feat: land superpaper v1 notes scaffold
Add the ledger schema, class router, lint codes, ingest/build pipeline, and three work-tree examples: align derivation, superfig delegation, and supertensor delegation.
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一次前向 $x\to f_\theta\to\hat y$。损失挂在预测下方,不是主路车站。
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# Figure request F1
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toolkit: superfig
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language: cjk
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claim: 一次前向是 x → f_θ → ŷ;损失不在主路上。
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grammar: pipeline
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work_rel_dir: figures/F1
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## Roles
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- {role: input, color: sfTeal}
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- {role: model, color: sfOrange}
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- {role: loss, color: sfCoral}
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- {role: output, color: sfViolet}
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## Flow (cursor). \sfconn 没有 endpoints。
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stage: {name: SA, text: "推理流程:一次前向"}
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row: {name: R1, height: 16mm}
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in_row:
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- {macro: sfnode, name: x, role: input, label: "输入 $x$", w: 16mm, h: 12mm}
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- {macro: sfconn, name: e1, label: 预处理}
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- {macro: sfnode, name: f, role: model, label: "模型 $f_\\theta$", w: 18mm, h: 12mm}
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- {macro: sfconn, name: e2, label: logits}
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- {macro: sfnode, name: y, role: output, label: "预测 $\\hat y$", w: 16mm, h: 12mm}
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## Fixed topology
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- {macro: sfnode, name: s, role: loss, label: "损失 $L$", w: 14mm, h: 12mm,
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at: "($(y.south)+(0,-22mm)$)"}
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- {macro: sfarrowlabel, from: "y.south", to: "s.north", label: "$L(\\hat y, y)$"}
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% superfig golden example 1 -- one horizontal paper-figure pipeline.
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% Main path is input -> model -> prediction. Loss is a side object, not a
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% station on the forward path.
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% ../scripts/build.sh pipeline.tex
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\documentclass[border=10pt]{standalone}
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\usepackage[cjk]{superfig}
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\sfsetrole{input}{sfTeal}
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\sfsetrole{model}{sfOrange}
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\sfsetrole{loss}{sfCoral}
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\sfsetrole{output}{sfViolet}
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\begin{document}
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\begin{tikzpicture}
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\sfstage{SA}{推理流程:一次前向}
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\sfrow{R1}{16mm}
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\sfnode[role=input]{x}{输入 $x$}{16mm}{12mm}
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\sfconn{e1}{预处理}
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\sfnode[role=model]{f}{模型 $f_\theta$}{18mm}{12mm}
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\sfconn{e2}{logits}
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\sfnode[role=output]{y}{预测 $\hat y$}{16mm}{12mm}
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\sfrowend
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% Loss compares the prediction with the target; it is not on the main path.
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% Hang it below ŷ with enough shaft that no caption sits on the arrow.
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\sfnode[role=loss, at={($(y.south)+(0,-22mm)$)}]{s}{损失 $L$}{14mm}{12mm}
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\sfarrowlabel{y.south}{s.north}{$L(\hat y,y)$}
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\sflane{R1}
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\sfcaption{x}{$x$}{原始输入}
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\sfcaption{f}{$f_\theta$}{可学习参数}
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\sfnolane
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\sfcaption{s}{$L$}{与真值比较}
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\sfbbox{all}
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\sftopformula{F}{%
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$x \;\xrightarrow{\;f_\theta\;}\; \hat y,\qquad
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\min_\theta\; L\bigl(f_\theta(x),\,y\bigr)$}
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\sfmeaningbox{mb}{96mm}{all}
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{一次从输入到预测的前向;损失在预测之后单独计算}
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{数据、模型参数、预测、损失}
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{预处理后送入模型;模型产生 logits 得到预测;损失比较预测与真值,梯度再回到参数}
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\sfsignature{推理流程示意}{mb}
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\end{tikzpicture}
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\end{document}
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\documentclass[a4paper]{article}
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\input{notes-macros}
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\renewcommand{\notetitle}{一次前向与侧路损失}
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\renewcommand{\notepaper}{A One-Step Predictor}
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\begin{document}
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\tableofcontents
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\newpage
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\input{sections/sec-01.tex}
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\input{sections/sec-02.tex}
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\input{sections/sec-03.tex}
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\appendix
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\section{符号表}
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\input{sections/symbols.tex}
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\end{document}
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\section{这篇论文在问什么}
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预测怎么从输入算出来,损失该不该站在前向主路上?
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\section{主张与贡献}
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\splabel{C1}
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一次前向是 $x\to f_\theta\to\hat y$。损失在预测之后单独比较。
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\spfig{F1}{一次前向与侧路损失。}{重绘自 fixture Figure~1;toolkit: \texttt{superfig};ledger id: F1。}
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\subsection{本章小结}
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损失不是前向的一站。
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\section{总结与延伸}
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这张图走 superfig,因为要画的是谁吃谁,不是轴长。
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