README.md

---
license: apache-2.0
task_categories:
- multiple-choice
language:
- en
- zh
tags:
- audio-visual
- omnimodality
- multi-modality
- benchmark
pretty_name: 'XModBench '
size_categories:
- 10K<n<100K
---

<h1 align="center">
XModBench: Benchmarking Cross-Modal Capabilities and Consistency in Omni-Language Models
</h1>

<p align="center">
  <img src="https://xingruiwang.github.io/projects/XModBench/static/images/teaser.png" width="90%" alt="XModBench teaser">
</p>

<p align="center">
  <a href="https://arxiv.org/abs/2510.15148">
    <img src="https://img.shields.io/badge/Arxiv-Paper-b31b1b.svg" alt="Paper">
  </a>
  <a href="https://xingruiwang.github.io/projects/XModBench/">
    <img src="https://img.shields.io/badge/Website-Page-0a7aca?logo=globe&logoColor=white" alt="Website">
  </a>
  <a href="https://huggingface.co/datasets/RyanWW/XModBench">
    <img src="https://img.shields.io/badge/Huggingface-Dataset-FFD21E?logo=huggingface" alt="Dataset">
  </a>
<a href="https://github.com/XingruiWang/XModBench">
  <img src="https://img.shields.io/badge/Github-Code-181717?logo=github&logoColor=white" alt="GitHub Repo">
</a>
  <a href="https://opensource.org/licenses/MIT">
    <img src="https://img.shields.io/badge/License-MIT-green.svg" alt="License: MIT">
  </a>
</p>



XModBench is a comprehensive benchmark designed to evaluate the cross-modal capabilities and consistency of omni-language models. It systematically assesses model performance across multiple modalities (text, vision, audio) and various cognitive tasks, revealing critical gaps in current state-of-the-art models.

### Key Features

- **🎯 Multi-Modal Evaluation**: Comprehensive testing across text, vision, and audio modalities
- **🧩 5 Task Dimensions**: Perception, Spatial, Temporal, Linguistic, and Knowledge tasks
- **📊 13 SOTA Models Evaluated**: Including Gemini 2.5 Pro, Qwen2.5-Omni, EchoInk-R1, and more
- **🔄 Consistency Analysis**: Measures performance stability across different modal configurations
- **👥 Human Performance Baseline**: Establishes human-level benchmarks for comparison


## 🚀 Quick Start

### Installation

```bash
# Clone the repository
git clone https://github.com/XingruiWang/XModBench.git
cd XModBench

# Install dependencies
pip install -r requirements.txt
```

## 📂 Dataset Structure

### Download and Setup

After cloning from HuggingFace, you'll need to extract the data:

```bash
# Download the dataset from HuggingFace
git clone https://huggingface.co/datasets/RyanWW/XModBench

cd XModBench

# Extract the Data.zip file
unzip Data.zip

# Now you have the following structure:
```

### Directory Structure

```
XModBench/
├── Data/                              # Unzipped from Data.zip
│   ├── landscape_audiobench/          # Nature sound scenes
│   ├── emotions/                      # Emotion classification data
│   ├── solos_processed/               # Musical instrument solos
│   ├── gtzan-dataset-music-genre-classification/  # Music genre data
│   ├── singers_data_processed/        # Singer identification
│   ├── temporal_audiobench/           # Temporal reasoning tasks
│   ├── urbansas_samples_videos_filtered/  # Urban 3D movements
│   ├── STARSS23_processed_augmented/  # Spatial audio panorama
│   ├── vggss_audio_bench/             # Fine-grained audio-visual
│   ├── URMP_processed/                # Musical instrument arrangements
│   ├── ExtremCountAV/                 # Counting tasks
│   ├── posters/                       # Movie posters
│   └── trailer_clips/                 # Movie trailers
│
└── tasks/                             # Task configurations (ready to use)
    ├── 01_perception/                 # Perception tasks
    │   ├── finegrained/               # Fine-grained recognition
    │   ├── natures/                   # Nature scenes
    │   ├── instruments/               # Musical instruments
    │   ├── instruments_comp/          # Instrument compositions
    │   └── general_activities/        # General activities
    ├── 02_spatial/                    # Spatial reasoning tasks
    │   ├── 3D_movements/              # 3D movement tracking
    │   ├── panaroma/                  # Panoramic spatial audio
    │   └── arrangements/              # Spatial arrangements
    ├── 03_speech/                     # Speech and language tasks
    │   ├── recognition/               # Speech recognition
    │   └── translation/               # Translation
    ├── 04_temporal/                   # Temporal reasoning tasks
    │   ├── count/                     # Temporal counting
    │   ├── order/                     # Temporal ordering
    │   └── calculation/               # Temporal calculations
    └── 05_Exteral/                    # Additional classification tasks
        ├── emotion_classification/    # Emotion recognition
        ├── music_genre_classification/ # Music genre
        ├── singer_identification/     # Singer identification
        └── movie_matching/            # Movie matching
```

**Note**: All file paths in the task JSON files use relative paths (`./benchmark/Data/...`), so ensure your working directory is set correctly when running evaluations.



### Basic Usage

```bash


#!/bin/bash
#SBATCH --job-name=VLM_eval        
#SBATCH --output=log/job_%j.out
#SBATCH --error=log/job_%j.log                        
#SBATCH --ntasks-per-node=1
#SBATCH --gpus-per-node=4

echo "Running on host: $(hostname)"
echo "CUDA_VISIBLE_DEVICES=$CUDA_VISIBLE_DEVICES"

module load conda
# conda activate vlm
conda activate omni

export audioBench='/home/xwang378/scratch/2025/AudioBench'

# python $audioBench/scripts/run.py \
#     --model gemini \
#     --task_name perception/vggss_audio_vision \
#     --sample 1000


# python $audioBench/scripts/run.py \
#     --model gemini \
#     --task_name perception/vggss_vision_audio \
#     --sample 1000

# python $audioBench/scripts/run.py \
#     --model gemini \
#     --task_name perception/vggss_vision_text \
#     --sample 1000

# python $audioBench/scripts/run.py \
#     --model gemini \
#     --task_name perception/vggss_audio_text \
#     --sample 1000

# Qwen2.5-Omni

# python $audioBench/scripts/run.py \
#         --model qwen2.5_omni \
#         --task_name perception/vggss_audio_text \
#         --sample 1000

python $audioBench/scripts/run.py \
        --model qwen2.5_omni \
        --task_name perception/vggss_vision_text \
        --sample 1000


```



## 📈 Benchmark Results

### Overall Performance Comparison

| Model | Perception | Spatial | Temporal | Linguistic | Knowledge | Average |
|-------|------------|---------|----------|------------|-----------|---------|
| **Gemini 2.5 Pro** | 75.9% | 50.1% | 60.8% | 76.8% | 89.3% | 70.6% |
| **Human Performance** | 91.0% | 89.7% | 88.9% | 93.9% | 93.9% | 91.5% |

### Key Findings

#### 1️⃣ Task Competence Gaps
- **Strong Performance**: Perception and linguistic tasks (~75% for best models)
- **Weak Performance**: Spatial (50.1%) and temporal reasoning (60.8%)
- **Performance Drop**: 15-25 points decrease in spatial/temporal vs. perception tasks

#### 2️⃣ Modality Disparity
- **Audio vs. Text**: 20-49 point performance drop
- **Audio vs. Vision**: 33-point average gap
- **Vision vs. Text**: ~15-point disparity
- **Consistency**: Best models show 10-12 point standard deviation

#### 3️⃣ Directional Imbalance
- **Vision↔Text**: 9-17 point gaps between directions
- **Audio↔Text**: 6-8 point asymmetries
- **Root Cause**: Training data imbalance favoring image-to-text over inverse directions

## 📝 Citation

If you use XModBench in your research, please cite our paper:

```bibtex
@article{wang2024xmodbench,
  title={XModBench: Benchmarking Cross-Modal Capabilities and Consistency in Omni-Language Models},
  author={Wang, Xingrui, etc.},
  journal={arXiv preprint arXiv:2510.15148},
  year={2024}
}
```

## 📄 License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

## 🙏 Acknowledgments

We thank all contributors and the research community for their valuable feedback and suggestions.

## 📧 Contact

- **Project Lead**: Xingrui Wang
- **Email**: [xwang378@jh.edu]
- **Website**: [https://xingruiwang.github.io/projects/XModBench/](https://xingruiwang.github.io/projects/XModBench/)

## 🔗 Links

- [Project Website](https://xingruiwang.github.io/projects/XModBench/)
- [Paper](https://arxiv.org/abs/xxxx.xxxxx)
- [Leaderboard](https://xingruiwang.github.io/projects/XModBench/leaderboard)
- [Documentation](https://xingruiwang.github.io/projects/XModBench/docs)


## Todo

- [ ] Release Huggingface data
- [x] Release data processing code
- [x] Release data evaluation code
---

**Note**: XModBench is actively maintained and regularly updated with new models and evaluation metrics. For the latest updates, please check our [releases](https://github.com/XingruiWang/XModBench/releases) page.