Attention Sink in Transformers: A Survey on Utilization, Interpretation, and Mitigation

Zunhai Su; Hengyuan Zhang; Wei Wu; Yifan Zhang; Yaxiu Liu; He Xiao; Qingyao Yang; Yuxuan Sun; Rui Yang; Chao Zhang; Jing Xiong; Hui Shen; Keyu Fan; Weihao Ye; Chaofan Tao; Taiqiang Wu; Zhongwei Wan; Tiantian Zhang; Bowen Yan; Zhen Li; Yiming Zhang; Congkai Xie; Yulei Qian; Yuchen Xie; Yik-Chung Wu; Hongxia Yang; Ngai Wong

arXiv · 2604.10098

Attention Sink in Transformers: A Survey on Utilization, Interpretation, and Mitigation

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Abstract

As the foundational architecture of modern machine learning, Transformers have driven remarkable progress across diverse AI domains. Despite their transformative impact, a persistent challenge across various Transformers is Attention Sink (AS), in which a disproportionate amount of attention is focused on a small subset of specific yet uninformative tokens. AS complicates interpretability, significantly affecting the training and inference dynamics, and exacerbates issues such as hallucinations. In recent years, substantial research has been dedicated to understanding and harnessing AS. However, a comprehensive survey that systematically consolidates AS-related research and offers guidance for future advancements remains lacking. To address this gap, we present the first survey on AS, structured around three key dimensions that define the current research landscape: Fundamental Utilization, Mechanistic Interpretation, and Strategic Mitigation. Our work makes a pivotal contribution by highlighting the key concepts and main trends in the field, guiding researchers through the evolution of AS-related studies. We envision this survey as a valuable resource, empowering researchers to effectively manage AS within the current Transformer paradigm, while simultaneously inspiring innovative advancements for the next generation of Transformers. The paper list of this work is available at https://github.com/ZunhaiSu/Awesome-Attention-Sink.

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Zunhai Su, Hengyuan Zhang, Wei Wu, Yifan Zhang, Yaxiu Liu, He Xiao, Qingyao Yang, Yuxuan Sun, Rui Yang, Chao Zhang, Jing Xiong, Hui Shen, Keyu Fan, Weihao Ye, Chaofan Tao, Taiqiang Wu, Zhongwei Wan, Tiantian Zhang, Bowen Yan, Zhen Li, Yiming Zhang, Congkai Xie, Yulei Qian, Yuchen Xie, Yik-Chung Wu, Hongxia Yang, Ngai Wong. 2026-04-11. Attention Sink in Transformers: A Survey on Utilization, Interpretation, and Mitigation. https://arxiv.org/abs/2604.10098

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