arXiv Machine Learning
Jun 15

PCR-CA: Parallel Codebook Representations with Contrastive Alignment for Multiple-Category App Recommendation

arXiv:2508. 18166v5 Announce Type: replace-cross Abstract: Modern app store recommender systems struggle with multiple-category apps, as traditional taxonomies fail to capture overlapping semantics, leading to suboptimal personalization.

By Bin Tan, Wangyao Ge, Yidi Wang, Xin Liu, Jeff Burtoft, Hao Fan, Hui Wang
arXiv AI
Aug 3

Looks Right, Works Right: A Project-Level Benchmark for Multi-Screen Mobile App Generation

arXiv:2607. 28645v1 Announce Type: cross Abstract: Recent multimodal large language models can convert visual designs directly into executable code, but real mobile products require multiple screenshots to become a buildable codebase with shared components and working navigation.

By Fan Wu, Cuiyun Gao, Yiming Huang, Yang Xiao, Yujia Chen, Qing Liao
arXiv AI
Oct 28

MoI-Bench: A Benchmark for Image Generation and Editing on Mobile Devices

MoI-Bench is a new benchmark designed for on‑device image generation and editing, featuring 64 subtasks and 1150 samples that cover 16 operations and eight visual target categories. It introduces MoI‑Eval, a task‑specific evaluation framework that assesses instruction following, visual quality, and detail preservation, and aggregates results into a two‑dimensional Quality‑Deployability map. The benchmark reveals that current on‑device models excel at efficient generation or editing but need improved editing quality and broader task coverage, especially for text replacement and identity preservation.

By Kailai Feng, Haoxiang Rao, Yuxiang Wei, Bo Chen, Junsong Li, Yang Pan, Chengkun Cai, Jagmohan Chauhan
arXiv AI
Oct 9

MobileWorldBench: Towards Semantic World Modeling For Mobile Agents

MobileWorldBench introduces a benchmark for evaluating vision‑language models as world models in mobile GUI agents, shifting from pixel‑based predictions to natural‑language state transitions. The authors release MobileWorld, a 1.4 million‑sample dataset that enhances VLM world‑modeling performance. They also present a framework that integrates these semantic world models into mobile agent planning, showing improved task success rates.

By Shufan Li, Konstantinos Kallidromitis, Akash Gokul, Yusuke Kato, Kazuki Kozuka, Aditya Grover