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\title[LatentLLM]{\texorpdfstring{LatentLLM: Attention-Aware\\Joint Tensor Compression}{LatentLLM: Attention-Aware Joint Tensor Compression}}
\subtitle{Research paper discussion | 22 main frames + 6 backup frames}
\author{\texorpdfstring{Reading presentation by Codex\\[1mm]{\small Paper: Toshiaki Koike-Akino, Xiangyu Chen, Jing Liu,}\\{\small Ye Wang, Pu (Perry) Wang, Matthew Brand (MERL)}}{Reading presentation by Codex; paper by Toshiaki Koike-Akino et al.}}
\date{arXiv:2505.18413v1 | Read on 24 September 2026}
\hypersetup{pdftitle={LatentLLM: research paper reading},pdfauthor={Codex -- reading presentation},pdfsubject={arXiv:2505.18413v1}}
\begin{document}

% 1
\begin{frame}
\titlepage
\vspace{-4mm}
\begin{center}\small Preserve useful computations through statistical weighting, compact factors, and joint objectives.\end{center}
\end{frame}

% 2
\begin{frame}{The reported gain is a better quality--compression tradeoff}
\begin{columns}[T,onlytextwidth]
\column{0.48\textwidth}
\begin{block}{OPT-6.7B: WT2 perplexity $\downarrow$}
\centering
\begin{tabular}{lr}
Original & 10.9\\
Local RootCov & 13.5\\
\textbf{LatentLLM} & \textbf{11.5}
\end{tabular}
\end{block}
\column{0.48\textwidth}
\begin{block}{LLaVA: ScienceQA accuracy $\uparrow$}
\centering
\begin{tabular}{lr}
Original & 70.03\%\\
Local RootCov & 57.53\%\\
\textbf{LatentLLM} & \textbf{63.85\%}
\end{tabular}
\end{block}
\end{columns}
\medskip
Both compressed rows use the paper's \alert{20\% compression setting}.
\begin{itemize}
\item Calibration and factorization; no end-to-end fine-tuning.
\item Improvement over the tested local low-rank baselines; some quality loss remains.
\end{itemize}
\papersource{Paper, Table 2, PDF p.6; Table 4, PDF p.10. ``20\%'' is the nominal setting.}
\end{frame}

% 3
\begin{frame}{Compress interactions between adjacent projections}
\begin{columns}[c,onlytextwidth]
\column{0.61\textwidth}
\centering\includegraphics[width=\linewidth,height=0.70\textheight,keepaspectratio]{figures/fig-01-architecture.pdf}
\column{0.36\textwidth}
\begin{itemize}
\item Q/K: preserve attention scores.
\item V/O: approximate the composed output map.
\item U/D: coordinate both MLP layers.
\end{itemize}
\medskip
``Global'' here concerns coupled modules; no whole-network optimum is established.
\end{columns}
\papersource{Paper, Fig.1, Secs.3.1 and 4, PDF pp.2--5. Author figure reused.}
\end{frame}

% 4
\begin{frame}{RootCov solves the local activation reconstruction problem}
\[
W\in\R^{d'\times d},\quad X\in\R^{d\times l},\quad
\widehat W=BA,\quad \operatorname{rank}(BA)\leq r
\]
\[
\mathcal L_1=\mathbb E\norm{(W-BA)X}^{2}
=\norm{(W-BA)C^{1/2}}^{2},\quad C=\mathbb E[XX^\top].
\]
\begin{block}{A weighted SVD is enough for this objective}
\[
BAC^{1/2}=\operatorname{svd}_r(WC^{1/2}).
\]
\end{block}
\begin{itemize}
\item $C$ is an uncentered second moment; finite data use damping.
\item $P=C$ weights the error by $C^2$; $P=C^{1/2}$ matches the stated loss.
\item Optimality is local, with fixed rank and fixed statistics.
\end{itemize}
\papersource{Paper, Eqs.(1)--(5), Table 1, PDF pp.2--3; Appendix B, pp.13--15.}
\end{frame}

% 5
\begin{frame}{An identity block removes redundant factor parameters}
\[
USV=\operatorname{svd}_r(WP),\qquad
B=USJ,\quad A=J^{+}VP^{+}.
\]
\[
VP^{+}=[\,V_1\;V_2\,],\quad J=V_1
\quad\Longrightarrow\quad A=[\,I_r\;V_1^{-1}V_2\,].
\]
\begin{block}{Same low-rank product; fewer stored coefficients}
\[
r(d+d')\quad\longrightarrow\quad r(d+d')-r^2.
\]
\end{block}
\begin{itemize}
\item Requires an invertible block; pivot columns if needed.
\item At fixed rank, the product and its error are unchanged.
\item Practical savings require an implementation that omits the identity block.
\end{itemize}
\papersource{Paper, Eqs.(6)--(9), Remarks 4--5, PDF pp.3--4. Numerical conditioning is a reader concern.}
\end{frame}

% 6
\begin{frame}{Latent dimension, parameter budget, and FLOPs differ}
For a single square $d\times d$ layer, let $\rho=r/d$.
\[
\text{Dense-factor retention}=2\rho,\qquad
\text{Identity-block retention}=2\rho-\rho^2.
\]
\begin{center}
\begin{tabular}{ccc}
\toprule
Latent retention & Dense-factor parameters & Identity-block parameters\\
\midrule
75\% & 150\% & 93.75\%\\
50\% & 100\% & 75\%\\
\bottomrule
\end{tabular}
\end{center}
\medskip
\begin{itemize}
\item A 25\% latent reduction yields only a 6.25\% parameter reduction in the first example.
\item OPT-6.7B at nominal 20\%: $6.66\mathrm B\to5.53\mathrm B$ parameters, a 16.97\% reduction.
\end{itemize}
\papersource{Single-layer illustration from Sec.3.3, PDF p.3. Whole-model counts: Table 3, p.7. Percentages calculated.}
\end{frame}

% 7
\begin{frame}{Joint QK preserves the scores that determine attention}
\[
M_i=X^\top W_{q,i}^\top W_{k,i}X\in\R^{l\times l},
\qquad W_{q,i},W_{k,i}\in\R^{d_h\times d}.
\]
\[
\widehat M_i=X^\top A_q^\top B_{q,i}^\top B_{k,i}A_kX,
\qquad \mathcal L_2=\sum_{i=1}^{h}\norm{M_i-\widehat M_i}^{2}.
\]
\begin{itemize}
\item $A_q\in\R^{r_q\times d}$ and $A_k\in\R^{r_k\times d}$ are shared across heads.
\item $B_{q,i}, B_{k,i}$ are head-specific decompression factors.
\item The target is \alert{pre-softmax scores}; it does not directly optimize task loss.
\end{itemize}
\papersource{Paper, Eqs.(10)--(12), PDF p.4. The paper omits the usual score scale in this derivation.}
\end{frame}

% 8
\begin{frame}{The score objective becomes a Tucker approximation}
\centering
\includegraphics[width=0.88\linewidth,height=0.35\textheight,keepaspectratio]{figures/fig-03-tucker.pdf}
\[
G_i=C^{1/2}W_{q,i}^{\top}W_{k,i}C^{1/2},\quad
Q=A_qC^{1/2},\quad K=A_kC^{1/2}
\]
\[
\mathcal L_2=\sum_i\norm{G_i-Q^\top H_iK}^{2},\qquad
H_i=B_{q,i}^{\top}B_{k,i}.
\]
\raggedright
Two input modes are compressed; the head mode stays intact.\\
$Q,K$ denote weighted coordinates on this slide; the author diagram omits preconditioning.
\papersource{Paper, Fig.3 and Eq.(13), PDF p.4. Author diagram reused.}
\end{frame}

% 9
\begin{frame}{Alternating subspace updates give a practical solver}
With row-orthonormal $Q,K$, the optimal core is $H_i=QG_iK^\top$.
\[
K\leftarrow\teig_{r_k}\!\left(\sum_iG_i^\top Q^\top QG_i\right),
\]
\[
Q\leftarrow\teig_{r_q}\!\left(\sum_iG_iK^\top KG_i^\top\right).
\]
\begin{itemize}
\item Initialize from a marginal Gram matrix; alternate, then undo weighting.
\item Each step solves a conditional problem; a joint global optimum is not guaranteed.
\item Experiments use 8 QK iterations.
\end{itemize}
\papersource{Paper, Algorithm 1, p.5; Appendix E, pp.18--20; iteration count, p.8.}
\papersource{Reader notation: TopEigRows returns leading eigenvectors as rows. Q update follows the dimensionally consistent Appendix E; Algorithm 1 has a transpose error.}
\end{frame}

% 10
\begin{frame}{The simple joint VO objective misses attention weighting}
Let $S_i$ be the normalized attention weights, with a consistent orientation.
\[
Y'=\sum_iW_{o,i}W_{v,i}XS_i.
\]
The simple proxy instead minimizes
\[
\mathcal L_3=\sum_i\norm{W_{o,i}W_{v,i}X-B_oA_{o,i}B_{v,i}A_vX}^{2}.
\]
\begin{block}{The paper reports a negative result}
This joint VO approximation was not effective relative to separate V/O compression (Remark 11).
\end{block}
Appendix G explores actual attention-weighted statistics, but provides no separate end-to-end gain table for those alternatives.
\papersource{Paper, Sec.4.2, Eqs.(14)--(18), Remark 11, PDF p.5; Appendix G, pp.28--31.}
\end{frame}

% 11
\begin{frame}{Auxiliary variables coordinate both MLP projections}
\[
Z=W_uX,\quad Z'=\sigma(Z),\quad Y=W_dZ'
\]
\[
\mathcal L_4=\alpha\norm{W_uX-Z}^{2}
+\beta\norm{Z'-\sigma(Z)}^{2}
+\gamma\norm{W_dZ'-Y}^{2}.
\]
\begin{enumerate}
\item Update auxiliary activations using the fixed calibration pair $(X,Y)$.
\item Fit effective maps $ZX^+$ and $YZ'^{+}$ with weighted low-rank SVD.
\item Alternate the updates; the experiments use 4 UD iterations.
\end{enumerate}
\medskip
The closed-form nonlinear update is derived for \alert{ReLU}. Handling SiLU, GELU, or gated MLPs needs additional detail.
\papersource{Paper, Sec.4.3, Eqs.(19)--(22), PDF pp.5--7; Appendix H, pp.31--32. Iterations: p.8.}
\end{frame}

% 12
\begin{frame}{Extensions have different levels of validation}
\begin{center}
\begin{tabular}{>{\raggedright\arraybackslash}p{0.17\linewidth}>{\raggedright\arraybackslash}p{0.42\linewidth}>{\raggedright\arraybackslash}p{0.31\linewidth}}
\toprule
Extension & Mechanism & Evidence boundary\\
\midrule
Bias & Center activations; update bias & Derivations for linear, QK, VO\\[2mm]
GQA & Sum scores over KV groups and query heads & Derivation; no main GQA benchmark\\[2mm]
RoPE & Include relative-position rotations & 10-token proxy-loss experiment\\[2mm]
Quantization & STE on low-rank factors & Proposal; no main bit-width table\\
\bottomrule
\end{tabular}
\end{center}
\medskip
A listed architecture or derived extension is not an end-to-end validation result.
\papersource{Paper, Appendices B.2, E.2--E.3, F, I.1, J; PDF pp.14--15, 21--28, 34--37.}
\end{frame}

% 13
\begin{frame}{Calibration is small, but its data source matters}
\begin{columns}[T,onlytextwidth]
\column{0.48\textwidth}
\begin{block}{Language evaluation}
\begin{itemize}
\item OPT: 125M, 350M, 1.3B, 2.7B, 6.7B, 13B.
\item 64 C4 segments $\times$ 2048 tokens for calibration.
\item WT2, PTB, C4 perplexity.
\end{itemize}
\end{block}
\column{0.48\textwidth}
\begin{block}{Multimodal evaluation}
\begin{itemize}
\item LLaVA-v1.6-Vicuna-7B.
\item 64 ScienceQA \textbf{training} examples for calibration.
\item Overall and subgroup accuracy.
\end{itemize}
\end{block}
\end{columns}
\medskip
\begin{itemize}
\item A40 GPUs; PyTorch and HuggingFace Transformers.
\item ``No fine-tuning'' does not imply ``no task-specific calibration.''
\item FLOPs are profiled separately at 128 tokens.
\end{itemize}
\papersource{Paper, Sec.5, PDF pp.7--10. Evaluation details and run-to-run variation are incompletely reported.}
\end{frame}

% 14
\begin{frame}{RootCov is the most informative nearby baseline}
OPT-6.7B, nominal 20\% compression, WT2 perplexity $\downarrow$:
\begin{center}
\begin{tabular}{lr}
\toprule
Method & Perplexity\\
\midrule
Plain SVD & 67517.7\\
ASVD (Hessian) & 17.3\\
ASVD ($\ell_2$) & 14.6\\
ASVD (Cov) & 9842.6\\
ASVD (RootCov) & 13.5\\
\textbf{LatentLLM (RootCov)} & \textbf{11.5}\\
\bottomrule
\end{tabular}
\end{center}
The final gap also includes structural savings and joint updates.\\
It cannot be assigned entirely to the QK objective from this table.
\papersource{Paper, Table 2, PDF p.6. Baselines are local low-rank variants, not full quantization/pruning systems.}
\end{frame}

% 15
\begin{frame}{Quality degrades as compression becomes aggressive}
OPT-6.7B: WT2 / PTB / C4 perplexity $\downarrow$
\begin{center}
\begin{tabular}{lcc}
\toprule
Nominal compression & Local RootCov & LatentLLM\\
\midrule
Original & --- & 10.9 / 15.8 / 12.7\\
10\% & 11.8 / 17.7 / 14.2 & 10.7 / 16.1 / 13.0\\
20\% & 13.5 / 19.5 / 15.4 & 11.5 / 17.4 / 13.7\\
30\% & 17.0 / 23.9 / 17.8 & 13.5 / 19.2 / 15.3\\
40\% & 27.2 / 36.1 / 24.0 & 18.0 / 24.2 / 18.4\\
\bottomrule
\end{tabular}
\end{center}
\begin{itemize}
\item At 20\%: WT2 is 14.81\% lower than local RootCov, but 5.50\% above the original.
\item At 10\%: the small WT2 improvement does not extend to PTB or C4.
\end{itemize}
\papersource{Paper, Table 2, PDF p.6; percentages calculated from displayed values. No significance claim.}
\end{frame}

% 16
\begin{frame}{The OPT trend spans six evaluated model sizes}
WT2 perplexity at nominal 30\% compression $\downarrow$:
\begin{center}
\begin{tabular}{lrrr}
\toprule
OPT model & Original & Local RootCov & LatentLLM\\
\midrule
125M & 27.7 & 88.8 & 43.4\\
350M & 22.0 & 71.6 & 32.9\\
1.3B & 14.6 & 33.9 & 20.2\\
2.7B & 12.5 & 24.1 & 16.5\\
6.7B & 10.9 & 17.0 & 13.5\\
13B & 10.1 & 14.3 & 12.0\\
\bottomrule
\end{tabular}
\end{center}
Larger OPT models tolerate this setting better in these data.\\
The configuration tables include larger models; the main results stop at 13B.
\papersource{Paper, Table 2, PDF p.6; Figs.4--5, pp.8--9. This is an empirical family-specific trend.}
\end{frame}

% 17
\begin{frame}{Fewer counted FLOPs do not establish runtime speedup}
OPT-6.7B, \texttt{calflops}, 128-token input:
\begin{center}
\begin{tabular}{lrrr}
\toprule
Nominal compression & FLOPs & MACs & Parameters\\
\midrule
0\% & 1.70T & 851G & 6.66B\\
20\% & 1.36T & 681G & 5.53B\\
30\% & 1.19T & 596G & 4.87B\\
40\% & 1.02T & 511G & 4.20B\\
50\% & 851G & 425G & 3.54B\\
\bottomrule
\end{tabular}
\end{center}
\begin{itemize}
\item At 50\%: about half the FLOPs and 46.85\% fewer parameters.
\item No reported latency, throughput, peak-memory, or energy benchmark.
\item Real savings depend on factor layout, contraction order, and kernels.
\end{itemize}
\papersource{Paper, Table 3 and computational-complexity paragraph, PDF p.7. Parameter reduction calculated.}
\end{frame}

% 18
\begin{frame}{ScienceQA gains over RootCov coexist with absolute losses}
LLaVA-v1.6-Vicuna-7B: overall accuracy $\uparrow$
\begin{center}
\begin{tabular}{lrrrr}
\toprule
Compression & RootCov & LatentLLM & Gain (pp) & Loss vs. original (pp)\\
\midrule
0\% & --- & 70.03\% & --- & ---\\
10\% & 60.67\% & 65.76\% & +5.09 & 4.27\\
20\% & 57.53\% & 63.85\% & +6.32 & 6.18\\
30\% & 54.37\% & 60.13\% & +5.76 & 9.90\\
40\% & 52.23\% & 54.59\% & +2.36 & 15.44\\
50\% & 49.30\% & 52.25\% & +2.95 & 17.78\\
\bottomrule
\end{tabular}
\end{center}
\medskip
pp = percentage points. The 20\% gain is 6.32 pp, or 10.99\% relative to RootCov. Calibration uses ScienceQA training examples.
\papersource{Paper, Table 4, PDF p.10; calibration, p.7. Differences calculated.}
\end{frame}

% 19
\begin{frame}{Subgroup accuracy clarifies what remains after compression}
ScienceQA at nominal 20\% compression:
\begin{center}
\begin{tabular}{lrrr}
\toprule
Subgroup & Original & LatentLLM & Loss (pp)\\
\midrule
Natural science & 72.47 & 66.39 & 6.08\\
Social science & 69.18 & 61.19 & 7.99\\
Language science & 65.73 & 60.82 & 4.91\\
Image context & 68.82 & 63.41 & 5.41\\
No context & 65.99 & 60.62 & 5.37\\
Grades 1--6 & 72.72 & 66.41 & 6.31\\
Grades 7--12 & 65.19 & 59.26 & 5.93\\
\bottomrule
\end{tabular}
\end{center}
Accuracy is in percent. Groups describe different, potentially overlapping attributes; their simple mean is not the overall score.
\papersource{Paper, Table 4 and Fig.6, PDF p.10. Subgroup counts and full aggregation details are not reported.}
\end{frame}

% 20
\begin{frame}{Mechanism evidence is useful, but not a full ablation}
\begin{itemize}
\item \textbf{RootCov:} random-matrix experiments support the statistical weighting (Fig.7).
\item \textbf{Joint QK:} attention-score error improves over activation-only fitting on synthetic inputs (Fig.10).
\item \textbf{Joint QKV and split heads:} sharing and objective choice have different tradeoffs (Figs.8--9).
\item \textbf{Joint VO:} the simple proxy has no reported advantage (Remark 11).
\end{itemize}
\begin{block}{Missing separation}
The main tables do not isolate RootCov, identity blocks, QK, and UD under fully matched parameter/rank budgets.
\end{block}
\papersource{Paper, Remark 11, p.5; Tables 2 and 4, pp.6 and 10; Figs.7--10, pp.15--17 and 21.}
\end{frame}

% 21
\begin{frame}{The next validation should target deployment and robustness}
\begin{enumerate}
\item \textbf{Optimization:} quantify the gap from score reconstruction to full-model quality.
\item \textbf{Robustness:} vary calibration data, damping, conditioning, and seeds.
\item \textbf{Architecture:} verify RoPE, GQA, and non-ReLU/gated MLP handling on real models.
\item \textbf{Systems:} measure KV bytes, prefill/decode latency, throughput, and compression cost.
\end{enumerate}
\medskip
\alert{Reader assessment:} the current evidence is strongest for the reported quality--compression tradeoffs.
\papersource{Evidence boundaries from Secs.4--5 and Appendices E--J. These validation priorities are this reading's analysis.}
\end{frame}

% 22
\begin{frame}{Three ideas to retain from LatentLLM}
\begin{block}{Metric}
Weight errors by the activation statistics that determine their effect.
\end{block}
\begin{block}{Representation}
Use factorization freedom to remove redundant parameters.
\end{block}
\begin{block}{Objective}
Approximate coupled computations when local projections are insufficient.
\end{block}
\papersource{Primary source: Koike-Akino et al., LatentLLM: Attention-Aware Joint Tensor Compression, arXiv:2505.18413v1, 23 May 2025.}
\papersource{\href{https://arxiv.org/abs/2505.18413v1}{arxiv.org/abs/2505.18413v1}. Reading uses the matching author TeX and 37-page PDF; no model experiments were independently reproduced.}
\end{frame}

\appendix
% 23
\begin{frame}{Backup: why the alternating QK updates follow}
For row-orthonormal $Q,K$, define $H_i=QG_iK^\top$.
\[
\begin{aligned}
\mathcal L_2
&=\sum_i\norm{G_i-Q^\top H_iK}^{2}\\
&=\sum_i\left(\norm{G_i}^{2}-\norm{QG_iK^\top}^{2}\right).
\end{aligned}
\]
With $K$ fixed, minimizing the loss means maximizing
\[
\operatorname{tr}\left[Q\left(\sum_iG_iK^\top KG_i^\top\right)Q^\top\right],
\qquad QQ^\top=I_{r_q}.
\]
The leading eigenspace solves this conditional problem. The symmetric argument updates $K$; coupling still makes the joint problem nonconvex.
\papersource{Paper, Appendix E, PDF pp.18--20. Q and K are this presentation's weighted-coordinate notation.}
\end{frame}

% 24
\begin{frame}{Backup: bias changes the statistics to be preserved}
\[
Y=WX+b\boldsymbol{1}^\top,\qquad \mu=X\boldsymbol{1}/l.
\]
For fixed $BA$, the optimal bias is
\[
\widehat b=b+(W-BA)\mu.
\]
Substitution leaves a centered reconstruction objective:
\[
\norm{(W-BA)(X-\mu\boldsymbol{1}^\top)}^{2}
=\norm{(W-BA)C_0^{1/2}}^{2}.
\]
\begin{itemize}
\item $C_0=(X-\mu\boldsymbol{1}^\top)(X-\mu\boldsymbol{1}^\top)^\top$ here is unnormalized.
\item Joint QK bias requires an augmented, structurally constrained formulation.
\end{itemize}
\papersource{Paper, Appendix B.2, Eqs.(45)--(49), PDF p.15; Appendix E.2, pp.21--25.}
\end{frame}

% 25
\begin{frame}{Backup: RoPE evidence uses a short synthetic window}
\begin{columns}[c,onlytextwidth]
\column{0.57\textwidth}
\centering\includegraphics[width=\linewidth,height=0.66\textheight,keepaspectratio]{figures/fig-12-rope.png}
\column{0.40\textwidth}
\begin{itemize}
\item Rank on the x-axis; loss in dB on the y-axis, lower is better.
\item 10-token window, $d=768$, RoPE base $10^4$.
\item Authors report roughly 1--2 dB additional improvement.
\item Derivation assumes independent tokens; no long-context benchmark follows.
\end{itemize}
\end{columns}
\papersource{Paper, Appendix F.3, Fig.12, PDF pp.27--28. Author figure reused; dB is not percent accuracy.}
\end{frame}

% 26
\begin{frame}{Backup: UD updates combine regression and weighted SVD}
Fixed $Z,W_d$ give
\[
Z'=(\gamma W_d^\top W_d+\beta I)^+
\left(\beta\sigma(Z)+\gamma W_d^\top Y\right).
\]
Then approximate the effective maps using
\[
\operatorname{svd}_{r_u}\!\left(ZX^+C^{1/2}\right),\qquad
\operatorname{svd}_{r_d}\!\left(YZ'^+C_d^{1/2}\right),\quad C_d=Z'Z'^\top.
\]
\begin{itemize}
\item ReLU permits piecewise candidates for $Z$; branch constraints must be respected.
\item $X,Y$ remain the calibration pair; auxiliary variables and factors are updated.
\item All penalty weights and non-ReLU implementation details are not specified.
\end{itemize}
\papersource{Paper, Sec.4.3, PDF pp.5--7; Appendix H, pp.31--32. Bias-free notation for clarity.}
\end{frame}

% 27
\begin{frame}{Backup: sparse and quantized extensions remain exploratory}
\[
\widehat W=BA+D,\qquad \|D\|_0\leq\kappa.
\]
\begin{itemize}
\item Alternate a low-rank fit with sparse approximation of the residual.
\item Synthetic tests favor some sparse-only solutions over low-rank plus sparse ones (Figs.13--16).
\item Nonzero indices and execution kernels still determine storage and speed.
\item STE can put quantized $A,B$ in the forward pass; no real-model bit-width sweep is reported.
\end{itemize}
\medskip
These are extension directions and limited proxy-loss results, not a validated combined compression system.
\papersource{Paper, Appendix I--I.1, PDF pp.32--36. Here $\|D\|_0$ counts nonzeros, rather than a matrix norm.}
\end{frame}

% 28
\begin{frame}{Backup: reproducibility requires resolving notation issues}
\begin{itemize}
\item \textbf{Algorithm 1:} the Q update needs $K^\top K$ for row-shaped $K$; the printed $KK^\top$ is dimensionally inconsistent.
\item \textbf{Appendix B, Eq.(42):} an extra singular-value factor conflicts with the main-text factorization.
\item \textbf{VO:} fix the softmax orientation and re-count head-dependent attention multiplications.
\item \textbf{Table 2:} OPT-125M repeats PTB/C4 values at 30\% and 40\%; the source does not explain the repetition.
\end{itemize}
\medskip
The reading retains reported data, labels its corrections, and uses the consistent main/appendix derivations. It is not an author erratum or an independent reproduction.
\papersource{Paper, Algorithm 1, p.5; Eq.(42), p.14; Eqs.(14)--(18), p.5; Table 2, p.6.}
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