AI RESEARCH
Neural Attention Search Linear: Towards Adaptive Token-Level Hybrid Attention Models
arXiv CS.LG
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ArXi:2602.03681v2 Announce Type: replace-cross The quadratic computational complexity of softmax transformers has become a bottleneck in long-context scenarios. In contrast, linear attention model families provide a promising direction towards a efficient sequential model. These linear attention models compress past KV values into a single hidden state, thereby efficiently reducing complexity during both