@inproceedings{hu-etal-2026-optimizing,
    title = "Optimizing Native Sparse Attention with Latent Attention and Local Global Alternating Strategies",
    author = "Hu, Yuxuan  and
      Tan, Jianchao  and
      Zhang, Jiaqi  and
      Zan, Wen  and
      Sun, Pingwei  and
      Lu, Yifan  and
      Cai, Xunliang  and
      Zhang, Jing",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.695/",
    pages = "14209--14220",
    ISBN = "979-8-89176-395-1",
    abstract = "In this work, we conduct a systematic analysis of Native Sparse Attention (NSA) and propose targeted improvements that enhance long-context modeling. A key insight is that alternating between local (sliding-window) and global (compression/selective) attention across layers, rather than using fixed patterns, enables more effective propagation of long-range dependencies and substantially boosts performance on long-sequence tasks. Meanwhile, we further refine NSA{'}s branches with Latent Attention that the sliding-window branch is enhanced with Multi-head Latent Attention (MLA) while compression and selective branches adopt Group-head Latent Attention (GLA). These changes reduce KV-cache memory by 50{\%} versus NSA while improving the model{'}s common-sense reasoning and long-text understanding capabilities. Experiments on models from 340M to 1.3B parameters (trained on 15B and 100B tokens) show our method matches or exceeds full attention and native sparse attention in both common-sense reasoning and long-context understanding tasks."
}