--- license: apache-2.0 task_categories: - multiple-choice language: - en - zh tags: - audio-visual - omnimodality - multi-modality - benchmark pretty_name: 'XModBench ' size_categories: - 10K XModBench: Benchmarking Cross-Modal Capabilities and Consistency in Omni-Language Models

XModBench teaser

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.