Audio-Reasoner: Improving Reasoning Capability in Large Audio Language Models
Xie Zhifei, Mingbao Lin, Zihang Liu, Pengcheng Wu, Shuicheng Yan, Chunyan Miao
Abstract
Recent advancements in multimodal reasoning overlook the audio modality. We introduce Audio-Reasoner, a large-scale audio language model for deep reasoning. We meticulously curated a large-scale and diverse multi-task audio dataset with simple annotations. Then, we leverage closed-source models to conduct secondary labeling, QA generation, along with structured COT process. These datasets together form a high-quality reasoning dataset with 1.2 million reasoning-rich samples, which we name CoTA. Following inference scaling principles, we train Audio-Reasoner on CoTA, enabling it to achieve great logical capabilities in audio reasoning. Experiments show state-of-the-art performance across key benchmarks, including MMAU-mini (+25.42%), AIR-Bench chat/foundation (+14.57%/+10.13%), and MELD (+8.01%). Our findings stress the core of structured CoT training in advancing audio reasoning. The model, dataset, and code are open-sourced at [https://github.com/xzf-thu/Audio-Reasoner](https://github.com/xzf-thu/Audio-Reasoner) or [https://huggingface.co/datasets/zhifeixie/Audio-Reasoner-CoTA](https://huggingface.co/datasets/zhifeixie/Audio-Reasoner-CoTA).
BibTeX
@inproceedings{emnlp2025_audioreasonerimp,
title = {Audio-Reasoner: Improving Reasoning Capability in Large Audio Language Models},
author = {Xie Zhifei and Mingbao Lin and Zihang Liu and Pengcheng Wu and Shuicheng Yan and Chunyan Miao},
booktitle = {EMNLP 2025},
year = {2025}
}