时空ver最后的回忆

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panda361
2026-03-19 02:28:50 +08:00
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\begin{figure}[t]
% \captionsetup[subfigure]{labelformat=simple, labelsep=period}
% \renewcommand\thesubfigure{\alph{subfigure})} % 将子标题的标签格式改为 "a)"
\centering
\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
\hspace{-3px} % PD: 往左挪点防止重心偏右
\includegraphics[width=\linewidth]{assets/Layer10.pdf}
% \includegraphics[width=\linewidth]{assets/Layer10_norubost.pdf}
\caption{Across Head Dimensions} % 子图标题留空即可自动生成 (a)
\label{fig:attnindim}
\end{subfigure}
\hfill % 在两张图之间插入一个弹性空白,使它们左右对齐
\begin{subfigure}[b]{0.48\linewidth}
\hspace{-3px} % PD: 往左挪点防止重心偏右
\includegraphics[width=\linewidth]{assets/AcrossLayer.pdf}
\caption{Across Layers}
\label{fig:attninlayer}
\end{subfigure}
\caption{Q-state activation strength visualizations in LLaMA-2-7B.
We compute the average L2 norm per attention head to quantify activation strength.
Stronger activations are concentrated in high-indexed (\ie low-RoPE frequency) dimensions and vary across layers, highlighting both dimension-wise and layer-wise heterogeneity.
}
\label{fig:hotattn}
\end{figure}
% \py{font size of figure is too small}

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% \begin{figure*}[ht]
% \centering
% \includegraphics[width=0.7\linewidth]{assets/model2.pdf}
% \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.}
% % \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.
% \label{fig:framework}
% \end{figure*}

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