发表机构
The Hong Kong Polytechnic University; Hangzhou Diagens Biotechnology Co., Ltd.（香港理工大学; 杭州迪基因生物技术有限公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对移动设备上代理框架存在的问题，提出PalmClaw开源框架，能在手机上原生运行并管理相关要素，将设备能力以工具形式呈现，实验显示该框架提升了任务成功率、缩短完成时间且降低设置负担。
AI 中文摘要
大语言模型（LLM）代理已从生成响应发展到通过调用工具、观察结果和迭代决定下一步行动来执行多步任务。大多数代理系统运行在桌面或服务器上，而移动设备也是重要的代理环境。现有移动代理主要通过图形用户界面（GUI）操作运行，存在诸多问题。本文提出PalmClaw，一个在手机上原生运行的开源代理框架，它能直接在设备上管理会话、内存、技能、工具和代理循环，将设备能力作为设备工具暴露，实验表明其在任务成功率和完成时间上有显著提升，且设置负担更低。
英文摘要
Large Language Model (LLM) agents have moved beyond generating responses to executing multi-step tasks by calling tools, observing the results, and iteratively deciding the next action. Most agent systems run on desktops or servers, which support tool use and task automation. Mobile devices are also important agent environments because they are widely accessible and contain users' data, sensors, and daily-use applications. Existing mobile agents mainly operate smartphones through graphical user interface (GUI) actions such as tapping, swiping, and typing, which often form long, interface-dependent sequences, cannot directly access device capabilities, and make execution boundaries difficult to define. We present PalmClaw, an open-source agent framework that runs natively on mobile phones and manages the sessions, memory, skills, tools, and agent loop directly on the device. PalmClaw exposes device capabilities as device tools with explicit arguments, structured results, and clearly defined execution boundaries. This design enables agents to use mobile capabilities directly while keeping each action explicit and controlled. Experiments show an 11.5% relative improvement in task success and a 94.9% reduction in completion time over the strongest baseline, with lower setup burden and traces illustrating how execution boundaries are applied. Code is available at https://github.com/ModalityDance/PalmClaw.
CommentsAccepted by EMNLP 2026 System Demonstration
