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\title[LightTransfer]{LightTransfer}
\subtitle{Your Long-Context LLM is Secretly a Hybrid Model\\with Effortless Adaptation}
\author{Research group paper discussion}
\date{arXiv:2410.13846v3 \quad | \quad 21 October 2026}

\begin{document}

\begin{frame}
  \titlepage
  \vspace{-8mm}
  \begin{center}
    {\small Paper authors: Xuan Zhang, Fengzhuo Zhang, Cunxiao Du, Chao Du,\\
    Tianyu Pang, Wei Gao, and Min Lin}
  \end{center}
  \begin{block}{Central idea}
    Use learned attention locality to select which layers need a full KV cache.
  \end{block}
  \papersource{TMLR, 09/2025; arXiv v3 revised 18 May 2026. Main discussion: about 25 minutes.}
\end{frame}

\begin{frame}{The benefit depends on the task and inference budget}
  \begin{itemize}
    \item \textbf{Long inputs:} choose streaming layers during prefill, with no parameter updates.
    \item \textbf{Long reasoning:} preselect layers from training sequences, then adapt with about 5K examples.
    \item \textbf{System benefit:} evict caches during prefill so more requests fit in memory.
  \end{itemize}
  \begin{block}{Three results, three conditions}
    1.45-point mean LongBench drop at 50\% streaming layers;\\
    up to 2.17$\times$ throughput with each method's maximum batch size;\\
    53.3\% AIME24 accuracy after training adaptation.
  \end{block}
  \papersource{Paper, Tables 2--5, PDF pp. 8--11. The 1.45-point drop is calculated from Table 2 model averages.}
\end{frame}

\begin{frame}{A hybrid cache keeps global access in only some layers}
  \begin{itemize}
    \item Keep $P$ of $L$ layers global; use a fixed window of $W$ tokens elsewhere.
    \item Transformer blocks and feed-forward networks remain in place.
  \end{itemize}
  \[
    \frac{M_{\mathrm{hybrid}}}{M_{\full}}
    \approx \frac{PT+(L-P)W}{LT}
    =\alpha+(1-\alpha)\frac{W}{T},\qquad \alpha=\frac PL.
  \]
  \begin{block}{Worked estimate}
    $T=16{,}384$, $W=1{,}024$, $\alpha=0.5$:
    retain 53.1\% of KV storage, saving about 46.9\%.
  \end{block}
  Global layers still require cache that grows with context length.
  \papersource{Teaching derivation from Sec. 5.1; equal KV dimensions across layers, $T\geq W$; excludes weights and workspace.}
\end{frame}

\begin{frame}{Some layers remain local across prefill and decoding}
  \centering
  \includegraphics[width=0.97\linewidth,height=0.48\textheight,keepaspectratio]{figures/fig-02-attention-patterns.pdf}
  \begin{itemize}
    \item Heatmap: tokens on the horizontal axis, layers on the vertical axis.
    \item Red means more attention mass on initial and recent tokens.
    \item Layer choices can differ across prompts: identify them per request.
  \end{itemize}
  \papersource{Paper, Sec. 4 and Fig. 2, PDF pp. 4--5; additional examples in Fig. 9, p. 21.}
\end{frame}

\begin{frame}{The lazy ratio measures attention mass that will survive}
  \[
    r_i=\frac{1}{w_{\mathrm{last}}}
    \sum_{\hat x\in X_{\mathrm{last}}}
    \sum_{x\in X_{\mathrm{initial}}\cup X_{\mathrm{recent}}}
    A_i(\hat x,x).
  \]
  \begin{itemize}
    \item $A_i$: attention weights averaged across heads at layer $i$.
    \item $X_{\mathrm{last}}$: the last $w_{\mathrm{last}}$ prompt queries.
    \item Default retained keys: first 4 tokens and most recent 1020 tokens.
    \item Large $r_i$ means little attention mass is discarded by streaming.
  \end{itemize}
  \begin{block}{Selection rule}
    Preserve full attention in the $P$ layers with the \emph{lowest} $r_i$.
  \end{block}
  \papersource{Paper, Sec. 5.1, Eq. (1), PDF pp. 5--6; window defaults in App. A, p. 16.}
\end{frame}

\begin{frame}{Online selection releases KV before all layers finish}
  \centering
  \includegraphics[width=0.94\linewidth,height=0.48\textheight,keepaspectratio]{figures/fig-03-test-method.pdf}
  \begin{enumerate}
    \item Run the current layer's full prefill and estimate $r_i$.
    \item Insert $(r_i,i)$ into a max-heap with capacity $P$.
    \item On overflow, evict the largest-$r_i$ layer's middle KV entries.
  \end{enumerate}
  \papersource{Paper, Fig. 3 and Sec. 5.1, PDF pp. 5--6. Current-layer KV and activations still require transient memory.}
\end{frame}

\begin{frame}{A bounded heap implements the global ranking}
  \begin{center}
  \begin{tabular}{cccl}
    \toprule
    Layer & Lazy ratio & Heap after this layer & Cache decision \\
    \midrule
    1 & 0.93 & $\{1\}$ & Keep full for now \\
    2 & 0.40 & $\{1,2\}$ & Keep full for now \\
    3 & 0.86 & $\{2,3\}$ & Layer 1 becomes streaming \\
    4 & 0.60 & $\{2,4\}$ & Layer 3 becomes streaming \\
    \bottomrule
  \end{tabular}
  \end{center}
  \begin{block}{Four-layer example with $P=2$}
    Final global layers: 2 and 4. Final streaming layers: 1 and 3.
  \end{block}
  During decoding, streaming layers keep sinks and a moving recent window.
  \papersource{Teaching example of the priority-queue algorithm in Sec. 5.1; the ratios above are illustrative.}
\end{frame}

\begin{frame}{Reuse FlashAttention's denominator to avoid full rescoring}
  For each head $h$ and final query $t$, let $S_t$ contain retained, causal keys:
  \[
    \rho_{h,t}=\exp\!\left(
      \LSE_{j\in S_t} z_{h,t,j}-\ell^{\mathrm{all}}_{h,t}
    \right),\qquad
    r_i=\frac{1}{H w_{\mathrm{last}}}\sum_{h,t}\rho_{h,t}.
  \]
  \begin{itemize}
    \item $z$: scaled and masked query--key logits.
    \item $\ell^{\mathrm{all}}$: LSE returned by full FlashAttention.
    \item Extra work: $O(Hw_{\mathrm{last}}W d_k)$, constant in context length for fixed windows.
  \end{itemize}
  Reported normalized throughput loss from identification: 0.58\% to 0.14\% over 4K--32K inputs.
  \papersource{Paper, Sec. 5.1 and Table 1, PDF p. 6. Equations expand the schematic code; average probability mass after exponentiation.}
\end{frame}

\begin{frame}{Short prompts require a different selection signal}
  \begin{block}{Why test-time ranking can fail}
    When the prompt fits inside the retained window, $r_i\approx1$ for every layer.
  \end{block}
  \begin{enumerate}
    \item Analyze \textbf{training questions plus answers} to expose long histories.
    \item Fix layers that most frequently appear lazy across training samples.
    \item Supervise the hybrid model using about 5K examples.
  \end{enumerate}
  \medskip
  Experiment: Qwen2.5-32B-Instruct with the public QwQ-STILL distillation recipe; 50\% streaming layers; Flex Attention for training.
  \papersource{Paper, Secs. 5.2 and 6.2, PDF pp. 6 and 9--10. Inference uses the fixed selection, with no test-answer access.}
\end{frame}

\begin{frame}{Discarded attention mass controls local output error}
  For fixed queries, keys and values, let $\delta$ be the attention mass removed:
  \[
    \|o_{\full}-o_{\trim}\|_2\leq 2\delta V_{\max},
    \qquad V_{\max}=\max_j\|v_j\|_2.
  \]
  \begin{itemize}
    \item Removed values contribute at most $\delta V_{\max}$.
    \item Renormalizing retained weights contributes at most another $\delta V_{\max}$.
    \item The network bound propagates these perturbations through residual blocks.
  \end{itemize}
  \begin{block}{An important gap}
    The formal bound uses the worst query; the algorithm averages a few final queries. Attention stability motivates this proxy.
  \end{block}
  \papersource{Paper, Lemma G.4, PDF pp. 25--26; Theorem F.3, p. 23; Eq. (1), p. 5.}
\end{frame}

\begin{frame}{Quality experiments use different global-layer budgets}
  \begin{center}
  \begin{tabular}{llll}
    \toprule
    Evaluation & Model(s) & Full layers & Adaptation \\
    \midrule
    LongBench & LLaMA2/3, Mistral & 50\% & Test only \\
    NIAH / RULER & Mistral-7B & \textbf{75\%} & Test only \\
    Math reasoning & QwQ-STILL recipe & 50\% & About 5K samples \\
    \bottomrule
  \end{tabular}
  \end{center}
  \begin{itemize}
    \item A100; BF16 weights, activations and KV; no quantization.
    \item Greedy decoding; reasoning outputs capped at 32K tokens.
    \item LongBench reports 16 tasks with heterogeneous task scores.
    \item MiniCache merges adjacent-layer KV; maximum nominal reduction: 25\%.
    \item SqueezeAttention allocates KV across layers; matched nominal budget.
  \end{itemize}
  \papersource{Paper, Sec. 6 and App. A, PDF pp. 7--11 and 16. LongBench averages are not one common accuracy measure.}
\end{frame}

\begin{frame}{LongBench averages hide substantial task variation}
  \begin{center}
  \begin{tabular}{lrrr}
    \toprule
    Model & Standard & LightTransfer & Change (points) \\
    \midrule
    LLaMA2-7B-chat & 31.8 & 29.5 & $-2.3$ \\
    Mistral-7B-Instruct & 47.6 & 47.4 & $-0.2$ \\
    LLaMA3-8B-Instruct & 40.8 & 38.7 & $-2.1$ \\
    LLaMA3-70B-Instruct & 44.8 & 43.6 & $-1.2$ \\
    \bottomrule
  \end{tabular}
  \end{center}
  \begin{itemize}
    \item Mean model-average drop: \textbf{1.45 score points}, about 3.52\% relative to the mean Standard score.
    \item LLaMA3-8B Qasper: 32.8 $\rightarrow$ 18.3, despite a much smaller average drop.
  \end{itemize}
  \papersource{Paper, Table 2, PDF p. 8; 50\% streaming layers. Calculations use the displayed, rounded model averages.}
\end{frame}

\begin{frame}{Retrieval stays close with a more conservative budget}
  Mistral-7B, 32K input, \textbf{75\% full-attention layers}:
  \begin{center}
  \begin{tabular}{lrrl}
    \toprule
    NIAH task & Standard & LightTransfer & Source \\
    \midrule
    Single-key & 96.6\% & 96.7\% & Sec. 6.1.2 text \\
    Multiple-key & 78.9\% & 78.2\% & Sec. 6.1.2 text \\
    \bottomrule
  \end{tabular}
  \end{center}
  \begin{block}{The paper's multiple-key figure conflicts with its text}
    Fig. 4 plots both 32K bars around 96--97\%, rather than 78--79\%.
    The exact multiple-key score needs clarification.
  \end{block}
  These quality results use a different budget from the 50\%-global throughput experiment.
  \papersource{Paper, Sec. 6.1.2 and Fig. 4, PDF p. 9; App. A.2, p. 16. Original figure in backup slides.}
\end{frame}

\begin{frame}{Training adaptation matters for long reasoning}
  \begin{center}
  \begin{tabular}{lrrr}
    \toprule
    Method & MATH-OAI & AIME24 & GSM8K \\
    \midrule
    QwQ-STILL & 90.2 & 46.7 & 95.6 \\
    LongGen & 78.2 & 16.7 & 95.4 \\
    LightTransfer-Test & 85.0 & 40.0 & --- \\
    LightTransfer-Train & \textbf{90.7} & \textbf{53.3} & 95.5 \\
    \bottomrule
  \end{tabular}
  \end{center}
  \begin{itemize}
    \item Train versus QwQ-STILL: $+0.5$, $+6.6$, and $-0.1$ percentage points.
    \item The Test variant still loses accuracy on MATH-OAI and AIME24.
    \item About 5K training examples can contain long reasoning trajectories; example count alone does not measure compute.
  \end{itemize}
  \papersource{Paper, Tables 3--4 and Sec. 6.2, PDF pp. 9--10. Accuracy in percent; no repeated-run intervals reported.}
\end{frame}

\begin{frame}{Layer position alone does not predict attention locality}
  \centering
  \includegraphics[width=0.95\linewidth,height=0.52\textheight,keepaspectratio]{figures/fig-05-lazy-ratio.pdf}
  \begin{itemize}
    \item QwQ-STILL: lazy ratio varies non-monotonically over 64 layers.
    \item Some early layers have low ratios and still use middle-context information.
    \item Fixed replacement of both ends can remove useful global access.
  \end{itemize}
  \papersource{Paper, Fig. 5, PDF p. 10. Black boxes show LongGen's predefined replacement regions.}
\end{frame}

\begin{frame}{The 2.17$\times$ result is a maximum-batch throughput gain}
  \begin{center}
  \begin{tabular}{lrrrr}
    \toprule
    Method & 4K & 8K & 16K & 32K \\
    \midrule
    SqueezeAttention & 1.03 & 1.09 & 1.12 & 1.04 \\
    MiniCache & 1.26 & 1.29 & 1.52 & 1.41 \\
    LightTransfer & \textbf{1.44} & \textbf{1.78} & \textbf{2.17} & \textbf{1.75} \\
    \bottomrule
  \end{tabular}
  \end{center}
  \begin{itemize}
    \item Mistral-7B on RULER; 50\% full-attention layers.
    \item Each method uses its own maximum batch size; full-cache throughput $=1$.
    \item Early eviction reduces prefill peak memory and enables larger batches.
  \end{itemize}
  \begin{block}{Interpretation}
    Aggregate generation throughput includes memory-capacity benefits. The table does not measure a matched-batch single-request speedup.
  \end{block}
  \papersource{Paper, Sec. 6.3 and Table 5, PDF p. 11.}
\end{frame}

\begin{frame}{The number of global layers controls the quality tradeoff}
  \centering
  \includegraphics[width=0.84\linewidth,height=0.63\textheight,keepaspectratio]{figures/fig-06-retention.pdf}
  \par\medskip
  More retained global layers improve scores; LightTransfer exceeds SqueezeAttention at the plotted budgets.
  \papersource{Paper, Fig. 6, PDF p. 11. Endpoints correspond to 25\%, 50\%, and 75\% full-attention layers.}
\end{frame}

\begin{frame}{Keeping attention sinks is a critical design choice}
  \begin{center}
  \begin{tabular}{lrrrr}
    \toprule
    Sink count & 0 & 2 & 4 & 6 \\
    \midrule
    Reported score & \textbf{26.5} & 39.8 & 39.8 & 39.9 \\
    \bottomrule
  \end{tabular}
  \end{center}
  \begin{columns}[T,onlytextwidth]
    \begin{column}{0.48\textwidth}
      \begin{block}{Recent window}
        252 to 2044 tokens:\\
        scores range from 39.5 to 40.1.
      \end{block}
    \end{column}
    \begin{column}{0.48\textwidth}
      \begin{block}{Final query count}
        $w_{\mathrm{last}}=8$ to 64:\\
        scores range from 39.7 to 39.9.
      \end{block}
    \end{column}
  \end{columns}
  \medskip
  Window and query-count changes are modest; removing all sinks is costly.
  \papersource{Paper, Table 7, PDF p. 18; App. C.1, p. 19. The table does not clearly identify its model/task aggregation.}
\end{frame}

\begin{frame}{Additional experiments extend the evidence}
  \begin{itemize}
    \item \textbf{Head-wise comparison:} LightTransfer 40.64 versus DuoAttention 40.98 on Gradient-1048K, at 50\% sparsity over 11 tasks.
    \medskip
    \item \textbf{MoE:} Qwen1.5-MoE average 36.26 $\rightarrow$ 35.43, a 0.83-point drop with 50\% streaming layers.
    \medskip
    \item \textbf{Composition:} apply SnapKV to non-lazy layers and LightTransfer to lazy layers; Fig. 8 reports improved compression and score.
  \end{itemize}
  \begin{block}{Scope}
    These are separate models and task subsets; their averages should not be combined with the main LongBench table.
  \end{block}
  \papersource{Paper, App. C.2--C.4, Fig. 8 and Tables 9--10, PDF pp. 19--20.}
\end{frame}

\begin{frame}{Attention-based selection is useful when coupled to memory timing}
  \begin{enumerate}
    \item \textbf{Mechanism:} estimate which layers can give up middle-context KV using attention mass.
    \medskip
    \item \textbf{Systems:} evict during prefill to make the reduced cache useful for batching.
    \medskip
    \item \textbf{Task dependence:} long prompts support online selection; short-prompt reasoning benefits from training adaptation.
  \end{enumerate}
  \begin{block}{Research question}
    Can a model cheaply detect changing global-context needs during generation, and recover access after KV eviction?
  \end{block}
  \papersource{Synthesis of Secs. 4--6. The final question is a proposed direction, not an evaluated result.}
\end{frame}

\begin{frame}{Sources and discussion map}
  \textbf{Primary paper}
  \medskip

  Xuan Zhang, Fengzhuo Zhang, Cunxiao Du, Chao Du, Tianyu Pang, Wei Gao, and Min Lin.
  \emph{LightTransfer: Your Long-Context LLM is Secretly a Hybrid Model with Effortless Adaptation.}
  TMLR (09/2025), arXiv:2410.13846v3.
  \par\medskip
  \href{https://arxiv.org/abs/2410.13846v3}{arxiv.org/abs/2410.13846v3}

  \medskip
  \textbf{Metric reference}
  \par
  \href{https://github.com/THUDM/LongBench/blob/main/LongBench/eval.py}{LongBench official evaluation code: task-specific metric mapping.}

  \medskip
  Backup: formal bound, its interpretation, layer-selection baselines, head-wise methods, parallelism, NIAH discrepancy, SnapKV, implementation details.
\end{frame}

\appendix

\begin{frame}{Backup: the formal bound includes error propagation}
  Define $e_i=\|X^{(i)}-\widetilde X^{(i)}\|_{2,\infty}$ and
  \[
    C_1=HB+L_{\mathrm{lip}}B^2+4HB^3,\quad
    C_2=1+HB(1+4B^2).
  \]
  \[
    e_i\le e_{i-1}+C_1\min\{2,C_2e_{i-1}\}
      +2H(B+L_{\mathrm{lip}}B^3)\mathbb{I}\{i\in\mathcal I\}s_i.
  \]
  \[
  \begin{aligned}
    \|\widetilde f(X)-f(X)\|_{2,\infty}
    \le{}&2LB^2(H+L_{\mathrm{lip}}B+4HB^2)\\
    &+2HB^2(1+L_{\mathrm{lip}}B^2)\sum_{i\in\mathcal I}s_i.
  \end{aligned}
  \]
  $B$: weight-norm bound; $H$: head count; $L_{\mathrm{lip}}$: activation Lipschitz constant.
  The proof uses unit-ball projection for normalization.
  \papersource{Paper, Theorem F.3, Eqs. (4)--(5), PDF p. 23; normalization definition, p. 22.}
\end{frame}

\begin{frame}{Backup: the theoretical objective and empirical score differ}
  Let $D_t$ be the discarded key positions for query $t$.
  \[
    s_i=\max_t\frac1H\sum_h\sum_{j\in D_t}A_{i,h}(t,j),
    \qquad
    1-r_i=\operatorname{mean}_{t\in X_{\mathrm{last}},h}
    \sum_{j\in D_t}A_{i,h}(t,j).
  \]
  \begin{itemize}
    \item Worst query versus a small tail-query average: not identical quantities.
    \item The relaxed bound has an additive constant independent of discarded mass.
    \item App. B.3 reports negative near-zero KL values; true KL is nonnegative.
    \item Small measured discrepancies alone do not establish that the logit bound is tight.
  \end{itemize}
  \papersource{Analysis of Eq. (1), Theorem F.3 and Table 8, PDF pp. 5, 23 and 18. Negative KL may reflect numerical or implementation issues.}
\end{frame}

\begin{frame}{Backup: data-driven layer selection beats these alternatives}
  \begin{center}
  \begin{tabular}{lrrr}
    \toprule
    Strategy & HotpotQA & MuSiQue & NarrativeQA \\
    \midrule
    Shapley value--based & 39.97 & 18.18 & 19.63 \\
    BERTology & 42.48 & 18.13 & 20.91 \\
    LightTransfer & \textbf{43.70} & \textbf{20.90} & \textbf{23.20} \\
    \bottomrule
  \end{tabular}
  \end{center}
  \begin{itemize}
    \item LLaMA3-8B under the same stated inference budget.
    \item HotpotQA gain over the best baseline: $43.70-42.48=1.22$ points.
    \item The text's ``3.7'' gain matches Shapley, not the next-best method.
  \end{itemize}
  \papersource{Paper, App. C.5 and Table 11, PDF pp. 19--21. Main Fig. 7 also compares random and pyramid strategies.}
\end{frame}

\begin{frame}{Backup: head-wise comparisons depend on calibration protocol}
  \begin{center}
  \begin{tabular}{lrl}
    \toprule
    Method & Mean score & Selection protocol in this comparison \\
    \midrule
    RazorAttention & 31.63 & LongBench inputs for calibration \\
    HeadKV & 35.33 & LongBench inputs for calibration \\
    LightTransfer & 40.64 & Online, without a separate set \\
    DuoAttention & 40.98 & Trained head selection \\
    \bottomrule
  \end{tabular}
  \end{center}
  \begin{itemize}
    \item LLaMA3-8B-Instruct-Gradient-1048K; 50\% sparsity; 11 tasks.
    \item This is a different checkpoint and task set from Table 2.
    \item QwQ-STILL reasoning comparison: DuoAttention 79.2/33.3 versus LightTransfer-Test 85.0/40.0 on MATH-OAI/AIME24.
  \end{itemize}
  \papersource{Paper, App. C.3 and Table 9, PDF pp. 19--20; Table 4, p. 10.}
\end{frame}

\begin{frame}{Backup: layer-wise design fits the tested parallel setup}
  LLaMA3-70B on $8\times$A100 40GB, using the paper's implementations:
  \begin{center}
  \begin{tabular}{lrrrr}
    \toprule
    Attention / FFN parallelism & 4K & 8K & 16K & 32K \\
    \midrule
    DP / TP & 145.6 & OOM & OOM & OOM \\
    TP / TP & 1981.9 & 1188.6 & 772.1 & 360.0 \\
    \bottomrule
  \end{tabular}
  \end{center}
  \begin{itemize}
    \item Values are tokens/s; the paper attributes the gap to replication, KV sharding, and load balancing.
    \item Different cache lengths across head shards can cause synchronization waits.
    \item This directly tests parallel strategies, not a universal limit on head-wise compression.
  \end{itemize}
  \papersource{Paper, App. B.2 and Table 6, PDF pp. 17--18. DP: data parallelism; TP: tensor parallelism.}
\end{frame}

\begin{frame}{Backup: the original NIAH plot disagrees with the text}
  \centering
  \includegraphics[width=0.92\linewidth,height=0.61\textheight,keepaspectratio]{figures/fig-04-niah.pdf}
  \par\medskip
  Multiple-key 32K bars are near 96--97\%; the paragraph reports 78.9\% Standard and 78.2\% LightTransfer.
  \papersource{Paper, Fig. 4 and Sec. 6.1.2, PDF p. 9. Figure reproduced without changing its data.}
\end{frame}

\begin{frame}{Backup: layer selection composes with within-layer pruning}
  \begin{columns}[c,onlytextwidth]
    \begin{column}{0.52\textwidth}
      \includegraphics[width=\linewidth,height=0.65\textheight,keepaspectratio]{figures/fig-08-snapkv.pdf}
    \end{column}
    \begin{column}{0.44\textwidth}
      \begin{itemize}
        \item Qwen2.5-3B-chat-32K.
        \item Lazy layers: LightTransfer.
        \item Other layers: SnapKV.
        \item Bars: score, left axis.
        \item Line: compression ratio, right axis.
      \end{itemize}
    \end{column}
  \end{columns}
  \medskip
  The plot reports improvements in both quantities; exact point values are not labeled.
  \papersource{Paper, App. C.2 and Fig. 8, PDF p. 19.}
\end{frame}

\begin{frame}{Backup: implementation details matter for the lazy ratio}
  \begin{itemize}
    \item Match the full kernel's query--key scale, positional transforms, causal mask, and LSE query indices.
    \medskip
    \item Deduplicate initial/recent keys when short inputs make the sets overlap.
    \medskip
    \item Exponentiate each log ratio, then average over heads and tail queries.
    \medskip
    \item Table 1 is schematic: it omits final aggregation and has inconsistent tensor-layout comments.
  \end{itemize}
  \begin{block}{Scope of the extra computation}
    Fixed-window identification is constant in context length; full prefill computation still grows with sequence length.
  \end{block}
  \papersource{Implementation interpretation of Sec. 5.1, Eq. (1) and Table 1, PDF pp. 5--6.}
\end{frame}

\end{document}