Editable Visual Design introduces a new design paradigm that combines a Coding Agent with a Vision‑Language Model (VLM) and an image generation model. The VLM acts as the creative brain, understanding requirements, planning tasks, and judging aesthetics, while the image generator produces isolated visual assets on demand. The agent follows an "imagine first, then act" workflow, generating assets, writing native HTML/CSS, and refining the design through visual feedback, ultimately producing editable, layer‑wise artifacts with real text that can be adjusted via a graphical interface.
By Junyan Ye, Wei Liu, Dongzhi Jiang, Zichen Wen, HaoDong Li, Zhutao Lv, Jiaxin Lin, Jinhua Yu, Jun He, Zilong Huang, Rui Chen, Weijia Li
arXiv:2608.24169v1 Announce Type: new
Abstract: 3D geometry editing is a critical yet labor-intensive part of the graphics pipeline, requiring artists to translate creative intent into precise operat...
By Bo Pang, Jiaqi Pan, Xiaocheng Zhang, Jiacheng Xu, Guoping Wang, Peng-Shuai Wang
arXiv:2607. 22928v1 Announce Type: new Abstract: Generative UI tools promise to democratize UI design by turning natural language descriptions into complete interfaces.
By Kashif Imteyaz, Kaif Imteyaz, Nakul Rajpal, Kaif Shaikh, Michael Muller, Saiph Savage
arXiv:2610.18860v1 Announce Type: new
Abstract: Generating information-rich visual artifacts such as slides, posters, and dashboards as code is appealing because the outputs are structured, editable,...
By Xin Gao, Yifan Yang, Xin Li, Pan Wang, Yan Li, Muzhao Tian, Mingxi Cheng, Bei Liu, Kai Qiu, Qi Dai, Dong Chen, Yue Dong, Leandros Tassiulas, Rex Ying, Ji Li, Chong Luo
arXiv:2607. 01709v1 Announce Type: new Abstract: Agents are increasingly used to construct workflows and assist humans in completing recurring tasks more efficiently.
By Zongxia Li, Dawei Liu, Fuxiao Liu, Yuhang Zhou, Xiyang Wu, Jingxi Chen, Jing Xie, Xiaomin Wu, Lichao Sun
The paper introduces KNOWS, a benchmark for evaluating web agents that act as assistants by retrieving, synthesizing, and presenting information across complex, multi-step browser tasks. It outlines a task design rubric, evaluation protocol combining deterministic checks with LLM judgments, and reports that current agents achieve only modest success, with the best performing agent succeeding on less than 3% of tasks. The study highlights significant gaps in agents’ tool use, visual understanding, and long‑horizon reasoning.
By Alexander Gill, Md Farhan Ishmam, Xuyen Nguyen, Neha Bhat, Parker Henry DeYoung, Fateme Hashemi Chaleshtori, Nathan Stringham, Kenneth Marino, Ana Marasovi\'c