Unverified50% confidenceOpinionExact time
Confidence decoding measures how decisively the current position can be predicted, rather than how much revealing that position helps subsequent decoding.
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10/30/2026
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UnverifiedAt each step, the remask sampler prioritizes confirming high-confidence positions based on model predictions and then remasks the remaining positions.76% similarUnverifiedUsing a confidence-first strategy during decoding (prioritizing the positions the model is most confident about) typically delivers significantly better generation quality than uniformly revealing a fixed proportion of tokens, because it allows the model to decide on difficult positions after having more context.70% similarUnverifiedIn the context of this paper, equivariance requires that "first predicting in latent space and then encoding" and "first executing the true transition and then encoding" should yield consistent results.69% similarUnverifiedInference in classification models is non-autoregressive, requiring only a single forward pass to complete predictions for all positions, making computational efficiency significantly higher than that of generative models.68% similarUnverifiedThe decomposed mask method decouples 'where to change' from 'what to change into,' with mask prediction responsible for spatial grounding and the generative model responsible for appearance synthesis, reducing the risk of hallucination or tampering in irrelevant regions.68% similar
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