arXiv:2610.37024v1 Announce Type: new Abstract: Recent generative speech enhancement (SE) models achieve high perceptual quality, but their non-causal processing and expensive computation make them difficult to use for streaming, on-device inference. We propose StreamSE, a 20M-parameter streaming SE model that operates in the latent space of a frozen pretrained neural audio codec. At each frame, StreamSE directly regresses a clean continuous latent from the noisy continuous latent while conditioning on its own predictions from previous frames. Enhancement therefore requires a single forward pass per frame and no iterative sampling or look-ahead. We further truncate the codec's residual vector quantizer (RVQ) to its coarse levels, halving the number of tokens per frame while improving robustness to reverberation. StreamSE is the only streaming generative model in our comparison, yet it matches or exceeds the perceptual quality of the offline generative baselines. Furthermore, the complete StreamSE pipeline runs in real time on a single smartphone CPU core. Our results show that continuous latent regression provides a practical route to generative SE under fully causal, on-device streaming constraints.