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Basic Information

This repository contains the source code associated with the academic paper titled "Empowering LLM to use Smartphone for Intelligent Task Automation." It is intended to provide the implementations and assets that accompany the paper so readers can inspect, run, and build upon the experiments and techniques described by the authors. The main purpose is to demonstrate and deliver code that bridges large language models and smartphone capabilities to automate tasks on mobile devices. The README in the repository is minimal but the project identity and intent are clear from the repository name and description: offering reproducible research artifacts that enable LLM-driven smartphone task automation for researchers and developers interested in mobile automation and applied LLM interaction with device features.

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Features
The repository is primarily a codebase implementing the methods presented in the paper. It is expected to include the core implementation that links LLM outputs to smartphone actions, experimental scripts and evaluation harnesses used in the paper, and supporting materials such as examples that illustrate automated mobile tasks. The project likely contains documentation or usage notes to reproduce the paper"s experiments and may provide configuration or sample data required for running the provided code. Overall, the feature set centers on enabling LLM orchestration of smartphone interfaces and shipping runnable artifacts for the paper"s contributions.
Use Cases
This codebase helps researchers, engineers, and practitioners who want to reproduce, validate, or extend the paper"s approach for enabling LLM-driven mobile automation. It provides a concrete implementation that can serve as a reference or baseline for developing intelligent agents that interact with smartphone UIs and services. Users can inspect experimental setups, adapt scripts to new tasks, and use the repository as a starting point for further research on automating real-world mobile tasks with language models. The materials reduce the barrier to replicate results and to experiment with LLM-to-device control ideas.

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