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#machine learning Preprint Oct 2026

AURAL: Adaptive Latent Reasoning with Joint Chunk for Speech Language Models

Model intelligence and fast response jointly shape the quality of interaction with speech language models, yet remain difficult to achieve together. Explicit chain-of-thought (CoT) improves reasoning and audio understanding, but generating intermediate reasoning tokens delays responses. Describing fine-grained acoustic...

Yu-Xiang Wang, Kun-Yu Feng, Yuan-Cheng Wang et al. · 0 citations
Preprint Sep 2026

Interpreting and Evaluating Dynamic-Rate Speech Codec Boundaries

Dynamic-frame-rate neural speech codecs replace a uniform frame grid with variable-duration tokens, making boundary placement part of the representation itself. Yet it is unclear what these boundaries encode and whether interpretable boundaries are also useful for neural speech reconstruction. This work combines bounda...

Han Wang, Jia-Qi Li, Ying Shen et al. · 0 citations
#machine learning Preprint Sep 2026

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-im...

Yu-Xiang Wang, Sheng-Bo Cai, Ying Shen et al. · 0 citations

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