YC-born Interfaze open-sources diffusion-gemma-asr-small on July 2, officially dubbed the first multilingual open-source diffusion ASR. Trainable parameters only 42 million, 0.16% of the 26B DiffusionGemma backbone — the architectural approach "freeze everything, only train a tiny adapter": whisper-small encoder compresses 30 seconds of audio into 188 audio tokens, scattered into DiffusionGemma's <|audio|> slot, LoRA lets the backbone attend to the new modality, the diffusion decoder does 16-step bidirectional denoising on a 192-token canvas. The engineering key is CTC warmup: initially feeding audio directly to the frozen LLM, the loss stuck at 8 and didn't move — the attention learns to "ignore noise". The fix is to use CTC loss to strongly map audio tokens to transcriptions, within 300 steps CTC drops from 24 to 8.6, LibriSpeech test-clean English WER drops from 90% to 6.6%, surpassing Whisfusion (8.3%) and TransFusion proof-of-concept. There's still a 3-4 percentage point gap compared to autoregressive Whisper-small (~3.4%), the team attributes it to data volume rather than architecture. The 42M adapter covers English, German, French, Spanish, Hindi, Chinese six languages, FLEURS English WER 15.7%, Mandarin CER 29.6%. Diffusion ASR inference overhead is determined by the denoising step count, decoupled from audio length, 8 steps 14.9× real-time, suitable for batch transcription.