Duplicate from xiaomi-research/ProactiveMobile

Co-authored-by: KDZ <DZKong@users.noreply.huggingface.co>

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README.md

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+---
+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.

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