LLMver_v1

assets/unsorted/AgentCity/assets/.gitkeep

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-

assets/unsorted/AgentCity/figures/attn_vis.tex

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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}

assets/unsorted/AgentCity/figures/model_arch.tex

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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*}