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% Paper version: arXiv:2511.01581v1. Main talk: frames 1--21, about 25 minutes.
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\title[ExplicitLM]{ExplicitLM}
\subtitle{Decoupling Knowledge from Parameters\\via Explicit Memory Banks}
\author{Paper authors: Chengzhang Yu, Zening Lu, Chenyang Zheng,\\
Chiyue Wang, Yiming Zhang, and Zhanpeng Jin}
\date{arXiv:2511.01581v1 \quad $\cdot$ \quad Reading: 8 October 2026\\
\small Research group discussion $\cdot$ approximately 25 minutes}

\begin{document}
\begin{frame}
  \titlepage
  \vspace{-3mm}
  \begin{center}
    \small A readable memory interface improves access to preloaded facts.
  \end{center}
\end{frame}

% 2
\begin{frame}{Give factual knowledge an address}
  \begin{block}{Research question}
    Can a language model read inspectable knowledge entries while learning
    how to retrieve and use them?
  \end{block}
  \begin{itemize}
    \item Distributed parameters provide no direct record for each fact.
    \item ExplicitLM stores facts as token sequences in an external bank.
    \item Learned retrieval connects those records to network computation.
  \end{itemize}
  \medskip
  \alert{The experiments primarily test access to preloaded facts.}
  \papersource{Paper, Secs. 1 and 3, PDF pp. 1--6; evaluation scope from Sec. 4.2, p. 7.}
\end{frame}

% 3
\begin{frame}{All layers access a shared memory bank}
  \centering
  \includegraphics[width=.88\linewidth,height=.62\textheight,keepaspectratio]
    {figures/fig-01-overview.png}
  \par\medskip
  One shared bank $\;\longrightarrow\;$ layer-specific reads
  $\;\longrightarrow\;$ fusion with network computation.
  \papersource{Paper, Fig. 1 and Sec. 3.2, PDF p. 4. The exact fusion operator is not specified.}
\end{frame}

% 4
\begin{frame}{Readable content and retrieval keys are distinct}
  \[
    \mathbf m_i=\operatorname{Tokenize}(s_i),\qquad
    \mathbf E_i=\operatorname{Embed}(\mathbf m_i)\in\mathbb R^{d\times L}
  \]
  \begin{columns}[T,onlytextwidth]
    \begin{column}{.47\textwidth}
      \begin{block}{Stored content}
        $N=10^6$ entries\\
        Up to $L=16$ token IDs per entry\\[2mm]
        Decodable into text
      \end{block}
    \end{column}
    \begin{column}{.49\textwidth}
      \begin{block}{Learned addressing}
        Query $q(x)\in\mathbb R^d$\\
        Key $\mathbf k_i\in\mathbb R^d$\\[2mm]
        Selects a memory address
      \end{block}
    \end{column}
  \end{columns}
  \medskip
  Readability applies to the stored token sequence, not the key vector.
  \papersource{Paper, Secs. 3.2 and 3.4, PDF pp. 4--5.}
\end{frame}

% 5
\begin{frame}{Freeze facts; reserve capacity for learned patterns}
  \[
  \M=\M_f\cup\M_u,\qquad
  |\M_f|=\rho N,\qquad|\M_u|=(1-\rho)N
  \]
  \begin{columns}[T,onlytextwidth]
    \begin{column}{.48\textwidth}
      \begin{block}{Frozen partition: 20\% by default}
        200,000 entries\\
        Curated explicit facts\\
        Content preserved during training
      \end{block}
    \end{column}
    \begin{column}{.48\textwidth}
      \begin{block}{Updatable partition: 80\%}
        800,000 entries\\
        Intended to learn implicit patterns\\
        EMA proposed for gradual updates
      \end{block}
    \end{column}
  \end{columns}
  \medskip
  Fixed slot count; updates replace content in place.
  \papersource{Paper, Sec. 3.3, PDF pp. 4--5. EMA statistics and conversion back to token IDs are unspecified.}
\end{frame}

% 6
\begin{frame}{Retrieve candidates, then select a discrete entry}
  \centering
  \includegraphics[width=.96\linewidth,height=.56\textheight,keepaspectratio]
    {figures/fig-02-retrieval.png}
  \par\medskip
  \begin{enumerate}
    \item Product keys reduce the initial search.
    \item Cosine scores and Gumbel-Softmax drive the final choice.
  \end{enumerate}
  \papersource{Paper, Fig. 2 and Sec. 3.4, PDF p. 5.}
\end{frame}

% 7
\begin{frame}{Product keys factor a million-way search}
  \[
    \mathbf k=[c;c'],\quad q=[q^{(1)};q^{(2)}],\quad
    q^\top\mathbf k=(q^{(1)})^\top c+(q^{(2)})^\top c'
  \]
  \begin{itemize}
    \item Two sets of $\sqrt N=1{,}000$ subkeys address $N=10^6$ entries.
    \item Retain high-scoring subkeys and combine their scores.
    \item Teaching example: top 16 from each set gives $16^2=256$ pairs.
  \end{itemize}
  \begin{block}{Scope of the efficiency claim}
    Paper: $O(N|I|)\rightarrow O(\sqrt N|I|)$ for retrieval.\\
    Subkey scoring, candidate selection, and memory fusion still cost work.
    No end-to-end latency benchmark is reported.
  \end{block}
  \papersource{Paper, Sec. 3.4, p. 5; 16 candidates in Sec. 4.6, p. 9. Score expansion is a teaching derivation.}
\end{frame}

% 8
\begin{frame}{Hard choices use a soft gradient approximation}
  \[
  p_i=\frac{\exp((cs_i+g_i)/\tau)}
            {\sum_{j\in I}\exp((cs_j+g_j)/\tau)},\qquad
  cs_i=\cs(q,\mathbf k_i)
  \]
  \[
  g_i=-\log(-\log\epsilon_i),\quad \epsilon_i\sim U(0,1),
  \qquad \hat i=\arg\max_i p_i
  \]
  \begin{itemize}
    \item Forward: read the discrete entry $\mathbf m_{\hat i}$.
    \item Backward: use the soft selection distribution as a surrogate.
    \item Candidate set $I$; temperature $\tau$ controls the soft weights.
  \end{itemize}
  \medskip
  The first-stage candidate indices remain discrete.
  \papersource{Paper, Eq. (1) and Sec. 3.4, PDF p. 5. Inference noise policy is not specified.}
\end{frame}

% 9
\begin{frame}{Three objectives couple prediction and memory use}
  \[
  \mathcal L=\mathcal L_{\mathrm{CE}}
      +\lambda_{\mathrm{sim}}\mathcal L_{\mathrm{sim}}
      +\lambda_{\mathrm{div}}\mathcal L_{\mathrm{div}}
  \]
  \renewcommand{\arraystretch}{1.35}
  \begin{tabular}{@{}p{.22\linewidth}p{.69\linewidth}@{}}
    \toprule
    Component & Intended effect\\
    \midrule
    Cross-entropy & Predict the next token using context and memory.\\
    Relevance & Favor memories with high query--content similarity.\\
    Diversity & Reduce similarity among retrieved candidates.\\
    \bottomrule
  \end{tabular}
  \par\medskip
  Relevance and diversity create a trade-off controlled by the weights.
  \papersource{Paper, Sec. 3.5, Eqs. (2)--(4), PDF p. 6. Exact notation issues are discussed in backup slides.}
\end{frame}

% 10
\begin{frame}{Test facts are available inside the memory bank}
  \begin{itemize}
    \item \textbf{Corpus:} 10M entries from Wikipedia, Gutenberg, OpenWebText.
    \item \textbf{Memory initialization:} Wikipedia only; source UUIDs retained.
    \item \textbf{Split:} test examples derive from frozen memory entries;
      training excludes those entries' tokens, as stated in the paper.
    \item \textbf{Comparison:} ExplicitLM vs.\ a Transformer without memory;
      SFT data varies from 10k to 100k examples.
  \end{itemize}
  \begin{block}{What this protocol measures}
    Learning to use externally supplied facts beyond the training examples.
    The baseline has no equivalent retrieval access.
  \end{block}
  \papersource{Paper, Secs. 4.1--4.3, PDF pp. 6--7. Model size and matched compute budgets are not specified.}
\end{frame}

% 11
\begin{frame}{Three tasks probe different uses of stored facts}
  \renewcommand{\arraystretch}{1.5}
  \begin{tabular}{@{}p{.25\linewidth}p{.36\linewidth}p{.29\linewidth}@{}}
    \toprule
    Task & Input $\rightarrow$ output & Main capability\\
    \midrule
    Object prediction & Subject, relation $\rightarrow$ object & Exact factual recall\\
    Relation reasoning & Entity pair $\rightarrow$ relation & Relation identification\\
    Fact verification & Statement $\rightarrow$ true/false & Fact discrimination\\
    \bottomrule
  \end{tabular}
  \par\medskip
  All results report accuracy (\%, higher is better).
  \begin{itemize}
    \item Object prediction includes five distractors in the stated protocol.
    \item Fact verification uses a 50:50 positive/negative balance.
  \end{itemize}
  \papersource{Paper, Sec. 4.2, PDF p. 7. No separate multi-hop reasoning benchmark is reported.}
\end{frame}

% 12
\begin{frame}{Object prediction shows the most persistent gains}
  \centering
  \renewcommand{\arraystretch}{1.2}
  \begin{tabular}{@{}rrrr@{}}
    \toprule
    SFT data & Baseline (\%) & ExplicitLM (\%) & Gain (pp)\\
    \midrule
    10k  & 7.86 & 28.42 & \gain{20.56}\\
    25k  & 22.16 & 63.12 & \gain{40.96}\\
    50k  & 30.23 & 73.90 & \gain{43.67}\\
    75k  & 40.64 & 79.76 & \gain{39.12}\\
    100k & 56.80 & 80.94 & \gain{24.14}\\
    \bottomrule
  \end{tabular}
  \par\medskip
  \begin{block}{Two different quantities}
    10k: $28.42/7.86=3.62\times$ the baseline accuracy.\\
    50k: $73.90-30.23=43.67$ percentage points.
  \end{block}
  \papersource{Paper, Table 1, PDF p. 7. The abstract's 43.67\% improvement is an absolute accuracy difference.}
\end{frame}

% 13
\begin{frame}{Other task gaps narrow with more supervision}
  \centering
  \renewcommand{\arraystretch}{1.2}
  \begin{tabular}{@{}rrrrr@{}}
    \toprule
     & \multicolumn{2}{c}{Relation reasoning} & \multicolumn{2}{c}{Fact verification}\\
    SFT data & Baseline & ExplicitLM & Baseline & ExplicitLM\\
    \midrule
    10k  & 38.27 & 70.02 & 61.71 & 66.03\\
    25k  & 79.99 & 87.85 & 71.49 & 79.79\\
    50k  & 83.80 & 90.41 & 83.34 & 86.25\\
    75k  & 87.66 & 92.12 & 86.40 & 88.74\\
    100k & 91.91 & 92.73 & 88.92 & 89.75\\
    \bottomrule
  \end{tabular}
  \par\medskip
  Relation gain: \gain{31.75 pp at 10k} $\rightarrow$ \gain{0.82 pp at 100k}.\\
  Fact verification gain at 100k: \gain{0.83 pp}.
  \papersource{Paper, Table 1, PDF p. 7. All entries are accuracy (\%). At 25k, fact verification improves by 8.30 pp; the printed delta is 8.33.}
\end{frame}

% 14
\begin{frame}{Memory access varies across network depth}
  \centering
  \includegraphics[width=.97\linewidth,height=.79\textheight,keepaspectratio]
    {figures/fig-03-hit-rates.png}
  \papersource{Paper, Fig. 3, PDF p. 8. Full bar: correct. Bottom: incorrect. Top: difference.}
\end{frame}

% 15
\begin{frame}{Correct predictions have higher memory hit rates}
  \centering
  \renewcommand{\arraystretch}{1.2}
  \begin{tabular}{@{}rrrr@{}}
    \toprule
    SFT data & Hit $\mid$ correct & Hit $\mid$ incorrect & Gap (pp)\\
    \midrule
    10k  & 71\% & 22\% & 49\\
    25k  & 66\% & 21\% & 45\\
    50k  & 65\% & 21\% & 44\\
    75k  & 71\% & 37\% & 34\\
    100k & 71\% & 23\% & 48\\
    \bottomrule
  \end{tabular}
  \par\medskip
  \begin{itemize}
    \item Total hit: at least one layer retrieves relevant memory.
    \item These are $P(\mathrm{hit}\mid\mathrm{correct})$ comparisons:
      evidence of association, not a causal effect estimate.
  \end{itemize}
  \papersource{Paper, Fig. 3, PDF p. 8. Values follow figure labels; Sec. 4.4 prose says 5k where the figure says 75k.}
\end{frame}

% 16
\begin{frame}{The frozen fraction creates a nonmonotonic trade-off}
  \centering
  \includegraphics[width=.97\linewidth,height=.78\textheight,keepaspectratio]
    {figures/fig-04-freeze-rate.png}
  \papersource{Paper, Fig. 4, PDF p. 9. Left: accuracy (starts at 20\%). Right: relative gain over baseline.}
\end{frame}

% 17
\begin{frame}{A 40\% frozen bank often beats the 20\% default}
  \centering
  \renewcommand{\arraystretch}{1.2}
  \begin{tabular}{@{}rrrrr@{}}
    \toprule
    SFT data & Baseline & $\rho=0.2$ & $\rho=0.4$ & $\rho=0.6$\\
    \midrule
    10k  & 38.3 & 70.0 & \gain{84.7} & 78.8\\
    25k  & 80.0 & 87.8 & \gain{90.1} & 82.2\\
    50k  & 83.8 & 90.4 & \gain{92.3} & 88.4\\
    75k  & 87.7 & \gain{92.1} & 92.0 & 90.9\\
    100k & 91.9 & 92.7 & \gain{94.9} & 92.2\\
    \bottomrule
  \end{tabular}
  \par\medskip
  At 10k, $\rho=0.4$ adds \gain{14.7 pp} over the default.\\
  Hyperparameter choice matters even when every setting beats baseline.
  \papersource{Paper, Fig. 4, PDF p. 9; values as labeled to one decimal. Whether test facts are held fixed as the frozen partition changes is not specified.}
\end{frame}

% 18
\begin{frame}{Oracle memory replacement improves accuracy}
  Replace the top-ranked of 16 candidates at \textbf{L1 and L3}
  with the answer-relevant oracle entry.
  \medskip
  \begin{center}
  \renewcommand{\arraystretch}{1.25}
  \begin{tabular}{@{}lrrr@{}}
    \toprule
    Gain over Retain (pp) & 50k & 75k & 100k\\
    \midrule
    Object prediction & 3.62 & 2.73 & 1.88\\
    Relation reasoning & 2.25 & 1.62 & 1.75\\
    Fact verification & 2.12 & 2.25 & 0.75\\
    \bottomrule
  \end{tabular}
  \end{center}
  Mean gain: \gain{2.11 percentage points} across nine comparisons.
  \medskip
  \begin{itemize}
    \item Direct intervention supports a contribution from retrieval content.
    \item Only two layers are changed; this is not a universal upper bound.
  \end{itemize}
  \papersource{Paper, Sec. 4.6 and Table 2, PDF p. 9. Retain values differ from Table 1; comparisons stay within Table 2.}
\end{frame}

% 19
\begin{frame}{The strongest evidence concerns knowledge access}
  \renewcommand{\arraystretch}{1.35}
  \begin{tabular}{@{}p{.45\linewidth}p{.47\linewidth}@{}}
    \toprule
    Design and reported evidence & What the broader claims still need\\
    \midrule
    Better accuracy with preloaded memory & Comparisons with equal knowledge access\\
    Retrieval content affects predictions & Actual post-training fact-editing tests\\
    Readable token entries & Faithfulness of reasoning explanations\\
    Factorized candidate search & Measured latency and memory costs\\
    \bottomrule
  \end{tabular}
  \par\medskip
  The backbone still has parameters that can store knowledge.
  \papersource{Reading assessment based on Secs. 3--5, PDF pp. 3--10. Accuracy and intervention evidence: Tables 1--2.}
\end{frame}

% 20
\begin{frame}{Three experiments would strengthen the central claim}
  \begin{enumerate}
    \item \textbf{Equal knowledge access.}\\
      Compare RAG, context insertion, and layer-wise memory on the
      same facts, backbone, and resource budget.
      \medskip
    \item \textbf{Controlled fact editing.}\\
      Change one record; test update success, paraphrases, and side effects.
      \medskip
    \item \textbf{Complete the implementation and cost picture.}\\
      Specify EMA-to-token updates, pooling, and fusion; measure
      latency and ablate auxiliary losses.
  \end{enumerate}
  \papersource{Proposed follow-up experiments from this reading. The paper identifies manual knowledge curation as a limitation in Sec. 5, PDF p. 10.}
\end{frame}

% 21
\begin{frame}{A useful interface for readable external knowledge}
  \begin{enumerate}
    \item \textbf{Architecture:} token memory, product-key search,
      and trainable discrete selection.
      \medskip
    \item \textbf{Evidence:} strong factual recall gains in the reported
      memory-backed protocol; oracle reads add another 2.11 pp on average.
      \medskip
    \item \textbf{Open problem:} connect inspectable storage to reliable
      editing and well-specified, efficient computation.
  \end{enumerate}
  \vfill
  \small
  Chengzhang Yu et al., \emph{ExplicitLM: Decoupling Knowledge from
  Parameters via Explicit Memory Banks}.\\
  \href{https://arxiv.org/abs/2511.01581v1}{arXiv:2511.01581v1}, 3 November 2025.
  \papersource{Main discussion ends here. Four backup slides follow.}
\end{frame}

\appendix

% 22
\begin{frame}{Backup: a consistent straight-through surrogate}
  \[
  h_i=\mathbf 1[i=\hat i],\qquad
  z_i=\sg(h_i-p_i)+p_i,\qquad y=\sum_{i\in I}z_i v_i
  \]
  \begin{itemize}
    \item Forward: $z=h$, so the read is the selected value $v_{\hat i}$.
    \item Backward: differentiate through $p$; treat $\sg(\cdot)$ as constant.
  \end{itemize}
  \[
  \frac{\partial p_i}{\partial cs_j}
  =\frac{p_i}{\tau}(\delta_{ij}-p_j)
  \]
  \medskip
  Here $v_i$ is a pedagogical memory vector; candidates, noise, and values
  are held fixed for the score derivative.
  \papersource{Teaching derivation for Sec. 3.4, PDF p. 5. The paper's displayed query-gradient expression omits the selection Jacobian and differentiates discrete IDs.}
\end{frame}

% 23
\begin{frame}{Backup: two loss definitions need clarification}
  \textbf{Relevance loss, Eq. (3):}
  \[
  -\mathbb E_x\left[\sum_{i\in I}p_i
  \frac{q(x)^\top\mathbf E_i}{\|q(x)\|_2\|\mathbf E_i\|_2}\right]
  \]
  $q\in\mathbb R^d$, but $\mathbf E_i\in\mathbb R^{d\times L}$.
  An aggregation into a compatible vector is needed.
  \medskip

  \textbf{Diversity loss:} $K=|I|$, $\hat{\mathbf E}_i$ is normalized.
  \[
  \frac{2}{K(K-1)}
  \sum_{i\in I}\sum_{\substack{j\in I\\j\ne i}}
  \cs(\hat{\mathbf E}_i,\hat{\mathbf E}_j)
  \]
  Ordered pairs are counted twice. This equals twice the usual mean
  pairwise cosine, affecting the effective regularization weight.
  \papersource{Paper, Sec. 3.2, p. 4, and Eqs. (3)--(4), p. 6. Both observations are this reading's analysis.}
\end{frame}

% 24
\begin{frame}{Backup: full oracle intervention results}
  \centering
  \renewcommand{\arraystretch}{1.2}
  \begin{tabular}{@{}rlrrr@{}}
    \toprule
    SFT & Condition & Object & Relation & Fact\\
    \midrule
    50k & Retain & 70.87 & 89.87 & 85.12\\
        & Replace & 74.49 & 92.12 & 87.24\\
    \midrule
    75k & Retain & 77.12 & 90.25 & 88.00\\
        & Replace & 79.85 & 91.87 & 90.25\\
    \midrule
    100k & Retain & 79.12 & 90.50 & 90.37\\
         & Replace & 81.00 & 92.25 & 91.12\\
    \bottomrule
  \end{tabular}
  \par\medskip
  Accuracy (\%). Average gain by data scale: 2.66, 2.20, 1.46 pp.
  \papersource{Paper, Table 2, PDF p. 9. At 50k, Table 1 object accuracy is 73.90\%, whereas this table's Retain is 70.87\%; the run/configuration difference is unexplained.}
\end{frame}

% 25
\begin{frame}{Backup: details that affect reproduction}
  \begin{itemize}
    \item \textbf{Task scoring:} five distractors would suggest six choices;
      uniform chance would be 16.67\%, above the 10k baseline's 7.86\%.
      Candidate construction and scoring need clarification.
      \medskip
    \item \textbf{Model/training:} model size, optimizer settings, temperature
      schedule, loss weights, and EMA-to-ID mapping are not specified.
      \medskip
    \item \textbf{Figure 3:} use the plotted 75k label; the prose says 5k.
      The internal stacking legend also differs from the caption.
  \end{itemize}
  \medskip
  Appendix A.1 states NVIDIA A100 hardware and links an anonymous repository.
  \papersource{Paper, Secs. 3--4, PDF pp. 4--9, and Appendix A.1, p. 12. Statements concern the paper's specification.}
\end{frame}

\end{document}