Multi-State Linear Attention (MSLA) improves long‑sequence modeling by compressing context into separate memory states, but its aggregation only uses mean alignment, ignoring distribution within segments. MERLIN (MomEnt Routing for multi‑state LINear attention) enhances this by tracking first and second moments of each segment’s keys, enabling queries to recover both mean and variance of alignment for more content‑aware aggregation. Experiments on Gated DeltaNet‑1.3B and Mamba‑2‑780M demonstrate that MERLIN consistently outperforms existing aggregation methods on long‑context tasks, achieving significant accuracy gains while using less memory.
By Hee Seung Hwang, Sanghyuk Chun, Xindi Wu, Olga Russakovsky, Zhiwei Deng
arXiv:2609.39661v1 Announce Type: new
Abstract: Self-attention gives LLMs fine-grained, query-dependent access to context, but dense token interactions incur quadratic prefill cost and a key--value c...
By Zhentao Tan, Jingyi Shen, Yanbo Li, Yao Liu, Yue Wu, Jieping Ye
MS-GLA introduces Multi-Scale Gated Linear Attention, distributing attention heads across multiple temporal resolutions to address representational bottlenecks in GLA Transformers. Coarser resolutions pool longer token spans for long‑range dependencies, while finer heads capture local syntax, and a learnable fusion layer recombines outputs dynamically. Experiments on language modeling, recall‑intensive tasks, and long‑context generalization show MS‑GLA outperforms GLA with up to 18.9% improvement on recall tasks and 9.5% lower perplexity on language modeling benchmarks.
By Prasoon Dev, Anirudh Sankar, Vasudeva Varma
arXiv:2606. 01294v1 Announce Type: cross Abstract: Linear attention reduces the quadratic cost of softmax attention by maintaining a recurrent fast-weight state, but it consistently lags on in-context retrieval and long-context tasks.
By Dong Le, Thong Nguyen, Cong-Duy Nguyen, Anh Tuan Luu
arXiv:2608.30310v1 Announce Type: cross
Abstract: Hybrid large language models interleave full-attention layers with linear-attention layers to reduce the cost of long-context inference. This structu...
By Yirui Liu, Ruoling Qi, Xuaner Wu, Penghang Liu, Jian Chen
The scalability of Large Language Models (LLMs) to long contexts is fundamentally constrained by the quadratic complexity of standard attention, motivating the adoption of linear attention mechanisms with sub-quadratic cost. To improve representation capacity under long contexts, recent approaches organize memory in a multi-state manner.