Roman Bolshow

Status: training in progress. No weights are published yet — this card describes the recipe and the pilot results that motivate it. A ~1B masked-diffusion language model decoded with confidence-targeted steps, then spend a few extra passes rewriting only the tokens the model is least sure about. The point is inference cost. An autoregressive model needs one sequential forward pass per token. This one needs ~20 passes for a whole sequence, regardless of its length. Cost is K + R forward passes. One refill pass fixes any number of positions at once, because the model processes the whole sequence in parallel — that is what makes targeted repair cheaper than more denoising. Draft and refill are…