Kimi Linear introduces a novel attention architecture designed to enhance both expressiveness and computational efficiency in transformer models. It aims to reduce quadratic complexity while maintaining or improving performance on long-sequence tasks.
Background
Attention mechanisms are foundational to modern transformers but often suffer from high computational cost as sequence length grows. This paper proposes a linear-complexity alternative that preserves modeling capacity.
- Source
- Hacker News (RSS)
- Published
- Jul 28, 2026 at 06:52 PM
- Score
- 7.0 / 10