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README.md

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+# XModBench: Benchmarking Cross-Modal Capabilities and Consistency in Omni-Language Models
+
+[![Paper](https://img.shields.io/badge/Paper-arXiv-red.svg)](https://arxiv.org/abs/2510.15148)
+[![Website](https://img.shields.io/badge/Website-XModBench-green.svg)](https://xingruiwang.github.io/projects/XModBench/)
+[![Dataset](https://img.shields.io/badge/Dataset-XModBench-ffcc4d?logo=huggingface&logoColor=black)](https://huggingface.co/datasets/RyanWW/XModBench)
+[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
+
+
+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
+
+```
+XModBench/
+├── data/
+│   ├── text/
+│   │   ├── perception/
+│   │   ├── spatial/
+│   │   ├── temporal/
+│   │   ├── linguistic/
+│   │   └── knowledge/
+│   ├── vision/
+│   │   └── [same task categories]
+│   └── audio/
+│       └── [same task categories]
+├── models/
+│   └── evaluation_scripts/
+├── results/
+│   └── model_performances/
+└── analysis/
+    └── visualization/
+```
+
+
+
+### 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 and Others},
+  journal={arXiv preprint arXiv:xxxx.xxxxx},
+  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**: [xingrui.wang@example.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.
+