Raw waveform diffusion can now deliver the same—or even higher—audio fidelity that autoencoder‑based pipelines have long claimed as their exclusive domain. By discarding any latent compression step, WavFlow produces samples that listeners cannot distinguish from those generated by established latent diffusion models. For years the community has built audio generators on top of semantic‑acoustic autoencoders, a strategy epitomized by Stable Audio 3, which first compresses waveforms into a compact latent space before applying diffusion. This two‑stage design has been justified as necessary to tame the high dimensionality of raw audio and to keep training tractable. WavFlow’s VGGSound results prove that a pure‑waveform approach is competitive: “Experimental results show that WavFlow achieves competitive results on the video‑to‑audio benchmark VGGSound (FD 59.98, IS 17.40, DeSync 0.44) … matching or exceeding the performance of established latent‑based methods” [1] .…