Instruction-driven image editing with unified multimodal generative models has advanced rapidly, yet their underlying visual reasoning remains limited, leading to suboptimal performance on reasoning-centric edits. Reinforcement learning (RL) has been investigated for improving the quality of image editing, but it faces three key challenges: (1) limited reasoning exploration confined to denoising stochasticity, (2) biased reward fusion, and (3) unstable VLM-based instruction rewards. In this work, we propose ThinkRL-Edit, a reasoning-centric RL framework that decouples visual reasoning from image synthesis and expands reasoning exploration beyond denoising. To the end, we introduce Chain-of-Thought (CoT)-based reasoning sampling with planning and reflection stages prior to generation in online sampling, compelling the model to explore multiple semantic hypotheses and validate their plausibility before committing to a visual outcome. To avoid the failures of weighted aggregation, we propose an unbiased chain preference grouping strategy across multiple reward dimensions. Moreover, we replace interval-based VLM scores with a binary checklist, yielding more precise, lower-variance, and interpretable rewards for complex reasoning. Experiments show our method significantly outperforms prior work on reasoning-centric image editing, producing instruction-faithful, visually coherent, and semantically grounded edits.
Taming I2V models for Image HOI Editing: A Cognitive Benchmark and Agentic Self-Correcting Framework
Wangxuan Institute of Computer Technology, Peking University, Beijing, China（王轩计算机技术研究所，北京大学，北京，中国）；National Institute of Health Data Science, Peking University, Beijing, China（国家健康数据科学研究院，北京大学，北京，中国）
The designers' tendency to adhere to a specific mental set and heavy emotional investment in their initial ideas often hinder their ability to innovate during the design thinking and ideation process. In the fashion industry, in particular, the growing diversity of customers' needs, the intense global competition, and the shrinking time-to-market (a.k.a., "fast fashion") further exacerbate this challenge for designers. Recent advances in deep generative models have created new possibilities to overcome the cognitive obstacles of designers through automated generation and/or editing of design c…
University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）；University of Michigan（密歇根大学）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Dolby Laboratories（杜比实验室）
WISER: Wider Search, Deeper Thinking, and Adaptive Fusion for Training-Free Zero-Shot Composed Image Retrieval
Foundation Model Research Center, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所基础模型研究中心）；School of Advanced Interdisciplinary Sciences, University of Chinese Academy of Sciences（中国科学院大学先进交叉学科学院）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；National University of Singapore（新加坡国立大学）；Wuhan AI Research（武汉人工智能研究院）；Minzu University of China（民族大学）
EchoFoley: Event-Centric Hierarchical Control for Video Grounded Creative Sound Generation
ByteDance Intelligent Creation（字节跳动智能创作）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；University of California, Merced（加州大学默塞德分校）；University of California, Los Angeles（加州大学洛杉矶分校）
