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\begin{figure}[t]
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% \captionsetup[subfigure]{labelformat=simple, labelsep=period}
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% \renewcommand\thesubfigure{\alph{subfigure})} % 将子标题的标签格式改为 "a)"
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\centering
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\begin{subfigure}[b]{0.47\linewidth} % PD:平衡一下图片大小,如果一样的图可以都用0.48
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\hspace{-3px} % PD: 往左挪点防止重心偏右
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\includegraphics[width=\linewidth]{assets/Layer10.pdf}
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% \includegraphics[width=\linewidth]{assets/Layer10_norubost.pdf}
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\caption{Across Head Dimensions} % 子图标题留空即可自动生成 (a)
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\label{fig:attnindim}
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\end{subfigure}
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\hfill % 在两张图之间插入一个弹性空白,使它们左右对齐
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\begin{subfigure}[b]{0.48\linewidth}
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\includegraphics[width=\linewidth]{assets/AcrossLayer.pdf}
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\caption{Across Layers}
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\label{fig:attninlayer}
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\end{subfigure}
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\caption{Q-state activation strength visualizations in LLaMA-2-7B.
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We compute the average L2 norm per attention head to quantify activation strength.
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Stronger activations are concentrated in high-indexed (\ie low-RoPE frequency) dimensions and vary across layers, highlighting both dimension-wise and layer-wise heterogeneity.
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}
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\label{fig:hotattn}
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% \begin{figure*}[ht]
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% \centering
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% \includegraphics[width=0.7\linewidth]{assets/model2.pdf}
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% \caption{MESSA framework with shared--specific sparse updates. Sparse structures are learned via budget-aware soft gating and overlap regularization, and hardened through a soft-to-hard training process under a unified parameter budget.}
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% % \caption{The architecture of CAM and HyCAM framework. HyCAM applies a hybrid CAM mechanism to the output of the Attention module within each Transformer block, while the backbone LLM remains frozen. Specifically, HyCAM integrates a shared, full-parameter CAM module and multiple lightweight Specialized CAMs for common and task-specific knowledge.} % with a dynamic routing strategy. % adaptively coordinates the contributions of these specialized modules.
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% \label{fig:framework}
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% \end{figure*}
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