Speculative Decoding and the Economics of Feature-Level Drafting
Autoregressive token generation at the second-top-layer feature vector level circumvents the drafting bottleneck, yielding lossless three-fold acceleration for frontier reasoning engines.
- [ANALYSIS] Feature-level drafting operates on top-layer latent vectors rather than discrete token predictions, achieving higher verification acceptance rates.
- [ANALYSIS] Dynamic draft trees adjust speculative branching based on confidence distribution, delivering up to three-fold latency reduction without distributional shift.
