Unverified50% confidenceFactExact time
Diffusion LLMs operate on the entire sequence simultaneously in each iteration, so the decoding process is inherently parallel, and total latency depends on the number of denoising steps rather than output length.
1
Sources
50%
Confidence
Long-term
Relevance
10/22/2026
First Seen
Sources
Mercury 3: A Diffusion LLM That Scales 40x Without Slowing Down
producthuntProductHunt Daily10/22/2026
Related Entities
Related Claims
UnverifiedA Diffusion Language Model (DLM) first initializes the entire sequence as noise and then restores the text through multiple denoising steps; it can process the whole sequence in parallel and naturally supports scoring over a fixed candidate set.74% similarVerifiedThe denoising process of diffusion models typically requires tens to hundreds of iterations (called sampling steps), each of which requires a complete forward pass through the neural network.73% similarUnverifiedDuring each inference, an LLM only processes the token sequence of the current input and does not retain persistent state across sessions (statelessness).72% similarUnverifiedEvery tool call means at least one additional LLM inference step, and LLM inference itself is the main source of latency.71% similarUnverifiedMasked diffusion randomly masks some tokens in the sequence during training, and during generation starts from the masked state and gradually denoises through multiple iterations to restore the complete sequence, with each step able to predict tokens at multiple positions in parallel.71% similar
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