手机AI终于学会记事了：主动管理上下文，不再忘掉关键信息
现在的手机AI助手（比如让AI帮你订机票、填表单）在几步之内还行，但一旦任务拉长——比如跨多个App、需要记住中间结果——就很容易忘事。原因是它们像记流水账一样把所有历史堆在一起，导致关键信息被淹没。这篇论文换了个思路：让AI主动管理自己的记忆，把历史折叠成三个精简字段（操作历史、界面状态、最近一步），就像你写笔记时只记要点。他们用近3000条真实操作轨迹训练了一个8B模型，在长任务测试中达到开源8B模型最佳，还能泛化到没见过的App。它不是你明天就能装进手机用的，但指明了方向：AI助手要靠谱，得先学会“记什么、忘什么”。
📄 原文摘要(英文)
MLLM-based mobile GUI agents have made substantial progress on short-horizon tasks, yet remain unreliable on long-horizon tasks that require retaining intermediate facts across many steps and app transitions. We attribute this limitation to ReAct-style prompting, which passively accumulates per-step records, leading to prompt explosion and dilution of critical cross-app facts. To address this, we introduce MemGUI-Agent, an end-to-end long-horizon mobile GUI agent with proactive context management. MemGUI-Agent is built on Context-as-Action (ConAct), which casts context management as first-class actions emitted by the same policy that selects UI actions. Instead of passively appending history, ConAct maintains three structured context fields: folded action history, folded UI state, and recent step record, preserving critical UI facts while keeping context compact. To make proactive context management learnable across model scales, we construct MemGUI-3K, a 2,956-trajectory dataset with full ConAct annotations for supervised training and offline analysis. Training an 8B model on MemGUI-3K produces MemGUI-8B-SFT, an 8B MemGUI-Agent that achieves the best open-data 8B performance on MemGUI-Bench and generalizes to the out-of-distribution MobileWorld benchmark. Code, data, and trained models will be released at https://memgui-agent.github.io/.