@inproceedings{huang-etal-2026-apb,
    title = "{APB}-{V}: Accelerating Long-Video Understanding via Sequence-Parallelism-aware Approximate Attention",
    author = "Huang, Yuxiang  and
      Li, Mingye  and
      Han, Xu  and
      Xiao, Chaojun  and
      Zhao, Weilin  and
      Sun, Ao  and
      Yuan, Ziqi  and
      Zhou, Hao  and
      Meng, Fandong  and
      Liu, Zhiyuan",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.90/",
    pages = "2010--2025",
    ISBN = "979-8-89176-390-6",
    abstract = "The efficiency of long-video inference remains a critical bottleneck, mainly due to the dense computation in the prefill stage of Large Multimodal Models (LMMs). Existing methods either compress visual embeddings or apply sparse attention on a single GPU, yielding limited acceleration or degraded performance and restricting LMMs from handling longer, more complex videos. To overcome these issues, we propose APB-V, a sequence-parallel framework with optimized attention that accelerates long-video inference across multiple GPUs. By distributing approximate attention, APB-V reduces computation and increases parallelism, enabling efficient processing of more visual embeddings without compression and thereby improving task performance. System-level optimizations, such as load balancing and fused forward passes, further unleash the potential of APB-V, delivering speedups of 12.72x, 1.70x, and 1.18x over FlashAttn, ZigZagRing, and APB, without notable performance loss."
}