arXiv:2610.15509v1 Announce Type: cross
Abstract: KV caches make long-context serving efficient by reusing past computation, but model switching breaks this reuse and forces the receiver to prefill t...
By Haomiao Qiu, Yining Tang, Zijia Song, Ruizi Han, Ziyue Qiao, Miao Zhang
arXiv:2608.30963v1 Announce Type: cross
Abstract: Modern large language model (LLM) serving systems increasingly operate over repeated or shared context, yet each model typically performs its own pre...
By Yi Li, Dongming Jiang, Yi Zhao, Bingzhe Li
The paper introduces an LSTM-style transformer that shares a single key–value cache across its recurrent core, reducing parameters by 40.4% compared to an untied model while keeping forward FLOPs similar. It aligns parallel and continuation training passes with a consistency loss, eliminating 81–89% of the continuation‑loss penalty on FineWeb. Experiments across multiple widths and token budgets show that weight tying incurs the smallest loss cost at lower token budgets, highlighting its potential for data‑ and storage‑constrained settings.
By Andreas Kirsch
arXiv:2608. 11231v1 Announce Type: new Abstract: LLM serving is increasingly accelerated by position-independent caching (PIC).
By Yirui Liu, Ruoling Qi, Longwen Wang, Xuaner Wu, Jian Chen, Yuxin Jin, Jiawei Shao, Xuelong Li
The paper introduces LatentPort, a method that allows a language model to transfer its live memory to another model without requiring the receiver to reread the context. Experiments on a Qwen3.5 4B-to-9B sibling pair show that adding a Gated DeltaNet (GDN) persistent-state package reduces negative log‑likelihood by 0.747 nats/token and improves performance across 64 PG19 documents. The study also demonstrates that direct recurrent and convolution reuse outperforms learned GDN maps, and a 434,176‑parameter correction further narrows the performance gap to the native 9B model.
By Simon P. Villani
RaReCache is a framework that allows a large target language model to decode accurately from a KV cache prefilled by a much smaller source model by selectively recomputing only critical token positions. It identifies these positions using a rank disagreement metric that scores tokens based on the energy of their mapped KV in output directions weakly supported by calibration data. Across two model families and five benchmarks, RaReCache achieves up to a 3.04× prefill speedup and significantly reduces prefill latency while maintaining 95–99% of target accuracy with a 30–40% recompute budget.
By Sreetama Sarkar, Saptarshi Mitra, Sitao Huang, Souvik Kundu, Peter A. Beerel