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K3/notes/notes.tex
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dela 584f7e9e73 Initial K3 snapshot: 0.5B KDA/MLA/MoE train path
Standalone tree split from LLMRL/projects/kda. Includes Triton dt_bias
backward fix, train_k3 --preset 0.5b, SFT, Docker runtime, and tests.
2026-08-25 14:43:17 +08:00

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\documentclass[a4paper,11pt]{article}
\input{notes-macros}
\begin{document}
% ---------- 封面 ----------
\begin{titlepage}
\centering
\vspace{2cm}
{\huge\bfseries \notetitle\par}
\vspace{0.6cm}
{\Large \notesubtitle\par}
\vspace{0.8cm}
{\large \notedate\par}
\vspace{1.5cm}
\begin{tcolorbox}[width=0.88\textwidth, colback=black!2!white, colframe=black!60, sharp corners]
\textbf{项目}:\texttt{projects/kda/} — KDA 手写实现(naive recurrent → chunked → Triton)\\
\textbf{架构}:KDA + Gated MLA + Stable LatentMoE + AttnRes 深度残差 (K3-like)\\
\textbf{参考}:KDA arXiv:2510.26692; AttnRes arXiv:2603.15031; Kimi K3 architecture notes\\
\textbf{代码}:\texttt{kda/ops/}, \texttt{kda/layers/}, \texttt{kda/models/}
\end{tcolorbox}
\end{titlepage}
\tableofcontents
\newpage
\input{sections/sec-01} % KDA 递归核心
\input{sections/sec-02} % Gate 激活
\input{sections/sec-03} % 分块并行计算
\input{sections/sec-04} % GVA 分组值注意力
\input{sections/sec-05} % KDAAttention 层
\input{sections/sec-06} % Gated MLA 矩阵吸收版
\input{sections/sec-07} % SiTU-GLU 与 Stable LatentMoE
\input{sections/sec-08} % K3 混合架构
\input{sections/sec-09} % Attention Residual 深度残差
\input{sections/sec-10} % 反向传播推导
\input{sections/sec-11} % 符号表
\end{document}