CURE: Local Uncertainty Repair for Block-Parallel Speculative Decoding
Speculative decoding mitigates the latency of sequential generation in autoregressive Large Language Models (LLMs) by interleaving draft generation with target verification. However, existing parallel drafting backends often suffer from rapid accuracy degradation over long horizons, leading to high rejection rates during verification and suboptimal wall-clock speedups. We observe that drafting errors are not uniformly distributed but typically stem from localized high-uncertainty tokens that destabilize downstream generation trajectories. Motivated by this token error pattern, we propose CURE, a budget-aware dynamic repair tree designed to repair errors at uncertainty focal points without...
| Authors | Aofan Liu, Jingxiang Meng, Fangxin Liu et al. |
|---|---|
| Primary category | cs.AI |
| Published | 1 August 2026 |
| Last updated at source | 1 August 2026 |
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