--- 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: - 10KXModBench

Benchmarking Cross-Modal Capabilities and Consistency in Omni-Language Models

XModBench teaser

🎉 Accepted at ICLR 2026

## 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 `` (question stem) and four `` (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) └── // 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} } ```