Polished dataset card: ICLR 2026, HF configs, load examples, schema, Lite, lmms-eval, leaderboard, fixed license/citation

README.md

@@ -1,264 +1,221 @@
 ---
-license: apache-2.0
+license: mit
 task_categories:
 - multiple-choice
+- audio-classification
+- visual-question-answering
 language:
 - en
 - zh
 tags:
 - audio-visual
-- omnimodality
-- multi-modality
+- omni-modality
+- cross-modal
+- consistency
 - benchmark
-pretty_name: 'XModBench '
+pretty_name: XModBench
 size_categories:
 - 10K<n<100K
+configs:
+- config_name: audio_text
+  data_files: data/audio_text.jsonl
+- config_name: text_audio
+  data_files: data/text_audio.jsonl
+- config_name: audio_image
+  data_files: data/audio_image.jsonl
+- config_name: image_audio
+  data_files: data/image_audio.jsonl
+- config_name: image_text
+  data_files: data/image_text.jsonl
+- config_name: text_image
+  data_files: data/text_image.jsonl
+- config_name: audio_video
+  data_files: data/audio_video.jsonl
+- config_name: text_video
+  data_files: data/text_video.jsonl
+- config_name: video_audio
+  data_files: data/video_audio.jsonl
+- config_name: video_text
+  data_files: data/video_text.jsonl
+- config_name: lite_a2t
+  data_files: data_lite/a2t.jsonl
+- config_name: lite_a2v
+  data_files: data_lite/a2v.jsonl
+- config_name: lite_t2a
+  data_files: data_lite/t2a.jsonl
+- config_name: lite_t2v
+  data_files: data_lite/t2v.jsonl
+- config_name: lite_v2a
+  data_files: data_lite/v2a.jsonl
+- config_name: lite_v2t
+  data_files: data_lite/v2t.jsonl
 ---
 
-<h1 align="center">
-XModBench: Benchmarking Cross-Modal Capabilities and Consistency in Omni-Language Models
-</h1>
+<h1 align="center">XModBench</h1>
 
 <p align="center">
-  <img src="https://xingruiwang.github.io/projects/XModBench/static/images/teaser.png" width="90%" alt="XModBench teaser">
+  <b>Benchmarking Cross-Modal Capabilities and Consistency in Omni-Language Models</b>
 </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>
+  <a href="https://iclr.cc/Conferences/2026"><img src="https://img.shields.io/badge/ICLR-2026-8e44ad.svg" alt="ICLR 2026"></a>
+  <a href="https://arxiv.org/abs/2510.15148"><img src="https://img.shields.io/badge/arXiv-2510.15148-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://github.com/XingruiWang/XModBench"><img src="https://img.shields.io/badge/GitHub-Code-181717?logo=github&logoColor=white" alt="GitHub"></a>
+  <a href="https://github.com/XingruiWang/lmms-eval"><img src="https://img.shields.io/badge/lmms--eval-Integration-4b9cd3.svg" alt="lmms-eval"></a>
+  <a href="https://opensource.org/licenses/MIT"><img src="https://img.shields.io/badge/License-MIT-green.svg" alt="License: MIT"></a>
 </p>
 
+<p align="center">
+  <img src="https://xingruiwang.github.io/projects/XModBench/static/images/teaser.png" width="92%" alt="XModBench teaser">
+</p>
 
+<p align="center"><i>🎉 Accepted at <b>ICLR 2026</b></i></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
+## What is XModBench?
 
+**XModBench** is the first tri-modal (audio / vision / text) multiple-choice
+QA benchmark explicitly designed to measure **cross-modal consistency** — does
+an omni-language model give the same correct answer when the *same* semantic
+content is presented in different modalities?
 
-## 🚀 Quick Start
+Each item is a 4-choice question with a `<context>` (question stem) and four
+`<candidates>` (options). By permuting which modality carries the context vs.
+the candidates, every question is instantiated in **six modality
+configurations**, so no single modality is privileged.
 
-### Installation
+| | |
+|---|---|
+| **Samples** | 61,320 QA pairs |
+| **Task families** | 5 — Perception, Spatial, Temporal, Linguistic, Knowledge |
+| **Subtasks** | 17 |
+| **Modality configs** | 6 — A→T, A→V, T→A, T→V, V→A, V→T |
+| **Lite split** | 6,000 — balanced 5 families × 6 configs × 200 |
+| **Languages** | English, Chinese (speech translation) |
 
-```bash
-# Clone the repository
-git clone https://github.com/XingruiWang/XModBench.git
-cd XModBench
+## Repository layout
 
-# Install dependencies
-pip install -r requirements.txt
+```
+RyanWW/XModBench/
+├── data/            # 10 JSONL files, one per raw modality combination
+│   ├── audio_text.jsonl  text_audio.jsonl  audio_image.jsonl  ...
+├── data_lite/       # 6 JSONL — XModBench-Lite (a2t,a2v,t2a,t2v,v2a,v2t)
+├── Data/            # all media (audio .wav, images .jpg, videos .mp4)
+├── tasks/           # original per-subtask task definitions (JSON)
+└── eval_logs/       # released per-sample model outputs (reproduced via lmms-eval)
+    └── <model>/<lite|full>/  samples_*.jsonl + summary.json
 ```
 
-## 📂 Dataset Structure
-
-### Download and Setup
+## Loading the data
 
-After cloning from HuggingFace, you'll need to extract the data:
+```python
+from datasets import load_dataset
 
-```bash
-# Download the dataset from HuggingFace
-git clone https://huggingface.co/datasets/RyanWW/XModBench
+# one modality configuration (full set)
+ds = load_dataset("RyanWW/XModBench", "audio_text", split="train")
 
-cd XModBench
+# XModBench-Lite (balanced 6k)
+lite = load_dataset("RyanWW/XModBench", "lite_a2t", split="train")
 
-# Extract the Data.zip file
-unzip Data.zip
-
-# Now you have the following structure:
+# or stream a single file directly
+ds = load_dataset("json",
+     data_files="hf://datasets/RyanWW/XModBench/data/audio_text.jsonl",
+     split="train")
 ```
 
-### 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
+### Sample schema
+
+```json
+{
+  "index": 1,
+  "subtask": "01_perception/finegrained",
+  "question": "Listen to this audio clip. Which text description best matches the sound you hear? Answer with A, B, C, or D",
+  "conditions": { "modality": "Audio", "input": "Data/vggss_audio_bench/ymuNh7Cwhrs_000040.wav" },
+  "options": {
+    "A": { "modality": "Text", "input": "dog howling" },
+    "B": { "modality": "Text", "input": "chicken clucking" },
+    "C": { "modality": "Text", "input": "alligators, crocodiles hissing" },
+    "D": { "modality": "Text", "input": "cuckoo bird calling" }
+  },
+  "correct_answer": "A",
+  "category": "Animal Sounds"
+}
 ```
 
-**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"
+- `conditions.input` / `options[*].input` are **repo-relative media paths**
+  (`Data/...`) for non-text modalities, or the literal text for `Text`.
+- `correct_answer` ∈ {A, B, C, D}; `subtask` is `NN_family/subtask`.
 
-module load conda
-# conda activate vlm
-conda activate omni
+## Modality configurations
 
-export audioBench='/home/xwang378/scratch/2025/AudioBench'
+| Code | Context → Candidates |
+|------|----------------------|
+| A→T | Audio → Text |
+| A→V | Audio → Vision (image/video) |
+| T→A | Text → Audio |
+| T→V | Text → Vision |
+| V→A | Vision → Audio |
+| V→T | Vision → Text |
 
-# python $audioBench/scripts/run.py \
-#     --model gemini \
-#     --task_name perception/vggss_audio_vision \
-#     --sample 1000
+`data/` keeps Image and Video separate (10 files) for efficient loading;
+`data_lite/` merges Vision = Image ∪ Video into the 6 canonical configs.
 
+## XModBench-Lite
 
-# python $audioBench/scripts/run.py \
-#     --model gemini \
-#     --task_name perception/vggss_vision_audio \
-#     --sample 1000
+A 6,000-sample split, **balanced** across 5 task families × 6 configs × 200,
+for fast, low-cost evaluation. It tracks full-set model rankings closely
+(see leaderboard) and is the recommended quick-eval target.
 
-# python $audioBench/scripts/run.py \
-#     --model gemini \
-#     --task_name perception/vggss_vision_text \
-#     --sample 1000
+## Evaluate with lmms-eval
 
-# python $audioBench/scripts/run.py \
-#     --model gemini \
-#     --task_name perception/vggss_audio_text \
-#     --sample 1000
+XModBench is pre-integrated in
+[**XingruiWang/lmms-eval**](https://github.com/XingruiWang/lmms-eval); the
+dataset auto-downloads on first run.
 
-# 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
+```bash
+git clone https://github.com/XingruiWang/lmms-eval.git
+cd lmms-eval && pip install -e ".[all]"
 
+# XModBench-Lite, all 6 configs (resource-aware GPU profile)
+./submit_lite.sh qwen2_5_omni_interleave Qwen/Qwen2.5-Omni-7B qwenomni3
 
+# Level-2 metrics: by-config / by-family / disparity / imbalance
+python lmms_eval/tasks/xmod_bench/summarize.py \
+    --logs logs/xmod_bench_lite/results_qwen2_5_omni_interleave/
 ```
 
+Per-sample model outputs we reproduced are released here under
+[`eval_logs/`](https://huggingface.co/datasets/RyanWW/XModBench/tree/main/eval_logs).
 
+## Leaderboard — XModBench-Lite (reproduced via lmms-eval)
 
-## 📈 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% |
+By-config accuracy (%); **Avg.** is the mean over the six configs.
 
-### Key Findings
+| Model | A→T | A→V | T→A | T→V | V→A | V→T | Avg. |
+|-------|----:|----:|----:|----:|----:|----:|-----:|
+| Qwen3-Omni-30B | 71.6 | 52.0 | 62.5 | 67.0 | 55.6 | 83.1 | **65.3** |
+| Qwen2.5-Omni-7B | 63.1 | 49.8 | 59.2 | 62.5 | 50.3 | 76.4 | 60.2 |
+| Baichuan-Omni-1.5 | 52.5 | 32.0 | 47.6 | 56.6 | 47.0 | 77.7 | 52.2 |
+| OmniVinci | 62.2 | — | — | — | — | 78.8 | — |
 
-#### 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
+Qwen2.5-Omni matches its full-set paper numbers within 5 points on every
+configuration. Full-set numbers for all 14 paper models are on the
+[project website](https://xingruiwang.github.io/projects/XModBench/#leaderboard).
 
-#### 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
+## License
 
-#### 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
+Released under the **MIT License**. Media are redistributed for research use;
+please also respect the licenses of the underlying source datasets
+(VGG-Sound, STARSS23, GTZAN, URMP, MELD, URBANSAS, and others).
 
-## 📝 Citation
-
-If you use XModBench in your research, please cite our paper:
+## Citation
 
 ```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}
+@inproceedings{wang2026xmodbench,
+  title     = {XModBench: Benchmarking Cross-Modal Capabilities and Consistency in Omni-Language Models},
+  author    = {Wang, Xingrui and Liu, Jiang and Huang, Chao and Yu, Xiaodong and Wang, Ze and Sun, Ximeng and Wu, Jialian and Yuille, Alan and Barsoum, Emad and Liu, Zicheng},
+  booktitle = {International Conference on Learning Representations (ICLR)},
+  year      = {2026},
+  url       = {https://arxiv.org/abs/2510.15148}
 }
 ```
-
-## 📄 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.