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Massive Activations in Hybrid Linear Attention Large Language Models: Pre-Attention Spikes and Inter-Spike Plateaus

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TL;DR - This study identifies architecture-specific massive activation patterns in hybrid linear-attention LLMs. The findings link these patterns to full-attention placement and activation-cancellation timing, clarifying how hybrid architectures approach standard attention behavior.

  • Massive activations spike immediately before full-attention layers and may persist across intervening linear-attention layers.
  • Denser full attention increasingly connects these spikes into the stable activation pattern seen in full-attention LLMs.
  • The patterns recur across five architectures, six hybrid configurations, five data domains, and models from 1.2B to 397B parameters.
  • Full-attention output gating substantially reduces activation magnitude without removing its layerwise organization.

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Massive Activations in Hybrid Linear Attention Large Language Models: Pre-Attention Spikes and Inter-Spike Plateaus

arXiv cs.CL Zunhai Su, Bohan Sun, Xialie Zhuang, Shuibai Zhang, He Xiao, Jing Xiong, Hengyuan Zhang, Zhongzhu Zhou, Tiantian Zhang, Ngai Wong, Chuan-Wei Kuo 2026-08-12 arXiv:2608.12149
Public signals Hugging Face upvotes 30
Providers: Hugging Face · Upvotes 30 OpenAlex · N/A Publisher · N/A Semantic Scholar · N/A X · N/A Fetched 2026-09-12 14:27:40.361575 UTC

TL;DR - This study identifies architecture-specific massive activation patterns in hybrid linear-attention LLMs. The findings link these patterns to full-attention placement and activation-cancellation timing, clarifying how hybrid architectures approach standard attention behavior.

  • Massive activations spike immediately before full-attention layers and may persist across intervening linear-attention layers.
  • Denser full attention increasingly connects these spikes into the stable activation pattern seen in full-attention LLMs.
  • The patterns recur across five architectures, six hybrid configurations, five data domains, and models from 1.2B to 397B parameters.
  • Full-attention output gating substantially reduces activation magnitude without removing its layerwise organization.
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