Mobile Graphical User Interface (GUI) World Models (WMs) offer a promising path for improving mobile GUI agent performance at train- and inference-time. However, current approaches face a critical trade-off: text-based WMs sacrifice visual fidelity, while the inability of visual WMs in precise text rendering led to their reliance on slow, complex pipelines dependent on numerous external models. We propose a novel paradigm: visual world modeling via renderable code generation, where a single Vision-Language Model (VLM) predicts the next GUI state as executable web code that renders to pixels, rather than generating pixels directly. This combines the strengths of both approaches: VLMs retain their linguistic priors for precise text rendering while their pre-training on structured web code enables high-fidelity visual generation. We introduce gWorld (8B, 32B), the first open-weight visual mobile GUI WMs built on this paradigm, along with a data generation framework (gWorld) that automatically synthesizes code-based training data. In extensive evaluation across 4 in- and 2 out-of-distribution benchmarks, gWorld sets a new pareto frontier in accuracy versus model size, outperforming 8 frontier open-weight models over 50.25x larger. Further analyses show that (1) scaling training data via gWorld yields meaningful gains, (2) each component of our pipeline improves data quality, and (3) stronger world modeling improves downstream mobile GUI policy performance.
Generative Visual Communication in the Era of Vision-Language Models
Raymond and Beverly Sackler Faculty of Exact Sciences（雷蒙德和贝弗利·萨克勒精密科学学院）；Blavatnik School of Computer Science（布拉瓦尼克计算机科学学院）；School of Computer Science at Tel Aviv University（特拉维夫大学计算机科学学院）
InterChat: Enhancing Generative Visual Analytics using Multimodal Interactions
University of California, Davis（加利福尼亚大学戴维斯分校）；Bosch Research North America（博世北美研究院）；Bosch Center for Artificial Intelligence (BCAI)（博世人工智能中心）；Robert Bosch GmbH（罗伯特·博世有限公司）
Real-Time Pill Identification for the Visually Impaired Using Deep Learning
San Francisco Bay University（旧金山湾大学）；University of Science and Technology of China（中国科学技术大学）；University of Southampton（南安普顿大学）；Stevens Institute of Technology（史蒂文斯理工学院）；Georgia Institute of Technology（佐治亚理工学院）
ZipNet-GAN: Inferring Fine-grained Mobile Traffic Patterns via a Generative Adversarial Neural Network
大规模移动流量分析对于数字基础设施配置、公共交通、活动规划和其他领域变得至关重要。然而，监控全市范围的移动流量是一个复杂而昂贵的过程，依赖于专用探头。其中一些探测器的精度或覆盖范围有限，另一些每天收集数十GB的日志，这些日志独立提供有限的见解。提取细粒度的模式涉及昂贵的测量、存储和后处理的空间聚合。在本文中，我们提出了一种移动流量超分辨率技术，克服了这些问题，通过推断狭隘的本地化的流量消费从粗糙的测量。我们从图像处理中汲取灵感，设计了一个针对移动网络的深度学习架构，它结合了Zipper网络（ZipNet）和生成对抗神经网络（GAN）模型。这使得能够唯一地捕获在广泛覆盖区域上常规监测的交通量快照（“低分辨率”）与通常在密集计算之后获得的0.05 km $^2$水平上的相应消耗（“高分辨率”）之间的时空关系。我们进行的实验与现实世界的数据集表明，拟议的ZipNet（-GAN）推断流量消耗具有显着的准确性和高达100 $\times$更高的粒度相比，标准的探测，同时优于现有的数据插值技术。据我们所知，这是第一次将超分辨率概念应用于大规模移动交通分析，我们的解决方案是第一个从粗聚合物中推断细粒度城市交通模式的解决方案。
Joint Model Assignment and Resource Allocation for Cost-Effective Mobile Generative Services
School of Electronic Information and Communications, Huazhong University of Science and Technology（华中科技大学电子信息与通信学院）；School of Computer Science and Engineering, Central South University（中南大学计算机科学与工程学院）；University of Windsor（温莎大学）
On-demand Quantization for Green Federated Generative Diffusion in Mobile Edge Networks
School of Automation, Guangdong University of Technology（广东工业大学自动化学院）；School of Electronics Information and Electrical Engineering, Shanghai Jiao Tong University（上海交通大学电子信息与电气工程学院）；School of Computer Science and Engineering, Nanyang Technological University（南洋理工大学计算机科学与工程学院）；School of Software Engineering, Beijing Jiaotong University（北京交通大学软件学院）
Resource-Efficient Generative AI Model Deployment in Mobile Edge Networks
School of Electronic Information and Communications, Huazhong University of Science and Technology（华中科技大学电子信息与通信学院）；School of Cyber Science and Engineering, Huazhong University of Science and Technology（华中科技大学网络空间安全学院）；School of Computer Science and Engineering, Central South University（中南大学计算机科学与工程学院）
针对边缘资源受限下生成式 AI 模型部署难题，提出边云协同框架与模型级决策选择算法，联合优化资源消耗、模型切换时延及总体成本。
Filling the Missing: Exploring Generative AI for Enhanced Federated Learning over Heterogeneous Mobile Edge Devices
University of Macau（澳门大学）；Zhejiang University of Technology（浙江工业大学）；Guangdong University of Technology（广东工业大学）；Nanyang Technological University（南洋理工大学）；University of Waterloo（滑铁卢大学）
A generative, predictive model for menstrual cycle lengths that accounts for potential self-tracking artifacts in mobile health data
Columbia University（哥伦比亚大学）；Data Science Institute, Columbia University（哥伦比亚大学数据科学研究所）；Clue by BioWink（BioWink公司旗下Clue项目）；Department of Anthropology, Indiana University（印第安纳大学人类学系）；Department of Biomedical Informatics, Columbia University（哥伦比亚大学生物医学信息学系）
