ProactiveMobile

---
license: cc-by-nc-sa-4.0
language:
- zh
task_categories:
- text-generation
tags:
- mobile-agent
- proactive-agent
- benchmark
- function-calling
- gui
size_categories:
- 1K<n<10K
---

# ProactiveMobile

A comprehensive, **executable** benchmark for **proactive intelligence in mobile agents** — agents that anticipate user needs and act on their own, rather than passively executing explicit commands.

📄 Paper: [arXiv:2602.21858](https://arxiv.org/abs/2602.21858)  ·  🔗 Project: [xiaomi-research/proactive-mobile](https://github.com/xiaomi-research/proactive-mobile)

## Overview

Each instance asks a model to infer latent user intent from four dimensions of on-device context, then produce an **executable function sequence** drawn from a unified function pool.

- **3,658** instances (Chinese, `zh`)
- **Multi-answer** annotations: 1–3 target actions per instance
- **61** executable APIs as the unified function pool (`function_pool.json`)
- Difficulty levels: **1** (377) · **2** (1,324) · **3** (1,957)

> **Note on the function pool.** For this release, semantically overlapping functions were consolidated, and the pool was pruned to the **61 APIs** used by the benchmark.

## Data Format

A single JSON array (`benchmark_zh.json`). Each record:

```json
{
  "benchmark_metadata": {
    "id": "deb6a1f7-7db4-4dea-b32c-bba2bae9246a",
    "difficulty_level": 3
  },
  "reference_information": {
    "profile": "用户是一位35岁的国际关系分析师……",
    "phone":   "当前设备时间为晚上11点31分。手机型号为Pixel……",
    "world":   "今天是10月10日，星期一……",
    "trace": [
      "用户打开了 Google Scholar……",
      { "source": "picture", "picture": "Benchmark/aitz/android_in_the_zoo/.../xxx.png" }
    ]
  },
  "recommendations": [
    {
      "instruction": "立即打开'飞书'应用，并定位至设计团队的群聊……",
      "thinking": "用户在下午3点会议提醒后开启了勿扰模式……",
      "function": [
        {
          "name": "view_chat_history",
          "parameters": { "chat_criteria": "设计团队", "app_name": "飞书" }
        }
      ]
    }
  ],
  "language": "zh"
}
```

### Fields

| Field | Description |
|---|---|
| `benchmark_metadata.id` | Unique instance ID. |
| `benchmark_metadata.difficulty_level` | Difficulty, 1 (easy) – 3 (hard). |
| `reference_information.profile` | **User Profile** — attributes, habits, preferences. |
| `reference_information.phone` | **Device Status** — time, model, battery, network, notifications. |
| `reference_information.world` | **World Information** — date, weather, holidays, events. |
| `reference_information.trace` | **Behavioral Trajectory** — interaction history; each step is a text description or a screenshot reference. |
| `recommendations` | 1–3 target actions (the multi-answer ground truth). |
| `recommendations[].instruction` | Natural-language description of the proactive action. |
| `recommendations[].thinking` | Rationale linking context to the action. |
| `recommendations[].function` | Executable function sequence (`name` + `parameters`) from the function pool. An empty list means "no recommendation". |

> **Note on screenshots.** `trace` entries with `"source": "picture"` reference image paths (e.g. `Benchmark/aitz/...`, `Benchmark/GUI-Odyssey/...`, `Benchmark/MobileAgentBench/...`, `Benchmark/CAGUI/...`). The images are **not** included here — they come from the public **AITZ (Android in the Zoo)**, **GUI-Odyssey**, **MobileAgentBench**, and **CAGUI** datasets. Download them from their original sources and keep the relative paths to use the visual trajectories.

## Function Pool

`function_pool.json` defines the **61 executable APIs**, grouped into 15 functional categories (e.g. 娱乐与媒体, 个人管理, 购物消费, 交通出行). Each entry specifies the function name, a description, and its parameter schema. The `name` values in `recommendations[].function` are drawn from this pool.

## Usage

ProactiveMobile is an **evaluation benchmark** (test set). The data is a single JSON array, so loading it directly is the simplest:

```python
import json

data = json.load(open("benchmark_zh.json", encoding="utf-8"))
print(len(data), "instances")  # 3658
```

Alternatively, with 🤗 `datasets`:

```python
from datasets import load_dataset

ds = load_dataset("xiaomi-research/ProactiveMobile", data_files="benchmark_zh.json", split="test")
```

## Citation

```bibtex
@article{kong2026proactivemobile,
  title   = {ProactiveMobile: A Comprehensive Benchmark for Boosting Proactive Intelligence on Mobile Devices},
  author  = {Kong, Dezhi and Feng, Zhengzhao and Liang, Qiliang and others},
  journal = {arXiv preprint arXiv:2602.21858},
  year    = {2026}
}
```

## License

[CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) — non-commercial use, attribution required, share-alike.