Token-to-Mask remasking improves self-correction in diffusion LLMs by resetting erroneous commitments to masks rather than overwriting them, yielding +13.33 points on AIME 2025 and +8.56 on CMATH.
The baseline uses unmodified T2T editing at τt2t=0.5, with all inference parameters taken from the LLaDA2.1- mini Q Mode defaults (3)
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Remask, Don't Replace: Token-to-Mask Refinement in Diffusion Large Language Models
Token-to-Mask remasking improves self-correction in diffusion LLMs by resetting erroneous commitments to masks rather than overwriting them, yielding +13.33 points on AIME 2025 and +8.56 on CMATH.