Native Hybrid Attention for Efficient Sequence Modeling
Authors: name: Jusen Du, name: Jiaxi Hu, name: Tao Zhang, name: Weigao Sun, name: Yu Cheng

Transformers excel at sequence modeling but face quadratic complexity, while
linear attention offers improved efficiency but often compromises recall
accuracy over long contexts. In this work, we introduce Native Hybrid Attention
(NHA), a novel hybrid architecture of linear and full attention that integrates
both intra \& inter-layer hybridization into a unified layer design. NHA
maintains long-term context in key-value slots updated by a linear RNN, and
augments them with short-term tokens from a sliding window. A single
softmax attention operation is then applied over all keys and values,
enabling per-token and per-head context-dependent weighting without requiring
additional fusion parameters. The inter-layer behavior is controlled through a
single hyperparameter, the sliding window size, which allows smooth adjustment
between purely linear and full attention while keeping all layers structurally
uniform. Experimental results show that NHA surpasses Transformers and other
hybrid baselines on recall-intensive and commonsense reasoning tasks.
Furthermore, pretrained LLMs can be structurally hybridized with NHA, achieving
competitive accuracy while delivering significant efficiency gains. Code is
available at https://github.com/JusenD/NHA.