| --- |
| license: mit |
| task_categories: |
| - multiple-choice |
| - audio-classification |
| - visual-question-answering |
| language: |
| - en |
| - zh |
| tags: |
| - audio-visual |
| - omni-modality |
| - cross-modal |
| - consistency |
| - benchmark |
| 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</h1> |
|
|
| <p align="center"> |
| <b>Benchmarking Cross-Modal Capabilities and Consistency in Omni-Language Models</b> |
| </p> |
|
|
| <p align="center"> |
| <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> |
|
|
| ## 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? |
|
|
| 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. |
|
|
| | | | |
| |---|---| |
| | **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) | |
|
|
| ## Repository layout |
|
|
| ``` |
| 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.zip # ALL media (audio/image/video) — download + unzip → Data/ |
| ├── 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 |
| ``` |
|
|
| > **Media live in `Data.zip`.** The JSONL question files (`data/`, |
| > `data_lite/`) reference media by repo-relative paths like |
| > `Data/vggss_audio_bench/xxx.wav`. Download and unzip `Data.zip` once so |
| > those paths resolve. (`Data.zip` was rebuilt with Chapter-stripped |
| > `emotions/` clips — a fix for a moviepy parsing crash; see Changelog.) |
| |
| ## Loading the data |
| |
| **1. Get the media** (one-time, ~30 GB): |
| |
| ```bash |
| huggingface-cli download RyanWW/XModBench Data.zip \ |
| --repo-type dataset --local-dir . |
| unzip Data.zip # → ./Data/... (matches the JSONL paths) |
| ``` |
| |
| **2. Load the questions**: |
| |
| ```python |
| from datasets import load_dataset |
|
|
| # one modality configuration (full set) |
| ds = load_dataset("RyanWW/XModBench", "audio_text", split="train") |
|
|
| # XModBench-Lite (balanced 6k) |
| lite = load_dataset("RyanWW/XModBench", "lite_a2t", split="train") |
|
|
| # media path for the first item (resolve against the unzipped Data/) |
| print(ds[0]["conditions"]["input"]) # e.g. Data/vggss_audio_bench/....wav |
| ``` |
| |
| The [lmms-eval port](https://github.com/XingruiWang/lmms-eval) handles the |
| download + path resolution automatically — no manual unzip needed there. |
| |
| ### 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" |
| } |
| ``` |
| |
| - `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`. |
|
|
| ## Modality configurations |
|
|
| | 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 | |
|
|
| `data/` keeps Image and Video separate (10 files) for efficient loading; |
| `data_lite/` merges Vision = Image ∪ Video into the 6 canonical configs. |
|
|
| ## XModBench-Lite |
|
|
| 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. |
|
|
| ## Evaluate with lmms-eval |
|
|
| XModBench is pre-integrated in |
| [**XingruiWang/lmms-eval**](https://github.com/XingruiWang/lmms-eval); the |
| dataset auto-downloads on first run. |
|
|
| ```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) |
|
|
| By-config accuracy (%); **Avg.** is the mean over the six configs. |
|
|
| | 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 | — | |
|
|
| 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). |
|
|
| ## Changelog |
|
|
| - **2026-05**: `Data.zip` rebuilt — the `emotions/` MELD clips had MP4 |
| *Chapter* metadata that crashed `moviepy`'s parser (used by some |
| evaluation backends). All emotion clips were re-muxed with |
| `ffmpeg -map_chapters -1` (video/audio streams untouched). Frame content |
| is identical; only the Chapter atom was removed. No other media changed. |
|
|
| ## License |
|
|
| 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 |
|
|
| ```bibtex |
| @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} |
| } |
| ``` |
|
|
