聚焦移动端智能体高效学习、持续演化与轻量执行，面向真实受限设备环境，研究大规模强化学习、自演化记忆与轻量级执行引擎，推动端侧智能体从“能用”向“好用、常用、可持续演进”升级。相关成果发表于 ACM MobiCom、MobiSys 等顶级会议。
构建高并发、高保真的移动端交互基础设施，提升端侧智能体在有限算力下的大规模在线强化学习效率。
研究基于长期交互轨迹的自演化记忆机制，提升智能体在个性化服务和长尾场景中的适应能力。
研究轻量级任务执行引擎，提升端侧设备低功耗、低时延、高鲁棒的执行能力。
2025.12.11
The rapid development of mobile GUI agents has stimulated growing research interest in long-horizon task automation. However, building agents for these tasks faces a critical bottleneck: the reliance on ever-expanding interaction history incurs substantial context overhead…
2025.11.21
Large language models (LLMs) have opened new opportunities for automated mobile app exploration, an important and challenging problem that used to suffer from the difficulty of generating meaningful UI interactions. However, existing LLM-based exploration approaches rely heavily on…
2025.09.25
Mobile task automation is an attractive technique that aims to enable voice-based hands-free user interaction with smartphones. However, existing approaches suffer from poor scalability due to the limited language understanding ability and the non-trivial manual efforts…
