arXiv:2606. 02631v1 Announce Type: cross Abstract: This paper studies whether audio, images, and video can share a common wavelet token schema rather than relying on separate modality-specific latent grids.
By Shenghao Ding
arXiv:2606. 09605v1 Announce Type: new Abstract: Foundation models offer a promising route to compress multi-modal physiological signals into compact representations of human health, with broad applications across sleep medicine, cardiology, neurology and other healthcare domains.
By Jonathan F. Carter, Lionel Tarassenko
arXiv:2512.21653v2 Announce Type: replace-cross
Abstract: Speech codecs are usually optimized for waveform fidelity, allocating bits to acoustic detail that can be inferred from linguistic structure....
By Liuyang Bai, Weiyi Lu, Li Guo
The paper introduces Triage, a method that predicts the attention distribution of audio tokens before a language model processes them, enabling early pruning of less important tokens. By fitting a linear map to encoder outputs, Triage achieves high correlation (ρ ≥ 0.69) with full-model attention across eleven of thirteen large audio language models. Using this prediction, Triage compresses audio inputs while maintaining near‑full performance, outperforming baselines in transcription accuracy and significantly increasing the amount of audio that fits within a model’s context window.
By Kyoungjun Park, Yunzhe Li, Lili Qiu
TokenMapper is a framework that enables direct translation between different speech tokenizers, allowing heterogeneous speech models to communicate without converting tokens to waveform audio. It handles mismatched token spaces, including single and multi-codebook representations, while maintaining a shared effective token rate. Experiments on GLM-4-Voice, MiMi, and DualCodec show that TokenMapper achieves word error rates close to native reconstructions, comparable human MOS scores, and significantly reduces latency compared to waveform bridging.
By Tal Kozakov, Tal Rosenwein, Eliya Nachmani
The paper introduces a method to prune six layers from the encoder of OpenAI’s Whisper ASR model, reducing the encoder stack by 18.5% without requiring custom inference code. Layers are selected based on their minimal impact on Word Error Rate when removed. After pruning, the model’s WER rises from 18.2% to 21.9%, but distillation with unlabeled monolingual speech data lowers it to 20.1%.
"whyItMatters":"The approach offers a straightforward way to accelerate Whisper inference by simplifying the encoder while maintaining acceptable accuracy, and the released code and model enable immediate adoption by the community."
By Rasmus Aagaard, Nicki Skafte Detlefsen