Datasets:

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Audio-Cogito

The released data contains 545k high-quality audio reasoning samples spanning sound, speech, and music domains. Each sample includes label annotations, Chain-of-Thought (CoT) annotations, and final answers.

Dataset Description

Audio-Cogito is designed to elicit and study deep audio reasoning capabilities in Large Audio Language Models (LALMs). The dataset is constructed with Cogito-Pipe, a four-stage pipeline for audio reasoning data construction:

Data Collection: Gathering data from multi-domain audio sources spanning sound, speech, and music.
QA Construction: Synthesizing diverse and challenging QA pairs based on the collected audio.
CoT Construction: Producing detailed Chain-of-Thought reasoning annotations for each task.
Quality Verification: Enforcing consistency between QA pairs and CoT rationales while filtering hallucinated or low-quality samples.

Data Format

The dataset is provided as a JSONL file. Each line contains a conversation-style sample and an associated audio path identifier.

{
  "messages": [
    {
      "role": "user",
      "content": "<audio>Question text ..."
    },
    {
      "role": "assistant",
      "content": "<think>CoT annotation ...</think>\n\nFinal answer"
    }
  ],
  "audios": [
    "audiocap/audios/audio_00000002.wav"
  ]
}

The messages field contains the user query and the annotated assistant response. The assistant response includes both CoT annotations and the final answer. The audios field stores the corresponding audio path identifier.

Dataset Statistics

Domain	Dataset Source	Main Skills Learning	Quantity	Ratio (%)
Sound	AudioSet	General Audio Event	179k	32.53
Sound	Clotho	Audio Captioning	6k	1.14
Sound	AudioCaps	Audio Captioning	40k	7.20
Sound	ComplexAudio	Complex Audio	37k	6.66
Speech	MELD	Speech Emotion	24k	4.50
Speech	CoVoST2	Speech Translation	56k	10.10
Speech	DailyTalk	Spoken Dialogue	9k	1.64
Music	MusicBench	General Music	88k	16.04
Music	FMA	Music Genre	76k	13.81
Music	Medley-solos-DB	Instrument Analysis	35k	6.38

Main Results

Audio-Cogito achieves top-tier performance in the Interspeech 2026 Audio Reasoning Challenge and sets new state-of-the-art results among open-source models on the MMAR benchmark.

Model	Size	Sound	Music	Speech	S-M	S-S	M-S	S-M-S	Avg (%)	Rubrics (%)	CRS
Qwen3-Omni-Thinking	30B	64.24	50.00	79.25	54.55	72.48	69.51	70.83	68.00	57.97	0.85
Audio-Cogito	30B	66.67	53.40	79.25	90.91	79.90	76.83	79.17	71.70	62.22	0.87

Notes: S-M: Sound-Music, S-S: Sound-Speech, M-S: Music-Sound, S-M-S: Sound-Music-Speech.

Citation

If you find Audio-Cogito useful for your research, please cite our paper:

@misc{li2026audiocogitodeepaudioreasoning,
      title={Audio-Cogito: Towards Deep Audio Reasoning in Large Audio Language Models}, 
      author={Longhao Li and Hongjie Chen and Zehan Li and Qihan Hu and Jian Kang and Jie Li and Lei Xie and Yongxiang Li},
      year={2026},
      eprint={2604.12527},
      archivePrefix={arXiv},
      primaryClass={eess.AS},
      url={https://arxiv.org/abs/2604.12527}, 
}

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

Links

Dataset Description

Data Format

Dataset Statistics

Main Results

Citation