FOLD: Position-Free Far Memory with Local Order for Long-Context Speculative Decoding
Speculative decoding accelerates LLM generation through lightweight drafting and parallel verification. It is particularly promising for long inputs, where KV-cache access costs can be amortized over multiple accepted tokens. Yet most drafters are trained on short sequences and degrade on long inputs, while long-context training is costly. Existing training-free approaches restrict the context window, rescale positions, or augment recent context with retrieved distant entries. Drafters, however, read target hidden states that already encode contextual and positional information, and their lightweight architecture makes full-history attention affordable. We propose FOLD, a training-free me...
| Authors | Shuxuan Li, Jialiang Kang, Yanghao Zhou et al. |
|---|---|
| Primary category | cs.CL |
| Published | 14 October 2026 |
| Last updated at source | 14 October 2026 |
Popularity, last 3 days
0 now. Counts as reported by the source, snapshotted daily by Silytics.
Silytics shows public metadata from arXiv and links to the original. Information may be incomplete or out of date. Last refreshed 17 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.