Modern language models are built primarily from Transformers, recurrent models, and their hybrid architectures. Transformers rely on token-level attention memories, while recurrent models such as state space models (SSMs) and linear attention maintain compact recurrent states.
arXiv:2608. 02032v1 Announce Type: new Abstract: Modern language models are built primarily from Transformers, recurrent models, and their hybrid architectures.
By Yixiao Qian, Song Chen, Pengkai Wang, Jiaxu Liu, Shengze Cai, Chao Xu
arXiv:2604. 20920v2 Announce Type: replace Abstract: Sparse attention can reduce the cost of long-context inference, but most variants introduce new architectural components.
By Yuzhen Mao, Michael Y. Li, Emily B. Fox
The paper surveys memory‑centric compression techniques for large language models, framing long‑context handling as a memory‑management problem. It distinguishes compute‑active memory from compute‑inactive memory, and models operations such as writing, offloading, recall, and forgetting as state transitions within an information lifecycle. The authors propose a unified cost model and argue that future research should focus on dynamic, lifecycle‑aware memory management rather than static compression.
By Chaogui Gou
VisCache introduces a two-stage, plug‑and‑play framework for pruning visual key‑value caches in Vision Large Language Models without retraining. The first stage filters out temporally redundant keyframes, while the second stage, PruneKV, applies a parabolic layer‑wise budget and asymmetric update to selectively prune keys and fuse values, preserving essential context. Experiments show up to 2.35× speedup and significant memory savings with only 19–28% of the original cache retained, outperforming existing baselines.
By Lyuke Wang, Zhuo Li, Guangxu Zhu
Raincheck is a framework that defers multimodal prefill for large language models, allowing initial token selection to be decoupled from later information needs. It preserves unselected tokens and progressively prefills them during decoding, using unused compute capacity and scheduling gaps. Experiments on Qwen3-VL-8B and Qwen3.5-9B show that Raincheck recovers a significant portion of quality lost to hard pruning and reduces first-turn time-to-first-token by up to 24.1% with minimal impact on token generation speed.
By Wenjie Huang, Jun Zhang, Hongyaoxing Gu, Haoyang Huang, Tianqi Xu, Xinyue Hao, Xinyi Hu, Xinle Du, Jun Dai, Shuang Ge, Chen Lei, Yue Li, Mingcheng Wan