Kimi Linear: New Attention Architecture Balances Expressiveness and Efficiency

Researchers have proposed Kimi Linear, a novel attention architecture designed for large language models. The work, published on arXiv, aims to address the trade-off between expressiveness and computational efficiency in transformer-based systems. Linear attention mechanisms have long been explored as faster alternatives to standard softmax attention, though often at the cost of model quality. Kimi Linear appears to tackle this limitation by offering an approach that retains strong representational capacity while reducing computational overhead. The paper is available on arXiv under identifier 2510.26692.