license: other
license_name: avi-bench-data-use-policy-v1
license_link: https://github.com/FudanCVL/AVI-Bench/blob/main/DATA_USE_POLICY.md
pretty_name: AVI-Bench
viewer: true
tags:
- audio-visual
- omni-mllm
- multimodal-benchmark
- icml-2026
- evaluation-only
- no-training
- anti-contamination
task_categories:
- audio-classification
- image-classification
- video-classification
- visual-question-answering
- audio-text-to-text
- video-text-to-text
- image-text-to-text
- any-to-any
language:
- en
size_categories:
- 1K<n<10K
configs:
- config_name: AMIC
data_files:
- split: test
path: levels/perception/AMIC/data.json
- config_name: VMIC
data_files:
- split: test
path: levels/perception/VMIC/data.json
- config_name: AVL
data_files:
- split: test
path: levels/perception/AVL/data.json
- config_name: AVM
data_files:
- split: test
path: levels/perception/AVM/data.json
- config_name: VAR
data_files:
- split: test
path: levels/understand/VAR/data.json
- config_name: AVR
data_files:
- split: test
path: levels/understand/AVR/data.json
- config_name: AVC
data_files:
- split: test
path: levels/understand/AVC/data.json
- config_name: AVH
data_files:
- split: test
path: levels/reasoning/AVH/data.json
- config_name: VAH
data_files:
- split: test
path: levels/reasoning/VAH/data.json
- config_name: AVQA
data_files:
- split: test
path: levels/reasoning/AVQA/data.json
- config_name: AVLG
data_files:
- split: test
path: levels/reasoning/AVLG/data.json
- config_name: ASQA
data_files:
- split: test
path: levels/sensation/ASQA/data.json
- config_name: VSQA_I
data_files:
- split: test
path: levels/sensation/VSQA_I/data.json
- config_name: VSQA_V
data_files:
- split: test
path: levels/sensation/VSQA_V/data.json
- config_name: AVSQA
data_files:
- split: test
path: levels/sensation/AVSQA/data.json
extra_gated_prompt: |
## AVI-Bench Data Use Policy v1.0
AVI-Bench is released **for academic evaluation only**.
By requesting access you confirm that you have read and accept the
AVI-Bench Data Use Policy v1.0 in full:
https://github.com/FudanCVL/AVI-Bench/blob/main/DATA_USE_POLICY.md
In particular, you agree:
* **NOT** to use the dataset, in whole or in part, to train,
fine-tune, distil, align, or otherwise update **any** machine-
learning model — including LLMs, VLMs, audio-language models,
omni-modal foundation models, diffusion models, and any subsequent
model class.
* **NOT** to construct training data through paraphrasing,
translation, augmentation, synthetic generation, or LLM-assisted
relabelling of AVI-Bench content.
* **NOT** to redistribute, mirror, or rehost the dataset outside this
Hugging Face repository.
* **NOT** to crawl or batch-download the dataset by automated means.
* To use the dataset for evaluation, reproducibility, methodology
research, and benchmarking (academic or commercial), and not for
any other purpose.
* To cite the AVI-Bench paper in any derivative publication.
Misuse may result in access revocation and, where applicable, legal
remedy.
extra_gated_fields:
Full Name: text
Affiliation: text
Country: country
Intended use (one sentence): text
I have read and accept the AVI-Bench Data Use Policy: checkbox
extra_gated_button_content: I accept the policy and request access
⚠️ Evaluation-only dataset. AVI-Bench is licensed under the AVI-Bench Data Use Policy v1.0 (CC BY-ND 4.0 + Anti-Training Addendum). Using this dataset, in whole or in part, to train, fine-tune, distil, align, or otherwise update any machine-learning model is expressly prohibited. Bulk redistribution and automated scraping are also prohibited. Commercial evaluation and benchmarking are permitted. See the full policy before downloading.
AVI-Bench
AVI-Bench: Toward Human-like Audio-Visual Intelligence of Omni-MLLMs (ICML 2026)
A cognitively inspired benchmark for evaluating audio-visual intelligence of Omni-MLLMs. 5,864 samples, 14 tasks, scored by 9 metrics and summarised through a four-level AVI taxonomy.
- Code & docs: https://github.com/FudanCVL/AVI-Bench
- Project page: https://fudancvl.github.io/AVI-Bench/
- Data Use Policy: https://github.com/FudanCVL/AVI-Bench/blob/main/DATA_USE_POLICY.md
Layout
levels/
├── perception/ # AMIC, VMIC, AVL, AVM
├── understand/ # VAR, AVR, AVC
├── reasoning/ # AVH, VAH, AVQA, AVLG
└── sensation/ # ASQA, VSQA_I, VSQA_V, AVSQA
Each task folder contains a single data.json (JSON array of records,
schema: id, task, subtask, input, output). The HF viewer
exposes each task as a separate config with a single test split.
Loading
from datasets import load_dataset
# Pick a task
ds = load_dataset("FudanCVL/AVIBench", "AVQA", split="test")
print(len(ds), ds.features) # 469 rows
Tasks and sample counts
| Stage | Tasks | Samples |
|---|---|---|
| Perception | AMIC (518), VMIC (521), AVL (205), AVM (250) | 1,494 |
| Understanding | VAR (264), AVR (264), AVC (280) | 808 |
| Reasoning | AVH (250), VAH (250), AVQA (469), AVLG (503) | 1,472 |
| Primitive Sensation | ASQA (502), VSQA_I (620), VSQA_V (580), AVSQA (388) | 2,090 |
| Total | 14 tasks | 5,864 |
Citation
@inproceedings{wang2026avibench,
title = {AVI-Bench: Toward Human-like Audio-Visual Intelligence of Omni-MLLMs},
author = {Wang, Yaoting and Zhang, Ziyi and Tu, Wenming and Xu, Shaoxuan and Du, Wenjie and Liang, Cheng and Wang, Weijun and Li, Yuanchao and Li, Guangyao and Fei, Hao and Li, Yuanchun and Ding, Henghui and Liu, Yunxin},
booktitle = {Proceedings of the 43rd International Conference on Machine Learning (ICML)},
year = {2026}
}
Attribution
AVI-Bench bundles content derived from several public sources (including MusicAVQA, AV-Caps, AVHBench, and others). Each upstream source retains its own license. The Anti-Training Addendum of the AVI-Bench Data Use Policy governs the AVI-Bench annotations, organisation, and processing layered on top.
If you believe any bundled source content infringes your rights, please open an issue at https://github.com/FudanCVL/AVI-Bench/issues with the subject "Removal request"; we will respond within 14 calendar days.