EvoAudio: Recursive Self-Improvement for Audio Understanding
Audio language models understand what is said far better than how it sounds. Closing this gap takes more than data. Detailed acoustic annotation is costly, labels from stronger models inherit their errors and limits, and fixed data cannot adapt as the learner improves. We therefore propose EvoAudio, a recursive self-improvement system for audio understanding. To our knowledge, it is the first to evolve the model, waveforms, questions, and difficulty in one closed loop. EvoAudio uses the current model's performance to set the focus and difficulty of the next training data. A library of audio tools then constructs questions whose answers follow from how the audio was made, providing verifia...
| Authors | Yuxiang Wang, Shengbo Cai, Yingda Shen et al. |
|---|---|
| Primary category | cs.SD |
| Published | 23 September 2026 |
| Last updated at source | 23 September 2026 |
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