Datasets:
MobileForge Generated Tasks
Anonymous project: https://mobileforge-anonymous.github.io/
Anonymous code: https://github.com/mobileforge-anonymous/MobileForge
This dataset contains the consolidated task pool generated by MobileGym-Curriculum from target-app exploration trajectories. These tasks are used by MobileForge for rollout collection and annotation-free adaptation.
Release inventory payloads: files=1; bytes=2028399; sha256=66d0c571583fad088279323c7332ef99ad1d21a3df76f0d969ce698c62f4ebfb
Dataset summary
| File | Rows | Apps | Size | Description |
|---|---|---|---|---|
generated_tasks_26020301-all.csv |
3,249 | 20 | 1.93 MB | Consolidated AndroidWorld-side MobileForge task pool. |
The task pool is generated from real target-app exploration traces. Each row describes an executable mobile GUI task candidate together with its source app, source trajectory id, task-generation metadata, and coarse feasibility signals from the task-generation stage.
Load with datasets
from datasets import load_dataset
ds = load_dataset("mobileforge-anonymous/mobileforge-generated-tasks", split="train")
print(ds[0])
Load with pandas:
import pandas as pd
df = pd.read_csv("generated_tasks_26020301-all.csv")
print(df[["app_name", "task_description"]].head())
Columns
| Column | Description |
|---|---|
task_identifier |
Public task id formed from the source trajectory id and task index. |
task_description |
Natural-language mobile GUI task. |
golden_steps |
Estimated number of reference steps from task generation. |
app_package |
Android package name. |
app_name |
Human-readable app name. |
trajectory_id |
Source exploration trajectory id. |
original_goal |
Exploration goal that grounded the generated task. |
task_reasonable |
Whether the task was judged reasonable by the generation pipeline. |
task_completed |
Whether the source trajectory completed the grounding goal. |
task_id |
Per-trajectory task index. |
difficulty_level |
Optional difficulty annotation; empty in this release. |
core_functionality |
Generated functionality label. |
variation_type |
Type of task variation, such as scenario application or step progression. |
prerequisites |
Preconditions assumed by the generated task. |
App distribution
| App | Tasks |
|---|---|
| Files | 258 |
| Pro Expense | 247 |
| Broccoli Recipe | 241 |
| Simple SMS Messenger | 240 |
| Markor | 233 |
| Clock | 215 |
| Retro Music | 189 |
| Contacts | 166 |
| VLC | 161 |
| Chrome | 157 |
| Tasks | 150 |
| Simple Draw Pro | 145 |
| OsmAnd | 136 |
| Joplin | 130 |
| Simple Calendar Pro | 107 |
| OpenTracks | 104 |
| Settings | 102 |
| Camera | 94 |
| Audio Recorder | 91 |
| Simple Gallery Pro | 83 |
Generation metadata
- Reasonable tasks: 3,213 / 3,249.
- Tasks grounded in completed source trajectories: 1,691 / 3,249.
- Common variation types include
scenario_application,step_progression,multi-step_workflow, andparameter_change.
The file manifest.json provides machine-readable file metadata, including the row count and column list.
Relationship to other MobileForge artifacts
This dataset is the bridge between:
mobileforge-anonymous/mobileforge-exploration-trajectories: target-app exploration traces.mobileforge-anonymous/mobileforge-training-data: hint-contextualized step-level GRPO samples produced after rollout and hierarchical evaluation.mobileforge-anonymous/mobileforge-benchmark-results: AndroidWorld and MobileWorld evaluation artifacts.
Limitations
The tasks are automatically generated from exploration trajectories and may include infeasible or environment-state-dependent assumptions. MobileForge handles this downstream through rollout feedback, task filtering, and step-level policy optimization.
Citation
Citation information is withheld during double-blind review.
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