כרטיס מסגרות למסגרות: תפיסת רעש משותפת להכנה של חלקה אחת בדיפוזיה של דגמי שפה

תקציר מקורי באנגליתarXiv:2609.06324v2 Announce Type: replace Abstract: Diffusion language models (dLLMs) predict all tokens of a block in parallel, but a single forward pass samples each position from its own marginal distribution, so the tokens need not form a coherent block. We ask whether a discrete masked model can commit an entire block in one pass when its mask embeddings are perturbed by a sampled Gaussian noise field: the same noise should give the same coherent continuation, and different noise should give different ones. We propose CONDOR (Coupled-Noise Distillation for One-Step Readout), which trains such a model from scratch without a target-side encoder or an autoregressive teacher. Training combines two signals. On real text, the model predicts masked tokens under several noise samples and is s