Recent advances in multi-modal generative models have driven substantial improvements in image editing. However, current generative models still struggle with handling diverse and complex image editing tasks that require implicit reasoning, underscoring the need for a comprehensive benchmark to systematically assess their performance across various reasoning scenarios. Existing benchmarks primarily focus on single-object attribute transformation in realistic scenarios, which, while effective, encounter two key challenges: (1) they largely overlook multi-object interactions as well as game-world scenarios that involve human-defined rules, which are common in real-life applications; (2) they only rely on textual references to evaluate the generated images, potentially leading to systematic misjudgments, especially in complex reasoning scenarios. To this end, this work proposes UniREditBench, a unified benchmark for reasoning-based image editing evaluation. It comprises 2,700 meticulously curated samples, covering both real- and game-world scenarios across 8 primary dimensions and 18 sub-dimensions. To improve evaluation reliability, we introduce multimodal dual-reference evaluation, providing both textual and ground-truth image references for each sample assessment. Furthermore, we design an automated multi-scenario data synthesis pipeline and construct UniREdit-Data-100K, a large-scale synthetic dataset with high-quality chain-of-thought (CoT) reasoning annotations. We fine-tune Bagel on this dataset and develop UniREdit-Bagel, demonstrating substantial improvements in both in-domain and out-of-distribution settings. Through thorough benchmarking of both open-source and closed-source image editing models, we reveal their strengths and weaknesses across various aspects.
InterCoG: Towards Spatially Precise Image Editing with Interleaved Chain-of-Grounding Reasoning
Faculty of Computing, Harbin Institute of Technology（哈尔滨工业大学计算机学院）；Zhengzhou Advanced Research Institute of Harbin Institute of Technology（哈尔滨工业大学郑州先进研究院）；Huawei Noah’s Ark Lab（华为诺亚实验室）；Artificial Intelligence Research Institute, Shenzhen University of Advanced Technology（深圳先进技术大学人工智能研究院）
Understanding the Implicit User Intention via Reasoning with Large Language Model for Image Editing
Southern University of Science and Technology, Shenzhen, China（南方科技大学）；Johns Hopkins University, Baltimore, USA（约翰霍普金斯大学）；Pengcheng Laboratory, Shenzhen, China（鹏城实验室）
Reasoning to Edit: Hypothetical Instruction-Based Image Editing with Visual Reasoning
Tencent Youtu Lab（腾讯云图实验室）；Sichuan University（四川大学）；University of the Chinese Academy of Sciences（中国科学院大学）；Fudan University（复旦大学）；Zhejiang University（浙江大学）；National University of Singapore（新加坡国立大学）；Nanyang Technological University（南洋理工大学）
DeepGen 1.0: A Lightweight Unified Multimodal Model for Advancing Image Generation and Editing
Shanghai Innovation Institute（上海创新研究院）；Fudan University（复旦大学）；University of Science and Technology of China（中国科学技术大学）；Shanghai Jiao Tong University（上海交通大学）；Zhejiang University（浙江大学）；Westlake University（西湖大学）；Nanjing University（南京大学）；University of Southern California（南加州大学）
Beyond Textual CoT: Interleaved Text-Image Chains with Deep Confidence Reasoning for Image Editing
Shanghai Jiaotong University（上海交通大学）；Huawei Noah’s Ark Lab（华为诺亚实验室）；East China Normal University（华东师范大学）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（深圳先进技术研究院，中国科学院）
Southeast University（东南大学）；Max Planck Institute for Informatics（马克斯·普朗克信息研究所）；Shanghai Jiao Tong University（上海交通大学）；StepFun；University of California, Berkeley（加州大学伯克利分校）；University of California, Merced（加州大学默塞德分校）
SpatialReward: Bridging the Perception Gap in Online RL for Image Editing via Explicit Spatial Reasoning
Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））；The Hong Kong University of Science and Technology（香港科学与技术大学）；Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生学院，清华大学）
EditRefiner: A Human-Aligned Agentic Framework for Image Editing Refinement
Shanghai Jiao Tong University（上海交通大学）；Vivo Mobile Communication Co., Ltd（Vivo移动通信有限公司）；University of Electronic Science and Technology of China（电子科学与技术大学）
I2E: From Image Pixels to Actionable Interactive Environments for Text-Guided Image Editing
Huazhong University of Science and Technology（华中科技大学）；Kuaishou Technology（快手科技）；Central China Normal University（华中师范大学）；Tsinghua University（清华大学）；Shanghai AI Laboratory（上海人工智能实验室）
Factuality Matters: When Image Generation and Editing Meet Structured Visuals
CUHK MMLab（香港大学多模态实验室）；Beihang University（北京航空航天大学）；Krea AI（Krea人工智能）；Shanghai Jiao Tong University（上海交通大学）；Shanghai AI Lab（上海人工智能实验室）；Hugging Face；National University of Singapore（新加坡国立大学）；ByteDance（字节跳动）；The University of Hong Kong（香港大学）
VIRAL: Visual In-Context Reasoning via Analogy in Diffusion Transformers
School of Data Science and Engineering, East China Normal University, Shanghai, China（数据科学与工程学院，东华大学，上海，中国）；Alibaba Group, Hangzhou, China（阿里巴巴集团，杭州，中国）
SmartEdit: Exploring Complex Instruction-based Image Editing with Multimodal Large Language Models
University of Macau（澳门大学）；ARC Lab, Tencent PCG（腾讯平台与内容事业群ARC实验室）；Tencent AI Lab（腾讯人工智能实验室）；Shenzhen Institute of Advanced Technology（深圳先进技术研究院）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Shenzhen Research Institute of Big Data（深圳大数据研究院）；Tsinghua University（清华大学）
Leveraging Verifier-Based Reinforcement Learning in Image Editing
School of Computing and Data Science, The University of Hong Kong（计算与数据科学学院，香港大学）；Center for Embodied AI and Computer Vision, Shenzhen Loop Area Institute（具身人工智能与计算机视觉中心，深圳Loop Area研究院）
Best of Both Worlds: Multimodal Reasoning and Generation via Unified Discrete Flow Matching
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Google Research（谷歌研究）；Sony Research, India（索尼印度研究）；Indian Institute of Technology, Roorkee（印度理工学院罗尔基分校）；University of Texas at Austin（德克萨斯大学奥斯汀分校）
