The advent of large language models (LLMs) has opened up new opportunities in the field of mobile task automation. Their superior language understanding and reasoning capabilities allow users to automate complex and repetitive tasks. However, due to the inherent unreliability and high operational cost of LLMs, their practical applicability is quite limited. To address these issues, this paper introduces MemoDroid, an innovative LLM-based mobile task automator enhanced with a unique app memory. MemoDroid emulates the cognitive process of humans interacting with a mobile app -- explore, select, derive, and recall. This approach allows for a more precise and efficient learning of a task's procedure by breaking it down into smaller, modular components that can be re-used, re-arranged, and adapted for various objectives. We implement MemoDroid using online LLMs services (GPT-3.5 and GPT-4) and evaluate its performance on 50 unique mobile tasks across 5 widely used mobile apps. The results indicate that MemoDroid can adapt learned tasks to varying contexts with 100% accuracy and reduces their latency and cost by 69.22% and 77.36% compared to a GPT-4 powered baseline.

翻译：大语言模型（LLMs）的出现为移动任务自动化领域带来了新的机遇。其卓越的语言理解与推理能力使用户能够自动化处理复杂且重复的任务。然而，由于LLMs固有的不可靠性与高运行成本，其实际应用能力受到很大限制。为解决这些问题，本文提出MemoDroid——一种基于LLM的创新移动任务自动化系统，并配备独特的应用记忆功能。MemoDroid模拟人类与移动应用交互时的认知过程——探索（explore）、选择（select）、推导（derive）与回忆（recall）。该方法通过将任务步骤分解为可复用、可重排且能适应不同目标的小粒度模块化组件，实现了对任务流程更精准、更高效的学习。我们基于在线LLM服务（GPT-3.5与GPT-4）实现了MemoDroid，并在5款主流移动应用的50个独特任务上评估其性能。结果表明，MemoDroid能以100%的准确率将已学习任务适配至不同场景，相较于基于GPT-4的基线系统，其延迟与成本分别降低69.22%与77.36%。