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\newcommand{\kv}{\mathbf{kv}}
\newcommand{\refmean}{\overline{\mathbf{KV}}_{\mathrm{ref},i}}
\newcommand{\zres}{\mathbf z_{\Delta,i}}
\title[ResidualKV]{ResidualKV}
\subtitle{Residual-Based KV Cache Compression\\for Efficient Long-Context Inference}
\author{Paper by Jitai Hao, Qiang Huang, Yaowei Wang,\\Min Zhang, and Jun Yu}
\date{Research group discussion\quad $\vert$\quad 8 October 2026\\\small arXiv:2602.08005v2 \; (30 September 2026)}

\begin{document}

\begin{frame}
\titlepage
\vspace{-6mm}
\begin{center}\color{AcademicTeal}\large Shared historical references + compact token-specific residuals\end{center}
\papersource{Reading presentation; not authored by the paper's authors. \url{https://arxiv.org/abs/2602.08005v2}}
\end{frame}

\begin{frame}{Long-context inference has three coupled costs}
\begin{block}{Goal}
Preserve useful history while reducing both stored KV bytes and active attention reads.
\end{block}
\medskip
\begin{tabular}{p{.23\linewidth}p{.30\linewidth}p{.35\linewidth}}
\toprule
Family & Main saving & Remaining cost \\
\midrule
Static eviction & Fewer stored tokens & Deleted history cannot return \\
Dynamic selection & Fewer active tokens & Full history remains accessible \\
KV compression & Fewer bytes per token & Full-history processing \\
\bottomrule
\end{tabular}\par
\medskip
Full prefill attention is quadratic in length; a cached decode step reads history linearly.
\papersource{Paper, Secs. I--III, PDF pp. 1--3. Complexity distinction stated here for clarity.}
\end{frame}

\begin{frame}{Useful reference tokens are often far away}
\begin{columns}[T]
\column{.49\textwidth}\centering\fig[0.53]{fig03a-similarity}
\column{.49\textwidth}\centering\fig[0.53]{fig03b-distance}
\end{columns}
\medskip
\begin{itemize}
\item Mean \emph{best historical} cosine similarity: \alert{0.92}.
\item \alert{59.7\%} of nearest matches are more than 16 positions away.
\end{itemize}
\papersource{Paper, Sec. III, Fig. 3(a--b), PDF pp. 3--4; Qwen2.5-7B-Instruct-1M.}
\end{frame}

\begin{frame}{Reference subtraction reduces residual magnitude}
\begin{columns}[T]
\column{.49\textwidth}\centering\fig[0.50]{fig03d-norm}
\column{.49\textwidth}\centering\fig[0.50]{fig03f-residual}
\end{columns}
\begin{itemize}
\item Smaller norms and fewer extreme values favor low-bit encoding.
\item Lower energy does not imply lower rank: the residual spectrum is flatter.
\end{itemize}
\papersource{Paper, Fig. 3(c--f), PDF p. 4. Rank/energy distinction is the reading's interpretation.}
\end{frame}

\begin{frame}{The cache separates reusable structure from deviations}
\centering\fig[0.76]{fig04-framework}
\papersource{Paper, Fig. 4, PDF p. 5. A: encode; B: reconstruct; C: select; D: train.}
\end{frame}

\begin{frame}{The actual code subtracts two nonlinear encodings}
\begin{enumerate}
\item Concatenate pre-RoPE K and V: $\kv_i\in\mathbb R^{2d_k}$.
\item Retrieve $k_i$ nearest references by squared $L_2$ distance; average them.
\item Encode the token and the reference mean separately:
\end{enumerate}
\[
f_c(x)=\bigl(\operatorname{Swish}(xW_{c1})\odot xW_{c2}\bigr)W_{c3},
\qquad f_c:\mathbb R^{2d_k}\to\mathbb R^{d_c}
\]
\begin{block}{Residual code}
\[\zres=f_c(\kv_i)-f_c(\refmean)\]
\end{block}
\centering This generally differs from $f_c(\kv_i-\refmean)$.
\papersource{Paper, Sec. IV-A, PDF pp. 4--5. Llama-3.1-8B: $2d_k=2048$, $d_c=512$.}
\end{frame}

\begin{frame}{Token-wise quantization enables selective reconstruction}
\begin{columns}[T]
\column{.49\textwidth}
\textbf{Store}
\begin{itemize}
\item Groups of at most 32 latent coordinates, within each token.
\item Asymmetric min--max quantization.
\item INT4 residuals by default; INT2 in multimodal budget experiments.
\item References stored in FP8 in deployment.
\end{itemize}
\column{.49\textwidth}
\textbf{Recover}
\[
\widetilde{\zres}=Q(\zres)
\]
\[
\widehat{\mathbf{KV}}_i
=\widetilde{\zres}W_d+\refmean
\]
\begin{block}{Cheap repeated read}
One bias-free linear map; split K/V and use the original token's position.
\end{block}
\end{columns}
\papersource{Paper, Secs. IV-A--B and V-A, PDF pp. 5--6, 9. Full-attention layers use 4-bit KV.}
\end{frame}

\begin{frame}{OmniKV selects tokens; ResidualKV reconstructs them}
\begin{columns}[c]
\column{.56\textwidth}\centering\fig[0.68]{fig07-sparse}
\column{.41\textwidth}
\begin{enumerate}
\item Filter layers scan their full history.
\item Selected indices guide sparse layers.
\item Fetch and decode only those residuals and references.
\end{enumerate}
\medskip
Unselected history remains available in the dynamic combination.
\end{columns}
\papersource{Paper, Sec. IV-C, Fig. 7, PDF p. 7. Static-eviction combinations only retain their selected subsets plus references.}
\end{frame}

\begin{frame}{Dynamic stride limits growth of the reference pool}
Fixed stride keeps every $s$-th token; default $s=10$.
\[
p_{m+1}=p_m+s_0+\lfloor\alpha(p_m-n_{\mathrm{sink}})\rfloor
\]
For fixed $\alpha>0$, ignoring rounding:
\[
N_{\mathrm{ref}}(T)\approx
\log_{1+\alpha}\left(1+\frac{\alpha(T-n_{\mathrm{sink}})}{s_0}\right).
\]
\begin{itemize}
\item $\alpha=0$: fixed-stride limit; reference count grows linearly.
\item $\alpha>0$: approximately logarithmic reference count.
\item \alert{Total cache still grows linearly}: token codes and full layers remain.
\end{itemize}
\papersource{Paper, Sec. IV-A, PDF pp. 5--6. $T$ denotes sequence length here.}
\end{frame}

\begin{frame}{Closer references tighten a KV reconstruction bound}
Let $\Delta_i=\kv_i-\refmean$. The retrieval radius satisfies
\[
\rho_i^2=\frac1{k_i}\sum_{j\in\mathcal R_i}\|\kv_i-\kv_j\|_2^2
=\|\Delta_i\|_2^2+V_i,
\]
where $V_i$ is reference dispersion around their mean.
\begin{block}{Theorem IV.1}
\[
\|\kv_i-\widehat{\mathbf{KV}}_i\|_2
\le(\eta_{\mathcal S}+C_b)\sqrt{\rho_i^2-V_i}
\le(\eta_{\mathcal S}+C_b)\rho_i.
\]
\end{block}
\begin{itemize}
\item $\eta_{\mathcal S}$: codec relative error on the residual support.
\item $C_b$: quantization term, depending on code gain and decoder norm.
\item Top-$k$ retrieval minimizes $\rho_i$; task accuracy is evaluated empirically.
\end{itemize}
\papersource{Paper, Sec. IV-B, Theorem IV.1, PDF pp. 6--7. Proof and assumptions in backup slides.}
\end{frame}

\begin{frame}{Train the codec for both reconstruction and prediction}
\[
\mathcal L=\underbrace{\sum_{l,t}\|\mathbf{KV}_{l,t}-\widehat{\mathbf{KV}}_{l,t}\|_2^2}_{\mathcal L_{\mathrm{rec}}}
+\underbrace{\mathcal L_{\mathrm{ntp}}(\phi;\theta)}_{\text{prediction through recovered KV}}
\]
\begin{itemize}
\item Backbone $\theta$ frozen; only codec parameters $\phi$ updated.
\item Reconstruction preserves values; NTP protects prediction-sensitive directions.
\item Text 7B/8B: 160M FineWeb-Edu tokens, 8,192-token sequences.
\item About 8 GPU-hours on one RTX PRO 6000 Blackwell; 32B: 14 hours.
\end{itemize}
\medskip
Qwen3-VL uses a separate multimodal setup: 30k steps on streamed LLaVA-OV data.
\papersource{Paper, Sec. IV-D and Sec. V-A, PDF p. 8. The 8-hour figure is not a universal training cost.}
\end{frame}

\begin{frame}{The evaluation spans different uses of history}
\renewcommand{\arraystretch}{1.22}
\begin{tabular}{p{.20\linewidth}p{.35\linewidth}p{.34\linewidth}}
\toprule
Evaluation & Question & Protocol \\
\midrule
LongBench & Long-input understanding & 16 datasets; $\alpha=0.001$ \\
SCBench & Repeated context queries & SCDQ subset; $\alpha=0$ \\
AIME & Long reasoning traces & Up to 32,768 output tokens \\
Multimodal & Visual understanding & 7 benchmarks; INT2 \\
Throughput & Real decode benefit & One H100, batch size 1 \\
\bottomrule
\end{tabular}\par
\medskip
Baselines cover static eviction, dynamic selection, quantization, low-rank compression, and visual-token reduction.
\papersource{Paper, Sec. V-A, PDF pp. 8--9. Task scores and throughput: higher is better. KR and CR: lower is better.}
\end{frame}

\begin{frame}{Stored bytes, attention reads, and latency are distinct}
Let $\beta$ be the fraction of full-attention layers, $r$ the sparse-layer access fraction.
\[
q_b=\frac b{16}+\frac2{32},\qquad
q_4=31.25\%,\quad q_2=18.75\%
\]
\[
\mathrm{KR}=\beta q_4+(1-\beta)
\left(\frac12\rho_{\mathrm{ref}}+q_b\frac{d_c}{2d_k}\right),
\qquad
\mathrm{CR}=\beta+(1-\beta)r
\]
\begin{itemize}
\item KR: KV storage relative to full FP16; includes quantizer extrema.
\item CR: layer-averaged historical attention access; \alert{$\mathrm{CR}\ge\beta$}.
\item Llama, 5/32 full layers, 10\% references, $d_c/(2d_k)=1/4$: KR $\approx15.7\%$.
\end{itemize}
\papersource{Paper, Sec. V-A, PDF p. 9. The formula does not itemize reference indices, workspaces, or codec weights.}
\end{frame}

\begin{frame}{LongBench retains competitive quality at lower storage}
\centering
\renewcommand{\arraystretch}{1.25}
\begin{tabular}{lrrrr}
\toprule
Backbone & KR (\%) & CR (\%) & ResidualKV & OmniKV \\
\midrule
Llama-3.1-8B & 13 & 30 & 50.3 & 50.2 \\
Qwen2.5-7B & 16 & 30 & 47.3 & 47.2 \\
Qwen2.5-32B & 13 & 30 & 48.4 & 48.5 \\
Qwen3-4B & 13 & 30 & 47.9 & 47.9 \\
\bottomrule
\end{tabular}\par
\medskip
\begin{itemize}
\item OmniKV retains 100\% KV storage at the table's 30\% CR.
\item Average is over 16 datasets, not an unweighted mean of six categories.
\item Other strong rows: Llama ResidualKV$^\dagger$ 50.4; Qwen2.5-7B KVzip 47.4.
\end{itemize}
\papersource{Paper, Table III, PDF p. 10. $\dagger$: no neural compressor, with a different storage budget.}
\end{frame}

\begin{frame}{Multi-query gains coexist with retrieval failures}
\centering
\renewcommand{\arraystretch}{1.25}
\begin{tabular}{lrrr}
\toprule
SCBench backbone & ResidualKV & Best listed alternative & Gain \\
\midrule
Qwen2.5-7B & 40.7 & 36.7 (KVzip) & +4.0 \\
Qwen3-4B & 32.9 & 22.8 (SnapKV) & +10.1 \\
\bottomrule
\end{tabular}\par
\medskip
\begin{itemize}
\item Qwen2.5 key-value retrieval: \alert{52.6}, vs. KVzip 36.2 and SnapKV 1.4.
\item Its English QA remains below KVzip: 29.9 vs. 44.0.
\item Qwen3 prefix-suffix retrieval is still \alert{0.0}.
\end{itemize}
\papersource{Paper, Table IV, PDF p. 10. Reported KR/CR: 19/20\% and 16/20\%. Selected SCDQ tasks; no full-cache row.}
\end{frame}

\begin{frame}{Multimodal quality is strong; budget accounting conflicts}
\centering
\renewcommand{\arraystretch}{1.25}
\begin{tabular}{lrrr}
\toprule
Backbone & Full cache & ResidualKV & Reported KR/CR \\
\midrule
LLaVA-OV-7B & 73.8 & 73.6 & 8\% / 10\% \\
Qwen3-VL-8B & 74.5 & 74.4 & 10\% / 10\% \\
\bottomrule
\end{tabular}\par
\medskip
Qwen3-VL exceeds DivPrune by 7.1 points (74.4 vs. 67.3).
\begin{block}{A configuration discrepancy in v2}
Table I lists 7 full-attention layers for Qwen3-VL-8B. Its official decoder has 36 layers: $\mathrm{CR}\ge7/36=19.4\%$, above the reported 10\%.
\end{block}
\papersource{Paper, Tables I--II and Sec. V-A--B, PDF pp. 8--9. Full-cache scores are in the prose. Official Qwen model config linked in references.}
\end{frame}

\begin{frame}{AIME preserves the measured full-cache score}
\centering
\renewcommand{\arraystretch}{1.5}
\begin{tabular}{lrrrr}
\toprule
Qwen3-4B-Think & Full & SnapKV & OmniKV & ResidualKV \\
\midrule
AIME score & 80.0 & 60.0 & 76.7 & \alert{80.0} \\
\bottomrule
\end{tabular}\par
\bigskip
\begin{itemize}
\item Up to 32,768 generated tokens for reasoning.
\item +20.0 points over SnapKV; +3.3 over OmniKV.
\item Table V does not specify KR/CR: do not attach the LongBench budget to this result.
\end{itemize}
\papersource{Paper, Table V, PDF p. 10; output-length setting in Sec. V-A, PDF p. 9.}
\end{frame}

\begin{frame}{Quantization changes the practical speed trade-off}
\begin{columns}[c]
\column{.60\textwidth}\centering\fig[0.65]{fig08-throughput}
\column{.37\textwidth}
\textbf{One H100; batch size 1}
\begin{itemize}
\item Up to $1.5\times$ with KV quantization.
\item Up to $3.4\times$ without KV quantization.
\item Full-attention layers still decode their whole low-bit cache.
\end{itemize}
\end{columns}
\papersource{Paper, Fig. 8 and Sec. V-B, PDF p. 10. Speed maxima and minimum storage do not describe a single configuration.}
\end{frame}

\begin{frame}{A much smaller reference pool can preserve quality}
\centering
\renewcommand{\arraystretch}{1.16}
\begin{tabular}{rrrr}
\toprule
$\alpha$ & References at 262K & Reference fraction & LongBench \\
\midrule
0 & 26,189 & 9.99\% & 47.4 \\
0.001 & 3,354 & 1.28\% & 47.4 \\
0.020 & 319 & 0.12\% & 47.3 \\
0.050 & 149 & 0.06\% & 47.0 \\
0.100 & 83 & 0.03\% & 47.1 \\
\bottomrule
\end{tabular}\par
\medskip
\begin{itemize}
\item $\alpha=0.001$: about $7.8\times$ fewer references, same reported score.
\item Counts are measured at 262K; the quality column is a LongBench aggregate.
\end{itemize}
\papersource{Paper, Table VI, PDF p. 11; Qwen2.5-7B-Ins. Fig. 5 additionally tests RULER variable tracking up to 96K.}
\end{frame}

\begin{frame}{Both learned coding and references matter}
\centering
\renewcommand{\arraystretch}{1.3}
\begin{tabular}{lrrr}
\toprule
Llama-3.1-8B ablation & KR / CR & Average & Change \\
\midrule
ResidualKV & 45\% / 30\% & 50.3 & --- \\
Without $f_c,f_d$ & 45\% / 30\% & 46.7 & $-3.6$ \\
Without reference tokens & 47\% / 30\% & 45.9 & $-4.4$ \\
\bottomrule
\end{tabular}\par
\bigskip
\begin{itemize}
\item Additional KV quantization disabled; $d_c=768$ instead of 512.
\item Table VIII reports 45--47\% KR; adjacent prose says about 35\%.
\item Interpret the matched-budget claim with this discrepancy in view.
\end{itemize}
\papersource{Paper, Sec. V-D and Table VIII, PDF p. 11. Values here follow the displayed table.}
\end{frame}

\begin{frame}{The representation also works without dynamic selection}
\centering
\renewcommand{\arraystretch}{1.35}
\begin{tabular}{lrrr}
\toprule
Llama-3.1-8B / LongBench & KR (\%) & CR (\%) & Average \\
\midrule
ResidualKV standalone & 16 & 100 & 50.5 \\
ResidualKV + OmniKV & 13 & 30 & 50.3 \\
ResidualKV + SnapKV & 17.5 & 40 & 50.1 \\
\bottomrule
\end{tabular}\par
\bigskip
\begin{itemize}
\item Standalone matches the full-cache average reported in the prose.
\item Dynamic selection adds attention savings and selective decompression.
\item Static combinations inherit the external policy's information loss.
\end{itemize}
\papersource{Paper, Sec. V-C and Table VII, PDF p. 11; composition mechanics in Sec. IV-C, PDF p. 7.}
\end{frame}

\begin{frame}{The main contribution is a reusable cache representation}
\begin{block}{Mechanism}
Store shared structure once, retain token-specific deviations, and reconstruct only active states.
\end{block}
\begin{itemize}
\item \textbf{Supported by results:} competitive LongBench quality, multi-query gains, and composability with selection policies.
\item \textbf{Engineering trade-off:} learned codec, reference retrieval, quantization, and full-layer overhead.
\item \textbf{What needs clarification:} multimodal CR accounting, ablation KR discrepancy, and exact serving configurations.
\end{itemize}
\papersource{Synthesis of Secs. IV--VI, PDF pp. 4--13. Detailed equations, diagnostics, and model settings follow.}
\end{frame}

\begin{frame}{References and supplementary discussion}
\textbf{Primary paper}
\medskip

Jitai Hao, Qiang Huang, Yaowei Wang, Min Zhang, and Jun Yu.\\
\emph{ResidualKV: Residual-Based KV Cache Compression for Efficient Long-Context Inference.}\\
arXiv:2602.08005v2, 30 September 2026.
\smallskip

\url{https://arxiv.org/abs/2602.08005v2}
\medskip

\textbf{Official model metadata used for the layer-count check}

\href{https://huggingface.co/Qwen/Qwen3-VL-8B-Instruct/raw/main/config.json}{Qwen/Qwen3-VL-8B-Instruct: config.json}\\
\texttt{text\_config.num\_hidden\_layers = 36} (accessed 8 October 2026).
\medskip

\textbf{Author implementation}\quad\url{https://github.com/CURRENTF/ResidualKV}
\papersource{The following slides are supplementary material for questions and discussion.}
\end{frame}

\appendix

\begin{frame}{Backup: quantization connects geometry to reconstruction}
\[
\eta_{\mathcal S}=\sup_i\frac{\|\Delta_i-f_d(\zres)\|_2}{\|\Delta_i\|_2},\qquad
\gamma_{\mathcal S}=\sup_i\frac{\|\zres\|_2}{\|\Delta_i\|_2}
\]
\centering\small Supremum over retrieval-induced support, excluding zero residuals.
\normalsize
\[
\|e_{q,i}\|_2\le
\frac{\sqrt{\sum_{\mathcal G}|\mathcal G|\,\operatorname{range}(z_{\mathcal G})^2}}{2(2^b-1)}
\le\frac{\sqrt{g/2}}{2^b-1}\|\zres\|_2
\]
\[
C_b=\frac{\|W_d\|_{\mathrm{op}}\gamma_{\mathcal S}\sqrt{g/2}}{2^b-1}
\]
\begin{itemize}
\item Split total error into unquantized codec error and decoded rounding error.
\item Apply the decoder operator norm and $\|\Delta_i\|_2^2=\rho_i^2-V_i$.
\item References denote the same effective stored values in encoder and decoder.
\end{itemize}
\papersource{Paper, Theorem IV.1 and proof, PDF pp. 6--7. $\eta_{\mathcal S}$ and $\gamma_{\mathcal S}$ are support-dependent, not universal constants.}
\end{frame}

\begin{frame}{Backup: averaging references has diminishing returns}
Assume compatible states $\kv_i=s_c+\epsilon_i$, with independent, zero-mean perturbations of covariance $\Sigma_c$.
\[
\Delta_i=\epsilon_i-\frac1{k_i}\sum_{j=1}^{k_i}\epsilon_{r_j}
\]
\[
\operatorname{Cov}(\Delta_i\mid c)
=\left(1+\frac1{k_i}\right)\Sigma_c
\]
\begin{itemize}
\item Shared component $s_c$ cancels exactly in this idealized model.
\item Reference noise falls as $1/k_i$; the current token's variation remains.
\item Actual nearest-neighbor selection need not satisfy independence or compatibility.
\end{itemize}
\papersource{Paper, Corollary IV.2, PDF p. 7. This is an explanatory model for benefit and saturation.}
\end{frame}

\begin{frame}{Backup: hyperparameters exchange quality for throughput}
\centering\fig[0.58]{fig11-hyperparameters}
\medskip
\begin{itemize}
\item Larger code/MLP: lower NTP loss, greater memory or compute cost.
\item More references saturate; denser reference spacing may cause OOM.
\end{itemize}
\papersource{Paper, Fig. 11, PDF p. 12. Throughput: context 128K, batch size 16. Blue: loss; red: throughput; crosses: OOM.}
\end{frame}

\begin{frame}{Backup: joint training addresses two different errors}
\centering\fig[0.57]{fig12-objectives}
\begin{itemize}
\item MSE-only training also improves next-token prediction.
\item NTP-only leaves substantial reconstruction error.
\item Joint optimization reaches low reconstruction and prediction losses.
\end{itemize}
\papersource{Paper, Fig. 12 and Sec. V-D, PDF p. 12. These are training loss curves, not downstream accuracy measurements.}
\end{frame}

\begin{frame}{Backup: K and V benefit from a shared compressor}
\begin{columns}[c]
\column{.62\textwidth}\centering\fig[0.65]{fig10-joint-kv}
\column{.35\textwidth}
\begin{itemize}
\item Matched parameter count, compression ratio, and training steps.
\item Joint K/V usually has lower NTP loss.
\item Both projections originate from the same hidden state.
\end{itemize}
\end{columns}
\papersource{Paper, Fig. 10, PDF p. 11. Curves are smoothed over 200 steps.}
\end{frame}

\begin{frame}{Backup: a heavier write path can make repeated reads cheaper}
\centering\fig[0.59]{fig06-latency}
\medskip
Prefill cost rises, but 512 repeated reconstruction steps dominate this test.
\papersource{Paper, Fig. 6, PDF p. 6. H100; 4,096-token prefill + 512 decode steps; codec-path latency, not whole-model request latency.}
\end{frame}

\begin{frame}{Backup: visual references capture distant structural cues}
\centering\fig[0.68]{fig13-visual-case}
\medskip
Targets on object boundaries retrieve distant edges; residuals concentrate near zero.
\papersource{Paper, Fig. 13, PDF p. 12; case study discussion, PDF pp. 12--13.}
\end{frame}

\begin{frame}{Backup: model dimensions and full-attention layers}
\centering\small
\renewcommand{\arraystretch}{1.25}
\setlength{\tabcolsep}{5pt}
\begin{tabular}{lrrrl}
\toprule
Model & $2d_k$ & $d_c$ & $d_{\mathrm{ff}}$ & Full-attention layer indices \\
\midrule
LLaVA-OV-7B & 1024 & 384 & 3072 & 0,1,4,7,14 \\
Qwen3-VL-8B & 2048 & 384 & 3072 & 0,1,2,3,10,21,29 \\
Llama-3.1-8B & 2048 & 512 & 3072 & 0,1,2,8,18 \\
Qwen2.5-7B & 1024 & 384 & 3072 & 0,1,2,4,7,14 \\
Qwen2.5-32B & 2048 & 512 & 4096 & 0,1,2,3,4,5,17,29,40 \\
Qwen3-4B Ins/Think & 2048 & 512 & 3072 & 0,3,9,13,16,21,28 \\
\bottomrule
\end{tabular}\par
\normalsize\medskip
Default retrieval: $k=4$, fixed stride $s=10$. LongBench uses dynamic stride $\alpha=0.001$.
\papersource{Paper, Table I and Sec. V-A, PDF pp. 8--9. Separate variants may require different layer configurations.}
\end{frame}

\begin{frame}{Backup: 10\% CR cannot follow from the listed VL configuration}
\[
\beta=\frac7{36},\qquad
\mathrm{CR}=\frac7{36}+\frac{29}{36}r,\quad r\ge0
\]
\begin{block}{Arithmetic consequence}
Even with no sparse-layer history reads, CR is at least 19.4\%. If sparse layers read 10\%, the same formula gives 27.5\% overall.
\end{block}
\begin{itemize}
\item Table II reports 10\%; Table I lists seven full-attention layers.
\item INT2 changes byte storage, not this attention-token ratio.
\item A different configuration or denominator could explain the report, but v2 does not specify that mapping.
\end{itemize}
\papersource{Reading's calculation using paper Sec. V-A and Tables I--II, PDF pp. 8--9, plus the official 36-layer Qwen3-VL-8B config.}
\end{frame}

\end{document}