Humans naturally communicate through abstract concepts like "mood". However, current image editing benchmarks focus primarily on explicit, literal commands, leaving abstract instructions largely underexplored. In this work, we first formalize the definition and taxonomy of abstract image editing. To measure instruction-following in this challenging domain, we introduce Entity-Rubrics, a framework that breaks down abstract edits into individual, entity-level assessments and achieves strong correlation with human judgment. Alongside this framework, we contribute AbstractEdit, the first benchmark dedicated to abstract image editing across diverse real-world scenes. Evaluating 11 leading models on this dataset reveals a fundamental challenge: standard architectures struggle to balance intent and preservation, commonly defaulting to under-editing or over-editing. Our analysis demonstrates that driving meaningful improvements relies heavily on integrating advanced LLM text encoders and iterative thinking. Looking forward, our entity-based paradigm can generalize beyond assessment to serve as a reward model, enable models to correctly interpret abstract communication, or highlight specific failures in test-time critique loops. Ultimately, we hope this work serves as a stepping stone toward seamless multimodal interaction, closing the gap between rigid machine execution and the natural, open-ended way humans communicate.
VRWKV-Editor: Reducing quadratic complexity in transformer-based video editing
International Artificial Intelligence Center of Morocco University Mohammed VI Polytechnic Rabat, Morocco（摩洛哥国际人工智能中心摩洛哥穆莱·伊沙克·穆莱·阿卜杜勒阿齐兹 polytechnic 大学拉巴特分校）；University Mohammed VI Polytechnic Rabat, Morocco（摩洛哥穆莱·伊沙克·穆莱·阿卜杜勒阿齐兹 polytechnic 大学拉巴特分校）；Sorbonne University, LIP6 - UMR 7606 CNRS, France（索邦大学，LIP6 - UMR 7606 CNRS，法国）
DisCo3D: Distilling Multi-View Consistency for 3D Scene Editing
Institute for Artificial Intelligence, Beijing National Research Center for Information Science and Technology, Department of Computer Science and Technology, Tsinghua University（人工智能研究院、北京信息科学与技术国家研究中心、计算机科学与技术系、清华大学）；College of Computer, National University of Defense Technology（计算机学院、国防科技大学）
Why Do DiT Editors Drift? Plug-and-Play Low Frequency Alignment in VAE Latent Space
Tsinghua University（清华大学）；Carnegie Mellon University（卡内基梅隆大学）；Peking University（北京大学）；CASIA；University of Electronic Science and Technology of China（电子科技大学）；Guangming Laboratory（光明实验室）
Refinement Is Inherently Editable: Training-Free Prompt-to-Prompt Image Editing with Generative Refinement Network
City University of Hong Kong (Dongguan)（香港城市大学（东莞））；City University of Hong Kong（香港城市大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
