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From Truncation to Commitment: Persistent Context in Uniform Discrete Diffusion

Hayakawa, Satoshi
arXiv (All) · Papers
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artificial-intelligencemachine-learningprobability
Uniform-state discrete diffusion models update all tokens in parallel while keeping every position revisable. Even when the commonly used top-$p$ rule leaves only one candidate at a position, that choice affects only the current reverse step and can be revised at the next sampling step. We ask what
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