New paper: Binary Discrete Diffusion denoises candidate token sets for language reasoning
LucaAmb · x · 2026-10-25
A new paper introduces Binary Discrete Diffusion for language and reasoning: instead of emitting one token at a time, it keeps a set of candidate tokens per position—like a human solving Sudoku—and denoises these sets using binary representations, iteratively narrowing them down.
In the quote-tweet, sedielem highlights the key conceptual point: continuous diffusion for language can, in principle, represent uncertainty in intermediate noisy states (superpositions of possible outcomes), and binary discrete diffusion is another neat way to achieve the same property.
The work is a non-autoregressive alternative worth watching in the diffusion-LM space.
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