COMPUTING GUIDANCE SCHEDULES FROM CONDITIONAL INFORMATION IN MASKED GENERATIVE MODELS

Masked generative models apply classifier-free guidance (CFG) repeatedly during parallel decoding, yet existing methods schedule guidance using a predefined function of normalized time. The appropriate time-based shape can vary across checkpoints, token grids, and reveal policies, requiring a new curve-family or exponent search for each setting. We introduce InfoProgress, an information-coordinate view of CFG scheduling. A short Monte Carlo probe of the frozen decoder measures the conditional-to-unconditional log-likelihood ratio carried by tokens committed at each round. Accumulating this evidence produces a checkpoint-specific information clock, from which dose normalization computes all per-round guidance weights in closed form. InfoProgress fixes a shared clock-to-guidance mapping and retains only the ordinary one-dimensional CFG-strength scan. On MaskGIT-256, it improves tuned constant CFG from FID 4.481 to 3.218 at 32 network evaluations and surpasses the checkpoint’s reported FID 4.17 at 64 evaluations. Across decoding budgets, token grids, model capacities, and MaskBit codebook widths, it consistently improves constant CFG and remains within 0.28 FID of the best hindsight-selected fixed curve.