Attention Sinks and Outliers in Attention Residuals

Haozheng Luo; Haoran Dai; Shaoyang Zhang; Xi Chen; Eric Hanchen Jiang; Yijiang Li; Jingyuan Huang; Chenghao Qiu; Chenwei Xu; Zhenyu Pan; Haotian Zhang; Binghui Wang; Yan Chen

arXiv · 2605.17887

Attention Sinks and Outliers in Attention Residuals

Haozheng Luo·Haoran Dai·Shaoyang Zhang·Xi Chen·Eric Hanchen Jiang·Yijiang Li·Jingyuan Huang·Chenghao Qiu·Chenwei Xu·Zhenyu Pan·Haotian Zhang·Binghui Wang·Yan Chen

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Abstract

We propose OASIS, an outlier- and sink-aware technique built on inter-layer null signaling. As AttnResidual architectures introduce an additional depth-wise normalization channel, they improve inter-layer routing flexibility but also exacerbate attention sinks, activation outliers, and the resulting degradation in inference stability and quantization robustness. OASIS addresses this issue by introducing a Softmax1-based null space and coupling token-level null evidence to depth routing through an inter-layer null signal, thereby reducing sink-dominated routing and improving structural robustness. Theoretically, we show that the dual-normalization design of AttnResidual intensifies sink formation and quantization brittleness. Experimentally, we compare OASIS against five baselines on three real-world datasets and observe consistent improvements in both attention sink and post-quantization performance. Notably, OASIS achieves an average reduction of 9.26% in maximum infinity norm and 2.60% in average kurtosis across the evaluated settings, while lowering perplexity by 75.85% under W8A8 and improving GSM8K Pass@1 by 12.42% under W4A4.

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Haozheng Luo, Haoran Dai, Shaoyang Zhang, Xi Chen, Eric Hanchen Jiang, Yijiang Li, Jingyuan Huang, Chenghao Qiu, Chenwei Xu, Zhenyu Pan, Haotian Zhang, Binghui Wang, Yan Chen. 2026-05-18. Attention Sinks and Outliers in Attention Residuals. https://arxiv.org/abs/2605.17887

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