AS$^2$D: Accelerating On-Demand Audio Understanding on Mobile Devices
Speculative decoding accelerates autoregressive generation by using a smaller drafter to propose tokens for batched verification by a larger target. However, conventional speculative decoding couples drafting to the target's evolving verified prefix, serializing drafting and verification. We ask whether this dependency is necessary for source-conditioned generation. Our key observation is that, for audio language models, the input audio and user request can provide useful speculative candidates without following the target's evolving text prefix. We propose AS$^2$D (Audio Speculative Speculative Decoding), which enables target-decoupled drafting: an audio-conditioned drafter follows its o...
| Authors | Yunzhe Li, Kyoungjun Park, Hongzi Zhu et al. |
|---|---|
| Primary category | cs.SD |
| Published | 29 September 2026 |
| Last updated at source | 29 September 2026 |
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