Datasets:
| task_categories: | |
| - image-text-to-text | |
| - video-text-to-text | |
| - audio-text-to-text | |
| # ThinkOmni Evaluation Dataset | |
| This repository contains the evaluation datasets for **ThinkOmni**, a training-free framework that lifts textual reasoning to omni-modal scenarios via guidance decoding. | |
| ThinkOmni enhances omni-modal large language models (OLLMs) with the reasoning capabilities of large reasoning models (LRMs) at decoding time, adaptively balancing perception and reasoning signals. | |
| - **Paper:** [ThinkOmni: Lifting Textual Reasoning to Omni-modal Scenarios via Guidance Decoding](https://huggingface.co/papers/2602.23306) | |
| - **Repository:** [https://github.com/1ranGuan/thinkomni](https://github.com/1ranGuan/thinkomni) | |
| ### Dataset Contents | |
| This collection includes the following evaluation sets used in the paper: | |
| - **MMAU** | |
| - **OmniBench** | |
| - **Daily-Omni** | |
| ### Citation | |
| If you find this work or these datasets useful, please cite: | |
| ```bibtex | |
| @inproceedings{guan2026thinkomni, | |
| title={ThinkOmni: Lifting Textual Reasoning to Omni-modal Scenarios via Guidance Decoding}, | |
| author={Guan, Yiran and Tu, Sifan and Liang, Dingkang and Zhu, Linghao and Ju, Jianzhong and Luo, Zhenbo and Luan, Jian and Liu, Yuliang and Bai, Xiang}, | |
| booktitle={International Conference on Learning Representations (ICLR)}, | |
| year={2026} | |
| } | |
| ``` |