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

arXiv: https://arxiv.org/abs/2604.12527 · HTML: https://mlsanity.com/paper/2604.12527/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Apr 2026.

## Built on by (most cited first, 3 of 3)

- [Audio Interaction Model](https://mlsanity.com/paper/2606.05121/) (2026)
- [X3-OPD: Distilling Reasoning into Large Audio-Language Models via On-Policy Alignment](https://mlsanity.com/paper/2607.21550/) (2026)
- [Reinforcement Learning with Evolving Rubrics as Rewards for Audio Reasoning](https://mlsanity.com/paper/2608.02831/) (2026)

## Abstract (arXiv)

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.