Analysis compares Qwen and MiniMax sparse attention implementations
stochasticchasm · x · 2026-08-28
A technical observer noted that Qwen's sparse attention appears to be a refined version of MiniMax's sparse attention. Both are block sparse, with the key difference being that Qwen performs pooling before SDPA (Scaled Dot-Product Attention), whereas MiniMax does so after. This pre-SDPA pooling design significantly reduces computation costs for long contexts. The post includes a link to the arXiv paper on MiniMax Sparse Attention.
More from Research
- LightSuite: fast cell detection and whole-brain atlas registration on a standard workstation — hisspikeness · 2026-10-15
- Pathway CSO pitches BDH as post-Transformer architecture uniting memory, reasoning, adaptation — schwentker · 2026-10-15
- AI-designed molecular scissors cut Alzheimer's proteins — but is it really AI? — gerardsans · 2026-10-15
- EmbodiedEvo tops RoboDojo at 38.95% success by letting robots inherit failed practice — thetripathi58 · 2026-10-15
- 797GB of n-gram counts in CPU RAM let 1B models match 57% more compute — PangWeiKoh · 2026-10-15
- Google's HARNESS² evolves agent harnesses without verifiers, up to +11.5 points on WorkBuddy-Bench — dair_ai · 2026-10-15