# MANA: Towards Efficient Mobile Ad Detection via Multimodal Agentic UI Navigation

arXiv: https://arxiv.org/abs/2603.20351 · HTML: https://mlsanity.com/paper/2603.20351/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 61 references that are in the map.
- First posted on arXiv 20 Mar 2026.

## Builds on (most cited first, 11 of 11)

- [LLaVA-OneVision: Easy Visual Task Transfer](https://mlsanity.com/paper/2408.03326/) (2024)
- [Navigating the Digital World as Humans Do: Universal Visual Grounding for GUI Agents](https://mlsanity.com/paper/2410.05243/) (2024)
- [AutoDroid: LLM-powered Task Automation in Android](https://mlsanity.com/paper/2308.15272/) (2023)
- [Ferret-UI: Grounded Mobile UI Understanding with Multimodal LLMs](https://mlsanity.com/paper/2404.05719/) (2024)
- [Explore, Select, Derive, and Recall: Augmenting LLM with Human-like Memory for Mobile Task Automation](https://mlsanity.com/paper/2312.03003/) (2023)
- [StoryDroid: Automated Generation of Storyboard for Android Apps](https://mlsanity.com/paper/1902.00476/) (2019)
- [LLM-Explorer: Towards Efficient and Affordable LLM-based Exploration for Mobile Apps](https://mlsanity.com/paper/2505.10593/) (2025)
- [MadDroid: Characterising and Detecting Devious Ad Content for Android Apps](https://mlsanity.com/paper/2002.01656/) (2020)
- [VET: Identifying and Avoiding UI Exploration Tarpits](https://mlsanity.com/paper/2102.06377/) (2021)
- [Studying Ad Library Integration Strategies of Top Free-to-Download Apps](https://mlsanity.com/paper/2104.00182/) (2021)
- [Careful About What App Promotion Ads Recommend! Detecting and Explaining Malware Promotion via App Promotion Graph](https://mlsanity.com/paper/2410.07588/) (2024)

## Abstract (arXiv)

Mobile advertising dominates app monetization but introduces risks ranging from intrusive user experience to malware delivery. Existing detection methods rely either on static analysis, which misses runtime behaviors, or on heuristic UI exploration, which struggles with sparse and obfuscated ads. In this paper, we present MANA, the first agentic multimodal reasoning framework for mobile ad detection. MANA integrates static, visual, temporal, and experiential signals into a reasoning-guided navigation strategy that determines not only how to traverse interfaces but also where to focus, enabling efficient and robust exploration. We implement and evaluate MANA on commercial smartphones over 200 apps, achieving state-of-the-art accuracy and efficiency. Compared to baselines, it improves detection accuracy by 30.5%-56.3% and reduces exploration steps by 29.7%-63.3%. Case studies further demonstrate its ability to uncover obfuscated and malicious ads, underscoring its practicality for mobile ad auditing and its potential for broader runtime UI analysis (e.g., permission abuse). Code and dataset are available at https://github.com/MANA-2026/MANA.