Code, data, and trained models will be released at <a href=\"https://memgui-agent.github.io\" rel=\"nofollow\">https://memgui-agent.github.io</a>.</p>\n","updatedAt":"2026-06-24T02:10:56.233Z","author":{"_id":"64d761b98ebc40443831f82a","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/64d761b98ebc40443831f82a/DHBOtOstiFp2-lDY6b9gb.png","fullname":"Guangyi Liu","name":"lgy0404","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":2,"isUserFollowing":false}},"numEdits":0,"identifiedLanguage":{"language":"en","probability":0.8865987658500671},"editors":["lgy0404"],"editorAvatarUrls":["https://cdn-avatars.huggingface.co/v1/production/uploads/64d761b98ebc40443831f82a/DHBOtOstiFp2-lDY6b9gb.png"],"reactions":[],"isReport":false}}],"primaryEmailConfirmed":false,"paper":{"id":"2606.19926","authors":[{"_id":"6a349ba04c5c5e0d69bf1b91","name":"Guangyi Liu","hidden":false},{"_id":"6a349ba04c5c5e0d69bf1b92","name":"Gao Wu","hidden":false},{"_id":"6a349ba04c5c5e0d69bf1b93","name":"Congxiao Liu","hidden":false},{"_id":"6a349ba04c5c5e0d69bf1b94","name":"Pengxiang Zhao","hidden":false},{"_id":"6a349ba04c5c5e0d69bf1b95","name":"Liang Liu","hidden":false},{"_id":"6a349ba04c5c5e0d69bf1b96","name":"Mading Li","hidden":false},{"_id":"6a349ba04c5c5e0d69bf1b97","name":"Qi Zhang","hidden":false},{"_id":"6a349ba04c5c5e0d69bf1b98","name":"Mengyan Wang","hidden":false},{"_id":"6a349ba04c5c5e0d69bf1b99","name":"Liang Guo","hidden":false},{"_id":"6a349ba04c5c5e0d69bf1b9a","name":"Yong Liu","hidden":false}],"publishedAt":"2026-06-18T00:00:00.000Z","submittedOnDailyAt":"2026-06-24T00:00:00.000Z","title":"MemGUI-Agent: An End-to-End Long-Horizon Mobile GUI Agent with Proactive Context Management","submittedOnDailyBy":{"_id":"64d761b98ebc40443831f82a","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/64d761b98ebc40443831f82a/DHBOtOstiFp2-lDY6b9gb.png","isPro":false,"fullname":"Guangyi Liu","user":"lgy0404","type":"user","name":"lgy0404"},"summary":"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. 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Cite arxiv.org/abs/2606.19926 in a Space README.md to link it from this page.