README.md

# OmniAgentBench Dataset Card

## Overview

**OmniAgentBench** is a comprehensive benchmark for evaluating multimodal agents under realistic "wild" conditions including speech input, acoustic noise, and complex multi-turn interactions.

**Organization:** `omniagentbench`  
**Dataset:** `OmniAgentBench`  
**Total Size:** 27.1 GB  
**Contributors:** Hodfa71, acbueff

---

## Dataset Structure

The dataset is organized into **separate benchmark folders** at the root level:

```
OmniAgentBench/
├── 📁 data/                      # Dataset metadata and manifests
├── 📁 dataset/mpcc/              # MPCC dataset files (tabular)
├── 📁 gui_odyssey/               # ⭐ GUI Odyssey (NEW - 1,800 wild audio samples)
│   ├── General_Tool/
│   ├── Information_Management/
│   ├── Multi_Apps/
│   ├── Media_Entertainment/
│   ├── Social_Sharing/
│   ├── Web_Shopping/
│   └── screenshots/              # GUI Odyssey screenshots ONLY
├── 📁 images/mpcc/               # ⚠️ MPCC screenshots ONLY (5,700 files)
├── 📁 mpcc/                      # MPCC audio and manifests
│   ├── manifest.json
│   └── *.wav (2,700+ audio files)
├── 📁 wild/                      # Shared noise resources (MUSAN, etc.)
└── 📁 wild_long_scattered/       # Additional wild variations
```

---

## ⚠️ Important: Folder Separation

**The `images/` folder contains ONLY MPCC screenshots!**

| Folder | Benchmark | Content |
|--------|-----------|---------|
| `images/mpcc/` | **MPCC** | 5,700 screenshots (flight schedules, calendars, meetings) |
| `gui_odyssey/screenshots/` | **GUI Odyssey** | 1,950 mobile app screenshots |
| `gui_odyssey/General_Tool/screenshots/` | **GUI Odyssey** | Per-category screenshots |

**They are NOT mixed** - each benchmark has its own dedicated folder.

---

## Benchmarks

### 1. MPCC (Multi-Modal Planning and Control Challenge)

Constraint planning over visual schedules (flights, calendars, meetings).

**Location:** `mpcc/`, `images/mpcc/`, `dataset/mpcc/`

**Contents:**
- Audio: 300 speech samples across 3 tasks × 3 difficulties
- Images: 5,700 screenshots (schedules, flight info, calendars)
- Text: Structured task instructions with JSON output format
- Ground Truth: Optimal plans with constraint satisfaction labels

**Format:**
```python
{
  "audio_file": "mpcc_flight_easy_1.wav",
  "image_paths": ["images/mpcc/flight_easy_1_img1.jpg"],
  "text_instruction": "Find flights from London to Vienna...",
  "gold_answer": {"flight_way": "...", "price": 291}
}
```

---

### 2. GUI Odyssey (NEW - Wild Audio)

Cross-app mobile GUI navigation with wild audio inputs.

**Location:** `gui_odyssey/`

**Contents:**
- **Audio:** 1,800+ wild samples (300 per category × 6 categories)
  - TTS-generated with Qwen3-TTS
  - Acoustic noise: coffee_shop, convention_hall, outdoor, etc.
  - SNR: 5dB, 10dB, 15dB
- **Images:** 1,950 mobile app screenshots
- **Text:** Task instructions (spoken in audio)
- **Ground Truth:** Click coordinates, text inputs, scroll actions

**Categories:**
- General_Tool (300 samples)
- Information_Management (300 samples)
- Web_Shopping (400 samples)
- Multi_Apps (300 samples)
- Media_Entertainment (400 samples)
- Social_Sharing (300 samples)

**Format per sample:**
```python
{
  "sample_id": "0182869798349621_step2",
  "instruction": "Use Bloomberg to search for Pfizer stock news...",
  "audio_path": "gui_odyssey/General_Tool/audio/0182869798349621_step2_summer_outdoor_snr15.wav",
  "screenshot_path": "gui_odyssey/screenshots/General_Tool/0182869798349621_2.png",
  "gt_action": "CLICK",
  "gt_x": 351.0,
  "gt_y": 54.0,
  "gt_all_steps": "[...full episode with 11 steps...]"  # JSON string
}
```

---

## Usage Examples

### Load MPCC
```python
from datasets import load_dataset

# Load MPCC manifest
import json
with open("mpcc/manifest.json") as f:
    mpcc_data = json.load(f)

# Access sample
sample = mpcc_data["mpcc_flight_easy_1"]
audio = sample["audio_file"]  # Path to .wav
images = sample["image_paths"]  # List of image paths
```

### Load GUI Odyssey
```python
import pandas as pd
from huggingface_hub import hf_hub_download

# Download parquet
parquet_path = hf_hub_download(
    repo_id="omniagentbench/OmniAgentBench",
    filename="gui_odyssey/General_Tool/gui_odyssey_General_Tool_wild.parquet",
    repo_type="dataset"
)

# Load
df = pd.read_parquet(parquet_path)

# Access sample
row = df.iloc[0]
print(row["instruction"])  # Text instruction
print(row["audio_path"])   # Path to audio
print(row["screenshot_path"])  # Path to screenshot
print(row["gt_action"])    # Ground truth: CLICK/TEXT/SCROLL
```

### Access Audio and Images
```python
from huggingface_hub import hf_hub_download

# MPCC audio
audio_path = hf_hub_download(
    repo_id="omniagentbench/OmniAgentBench",
    filename="mpcc/mpcc_flight_easy_1.wav",
    repo_type="dataset"
)

# MPCC image
image_path = hf_hub_download(
    repo_id="omniagentbench/OmniAgentBench",
    filename="images/mpcc/calendar_easy_0_img1.jpg",
    repo_type="dataset"
)

# GUI Odyssey audio
audio_path = hf_hub_download(
    repo_id="omniagentbench/OmniAgentBench",
    filename="gui_odyssey/General_Tool/audio/0182869798349621_step0_coffee_shop_snr5.wav",
    repo_type="dataset"
)

# GUI Odyssey screenshot
screenshot_path = hf_hub_download(
    repo_id="omniagentbench/OmniAgentBench",
    filename="gui_odyssey/screenshots/General_Tool/0182869798349621_0.png",
    repo_type="dataset"
)
```

---

## Statistics

| Benchmark | Audio Files | Images | Size | Samples |
|-----------|-------------|--------|------|---------|
| MPCC | 2,700+ | 5,700 | ~10 GB | 300 speech |
| GUI Odyssey | 1,900+ | 1,950 | ~3 GB | 1,800 wild |
| **Total** | **4,600+** | **7,650** | **~27 GB** | **2,100+** |

---

## Ground Truth Format

### MPCC
- **Task Type:** Constraint planning
- **Output:** JSON with flight_way/schedule + price
- **Evaluation:** Feasible rate, optimal rate

### GUI Odyssey
- **Task Type:** Mobile GUI navigation
- **Output:** Action (CLICK/TEXT/SCROLL) + coordinates
- **Evaluation:** Action accuracy, coordinate error

---

## Citation

```bibtex
@dataset{omniagentbench_2026,
  title = {OmniAgentBench: Wild Multimodal Agent Benchmark},
  author = {Hoda Fakharzadeh and Team},
  year = {2026},
  publisher = {HuggingFace Datasets},
  url = {https://huggingface.co/datasets/omniagentbench/OmniAgentBench}
}
```

---

## Contact

For issues or questions, please open an issue on the OmniAgentBench repository.