MemGUI-8B-SFT
This repository is an anonymous artifact prepared for peer review.
MemGUI-8B-SFT is an 8B MemGUI-Agent model trained from
Qwen3-VL-8B-Instruct on MemGUI-3K. It is designed for long-horizon mobile GUI
control with proactive context management.
The model follows the ConAct Context-as-Action protocol. At each step, it
produces a structured response with reasoning, history folding, a UI or memory
tool call, a grounded UI observation, and the next action intent. This allows
the agent to manage three context fields while acting: Folded Action History,
Folded UI State, and Recent Step Record.
Model Details
- Model type: multimodal mobile GUI agent
- Base model:
Qwen/Qwen3-VL-8B-Instruct
- Training data:
memgui-agent-anonymous/MemGUI-3K
- Training recipe: supervised fine-tuning with ms-swift
- Output protocol: ConAct 5-part structured output
- License: Apache 2.0
Intended Use
MemGUI-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.
Input and Output Format
The model expects a multimodal conversation with:
- a system prompt defining the MemGUI-Agent tools and response format,
- a user message containing
<image> plus the task goal and structured context,
- one screenshot image.
The assistant response follows this order:
1<thinking>...</thinking>
2<folding>{"range": [start_step, current_step], "summary": "..."}</folding>
3<tool_call>{"name": "mobile_use", "arguments": {...}}</tool_call>
4<ui_observation>...</ui_observation>
5<action_intent>...</action_intent>
For the first step of a trajectory, <folding> is omitted because there is no
previous step to fold.
Evaluation
| 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 in our experiments.
On MobileWorld GUI-Only, it transfers beyond the source benchmark and reaches
17.9% success rate.
Dataset
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.
