Unverified50% confidenceFactExact time
Eagle improves acceptance rates through feature-level prediction, while Lookahead Decoding avoids using an independent draft model through Jacobi iteration.
1
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50%
Confidence
Long-term
Relevance
9/11/2026
First Seen
Sources
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Related Claims
UnverifiedEAGLE's core innovation is training a lightweight autoregressive head that takes the target model's hidden states as input features to predict the feature representation of the next token, rather than directly predicting the token itself.67% similarUnverifiedAdaptiveSpec's progressive marginal rule allows a draft token to be accepted when the ratio of the target model's probability on that draft token to its top-1 token probability exceeds a threshold, even if the two do not exactly match.64% similarUnverifiedAnthropic's sparse autoencoder research has been able to isolate millions of interpretable features from model activations62% 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).61% similar
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