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arXiv · 2604.12527

Audio-Cogito: Towards Deep Audio Reasoning in Large Audio Language Models

Longhao Li·Hongjie Chen·Zehan Li·Qihan Hu·Jian Kang·Jie Li·Lei Xie·Yongxiang Li
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Abstract

Recent advances in reasoning models have driven significant progress in text and multimodal domains, yet audio reasoning remains relatively limited. Only a few Large Audio Language Models (LALMs) incorporate explicit Chain-of-Thought (CoT) reasoning, and their capabilities are often inconsistent and insufficient for complex tasks. To bridge this gap, we introduce Audio-Cogito, a fully open-source solution for deep audio reasoning. We develop Cogito-pipe for high-quality audio reasoning data curation, producing 545k reasoning samples. Based on this dataset, we adopt a self-distillation strategy for model fine-tuning. Experiments on the MMAR benchmark, the only audio benchmark evaluating the CoT process, show that our model achieves the best performance among open-source models and matches or surpasses certain closed-source models in specific metrics. Our approach also ranks among the top-tier systems in the Interspeech 2026 Audio Reasoning Challenge.

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Longhao Li, Hongjie Chen, Zehan Li, Qihan Hu, Jian Kang, Jie Li, Lei Xie, Yongxiang Li. 2026-04-14. Audio-Cogito: Towards Deep Audio Reasoning in Large Audio Language Models. https://arxiv.org/abs/2604.12527

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