Unverified50% confidenceFactExact time
Masked diffusion language models (such as the LLaDA and Dream series) start from a fully masked sequence at inference time and progressively unmask through multiple rounds of iteration, predicting and writing tokens at multiple positions in parallel each time.
1
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50%
Confidence
Long-term
Relevance
11/4/2026
First Seen
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Related Claims
VerifiedDiffusion language models can simultaneously perform parallel prediction and iterative refinement on all tokens in the entire sequence, rather than outputting them one by one.78% similarUnverifiedThe Clover model follows the diffusion language model route with a MoDE architecture, possesses global thinking, and generates results through multiple rounds of iteration.76% similarUnverifiedLarge language models generate text in an autoregressive manner, where the production of each token depends on the context formed by all preceding tokens, following the chain rule of probability.75% similarUnverifiedThe decode phase of large language model inference generates only one token at a time and is a memory-bandwidth-bound operation, because generating each token requires loading the entire model weights from memory once.74% similarUnverifiedDifferent models each have their strengths in tasks such as reasoning, writing, coding, and multilingual processing, and multi-model cross-validation can be used where one model generates a solution and another reviews it for flaws.73% similar
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