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lgy0404
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Model Details
Model Provider
lgy0404
Model Tree
Qwen/Qwen3-VL-8B-Instruct
Input Modalities
Output Modalities
Supported Functionality
Qwen/Qwen3-VL-8B-Instructlgy0404/MemGUI-3KMemGUI-8B-SFT is intended for research on mobile GUI agents, long-horizon GUI control, context management, UI memory, and history folding. It can be used as an action policy in mobile GUI environments that provide screenshots and execute structured tool calls.
This model is not a general-purpose chatbot. It expects the MemGUI-Agent system prompt, a screenshot, and a structured mobile GUI context state.
The model expects a multimodal conversation with:
<image> plus the task goal and structured context,The assistant response follows this order:
xml
<thinking>...</thinking><folding>{"range": [start_step, current_step], "summary": "..."}</folding><tool_call>{"name": "mobile_use", "arguments": {...}}</tool_call><ui_observation>...</ui_observation><action_intent>...</action_intent>
For the first step of a trajectory, <folding> is omitted because there is no
previous step to fold.
| Benchmark | Metric | Score |
|---|---|---|
| MemGUI-Bench | Pass@1 | 23.4 |
| MemGUI-Bench | Pass@3 | 35.9 |
| MemGUI-Bench | IRR | 30.2 |
| MobileWorld GUI-Only | Success Rate | 17.9 |
On MemGUI-Bench, MemGUI-8B-SFT improves over the Qwen3-VL-8B-Instruct baseline and achieves the best open-data 8B performance reported in our experiments. On MobileWorld GUI-Only, it transfers beyond the source benchmark and reaches 17.9% success rate.
MemGUI-3K contains 2,956 successful mobile GUI trajectories and 64,430 reasonable step-level training samples with ConAct annotations. The dataset includes full trajectories, screenshots, step-level reasonableness annotations, and multimodal training files.
Dataset page: https://huggingface.co/datasets/lgy0404/MemGUI-3K
bibtex
@article{memguiagent2026,title = {MemGUI-Agent: An End-to-End Long-Horizon Mobile GUI Agent with Proactive Context Management},author = {Guangyi Liu and Gao Wu and Congxiao Liu and Pengxiang Zhao and Liang Liu and Mading Li and Qi Zhang and Mengyan Wang and Liang Guo and Yong Liu},year = {2026},journal = {arXiv preprint arXiv:2606.19926}}