@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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T APB-V: Accelerating Long-Video Understanding via Sequence-Parallelism-aware Approximate Attention
%A Huang, Yuxiang
%A Li, Mingye
%A Han, Xu
%A Xiao, Chaojun
%A Zhao, Weilin
%A Sun, Ao
%A Yuan, Ziqi
%A Zhou, Hao
%A Meng, Fandong
%A Liu, Zhiyuan
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F huang-etal-2026-apb
%X 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.
%U https://aclanthology.org/2026.acl-long.90/
%P 2010-2025
Markdown (Informal)
[APB-V: Accelerating Long-Video Understanding via Sequence-Parallelism-aware Approximate Attention](https://aclanthology.org/2026.acl-long.90/) (Huang et al., ACL 2026)
ACL
- Yuxiang Huang, Mingye Li, Xu Han, Chaojun Xiao, Weilin Zhao, Ao Sun, Ziqi Yuan, Hao Zhou, Fandong Meng, and Zhiyuan Liu. 2026. APB-V: Accelerating Long-Video Understanding via Sequence-Parallelism-aware Approximate Attention. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2010–2025, San Diego, California, United States. Association for Computational Linguistics.