Sliding Window Attention Beats Linear Attention, but Hybrid Architectures Reign Supreme
ChengleiSi · x · 2026-09-01
A paper claims that sliding window attention (SWA) with attention sinks outperforms linear attention at no cost. Critics argue this is misleading: linear attention initialized on full-attention weights isn't optimally trained. Furthermore, frontier models favor hybrid architectures (e.g., Kimi's 3:1 ratio), which empirically outperform both pure linear and pure full attention mechanisms.
More from Models
- One Prompt, 629 Lines: IQuest-Q1 Builds a Deep-Sea Submarine Game in 2 Minutes — Div_pradeep · 2026-10-16
- Swapping RLM sub-calls for Jev: half the cost, 6x faster at 1M tokens — lateinteraction · 2026-10-16
- Stanford users say OpenAI cut ChatGPT Edu Pro usage 50% mid-contract — DJiafei · 2026-10-16
- MiniCPM-V 4.5 MoE reportedly hits 13+ tokens/s on iPhone with just 3GB memory — heyshrutimishra · 2026-10-16
- Trying Grok bot surfaces an unpaid Cursor balance, blocking account merging — haydendevs · 2026-10-16
- TensorFold 1.0.12 runs 120B vision model on 1-2 DGX Sparks, adds Mac image input — HankYeomans · 2026-10-16