Standard autoregressive decoding advances one token at a time, even when several upcoming tokens can be predicted together.
We propose CADENCE (Context-Adaptive DEcoding), a generation method that learns both what to draft and how far to draft.
Gaussian prediction heads represent future spans as continuous latent vectors, and a shared decoder converts the selected vector into token predictions in parallel.
A learned router selects the prediction length from the model's own hidden states and predicted Gaussian parameters, adapting generation granularity to the current context with single-token generation as a fallback.
The backbone verifies each proposed continuation and retains the prefix that matches its greedy predictions.
Experiments with Llama-3.1-8B-Instruct on Alpaca, GSM8K, and HumanEval show that router-selected positions yield higher draft acceptance than applying the corresponding prediction length everywhere.
CADENCE achieves up to 1.3x higher generation throughput than standard greedy decoding.