ThinkOmni: Lifting Textual Reasoning to Omni-modal Scenarios via Guidance Decoding
Paper • 2602.23306 • Published
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.
This collection includes the following evaluation sets used in the paper:
If you find this work or these datasets useful, please cite:
@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}
}