Contemporary GUI agents, while increasingly capable due to advances in Large Vision-Language Models (VLMs), often operate with a critical limitation: they treat each task in isolation, lacking a mechanism to systematically learn from past successes. This digital ''amnesia'' results in sub-optimal performance, repeated errors, and poor generalization to novel challenges. To bridge this gap, we introduce EchoTrail-GUI, a novel framework designed to mimic human-like experiential learning by equipping agents with a dynamic, accessible memory. Our framework operates in three distinct stages. First, during Experience Exploration, an agent autonomously interacts with GUI environments to build a curated database of successful task trajectories, validated by a reward model. Crucially, the entire knowledge base construction is thus fully automated, requiring no human supervision. Second, in the Memory Injection stage, upon receiving a new task, our system efficiently retrieves the most relevant past trajectories to serve as actionable ''memories''. Finally, during GUI Task Inference, these memories are injected as in-context guidance to inform the agent's reasoning and decision-making process. We demonstrate the efficacy of our approach on benchmarks including Android World and AndroidLab. The results show that EchoTrail-GUI significantly improves the task success rate and operational efficiency of baseline agents, validating the power of structured memory in creating more robust and intelligent GUI automation.

翻译：当前的图形用户界面（GUI）代理尽管因大型视觉语言模型（VLM）的进步而日益强大，但仍面临一个关键局限：它们孤立地处理每个任务，缺乏系统性地从过去成功经验中学习的机制。这种数字“遗忘症”导致性能欠佳、错误重复出现，以及对新挑战的泛化能力薄弱。为弥补这一不足，我们提出EchoTrail-GUI——一种新颖框架，旨在通过为代理配备动态、可访问的记忆来模拟人类经验学习。我们的框架分三个阶段运行。首先，在经验探索阶段，代理自主与GUI环境交互，构建经奖励模型验证的成功任务轨迹精选数据库。关键之处在于，整个知识库构建过程完全自动化，无需人工监督。其次，在记忆注入阶段，当接收到新任务时，系统高效检索最相关的过往轨迹作为可操作“记忆”。最后，在GUI任务推理阶段，这些记忆被注入作为上下文指引，以指导代理的推理和决策过程。我们在Android World和AndroidLab等基准上验证了该方法的效果。结果表明，EchoTrail-GUI显著提升了基线代理的任务成功率和操作效率，验证了结构化记忆在构建更鲁棒、更智能的GUI自动化中的强大能力。