From UI to Code: Mobile Ads Detection via LLM-Unified Static-Dynamic Analysis
- URL: http://arxiv.org/abs/2604.03561v1
- Date: Sat, 04 Apr 2026 03:04:03 GMT
- Title: From UI to Code: Mobile Ads Detection via LLM-Unified Static-Dynamic Analysis
- Abstract summary: ADWISE is a framework that formulates mobile ads detection as LLM-guided, ad-oriented UI exploration.<n>ADWISE first performs static program analysis to identify UI widgets used to place ads, which we call ad widgets.<n>ADWISE outperforms state-of-the-art baselines by 25.60% in ad widget detection.
- Score: 21.13861921564872
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Mobile advertisements (ads) are essential to the app economy, yet detecting them is challenging because ad content is dynamically fetched from remote servers and rendered through diverse user interfaces (UIs), making ads difficult to locate and trigger at runtime. To address this challenge, we present ADWISE, a novel framework that formulates mobile ads detection as LLM-guided, ad-oriented UI exploration. ADWISE first performs static program analysis to identify UI widgets used to place ads, which we call ad widgets. It then uses a grounded LLM reasoning loop to navigate toward and trigger these widgets under three complementary domain guidance signals: (1) WTG-based guidance, which provides global transition priors from a statically constructed window transition graph (WTG); (2) semantic guidance, which reasons over app functionality to prioritize user-likely interaction paths; and (3) structural guidance, which applies retrieval-augmented generation to match the current UI against recurring ad-heavy layouts from a knowledge base. By combining static program analysis with LLM-based reasoning over UI structure, app semantics, and retrieved analogies, ADWISE enables more effective ads detection in complex mobile UIs. Experiments on 100 benchmark apps show that ADWISE outperforms state-of-the-art baselines by 25.60% in ad widget detection. In addition, ADWISE uncovers 34.34% more ad regulation violations across six categories, directly benefiting downstream ad regulation.
Related papers
- LLaDA-UI: Bringing Block-wise Diffusion to Vision-Language GUI Agents [52.61417375636918]
We present LLaDA-UI, a 16.7B- parameter MoE-based, block-wise diffusion vision-language GUI agent.<n>We show that LLaDA-UI substantially outperforms Qwen2.5-VL-7B and surpasses Qwen3-VL-8B on four of six reported GUI benchmarks.
arXiv Detail & Related papers (2026-09-09T05:53:39Z) - When Ad Networks Misbehave: Understanding Risks of Semi-Drive-By Splash Ads [13.802323882269995]
We investigate the mobile splash ads ecosystem, where monetization relies on interaction signals that are difficult to verify end-to-end.<n>We uncover a previously less-known form of fraud at the ad-network layer in which splash ads are triggered not by intentional user actions but by incidental or indirect interactions.<n>By translating non-ad interactions into billable engagement events, ad networks can inflate performance metrics, overcharge advertisers, and erode user trust.
arXiv Detail & Related papers (2026-09-09T00:54:43Z) - GUIDE: Resolving Domain Bias in GUI Agents through Real-Time Web Video Retrieval and Plug-and-Play Annotation [27.67914789321192]
Guide is a training-free, plug-and-play framework that resolves GUI agent domain bias.<n>It autonomously acquires domain-specific expertise from web tutorial videos.<n>It consistently yields over 5% improvements without modifying any model parameters or architecture.
arXiv Detail & Related papers (2026-03-27T10:33:08Z) - MANA: Towards Efficient Mobile Ad Detection via Multimodal Agentic UI Navigation [28.21505173665395]
We present MANA, the first agentic multimodal reasoning framework for mobile ad detection.<n>We implement and evaluate MANA on commercial smartphones over 200 apps, achieving state-of-the-art accuracy and efficiency.
arXiv Detail & Related papers (2026-03-20T12:19:41Z) - RISER: Orchestrating Latent Reasoning Skills for Adaptive Activation Steering [62.63376387138257]
We propose a plug-and-play intervention framework that adaptively steers large language models (LLMs) reasoning in activation space.<n>RISER constructs a library of reusable reasoning vectors and employs a lightweight Router to dynamically compose them for each input.<n>The Router is optimized via reinforcement learning under task-level rewards, activating latent cognitive primitives in an emergent and compositional manner.
arXiv Detail & Related papers (2026-01-14T08:04:33Z) - Effective and Stealthy One-Shot Jailbreaks on Deployed Mobile Vision-Language Agents [29.62914440645731]
We present a one-shot jailbreak attack that leverages in-app prompt injections.<n> malicious apps embed short prompts in UI text that remain inert during human interaction but are revealed when an agent drives the UI via ADB.<n>Our framework comprises three crucial components: (1) low-privilege perception-chain targeting, which injects payloads into malicious apps as the agent's visual inputs; (2) user-invisible activation, a touch-based trigger that discriminates agent from human touches using physical touch attributes and exposes the payload only during agent operation; and (3) one-shot prompt efficacy, a stealthy-guided, character-level
arXiv Detail & Related papers (2025-10-09T05:34:57Z) - MobileGUI-RL: Advancing Mobile GUI Agent through Reinforcement Learning in Online Environment [63.62778707277929]
MobileGUI-RL is a scalable framework that trains GUI agent in online environment.<n>It synthesizes a curriculum of learnable tasks through self-exploration and filtering.<n>It adapts GRPO to GUI navigation with trajectory-aware advantages and composite rewards.
arXiv Detail & Related papers (2025-07-08T07:07:53Z) - Beyond Syntax: Action Semantics Learning for App Agents [60.56331102288794]
Action Semantics Learning (ASL) is a learning framework where the learning objective is capturing the semantics of the ground truth actions.<n>ASL significantly improves the accuracy and generalisation of App agents over existing methods.
arXiv Detail & Related papers (2025-06-21T12:08:19Z) - UI-TARS: Pioneering Automated GUI Interaction with Native Agents [58.18100825673032]
This paper introduces UI-TARS, a native GUI agent model that solely perceives the screenshots as input and performs human-like interactions.<n>In the OSWorld benchmark, UI-TARS achieves scores of 24.6 with 50 steps and 22.7 with 15 steps, outperforming Claude (22.0 and 14.9 respectively)
arXiv Detail & Related papers (2025-01-21T17:48:10Z) - Grounded GUI Understanding for Vision-Based Spatial Intelligent Agent: Exemplified by Extended Reality Apps [39.56688889845037]
We propose the first zero-shot cOntext-sensitive inteRactable GUI ElemeNT dEtection framework for virtual Reality apps, named Orienter.<n>By imitating human behaviors, Orienter observes and understands the semantic contexts of XR app scenes first, before performing the detection.
arXiv Detail & Related papers (2024-09-17T00:58:00Z) - Autoregressive Queries for Adaptive Tracking with Spatio-TemporalTransformers [55.46413719810273]
rich-temporal information is crucial to the complicated target appearance in visual tracking.
Our method improves the tracker's performance on six popular tracking benchmarks.
arXiv Detail & Related papers (2024-03-15T02:39:26Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.