When Hidden States Drift: Can KV Caches Rescue Long-Range Speculative Decoding?
Speculative decoding accelerates LLM inference, but SOTA hidden-state-based drafters suffer from long-range decay: draft accuracy degrades as the speculative step increases. Existing work attributes this decay to train-inference mismatch and proposes test-time training (TTT) as a remedy, yet we observe that long-range decay persists even in TTT-trained drafters. We revisit long-range decay from the perspective of context information preservation. In hidden-state reuse, we argue the target hidden state acts as a biased context compression: it aggregates historical token information according to the attention query at the current position, yielding a compact representation optimized for imm...
| Authors | Tianyu Liu, Yuhao Shen, Xinyi Hu et al. |
|---|---|
| Primary category | cs.CL |
| Published | 29 April 2026 |
| Last updated at source | 9 May 2026 |
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