Attention sinks arise from variance discrepancy in self-attention value aggregation, amplified by super neurons and first-token dimension disparity, and can be mitigated by head-wise RMSNorm to accelerate pre-training convergence.
Intactkv: Improving large language model quantization by keeping pivot tokens intact
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Attention sinks emerge in language models from softmax-induced token dependence on attention scores and do not appear when using sigmoid attention without normalization in models up to 1B parameters.
Attention-sink weight is recast as an implicit MoE router per head, motivating a sink-aware head-balancing loss that yields small, consistent benchmark gains across three attention variants but rests on a definitional reframing.
citing papers explorer
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The Structural Origin of Attention Sink: Variance Discrepancy, Super Neurons, and Dimension Disparity
Attention sinks arise from variance discrepancy in self-attention value aggregation, amplified by super neurons and first-token dimension disparity, and can be mitigated by head-wise RMSNorm to accelerate pre-training convergence.
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When Attention Sink Emerges in Language Models: An Empirical View
Attention sinks emerge in language models from softmax-induced token dependence on attention scores and do not appear when using sigmoid attention without normalization in models up to 1B parameters.
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Attention Sink Forges Native MoE in Attention Layers: Sink-Aware Training to Address Head Collapse
Attention-sink weight is recast as an implicit MoE router per head, motivating a sink-aware head-balancing loss that yields small, consistent benchmark gains across three attention variants but rests on a definitional reframing.