Unverified50% confidenceSolutionExact time
In a single one-step denoising step, the model directly reads out the probability distribution for each answer slot rather than sampling token by token.
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Long-term
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9/24/2026
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Related Claims
UnverifiedAutoregressive generative models sample the next token at each step based on the probability distribution of the preceding context; when a repetitive pattern gains a slightly higher probability, it forms a positive feedback loop that leads to repetition degeneration.78% similarUnverifiedSpeculative decoding generates candidate tokens through a lightweight draft model, with the main model verifying them in parallel in a single forward pass, accepting tokens that conform to the distribution and regenerating from the first rejected position.74% similarUnverifiedOn contaminated benchmarks, models can directly extract answers from parametric memory rather than relying on in-context retrieval, rendering the reported lossless-compression conclusions meaningless.73% similarUnverifiedFor multiple-choice questions, the logits over each option token can be normalized via softmax to obtain a probability distribution, where a highly concentrated distribution indicates the model is more confident in the answer73% similar
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