RIFT introduces selection‑based KV cache compression strategies that balance prefill information with recently generated tokens to mitigate decode staleness in speculative decoding. By refreshing the KV cache selection during token generation and employing an adaptive refresh strategy, RIFT improves drafter quality while managing decode latency. A compressibility‑driven drafter training phase further enhances performance, enabling Qwen3.5‑4B to retain over 98% of its full‑cache acceptance length within an 8K‑token KV cache budget on long‑context inputs.
By Dor Tsur, Maor Ashkenazi, Maximilian Golub, Peter Hicks, Yaniv Galron, Talor Abramovich, Yoav Miron, Tiyasa Mitra, Bita Darvish Rouhani, Ran Zilberstein
arXiv:2609.37988v1 Announce Type: new
Abstract: As the context size of text processed with an LLM grows, the size of KV caches can outstrip the memory allocated for the original model weights. This i...
By Joao Monteiro, Louis B\'ethune, Anastasiia Filippova, Sonia Laguna, David Grangier, Marco Cuturi
The paper addresses the challenge of long input contexts in Retrieval-Augmented Generation (RAG) systems, where concatenating many retrieved chunks increases prefill workload and time to first token (TTFT). It proposes a dual strategy: fine‑tuning the model to be aware of KV cache concatenation and selectively recomputing only part of the KV caches. Experiments on the RULER benchmark show that for a 124k‑token input, this combined method boosts the RULER score by 9.7 points over a baseline that recomputes caches only, while cutting TTFT by 80% compared with full attention.
By Fumihiko Tachibana, Daisuke Miyashita, Jun Deguchi
arXiv:2609.24197v1 Announce Type: new
Abstract: Speculative decoding losslessly accelerates large language model inference by having a lightweight draft model predict future tokens for verification b...
By Weifan Jiang, Krishna Teja Chitty-Venkata, Megan Flynn, Reed Meyerson, Zhenting Qi, Tianyu Wu, Eldar Kurtic, Minlan Yu, Alexandre Marques
arXiv:2606. 00144v1 Announce Type: cross Abstract: Speculative decoding speeds up autoregressive decoding by using a drafter to propose multiple tokens that a verifier validates in parallel.
By Liang He, Jingbo Wen, Qishi Zhan, Yixiong Chen, Kangning Cui, Qizhen Lan, Xilu Wang
arXiv:2608.30252v1 Announce Type: new
Abstract: Long-context LLM applications such as document summarization and multi-turn agents require generation from prefixes spanning tens of thousands of token...
By Tong Yuan, Chengxi Liao, Zeyi Wen