepondingg. file>
user requiremesss suset imear>
Figure 24: Prompt for generating the initial Chain-of-Thoughhe (COT) annotations.
Prompt for Productng Refined Col Annotations
Pruppi Genaating hatial Long Chain-uf- Thaughh with Counlinate Information
Please revise the provided Cluis (TanughiCCTT to follow these guidelines:
1. Thinking huvi: Use uaer youls and original image, incozed of config files tu ganntc alll ressasing staps For cxample Avcid using words that are olvionsll relsted to configummiin, such as "config files" ;
2. Expresiive style: Initutes the human like language patterss used to expeess though processes during intepprionaal communicatios. For example, it nzedd start with
"A light" and the untensos necal to he connectal rolunilly human specch;
3. Unforce
constrants: Compress
narratives atalysis original Call
150 through strotegis distillation of reasoning components:
stalysic
5. Originzl Image Analysi: The peinary clements of the oeiginal image analy sis from inttal thought process should be preserved, excompasinng both comeett analysis and ac acolatic codiatioo
Apply those rules rigerusly erouse that the final CoT accurately reflects the professional though process of image processng experts when they only have the original image materials and ourr waads.
Special Notes:
Oxpput modified diretlly without the insodaction weeds such "Alrigte, refiaed Chais Thoughh (CoT) that stricdly adieeres guidelines'
1. axipass must be striclly Faglish, prbiihingg the any lngwes.
3. image processing operations involred
recait specific adjusmmnt memtioned the original chain thought (CoT). 4. When mentoring rocediranes bounding baces, you mue enclose them within <bor-sboc tags. Forr examle: might comider soding a subble glow or holo amound the
penson =boxc-..14 1242, 0,599, 0,7453, 1 1.0000-baa""
5. <burtrohsss> lags must be in the form of choocrel yl ylohhoe to apraset bounding hasos, It important to note that the form of -haccra yls//bus is incomost.
6 Please stricty enforce the word limit.
CuTosoriginal CuT-
Robeive conniratos of the region interse descrcenniiattechhse>
Prompe for Geseruting Initial Long Chain- Thought without Coardiaate Information
Please revise the provided Chai of TTought(CT) to follow these guidelines:
1. Thinking baca: Use soer gools and original images insteed of config files to genrotee all reasoning steps. Foor example: Averd using words that are obviously reloned to configunsion, such as "config liles"; ::
2. Esprosise style Imitates the human-like langugg patierns used to thought proceaace during interperantal communication For comple, in noeak start with "Alnigh",, and sentences need be connected naturally
speech:
Eufoee langth
Cumpress auratives analysis orignnal
frough stnaigic distilltion key scasuning components,
aralysic main 5. Original Imuge Aradysis: The primay clanents, of the original image amly vis from insial thooght procos should be procrsal, excompusing both coneett anolysi and aesthetic evaltaton.
retined;
Apply thour miles rigennaly enaure that the firal Call secerately refleets the profescorall thoughtt process of image procescing experts when only have the original materials and user needs.
Special Notes:
Ostput modiifial dinectly witheut the introduztion auch "Alrigh,, karcs refined Clein Though (CuT) stricly gridulince
sutputs must be strecty English, protibiing the use any other languages.
3. All image prosseing openations involved should retain specilic aljustment values mentionad the original clain of thought (CoT).
4 Please srrelly enforce the word limit.
Coterigignall CuT>
Figure 25: Prompt for generating the refined Chain-of-Thought (COT) annotations.
40

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E Details of Retouching Tools in Lightroom
We provide an overview of key Lightroom tools generated by JarvisArt, focusing the functionality of retouching tools and their associated parameters:
Table 7: Lightroom Tools with Functional Description and Parameter Type.
Tool Name
Functional Description
Type
Basic Adjustments
WhiteBalance
Overall color temperature (As Shot, Auto, Custom)
Str.
Temperature
Blue-yellow balance (2000-10000 10000 Kelvin)
Num.
Tint
Green-magenta balance (-150 to +150)
Num.
Exposure2012
Overall brightness (-5.0 to +5.0 stops)
Num.
Contrast2012
Difference between light/dark arcas (-100 to +100)
Num.
Highlights2012
Adjusts bright arcas (-100 to +100)
Num.
Shadows2012
Adjusts dark areas (-100 to +100)
Num.
Whites2012
Fine-tunes brightest parts (-100 to +100)
Num.
Blacks2012
Fine-tunes darkest parts (-100 to +100)
Num.
Texture
Enhances/smooths medium textures (-100 to +100)
Num.
Clarity2012
Enhances/reduces local mid-tone contrast (-100 to + +100)
Num.
Dehaze
Reduces/adds atmospheric haze (-100 +100)
Num.
Vibrance
Saturation less-saturated colors (-100 to +100)
Num.
Saturation
Overall color intensity (-100 +100)
Num.
Ineremental/eempprrauuee
Relative temperature adjustment (-100 to +100)
Num.
Incremental/iin
Relative tint adjustment (-100 to +100)
Num.
Tone Curve
TuneCureeNmm22012
Predefined curve shape (Lincar, Custom) Custom RGB curve points (x.y: 0-255)
Str.
To1eCurePV22012
Dict.
ToneCurvePV2012RRG
Custom Red channel tone curve points
Dict.
ConeCurveX22112Grren
Custom Green channel tone curve points
Dict.
TomeCurvePV20112Blee
Custom Blue channel tone curve points
Dict.
ParametricShadows
Adjusts shadow tonal regions (-100 +100)
Num.
ParametricDarks
Adjusts dark tonal regions (-100 to +100)
Num.
ParametrieLight
Adjusts light tonal regions (-100 to +100)
Num.
Parametric Hightthtts
Adjusts highlight tonal regions (-100 to +100)
Num.
Parametric ShantorSii
Boundary: shadows/darks (10-50)
Num.
ParametricMiidooneSpltit
Boundary: darks/lights (25-75)
Num.
ParametricHHghlighsSpti
Boundary: lights/highlights (50-90)
Num.
Detail
Sharpness
Enhances edge definition (0-150)
Num.
SharpenRadius
Width of sharpening effect (0.5-3.0)
Num.
SharpenDetail
Amount of sharpening for details (0- (0-100)
Num.
SharpenEdgeMaskinng
Masks sharpening edges (0-100)
Num.
LuminanceSSmoothing
Reduces luminance noise (0-100)
Num.
ColorNoiseRccuuctinn
Reduces color noise (0-100)
Num.
ColorNoissRccductionDDeeiii ColoaNoiieReduuctttSmmmtttssss
Fine-tunes color noise reduction (0- (00)
Num.
Smoothness of color noise reduction (0-100)
Num.
HSI (per color: Red, Orange, Yellow, Green, Aqua, Blue, Purple, Magenta)
HueAAdjutmenttCClorzz
Shifts hue of specific color (-100 +100) Adjusts saturation specific color (-100 to +100) Adjusts brightness of specific color (-100 to +100)
Num.
Saturation AitrriiioonddusmmeeecCoooor
Num.
LuninanceAAjisteenn-CCooo
Num.
Color Grading
SplitToningShadowHue
Hue for shadows in split toning (0-359)
Num.
HerrrToninnHighighhhhi
Hue for highlights in split toning (0-359)
Num.
SaliTToningghadowSaurriiin
Saturation for shadows (0-10)
Num.
Saturation for highlights (0-100)
Num.
SplifToningBalance
Balance between shadow/highlight toning (-100 to +100)
Num.
ColorGraddMMidtonee
Midtone hue for color grading (0-359)
Num.
ColorGradeMMidtoness
Midtone saturation for color grading (0-100)
Num.
ColorGraadeMiddooneLLum
Midtone luminance color grading (0-100)
Num.
Continued next page
41

---

Table 7: Lightroom tools with functional description and parameter type. (Continued)
Tool Name
Functional Description
Type
ColorGradeShadowL..
Luminance for shadows (0-100)
Num.
ColorGradeHighlight..
Luminance for highlights (0-100)
Num.
ColorGrade Bendding
Blending of color grading effect (0-100)
Num.
ColonGradeGGoobllHue
Global hue adjustment (0-359)
Num.
ColonGradeGlooalSSn
Global saturation adjustment (0-100)
Num.
ColorGradeGloball..o
Global luminance adjustment (0-100)
Num.
Effects
PostCrop Amoutt GrainAmount
Darkens/lightens image corners (-100 to +100)
Num.
Adds film grain effect (0-100)
Num.
ShadowTint
Adjusts color tint shadows (-100 +100)
Num.
Camera Calibration (for Red, Green, Blue primary channels)
<PrmaryColorrHue
Shifts primary color's hue (-100 +100)
Num.
<PrimaryColor-Satueee
Adjusts primary color's saturation (-100 to +100)
Num.
Lens Blur (Overall: Dict.) LensBlur,
Enables/disahles lens blur effect
Bool.
LensBlur.Blun Amount
Strength of blur effect (0-100)
Num.
LensBlur.Focha FocalRange
Defines focal plane ("xl yl x2 y2")
Str.
LensBlur.ookehShae Shke
Bokeh shape identifier (default
Num.
LensBlurBokkchhhap Bww.Bookkhhhhpeeaaill
Definition of bokeh shape edges (0-100)
Num.
LensBlur. Highhlghts T Threshold
Brightness threshold for bokch (0-100)
Num.
HingBlu..
Enhances out-of out-of-focus highlights (0-100)
Num.
LensBlur, CatByl CatByeAAoous
Simulates cat's 's eye bokeh effect (0-100)
Num.
LensBlur.Catee Scale
Size cat's eye effect (0-100)
Num.
Advanced Color Grading (PointColors each point is Dict.)
SreHue
Source hue for adjustment (0-6.28 rad)
Num.
SreSat
Source saturation for adjustment (0-1.0)
Num.
SreLum
Source luminance for adjustment (0-1.0)
Num.
HueShift
Hue shift amount (-1 +1)
Num.
SatScale
Saturation scale (-1 to +1)
Num.
LumScale
Luminance scale (-1 to +1)
Num.
RangeAmount
Effect application amount (0-1.0)
Num.
HueRange
Falloff for hue adjustment (LowerNone, LowerFull,
Dict.
UpperFull, UpperNone: 0-1.0)
SatRange
Falloff for saturation adjustment (sub-props same as HucRange)
Dict.
LumRange
Falloff for luminance adjustment (sub-pruy props same HueRange)
Diet.
Look (Overall: Dict.)
Look.Name
Name the look preset
Str.
Look.Amount
Intensity of the look effect (0.0-1.0)
Num.
Look.Parameters
Dictionary of specific adjustments applied by the look
Dict.
(e.g., ProcessVersion,
TomeCurvePV2012, Parametric adjustments. SplifToning, Color- Grade, ConverTTeGrayscale
LookTable, RGBTable, RG- BTableAmount)
Localized Mask Adjustments (MsskGrroupasseCCoreetoss Array of Dicts.)
Per Correction Group: ComectionAmount
Amount correction group (0-1, default
Num.
ConectionAAtivee
Activates the correction group
Bool.
Nome
Name for the correction group
Str.
LocalExposuure211
Local exposure adjustment (-1 +1)
Num.
LocalContras22012
Local contrast adjustment (-1 +1)
Num.
LocalHiighlights2012
Local highlights adjustment (-1 to +1)
Num.
LocalShadows2012
Local shadows adjustment (-1 to +1)
Num.
Local
Local whites adjustment (-1 to+1)
Num.
Continued on next page
42

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Table 7: Lightroom tools with functional description and parameter type. (Continued)
Tool Name
Functional Description
Type
LocalBlacks2012
Local blacks adjustment (-1 to +1)
Num.
LocalClarity 2ocalClarity2012
Local clarity adjustment (-1 to +1)
Num.
LocalDehaze
Local dehaze adjustment (-1 to +1)
Num.
LocalTexture
Local texture adjustment (-1 +1)
Num.
LocalHue
Local hue adjustment (-1 +1)
Num.
LocalSaturation
Local saturation adjustment (-1 to +1)
Num.
LocalCurveReeineStturiiio
Local saturation curve refinement (0-100)
Num.
LocalToninglIue
Local toning hue (0-359)
Num.
LocalToningSatuuationn
Local loning saturation (-1 +1)
Num.
LocalTemperature
Local temperature adjustment (-1 to +1)
Num.
LocalTint
Local tint adjustment (-1 to +1)
Num.
LocalLuminanceNoise
Local luminance noise reduction (-1 +1)
Num.
LocalMoire
Local moire reduction (-1 +1)
Num.
LocalDefringe
Local defringe adjustment (-1 to +1)
Num.
LocalGrain
Local grain adjustment (-1 to +1)
Num.
LocalSharpness
Local sharpness adjustment (-1 to +1)
Num.
<Channeb>Curve
Local tone curve for Red, Green, Blue, Main channels
Dict.
(points "x.y")
LocalPointColors
Local specific color adjustments (dictionary string- encoded points)
Dict.
CossectionMasks
Array of mask definitions for the group
Array
Per Mask in CorrectionMasks:
What
Mask type (e.g.. "Mask/Image". "Mass/CineularGradii ent")
Str.
MaskActive
Activates this specific mask
Bool.
MaskName
Name of the mask (c.g.. "Subject", "Sky")
Str.
Mosk
Mask blending (0=Add, 1=Intersect)
Num.
MaskInverted
Inverts the mask area
Bool.
Mask
Mask opacity (0.0-1.0) AT Mask subtype (Subject, Sky, Person etc.) / Object type
Num.
MaskSubType
Num.
ReferencePoint
Center point
masks ("x y")
Str.
Gesture
Polygon points for object/region mask
Array
hop/Lccft/Bottoo/Riigtt
Coordinates for radial gradient (0-1)
Num.
Angle
Rotation angle for radial gradient (0-360)
Num.
Midpoint
Center point of radial gradient (0- (001
Num.
Feather
Edge feathering Flips radial gradient direction
radial gradient (0-100)
Num.
Flipped
Bool.
MaskSubCCteeoryID
Category ID for person parts mask (Face, Eyes, etc.)
Num.
43


Reviews:
[{"review_id": "JaaBSYxhsI", "reviewer": "Reviewer_NfhL", "summary": "The paper introduces JarvisArt, an agent for photo retouching based on a multi-modal large language model (MLLM) framework. The system allows lay users to apply high-quality Adobe Lightroom edits via natural language prompts. To support this, the authors construct a new dataset (MMArt) containing Chain-of-Thought (CoT) annotated samples that simulate artistic reasoning and tool-use skills. The dataset is generated using Qwen2.5-VL-72B and QVQ-max for image editing and reasoning, followed by human curation to select the most pleasing results.\n\nThe JarvisArt agent undergoes a two-stage post-training process: (1) CoT-supervised fine-tuning to build basic reasoning skills, and (2) Group Relative Policy Optimization for Retouching (GRPO-R), which refines the results through rewards targeting (i) output format, (ii) tool-use correctness, and (iii) perceptual quality.\n\nThe authors conduct quantitative comparisons against state-of-the-art models such as InstructPix2Pix, MagicBrush, OmniGen, VARGPT-v1.1, Step1X-Edit, Gemini-2-Flash, and GPT-4o. They report improvements in pixel-level metrics and user preference studies, particularly for content fidelity and subjective quality. An ablation study explores the contributions of the training stages and reward design.", "questions": "1. Could you clarify in detail the process from user prompt to ROC file generation? Specifically, how does CoT reasoning integrate with this?\n\n2. Given that competing models perform pixel-level edits while JarvisArt uses Lightroom, how do you ensure a fair comparison in your evaluation? Metrics like L1 and L2 naturally favor content-preserving approaches like yours, potentially overstating advantages.\n\n3. What are the actual contributions of this work, both general and specific? The paper mentions a practical system, dataset, and training strategy, but the distinction between engineering integration and conceptual novelty remains unclear. Could you clearly articulate what you see as the general research contributions to the field, beyond the applied success within Lightroom?\n\n4. Is GRPO-R conceptually novel, or is it a straightforward application of existing GRPO techniques? If novel, what are the technical distinctions compared to standard GRPO?\n\n5. The MMArt dataset would constitute a clear contribution if its construction were more robust. Did you consider incorporating multiple diverse VLMs for dataset generation and reasoning to reduce model-specific biases? How do you justify MMArt as a general research resource rather than a product of your specific model stack?", "limitations": "Yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 2, "significance": 2, "originality": 2, "strengths_and_weaknesses": "**Strengths**\n\n   - The paper presents very strong qualitative results, with realistic, professional-looking edits that preserve content fidelity better than competing methods.\n\n   - Introduction of the MMArt dataset combining image pairs, user instructions, Lightroom configurations, and CoT annotations is valuable for the community.\n\n   - Comprehensive and well-executed evaluation pipeline, including both automated metrics and a user preference study.\n\n   - The system shows a promising application of MLLMs to concrete artistic tasks, demonstrating that models can be trained to emulate expert-level tool use within professional software.\n\n**Weaknesses**\n\n   - The paper's methodological novelty is modest. The contribution lies in system integration and dataset curation rather than introducing fundamentally new algorithms. The GRPO-R extension appears to be an application of existing GRPO techniques, adapted for retouching with custom rewards, but not conceptually novel.\n\n   - Despite claims of generalizable artistic reasoning, the approach is narrowly scoped to Adobe Lightroom and to specific types of edits (primarily color, tone, and sharpness). More creative, structural, or semantic edits (e.g., object addition/removal) are out of scope.\n\n   - Competing models often perform generative edits directly on image pixels, whereas JarvisArt operates within Lightroom, inherently preserving more content. This fundamental difference makes direct pixel-level comparisons (L1, L2) potentially misleading. The paper acknowledges content preservation advantages but does not control for this disparity.\n\n   - The dataset generation process lacks sufficient robustness. Only Qwen2.5-VL-72B and QVQ-max VLMs are used, without clear consistency checks. This limits confidence in the dataset. Aggregating outputs from multiple VLMs and performing consensus filtering would significantly strengthen the dataset's credibility.\n\n   - The system pipeline is insufficiently explained in key areas. It is unclear how the user prompt is transformed through the CoT reasoning to generate the final ROC file with Lightroom instructions.\n\n   - Figures, particularly Figure 2 and Figure 3, are confusing and cluttered, making it difficult to grasp the workflow and design choices. Undefined terms (e.g., presets, functional descriptors) exacerbate this issue.\n\n\n**Suggestions for Improvement**\n\n   - Clarify the system architecture and pipeline flow. Explicitly describe the model components, how CoT reasoning integrates with prompt interpretation, and the step-by-step process from user prompt to ROC file generation.\n\n   - Rework Figure 2 and 3 for clarity. Simplify the diagram, clearly explain the transitions, define all terms, and distinguish between dataset generation, training, reasoning, and output stages.\n\n   - Define key terms upfront, particularly \"preset\" (used extensively but only vaguely implied to be parameter bundles in Lightroom). Also explain metrics like PQ and SC within the main text rather than relegating them to appendices. The Lightroom protocol in the main text only adds clutter, move it to the Appendix.\n\n   - Consider placing greater emphasis on the GRPO-R adaptation, particularly if the policy optimization introduces technical novelties beyond applying known GRPO techniques.\n\n**Typos**\n\n   - Line 66: \"pipelines pipelines\" — duplicated word.\n\n   - Figure 2 (Stage 3, Initial CoT annotations): \"configaration\" — typo.\n\n   - Figure 3 caption: \"two-stag\" — incomplete word.\n\n   - Figure 10 caption: Answer refers to image in Figure 11, not Figure 10 — mixed up.", "was_revised": true, "final_justification": "This paper presents strong experimental results and reflects a commendable engineering effort. The evaluation is thorough and indicates that the application is practically useful. While I remain uncertain about the depth of the research contribution, the practical strengths could justify acceptance.", "rebuttal": "We sincerely appreciate your feedback and recognition of our comprehensive evaluation, professional retouching results, the contribution of the MMArt dataset, and the potential of MLLM-based artistic agent applications. Please find our detailed responses to the review comments below.\n\n**Q1: Contributions of this work (infrastructure and GRPO-R).**\n> As you mentioned, one of our key contributions is the development of a robust and scalable infrastructure, comprising a data-synthesis pipeline, a fine-tuning framework, and an Agent-to-Lightroom protocol that effectively transform MLLMs into artistic agents. Additionally, we introduce GRPO-R, a tailored RL method for tool-integrated retouching tasks. In contrast to standard GRPO—which applies to mathematical tasks with a single, easily verifiable correct answer—retouching involves multi-dimensional complexity: **(1)** the model must predict multiple tools and their parameters, and **(2)** diverse parameter combinations may yield visually similar results. Designing effective reward signals in such a setting remains an open challenge. GRPO-R addresses this by incorporating two reward components: `retouching operation accuracy` for parameter prediction and `perceptual quality` for visual fidelity. This specific reward design enables scalable reinforcement learning, thereby enhancing the agent's reasoning ability, tool proficiency, and generalization. We believe this system design and tailored RL optimization can provide inspiration for the development of domain-specific intelligent agents, beyond the demonstrated success in Lightroom.\n\n**Q2: Scope limitations.**\n> This work focuses on a natural language-driven retouching task, which differs from generative editing. While generative editing involves creating new content or modifying image structures for creative purposes, photo retouching aims to enhance the original image by adjusting visual attributes—such as color, tone, and sharpness—without introducing external elements. Additionally, our method can be extended to support generative editing operations through the proposed data pipeline, which enables generating paired artist-edited data aligned with specific AI operations (e.g., addition, deletion), allowing the model to learn these capabilities.\n\n**Q3: Misleading pixel-level comparisons.**\n> To ensure a fair and comprehensive comparison, we evaluate JarvisArt against both traditional photo retouching methods and instruction-driven generative editing models. Following prior studies [1][2][3], we employ L1 and L2 metrics to assess pixel-level content preservation, and SC, PQ, and O metrics to measure instruction adherence. As shown in Tables 1 and 4 of the manuscript, our method achieves pixel-level fidelity comparable to traditional retouching while significantly outperforming generative models. Moreover, the SC and PQ results indicate competitive instruction-following performance relative to generative editing approaches. Through these multidimensional evaluations, we aim to highlight that JarvisArt integrates both the pixel fidelity of traditional retouching and the instruction-following capabilities of generative models, effectively achieving the strengths of both approaches.\n\n**Q4: Lack of sufficient robustness of dataset curation.**\n> To guarantee data quality, we employ a two-step verification process: **(1)** The Qwen2.5-VL-72B model evaluates the alignment between instructions and reasoning via multimodal input prompts. **(2)** Manual sampling is conducted for additional validation. This process will be clarified in the manuscript. Aggregating outputs from MLLMs and applying consensus-based filtering can enhance data quality, but it may incur higher computational and resource costs. To further improve sample quality, we currently regenerate instruction and chain-of-thought (CoT) annotations using Gemini-2.5 Pro. We plan to release this version as open-source.\n\n**Q5: Explanation of ROC file generation.**\n> As described in Section 3.2 of the manuscript, the retouching operation configuration (ROC) file and corresponding before/after-retouch images are prepared in advance during the instruction and chain-of-thought (CoT) reasoning generation process. After generating the instruction and reasoning text, the ROC file is inserted within the `<answer>` tag to produce an R1-style QA sample. The MLLM is not required to regenerate the ROC file, as its parameters are summarized manually by professional photographers.\n\n**Q6: Versatility of the MMArt dataset across research fields.**\n> The MMArt dataset may provide significant value for research in three key areas: **(1)** unified multimodal understanding and generation, **(2)** text-to-image generation, and **(3)** image-to-image generation. Specifically, the dataset includes diverse user instructions, high-quality photographic images, and chain-of-thought (CoT) annotations, all of which support the development and evaluation of models across these tasks.\n\n**Q7: Detail of technical terms.**\n> In Lightroom, a preset refers to a pre-saved retouching operation configuration (ROC) file, typically created by photographers or artists. It encapsulates stylistic parameters tailored for specific visual aesthetics—for example, a Japanese-style fresh look. Applying a preset enables users to efficiently reproduce a desired effect across multiple images. However, presets are often context-specific and may not yield satisfactory results for all image types. As outlined in Section 3.2, Stage I of the manuscript, we enhanced the accuracy of preset recommendations for the Qwen2.5-VL-72B model by summarizing each preset’s functional description. For example, the preset `{\"id\": \"PERSON-G20\", \"name\": \"Japanese Fresh Style\"}` is described as follows: `“This preset reduces overall saturation (-5) and warms tones (yellow/orange saturation -43/-42), emphasizes green brightness (+50), and enhances blue saturation (+31) to create a refreshing visual effect. It is suitable for outdoor portraits and Japanese-style compositions under natural lighting.”` These terms will be detailed in the manuscript.\n\n**Q8: Typo errors and paper presentation.**\n> All typo errors have been corrected in the revised manuscript. Additionally, we have simplified the diagram, clarified the transitions, defined all relevant terms, and explicitly distinguished the stages of dataset generation, training, reasoning, and output. PQ and SC metrics will be explained in the main text, with Lightroom protocol details included in the Appendix.\n\n\n---\nReferences:\n\n[1] MagicBrush: A Manually Annotated Dataset for Instruction-Guided Image Editing. NeurIPS 2023 Datasets and Benchmarks.\n\n[2] Emu Edit: Precise Image Editing via Recognition and Generation Tasks. CVPR 2024.\n\n[3] In-Context Edit: Enabling Instructional Image Editing with In-Context Generation in Large Scale Diffusion Transformer. Arxiv 2025, Github star 1.9k."}, {"review_id": "jngcysMsvZ", "reviewer": "Reviewer_3pvW", "summary": "The paper proposes JarvisArt, a Lightroom-based intelligent photo-retouching agent driven by a multimodal LLM. JarvisArt interprets both textual and visual user intent and can orchestrate more than 200 Lightroom tools for global and local, non-destructive edits. A two-stage training pipeline—Chain-of-Thought (CoT) supervised fine-tuning followed by Group Relative Policy Optimization for Retouching (GRPO-R)—plus an A2L protocol enables end-to-end automation. The authors also release a large CoT dataset (MMArt-55K) and an evaluation benchmark (MMArt-Bench). Experiments show that JarvisArt outperforms GPT-4o, Gemini-2-Flash and other state-of-the-art models in pixel-level metrics and instruction adherence, and wins a user-preference study with 80 participants.", "questions": "Questions and Actionable Requests for the Rebuttal\n1. GRPO-R scale and reproducibility. Please supply the full hyper-parameter table (learning-rate schedule, reward weights, KL coefficients, batch sizes, wall-clock time, GPU type/count) and clarify why only 5 K samples were used.\n2. Objective comparison with commercial auto-modes.  Add quantitative metrics (e.g., PSNR, LPIPS, SC, PQ) for Lightroom “Auto” and Google Photos “Enhance” on MMArt-Bench and briefly discuss any failure cases.\n3. Training and inference efficiency.  Report GPU hours for SFT and GRPO-R, and give average inference latency and VRAM use for a 24 MP image. Indicate whether mixed-precision or model pruning was applied.", "limitations": "1. Compute & efficiency – please quantify GPU hours for SFT/RL and give typical inference latency on a 24-MP image so readers can gauge practicality across hardware tiers.\n2. Privacy considerations – clarify how user-supplied photos are stored/processed and whether the A2L pipeline leaves any personal data on external servers.", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Major Strengths\n1. Clear Task Focus – Targets high-resolution, non-destructive photo retouching, a realistic need distinct from purely generative models.\n2. Complete Workflow – Covers data generation, model training and tight Lightroom integration, forming a deployable pipeline.\n3. Community Assets – MMArt-55K supplies CoT, local coordinates and ROC files—valuable resources for future work.\n\nMain Weaknesses \n1. GRPO-R uses only 5 K samples; more training details could appear in the supplement.\n2. Comparisons with commercial auto-retouch modes (Lightroom Auto, Google Photos) are visual only—no numerical metrics.\n3. Perceptual-quality reward requires rendering images; training cost and throughput are not quantified.\n4. The paper is long; some appendix material duplicates the main text and could be trimmed.\n\nSuggestions for Improvement\n1. Release additional GRPO-R hyper-parameters and training time to aid reproducibility.\n2. Add objective metrics (e.g., PSNR, LPIPS) versus Lightroom Auto in the appendix.\n3. Discuss inference cost on mobile or low-compute devices.\n4. Explore support for Lightroom’s newer AI features such as background removal.\n\nRecommended to add citations to the following work.\n1. WordArt designer: User-driven artistic typography synthesis using large language models. (2023). In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. \n2. Music2P: A multi-modal AI-driven tool for simplifying album cover design. (2024). In Proceedings of the 33rd ACM International Conference on Information and Knowledge Management (CIKM 2024). \n3. MetaDesigner: Advancing artistic typography through AI-driven, user-centric, and multilingual WordArt synthesis. (2025). In Proceedings of the Thirteenth International Conference on Learning Representations (ICLR 2025).", "was_revised": true, "final_justification": "The authors have provided the full details, comparisons with commercial auto-retouch tools, efficiency metrics, and a clear privacy guarantee, directly addressing my key concerns about reproducibility, evaluation rigor, and practicality. Thanks!", "rebuttal": "We appreciate your positive feedback and recognition of our clearly articulated motivation, comprehensive data generation and training workflow, and the contribution of the MMArt dataset. Please find our detailed responses to the review comments below.\n\n**Q1: Full hyper-parameter setting for SFT and GRPO-R.**\n> To ensure reproducibility, we provide the complete hyperparameter settings for both the SFT and GRPO-R phases in Table 1. These details are included in the revised manuscript.\n\n**Table 1: Hyper-parameter settings for SFT and GRPO-R**\n| Hyper-parameter | SFT | GRPO-R |\n|:-----|:-----|:-----|\n| Batch size | 2 | 2 |\n| Learning rate | 1e-5 | 1e-6 |\n| Weight decay | 0| 0 |\n| Optimizer | AdamW | AdamW |\n| Warmup ratio | 0.1 | 0.1 |\n| LR scheduler | cosine | cosine |\n| Training samples | 50K | 5K |\n| Training epochs | 2 | 2 |\n| Precision | bfloat16 | bfloat16 |\n| KL coefficient | - | 0.04 |\n| Reward coefficients | - | $R_f$:1, $R_{roa}$:1, $R_{pq}$:1 ($\\gamma$=0.4) |\n| Number of generations | - | 4 |\n| GPU resources | 8×A100 (～384 GPU hours) | 16×A100 (～2076 GPU hours) |\n\n**Q2: Rationale for 5K samples in GRPO-R training.**\n> We use 5K training samples for the GRPO-R phase for two main considerations. \n> - **First**, as shown in Table 2 of the manuscript, even this modest data yields substantial improvements over the SFT baseline: L1 and L2 errors drop notably (**14.42 → 12.44, 44.38 → 30.56**), while SC, PQ, and O all increase (**7.32 → 7.53, 8.67 → 9.82, 7.94 → 8.52**). \n> - **Second**, scaling beyond 5K samples significantly increases computational costs, especially due to the overhead of parallel Lightroom invocations. Given our current resource constraints, large-scale RL training is not yet feasible, though we anticipate further gains with increased data in future work.\n\n**Q3: Quantitative comparison with commercial photo retouching methods.**\n> We provide both scene- and region-level quantitative comparison, including commercial retouching tools and instruction-driven generative editing models. As shown in Table 2, JarvisArt combines superior pixel-level content preservation—comparable to commercial tools like Google Photos and Lightroom Auto—with advanced instruction-following capabilities on par with GPT-4o and Gemini-2-Flash. This integration positions JarvisArt as a versatile system that unifies the strengths of both paradigms. Notably, evaluating instruction-driven retouching tasks is inherently subjective: reference images represent only one of many acceptable outputs, and traditional image metrics often fail to reflect aesthetic quality or user preferences. As discussed in Section 4.2.2 of the manuscript, user studies may provide a more reliable and human-centric evaluation. \n\n**Table 2: Quantitative comparison on both scene-level and region-level**\n| **Method**            | **Instruction** | **Scene-level**  |  |  |  |  | **Region-level** |  |  |  |  |\n|-------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|\n|                   |          | $\\text{PSNR}$ ↑   | $\\text{LPIPS}$ ↓  | $\\text{SC}$ ↑     | $\\text{PQ}$  ↑     | $\\text{O}$ ↑      | $\\text{PSNR}^{RC}$ ↑   | $\\text{LPIPS}^{RC}$ ↓  | $\\text{SC}^{RC}$ ↑     | $\\text{PQ}^{RC}$ ↑     | $\\text{O}^{RC}$ ↑      |\n| Lightroom Auto    | ❌       | 21.19    | 0.198    | -        | -        | -        | 20.11    | 0.166    | -        | -        | -        |\n| Google Photos      | ❌       | 22.04    | 0.180    | -        | -        | -        | 23.30    | 0.134    | -        | -        | -        |\n| RSFNet            | ❌       | 21.68    | 0.194    | -        | -        | -        | 23.07    | 0.155    | -        | -        | -        |\n| 3DLUT             | ❌       | 21.77    | 0.193    | -        | -        | -        | 23.86    | 0.149    | -        | -        | -        |\n| InstructPix2Pix   | ✅       | 15.64    | 0.429    | 6.54     | 7.79     | 7.10     | 15.17    | 0.479    | 4.70     | 5.36     | 4.91     |\n| MagicBrush        | ✅       | 13.55    | 0.598    | 3.93     | 4.09     | 3.85     | 15.68    | 0.543    | 3.04     | 3.41     | 3.13     |\n| OmniGen           | ✅       | 8.68     | 0.821    | 4.25     | 4.42     | 4.13     | 8.61     | 0.784    | 6.17     | 7.56     | 6.72     |\n| VARGPT-v1.1       | ✅       | 9.20     | 0.743    | 1.83     | 1.38     | 1.48     | 9.17     | 0.747    | 1.38     | 1.15     | 1.08     |\n| Step1X-Edit       | ✅       | 11.20    | 0.600    | 7.52     | 8.67     | 8.01     | 14.80    | 0.358    | 8.32     | 9.04     | 8.66     |\n| Gemini-2-Flash    | ✅       | 11.38    | 0.565    | 7.62     | 8.78     | 8.08     | 13.87    | 0.448    | 8.04     | 9.25     | 8.61     |\n| GPT-4o            | ✅       | 11.54    | 0.629    | 8.73     | 9.66     | 9.18     | 13.91    | 0.585    | 8.59     | 9.48     | 9.03     |\n| **JarvisArt**     | ✅       | **21.54**| **0.263**| **7.53** | **9.82** | **8.52** | **22.78**| **0.142**| **8.08** | **9.39** | **8.69** |\n\n**Q4: Inference efficiency.**\n> Table 3 summarizes the average wall-clock time for processing 200 images at 24MP resolution across various deployment frameworks and precision settings. The inference process comprises two main components: **(1)** The VLM generates Lightroom retouching parameters; **(2)** these parameters are applied locally within the user's Lightroom environment. Notably, we set VLM to support input images up to 1,048,576 pixels for efficiency. For larger images, automatic resizing with aspect ratio preservation ensures no distortion. Since the VLM is solely responsible for parameter generation, the actual retouching is performed by Lightroom, which supports arbitrary resolutions and image formats. Results show that vLLM with bfloat16 precision yields the optimal wall-clock performance (18.21s total, with 12.51s for VLM and 5.69s for Lightroom). Future work will explore further acceleration strategies, such as model quantization and speculative sampling. As a pioneering effort, this study introduces the concept of an artist agent and establishes an effective fine-tuning framework. We will continue to enhance this paradigm.\n\n**Table 3: Comparison of inference time across frameworks and precision settings**\n| **Framework**| **Precision** | **Wall-clock Time (s)** | **VLM Processing (s)** | **Lightroom Processing (s)** | **Time to First Token (s)** | **Throughput (tokens/s)**  | **Avg Generated Tokens** | **VRAM Usage (GB)** |\n|:--------------------------|:----------|:------------------|:------------|:------------|:------------|:-------------------|:-----------|:------------\n| HuggingFace Transformers | float32      | 58.60        | 52.91 | 5.69 | 6.09   | 26.64 | 1241.6      | ~ 76.29     |\n| HuggingFace Transformers | bfloat16      | 30.14        | 24.45 | 5.69 | 4.45   | 53.54  | 1096.1     |  ~ 58.98       |\n| vLLM                       | float32      | 20.46        | 14.77 | 5.69 | 4.44   | 124.32 | 1241.6      |  ~ 44     |\n| **vLLM**                      | **bfloat16**      | **18.21**        | **12.51** | **5.69** | **3.61**   | **137.94** | **1227.8**      | **~ 29.6**      |\n| vLLm* | bfloat16      |   28.82   | 23.13 | 5.69 | 1.164 | 55.31 | 1214.3 | ~ 21.38 |\n\nSystem configurations:\n- VLM inference time is measured on an NVIDIA A100 GPU (80 GB).\n- \\* denotes VLM inference time measured on an NVIDIA RTX 4090 GPU (24 GB).\n- Lightroom processing is conducted on a Windows 11 system equipped with an AMD Ryzen 9 5900X CPU and an NVIDIA RTX 3090 GPU.\n\n**Q5: Privacy considerations.**\n> To ensure user privacy, the Agent-to-Lightroom (A2L) protocol adopts a strictly sandboxed execution architecture and a fully localized processing workflow. As detailed in Supplementary Section A, the protocol runs entirely within the user's local Lightroom environment: **(1)** File verification is performed via OS-level checks without any external transmission; **(2)** all operations are confined to Lightroom's Lua environment through official APIs; and **(3)** retouching tasks are processed asynchronously in the local background. This design guarantees that user photos never leave the local Lightroom instance—no data is transmitted or stored outside the user's device.\n\n**Q6: Inference cost on mobile or low-compute devices.**\n> In this work, we focus on pushing the boundaries of photo retouching and offer new insights into how artificial intelligence can empower and elevate artistic creation. Deploying models to mobile devices or edge devices is beyond the scope of this paper. For such scenarios, we believe that model compression techniques—such as quantization, pruning, and knowledge distillation—can effectively reduce model size and inference costs, thus supporting such applications.\n\n**Q7: Failure cases and future work.**\n> While JarvisArt is promising, the current system has several limitations. First, it lacks multilingual capabilities, which we aim to address by adding linguistically diverse training data. Second, it struggles to capture fine-grained user intent, particularly in localized facial edits (e.g., eyes and lips). To improve performance, we propose two strategies: **(1)** augmenting the training dataset with examples reflecting subtle and specific user editing preferences, and **(2)** designing fine-grained reward mechanisms in the RL phase to help the model better capture nuanced user intent and invoke appropriate retouching operations. \n\n**Q8: Support for Lightroom’s AI features.**\n> To facilitate the model's adaptation to AI functionalities in Lightroom—such as background removal—we will employ the proposed data pipeline to generate paired artist-edited datasets aligned with specific AI operations. Fine-tuning on this targeted data is expected to enhance the model’s proficiency with these tools.\n\n**Q9: Paper presentation and citations of related works.**\n> We have made the necessary revisions and will cite all listed works."}, {"review_id": "EhqMTiatK7", "reviewer": "Reviewer_VQZs", "summary": "This paper presents JarvisArt, an MLLM-based agent that interprets user instructions and derives Lightroom operations via chain-of-thought (CoT) reasoning. The authors curate a dataset and adopt Group Relative Policy Optimization (GRPO) to enhance the MLLM's decision-making. They also introduce a new evaluation set, MMArt-Bench, constructed from real-world edits.", "questions": "+ Although the performance is promising, the proposed pipeline—including SFT data curation and RL post-training—builds upon existing methods. The level of technical novelty may be a concern for this paper. I am not entirely sure whether it meets the NeurIPS acceptance bar.\n+ Since the operations are derived via an MLLM, how accurate are the intermediate steps? Is there a risk of error accumulation that could degrade final performance? How can such issues be mitigated?\n+ While Figure 7 shows an encouraging trend in RL training, there should be an analysis of the relationship between the reward score and actual performance. Does a higher reward score truly correspond to better outcomes?\n+ In addition to successful examples, a discussion of failure cases is important—particularly to highlight areas for improvement and guide future research.\n\n**Minor**\n+ Missing reference: Guiding Instruction-based Image Editing via Multimodal Large Language Models, which also adopts MLLMs to guide image editing.", "limitations": "yes", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 2, "originality": 3, "strengths_and_weaknesses": "**Main Strengths**\n- This paper is well-written and easy to follow.\n- The authors target a real-world application with significant potential for extension to various artistic design tasks.\n- They present numerous qualitative examples and detailed training curves, which are crucial for tracking and demonstrating the efficiency of GRPO.\n- The curated dataset leads to substantial improvements on the proposed benchmark.\n\n**Main Weakness**\n- One of the most valuable contributions of this paper is the curated dataset and the proposed MMArt-Bench. However, I could not find a shareable link in the draft. If the authors do not plan to open-source these resources, it may limit the potential impact of the work.", "was_revised": false, "final_justification": "", "rebuttal": "We are grateful for your positive feedback and your recognition of our writing quality, the promise of MLLM-driven artistic agents, and the value of the MMArt dataset. Please find our detailed responses to the review comments below.\n\n**Q1: Open-source plan for the MMArt dataset.**\n> The dataset will be released once the paper is accepted.\n\n**Q2: Technical novelty of this work.**\n> As acknowledged by Reviewer `NfhL`, one of our primary contributions is the development of a robust and scalable infrastructure, comprising a data-synthesis pipeline and a fine-tuning framework that effectively transform MLLMs into artistic agents. While our training framework relies on widely used techniques like SFT and RL, we propose GRPO-R, an RL method tailored for tool-integrated retouching tasks. In contrast to standard GRPO—which targets problems with single, verifiable answers—retouching tasks involve two key challenges: **(1)** predicting multiple tools and their parameters, and **(2)** managing the inherent ambiguity in parameter combinations that yield visually similar results. This complexity makes reward design particularly difficult. GRPO-R addresses this by incorporating two reward components: `retouching operation accuracy` for parameter prediction and `perceptual quality` for visual fidelity. This specific reward design enables scalable reinforcement learning, thereby enhancing the agent's reasoning ability, tool proficiency, and generalization. \n\n**Q3: Accuracy of the intermediate steps of MLLM.**\n> **During the data curation process**, to ensure the accuracy of intermediate reasoning steps generated by the MLLM, we adopt a two-stage verification approach: **(1)** the Qwen2.5-VL-72B model evaluates the alignment between instructions and reasoning via multimodal input prompts, and **(2)** manual sampling checks are conducted to further validate quality. This verification procedure will be clarified in the manuscript. \n>\n> **During JarvisArt's inference phase**, artistic reasoning may introduce error accumulation that degrades overall performance. For example, if the model predicts an incorrect adjustment such as `\"exposure +50\"`, it may produce suboptimal visual results. Since the model lacks immediate visual feedback during reasoning, such errors are not corrected and may compound in subsequent steps. Introducing stepwise visual rewards during the RL phase may allow the model to validate intermediate predicted parameters, thereby improving the reliability of the model during inference and reducing errors. Ongoing work aims to further optimize this implementation.\n\n**Q4: Impact of reward scores on performance outcomes.**\n> To analyze the correlation between reward scores and performance metrics, we conducted progressive assessments of checkpoints at four training milestones (**25%, 50%, 75%, and 100% completion**) during RL training. As shown in Table 1, results on MMArt-Bench demonstrate a statistically positive correlation between reward scores and performance metrics. This confirms that the GRPO-R can effectively enhance the model's reasoning, tool proficiency, and generalization during reward optimization.\n\n**Table 1: Analysis of correlation between rewards and performance**\n| **Training Phase** | **Reward Score ↑** | **L1×10² ↓** | **L2×10³ ↓** | **SC ↑** | **PQ ↑** | **O ↑** |\n|----------------|:--------------:|:--------:|:--------:|:----:|:----:|:----:|\n| 25% Completion | 2.071           | 14.23    | 43.75    | 7.41 | 8.73 | 8.04 |\n| 50% Completion | 2.094          | 14.14    | 40.92     | 7.44 | 8.81 | 8.09 |\n| 75% Completion | 2.313          | 13.46    | 35.15    | 7.49 | 9.37 | 8.37 |\n| 100% Completion| 2.416           | 12.44    | 30.56    | 7.53 | 9.82 | 8.52 |\n\n**Q5: Failure cases and future work.**\n> In this work, we focus on pushing the boundaries of photo retouching and offer new insights into how artificial intelligence can empower and elevate artistic creation. While promising, the current system has several limitations. First, it lacks multilingual capabilities, which we aim to address by adding linguistically diverse training data. Second, it struggles to capture fine-grained user intent, particularly in localized facial edits (e.g., eyes and lips). To improve performance, we propose two strategies: **(1)** augmenting the training dataset with examples reflecting subtle and specific user editing preferences, and **(2)** designing fine-grained reward mechanisms in the RL phase to help the model better capture nuanced user intent and invoke appropriate retouching operations. \n\n\n**Q6: Citations of related works.**\n> We will cite all listed works."}, {"review_id": "42IZrIqFhE", "reviewer": "Reviewer_xmNH", "summary": "The paper presents JarvisArt, an MLLM-driven photo retouching agent that integrates with Lightroom to automate professional-level edits.", "questions": "I don't have more questions", "limitations": "Yes", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths\n1. JarvisArt bridges the gap between natural language understanding and professional retouching workflows by orchestrating over 200 Lightroom tools. The two-stage training (CoT SFT + GRPO-R) and Agent-to-Lightroom Protocol show thoughtful design for real-world applicability.\n2. The MMArt dataset and MMArt-Bench benchmark provide a robust foundation for training and testing. Quantitative results show JarvisArt outperforms GPT-4o in content fidelity by 60% while maintaining instruction-following capabilities.\n3. The system addresses a critical pain point for non-experts by automating complex edits without sacrificing control. Visual comparisons highlight its superiority in preserving details and avoiding artifacts compared to baselines like InstructPix2Pix and MagicBrush.\n\nWeaknesses\n1. The user preference study recruits 80 participants but omits details on their expertise (e.g., amateur vs. professional) and the evaluation criteria. The 5-point ordinal scale may not capture nuanced differences in aesthetic judgment.", "was_revised": true, "final_justification": "My concern has been addressed.", "rebuttal": "We are grateful for your positive feedback and your recognition of our motivation, professional retouching results, and the contribution of the MMArt dataset. Please find our detailed responses to the review comments below.\n\n**Q1: Participant demographics in the user study.**\n> To ensure the comprehensiveness of our user study, we recruited 80 participants with diverse professional backgrounds, including art, computer science, photography, and design. The cohort comprises both male (50%) and female (50%) participants aged 18-35, including amateur/professional photographers (35%), digital artists (25%), and general users (40%). This multi-dimensional sampling strategy helps evaluate system performance across different user groups and application scenarios. This will be clarified in the manuscript.\n\n**Q2: Evaluation criteria in the user study.**\n> The evaluation criteria focus on two key dimensions: **(1)** image consistency, which measures how closely the content of the retouched image aligns with the source image; and **(2)** aesthetic quality, which evaluates the artistic merit of retouched images based on professional photography standards, including color harmony, composition balance, and visual impact. To maintain consistency with prior user studies [1][2], we adopt the five-level quality scale due to its simplicity, efficiency, and effectiveness in distinguishing significant differences in retouching quality. Notably, during the aesthetic assessment, participants are required to evaluate the overall aesthetic quality based on three key aspects—color harmony, lighting and shadow, and visual appeal—before scoring. This will be clarified in the manuscript.\n\n**Q3: Discussion of refined aesthetic evaluation.**\n> Pairwise comparison is a more refined evaluation method where participants are shown two images side-by-side and asked to choose their preference or identify the higher-quality one. This approach excels at capturing subtle differences in user preference and can mitigate biases like the anchoring effect, but its efficiency significantly decreases with a larger number of images due to the exponential increase in comparisons needed, potentially leading to participant fatigue and requiring more complex statistical analysis.\n\n---\nReferences:\n\n[1] Step1x-edit: A practical framework for general image editing. Arxiv 2025, Github star 1.5k.\n\n[2] MagicQuill: An Intelligent Interactive Image Editing System. CVPR 2025."}]

Author rebuttal:

Meta-review: This submission presents JarvisArt, a multimodal LLM-based agent for professional photo retouching. It is supported by the MMArt  and a two-stage training pipeline (SFT + GRPO-R). The reviewers praised its strong qualitative results, valuable dataset, comprehensive evaluation. I was most excited to see a practical integration with Lightroom. 

The main concerns centered on modest methodological novelty, the narrow scope restricted to Lightroom-based edits, potential limitations in dataset robustness, and questions about fair comparisons against pixel-level generative models. 

Overall, while not conceptually groundbreaking, the paper makes a meaningful contribution to the community.
Decision: Accept (poster)