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.
Video quality assessment (VQA) plays a critical role in optimizing video delivery systems. While numerous objective metrics have been proposed to approximate human perception, the perceived quality strongly depends on viewing conditions and display characteristics.
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.
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.
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.
arXiv:2606. 13192v1 Announce Type: new Abstract: User experience (UX) centered on usability, perceived consistency, and functional clarity is fundamental to real-world user interfaces (UI).
arXiv:2608. 05891v1 Announce Type: new Abstract: Mobile GUI agents can operate apps through pixel perception and touch actions, making them a promising interface for collecting and improving long-horizon mobile interaction policies.
arXiv:2608.21794v1 Announce Type: cross Abstract: GUI grounding evaluations that expose UI elements as text metadata often treat high instruction-element embedding similarity as evidence of semantic...
arXiv:2610.17661v2 Announce Type: replace Abstract: The rapid development of mobile imaging and social platforms has led to explosive growth in user-generated content images, making their systematic...
arXiv:2606. 09764v1 Announce Type: new Abstract: A useful phone agent needs to be personally intelligent.
arXiv:2508. 18636v2 Announce Type: replace-cross Abstract: Representing a new paradigm in software distribution, LLM app stores are rapidly emerging, offering users diverse choices for content generation, coding assistance, education, and more.
The paper investigates on-device language models (ODLMs) for predicting stress in a mobile health context, focusing on privacy-preserving, cloud-independent inference. Using zero‑shot prompting, the authors evaluate ODLMs across multimodal data—objective sensor features and subjective self‑reports—measuring predictive accuracy, latency, and throughput. Results indicate that sensor features slightly outperform self‑reports, and that lightweight sub‑2B models deliver low latency with predictable resource usage, underscoring both the potential and practical limits of ODLMs for mobile mental health.