Stop Thinking when Muscles Remember: Boosting Mobile GUI Agents with Memoir

Mobile graphical user interface (GUI) agents are gaining wide attention in both academia and industry thanks to their ability to automate everyday tasks. However, they suffer from poor execution efficiency due to the expensive per-step vision-language model (VLM) inference, which prohibits their wide deployment. Existing methods accelerate GUI agents by reducing input information for VLMs, overlooking the high sequential output overhead. In this paper, we identify that the long and redundant thinking tokens in VLM outputs are the key efficiency bottleneck for mobile GUI agents and propose to equip GUI agents with muscle memory to materialize redundant thinking at runtime. Based on the observation, we design Memoir, a Muscle-mEMOry traIning and Recall framework, to accelerate GUI agents. The framework adopts a domain-specific language (DSL), i.e., reduction DSL, to store muscle memory, an offline muscle training pipeline to learn muscle memory, and a dual-gated memory trigger to efficiently and reliably use muscle memory. Our evaluation results on AndroidControl show that Memoir accelerates GUI agents by up to 3.25x over state-of-the-art baselines while raising step accuracy from 65.2% to 69.3%.