本文介绍了一种名为Audio-Reasoner的音频语言模型，用于音频任务中的深度推理。通过构建大规模、多样化的多任务音频数据集并利用封闭源模型进行二次标注，实现了在音频推理任务上的突破。实验结果表明，Audio-Reasoner在多个关键基准测试中均取得了最先进的性能。

arXiv:2503.02318v2 Announce Type: replace-cross Abstract: Recent advancements in multimodal reasoning have largely overlooked the audio modality. We introduce Audio-Reasoner, a large-scale audio language model for deep reasoning in audio tasks. 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.