# LLM for Mobile: An Initial Roadmap

arXiv: https://arxiv.org/abs/2407.06573 · HTML: https://mlsanity.com/paper/2407.06573/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jul 2024.

## Built on by (most cited first, 11 of 11)

- [Large Language Model-Brained GUI Agents: A Survey](https://mlsanity.com/paper/2411.18279/) (2024)
- [Taming Various Privilege Escalation in LLM-Based Agent Systems: A Mandatory Access Control Framework](https://mlsanity.com/paper/2601.11893/) (2026)
- [Measuring and Augmenting Large Language Models for Solving Capture-the-Flag Challenges](https://mlsanity.com/paper/2506.17644/) (2025)
- [A Survey on Collaborative Mechanisms Between Large and Small Language Models](https://mlsanity.com/paper/2505.07460/) (2025)
- [LaSM: Layer-wise Scaling Mechanism for Defending Pop-up Attack on GUI Agents](https://mlsanity.com/paper/2507.10610/) (2025)
- [DiSCo: Device-Server Collaborative LLM-Based Text Streaming Services](https://mlsanity.com/paper/2502.11417/) (2025)
- [Recursive Offloading for LLM Serving in Multi-tier Networks](https://mlsanity.com/paper/2505.16502/) (2025)
- [LLM in the Middle: A Systematic Review of Threats and Mitigations to Real-World LLM-based Systems](https://mlsanity.com/paper/2509.10682/) (2025)
- [Towards Resilient and Autonomous Networks: A BlueSky Vision on AI-Native 6G](https://mlsanity.com/paper/2605.21395/) (2026)
- [On Emotion-Sensitive Decision Making of Small Language Model Agents](https://mlsanity.com/paper/2604.06562/) (2026)
- [mzCache: On-Device LLM Memory Management under Multitasking](https://mlsanity.com/paper/2609.01338/) (2026)

## Abstract (arXiv)

When mobile meets LLMs, mobile app users deserve to have more intelligent usage experiences. For this to happen, we argue that there is a strong need to appl LLMs for the mobile ecosystem. We therefore provide a research roadmap for guiding our fellow researchers to achieve that as a whole. In this roadmap, we sum up six directions that we believe are urgently required for research to enable native intelligence in mobile devices. In each direction, we further summarize the current research progress and the gaps that still need to be filled by our fellow researchers.