Title: JarvisArt: Liberating Human Artistic Creativity via an Intelligent Photo Retouching Agent
Venue: NeurIPS 2025
Accepted: yes
Keywords: AI agent; Photo retouching; Vision-foundation model
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.


Peer reviews:
comments: SUMMARY:
The paper presents JarvisArt, an MLLM-driven photo retouching agent that integrates with Lightroom to automate professional-level edits.

STRENGTHS AND WEAKNESSES:
Strengths
1. 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.
2. 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.
3. 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.

Weaknesses
1. 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.

QUESTIONS:
I don't have more questions

LIMITATIONS:
Yes
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-06-28

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\n**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.\n\nQUESTIONS:\n+ 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.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 5, "CLARITY": 4, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe 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.\n\nSTRENGTHS AND WEAKNESSES:\nMajor 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).\n\nQUESTIONS:\nQuestions 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.\n\nLIMITATIONS:\n1. 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.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe 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.\n\nSTRENGTHS AND WEAKNESSES:\n**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.\n\nQUESTIONS:\n1. 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?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-02"}