Unverified50% confidenceFactExact time
The core mechanism of DoubtLess is: let an auxiliary small model read the CoT already generated by the large model under two prompts, concise and verbose, respectively, then multiply the difference between the logit vectors under the two prompts by an intensity coefficient and add it to the logits of the main model's decoding at each step.
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10/14/2026
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UnverifiedDoubtLess is a training-free contrastive decoding method that uses a small reasoning model to guide a large model to generate more concise chain-of-thought without loss of accuracy.75% similarUnverifiedThe core idea of contrastive decoding is to run a strong model and a weak model simultaneously and subtract their output logits to amplify the strong model's distinctive tendencies relative to the weak model.74% 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 answer69% similarUnverifiedSpeculative decoding improves inference speed by using a small draft model to generate candidate tokens that are verified in parallel by the main model; sparse attention reduces computational complexity from O(n²) to near O(n).69% similar
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