你的AI助手终于能记住上次怎么帮你操作了
现在的AI助手每次帮你操作App都是从头学起，记不住你习惯的步骤。这篇论文让AI像真人助手一样，把每次操作的经验存进记忆库，下次遇到类似任务直接调用。研究者设计了一个双系统：一个大脑负责拆解任务、调用知识，另一个手专门操作界面，手会不断积累操作技巧。在手机、电脑、平板等跨平台测试中，这个系统在长任务基准上达到64.1%成功率，超过GPT-5.5等闭源模型。它不是你明天就能用的产品，但指明了方向：AI助手会越来越像有记忆的私人管家，而不是每次都要重新教的新手。
📄 原文摘要(英文)
OpenClaw has emerged as a leading agent framework for complex task automation, yet it faces insufficient cross-platform GUI interaction support and a well-built self-evolution mechanism. These flaws limit its adaptation to diverse device ecosystems and prevent performance improvements through continuous learning from execution experience. To resolve these issues, we propose the Know Deeply, Act Perfectly paradigm for personal assistants, which holds that accumulated user interaction and task-running experience directly improve execution accuracy and efficiency, unifying cognitive comprehension and operational execution. Based on this paradigm, we introduce KnowAct-GUIClaw, a novel Know-Route-Act-Reflect framework designed to address OpenClaw's GUI manipulation deficits and break through its cross-platform and recursive self-improvement constraints. First, the host agent leverages accumulated interaction experience and task-relevant knowledge for long-horizon task decomposition and allocation (Know). Second, a pluggable GUI subagent with an experience-attributable memory system (Know) and self-evolving skill library (Act), enabling seamless cross-platform migration and fast-path integration. Especially, this framework continuously stores user profiles and feedback to improve the accuracy of task decomposition and tool calls. Extensive experiments across Android, iOS, HarmonyOS and Windows show that KnowAct-GUIClaw achieves superior efficiency, accuracy and cross-platform adaptability. Especially, the GUIClaw with open-source Kimi-2.6 models achieves the best performance (64.1%) on the long-horizon MobileWorld benchmark, beating all agentical frameworks and closed-source agentical models, e.g., Seed-2.0-Pro and GPT-5.5. Additionally, the knowledgeable memory and execution skills supported by our framework are transferable across diverse base models, improving by 8.5% with Kimi-2.6.