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  \hspace{8mm}{\scriptsize\color{gray}GenMem / arXiv:2609.34633v1}%
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\newcommand{\mem}{\mathcal M}
\newcommand{\sid}{\mathbf z}
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\title[GenMem]{GenMem}
\subtitle{\texorpdfstring{Generative Symbolic Memory\\for Self-Evolving Harness}{Generative Symbolic Memory for Self-Evolving Harness}}
\author{\texorpdfstring{Paper: Xinke Jiang, Tao Feng, Weixuan Xu, et al.\\[4pt]
  {\small Independent paper-reading presentation}}{Independent paper-reading presentation}}
\date{arXiv:2609.34633v1 / September 28, 2026\\
  {\small Reading date: October 19, 2026}}

\begin{document}

% 1
\begin{frame}
\titlepage
\vspace{-6mm}
\begin{center}
\color{AcademicTeal}\large Learn stable addresses; revise the experience behind them.
\end{center}
\end{frame}

% 2
\begin{frame}{A revised memory should remain reachable}
\begin{columns}[T,onlytextwidth]
\column{.48\textwidth}
\begin{block}{The useful object is a procedure}
An agent reuses applicability rules, action sequences, verification steps, and lessons from failures.
\end{block}
\column{.48\textwidth}
\begin{block}{The procedure keeps changing}
Re-embedding revised text can change which queries retrieve an entry, even when its role is unchanged.
\end{block}
\end{columns}
\vspace{4mm}
\begin{itemize}
\item Relevant experience is sparse and often shares subskills.
\item Delayed task success gives weak credit to individual memory operations.
\item GenMem learns a context-to-address policy and edits addressed text.
\end{itemize}
\papersource{Paper, Sec. 1, PDF pp. 1--3; Appendix B.1--B.2, pp. 15--16.}
\end{frame}

% 3
\begin{frame}{Construction, learning, and evolution are separate}
\centering
\begin{tikzpicture}[>=Stealth,
 box/.style={draw=AcademicBlue,rounded corners=2pt,fill=SoftGray,
 text width=2.7cm,minimum height=1.35cm,align=center,font=\small}]
\node[box] (seed) {Training tasks\\and trajectories};
\node[box,right=5mm of seed] (quant) {Experience summaries\\and SID codebooks};
\node[box,right=5mm of quant] (learn) {Alignment, interaction\\training, and GRPO};
\node[box,right=5mm of learn] (online) {Online retrieval,\\execution, and revision};
\draw[->,thick] (seed)--(quant);
\draw[->,thick] (quant)--(learn);
\draw[->,thick] (learn)--(online);
\end{tikzpicture}
\vspace{6mm}
\begin{itemize}
\item Cold start: contrast successful, failed, and unverified attempts.
\item Training: learn where to read/write and how to use experience.
\item Online: freeze models and codebooks; update the external bank.
\end{itemize}
\papersource{Teaching diagram based on Fig. 2 and Secs. 2--3, PDF pp. 3--6.}
\end{frame}

% 4
\begin{frame}{80 symbols provide 49,152 possible addresses}
\[
 \sid=(s^1,\ldots,s^L)\in[N_1]\times\cdots\times[N_L]
\]
\begin{columns}[T,onlytextwidth]
\column{.48\textwidth}
\begin{block}{Address capacity}
\centering $K_{\rm addr}=\prod_{\ell=1}^{L}N_\ell$\\[2mm]
$48\times16\times8\times8=49{,}152$
\end{block}
\column{.48\textwidth}
\begin{block}{SID vocabulary}
\centering $V_{\rm SID}=\sum_{\ell=1}^{L}N_\ell$\\[2mm]
$48+16+8+8=80$
\end{block}
\end{columns}
\vspace{3mm}
Each address uses four level-specific tokens. Each occupied slot holds one current experience text.
\medskip

\alert{Capacity, populated slots, and payload storage are different quantities.}
\papersource{Paper, Eq. (1), PDF p. 3; Sec. 2, p. 4; Appendix C.1, p. 19.}
\end{frame}

% 5
\begin{frame}{Residual quantization constructs the initial SIDs}
\[
e_i=f_{\rm enc}(p_{\rm enc}\oplus x_i)\in\mathbb R^d,
\qquad r_i^{(1)}=e_i
\]
\[
\begin{aligned}
s_i^\ell&=\arg\min_{j\in[N_\ell]}\|r_i^{(\ell)}-c_j^{(\ell)}\|_2^2,\\
r_i^{(\ell+1)}&=r_i^{(\ell)}-c_{s_i^\ell}^{(\ell)},
\qquad \hat e_i=\sum_\ell c_{s_i^\ell}^{(\ell)}.
\end{aligned}
\]
\begin{itemize}
\item A frozen encoder represents reusable procedures in summary $x_i$.
\item Each level selects a centroid and passes the residual onward.
\item Experiences sharing a complete SID are consolidated.
\end{itemize}
\alert{Levels have no predefined domain/tool/strategy semantics.}
\papersource{Paper, Eqs. (2)--(3), p. 4; Appendices B.4--B.5 and C.1, pp. 17--20.\\
Equations show the Euclidean form; Table 15 selects a Weighted setting.}
\end{frame}

% 6
\begin{frame}{One address space supports both reading and writing}
\begin{columns}[T,onlytextwidth]
\column{.31\textwidth}
\begin{block}{1. MemRetriever}
Query/context\par$\downarrow$\par Generate SID\par$\downarrow$\par Lookup and rewrite
\end{block}
\column{.31\textwidth}
\begin{block}{2. StackPlanner}
Query + support\par$\downarrow$\par Execute task\par$\downarrow$\par Answer + trajectory
\end{block}
\column{.31\textwidth}
\begin{block}{3. MemEvolver}
Trajectory\par$\downarrow$\par Generate target SID\par$\downarrow$\par Insert or revise
\end{block}
\end{columns}
\vspace{4mm}
\[
p_\theta(\sid\mid c)=\prod_{\ell=1}^{L}p_\theta(s^\ell\mid c,s^{1:\ell-1})
\]
The write address is generated by MemEvolver. Updated text is not re-quantized to choose a replacement address.
\papersource{Paper, Eq. (4), p. 4; Sec. 3, pp. 5--6; Appendix B.3, p. 17.}
\end{frame}

% 7
\begin{frame}{Revision preserves the address and changes the rule}
\[
\mem_{t+1}[z]=
\begin{cases}
\tilde x_{e,t}, & z=z_{e,t},\\
\mem_t[z], & z\ne z_{e,t}.
\end{cases}
\]
\begin{block}{Example: fixed members in a selection task}
Same SID $(3,8,4,2)$:\par
``Use combinations for unordered selection''\par
$\downarrow$\par
``First remove fixed members; then choose the remaining members.''
\end{block}
For 12 players, 5 starters, and 2 fixed starters: $\binom{10}{3}=120$.
\medskip

\texttt{revise} can also retain, merge, or clear the payload.
\papersource{Teaching paraphrase of Appendix H, PDF p. 40. Update rule: Appendix C.2, pp. 20--21.}
\end{frame}

% 8
\begin{frame}{Alignment learns IDs; mid-training learns interactions}
\begin{columns}[T,onlytextwidth]
\column{.48\textwidth}
\begin{block}{Five alignment mappings}
\[
q\to z,\quad\tau\to z,\quad x\to z
\]
\[
z\to x,\qquad z\to d
\]
$q$: query; $\tau$: trajectory; $x$: experience; $d$: semantic description.
\end{block}
\column{.48\textwidth}
\begin{block}{Role-specific demonstrations}
MemRetriever: reason, address, lookup, rewrite.\par\medskip
MemEvolver: inspect a trajectory, address, compare, update.
\end{block}
\end{columns}
\[
\mathcal L_{\rm SID}(c,z)=-\sum_{\ell=1}^{L}
\log p_\theta(s^\ell\mid c,s^{1:\ell-1})
\]
Mid-training uses filtered ReAct-style traces and stronger-teacher targets.
\papersource{Paper, Eqs. (5)--(6) and Sec. 3, PDF pp. 4--5; Appendix B.5, p. 18.}
\end{frame}

% 9
\begin{frame}{Rewards evaluate both the operation and its utility}
\[
R_{\rm process}=R_{\rm format}+R_{\rm strategy}+R_{\rm SID}
\]
\[
R_{\rm out}=r_{\rm mem}-r_{\rm no\text{-}mem}
+\alpha_{\rm partial}\,\mathbb I[\text{both succeed}]
\]
\begin{center}
\begin{tabular}{llcl}
\toprule
With memory & Without memory & Reward & Interpretation\\
\midrule
Success & Failure & $+1$ & Helpful memory\\
Failure & Success & $-1$ & Harmful memory\\
Success & Success & $\alpha_{\rm partial}$ & Preserved success\\
Failure & Failure & $0$ & No success gain\\
\bottomrule
\end{tabular}
\end{center}
The table assumes binary task rewards. Strategy judging uses privileged reference information during training.
\papersource{Paper, Eqs. (7)--(8), PDF p. 5; Appendix C.3, pp. 21--22.}
\end{frame}

% 10
\begin{frame}{Memory-policy RL and execution-policy RL differ}
\begin{itemize}
\item \textbf{Memory-policy GRPO:} sample multiple outputs per context; prefer higher-reward operations while clipping updates and penalizing KL drift.
\item \textbf{Execution-policy Harness-RL:} train StackPlanner's task policy within its harness.
\item \textbf{Online evolution:} freeze all policies and codebooks; update only the external experience bank.
\end{itemize}
\begin{block}{How to read the main result rows}
GenMem and GenMem (RL) distinguish the configurations before and after \textbf{StackPlanner Harness-RL}. The former still uses trained memory modules.
\end{block}
\papersource{Paper, Eq. (9), p. 5; Sec. 4.1, pp. 6--7; Appendix F.1, p. 28.}
\end{frame}

% 11
\begin{frame}{Four evaluations answer different questions}
\small
\begin{tabular}{p{2.6cm}p{5.2cm}p{5.3cm}}
\toprule
Evaluation & Data / setting & Metric\\
\midrule
Main tasks & ALFWorld; WebShop; six search QA sets & Environment success; task score; answer token F1\\[2mm]
Transfer & Three harnesses under DeepSeek-V4-Flash & Search EM; Research RACE; SQL execution; code resolution\\[2mm]
Retrieval / evolution & Up to 50 candidates;\newline BGE reranking & Candidate Hit; Hit@1/5; task accuracy\\[2mm]
Symbolic input & GPQA, beam sizes 1/5/10 & Answer accuracy\\
\bottomrule
\end{tabular}\par\vspace{3mm}
\normalsize
Main GenMem modules: Qwen2.5-7B-Instruct.\par
QA average: mean of six dataset-level F1 scores.\par
Hit@k: presence of one reference SID, not precision of the returned set.
\papersource{Paper, Sec. 4.1, p. 6; Appendices E--F, pp. 25--28.}
\end{frame}

% 12
\begin{frame}{The full system improves interactive task results}
\centering
\begin{tabular}{lrrr}
\toprule
Method & ALFWorld All & WebShop Score & WebShop Success\\
\midrule
ReAct & 31.2 & 46.2 & 19.5\\
GenMem & 74.6 & 52.0 & 27.0\\
GRPO & 77.6 & 79.3 & 66.1\\
SkillRL & 89.9 & 85.2 & 72.7\\
Skill0 & 89.8 & 85.3 & 71.9\\
\textbf{GenMem (RL)} & \textbf{93.6} & \textbf{90.8} & \textbf{82.8}\\
\bottomrule
\end{tabular}
\vspace{2mm}
\begin{block}{Compared with SkillRL}
\centering ALFWorld: \alert{+3.7 percentage points}\qquad
WebShop success: \alert{+10.1 pp}
\end{block}
\raggedright
ALFWorld's Pick, Heat, and Cool have stronger alternatives in the table.
\papersource{Selected rows from Paper, Table 1, PDF p. 7. All values use the paper's percentage scale.}
\end{frame}

% 13
\begin{frame}{QA gains depend strongly on the trained task policy}
\centering\small
\setlength{\tabcolsep}{5pt}
\begin{tabular}{lrrrrrrr}
\toprule
Method & 2Wiki & Hotpot & Bambo. & MuSiQue & NQ & Trivia & Avg.\\
\midrule
ReAct & 27.51 & 42.81 & 27.63 & 19.34 & 30.01 & 54.55 & 33.6\\
GenMem & 42.91 & 35.83 & 22.21 & 19.85 & 33.88 & 47.62 & 33.7\\
SkillRL & 40.3 & 43.2 & 73.8 & 20.2 & 45.9 & 63.3 & 47.8\\
SDAR & 48.4 & 43.8 & 73.0 & 19.6 & 46.3 & 63.5 & 49.1\\
\textbf{GenMem (RL)} & \textbf{54.8} & \textbf{52.9} & 64.6 & \textbf{23.5} & \textbf{50.1} & \textbf{70.7} & \textbf{52.8}\\
\bottomrule
\end{tabular}
\normalsize\vspace{5mm}
\begin{itemize}
\item GenMem (RL) exceeds SDAR by \alert{3.7 pp} in average token F1.
\item Before StackPlanner RL, GenMem is near ReAct on average and lower on three QA datasets.
\item Bamboogle remains below SkillRL, even with the full system.
\end{itemize}
\papersource{Selected rows from Paper, Table 2, PDF p. 8. Answer token F1 (\%); higher is better.}
\end{frame}

% 14
\begin{frame}{Transferred skills help Research and SQL most consistently}
\centering
\begin{tabular}{lrrrr}
\toprule
Harness & Search & Research & SQL & Code\\
\midrule
StackPlanner & 0.00 & +2.07 & +5.00 & +1.00\\
TCRAG & $-1.00$ & +2.04 & +1.00 & +1.00\\
OpenHands & +1.00 & +2.12 & +2.00 & +2.00\\
\bottomrule
\end{tabular}
\vspace{4mm}
\begin{itemize}
\item Absolute score changes from skill-free to +GenMem Skills.
\item All three harnesses use DeepSeek-V4-Flash in this comparison.
\item Search changes are small and include one decrease.
\end{itemize}
\begin{block}{Scope}
Evidence for transferred skill support; the table does not by itself establish an identical online read--write loop in every harness.
\end{block}
\papersource{Paper, Table 3, p. 8; metrics in Table 10, p. 26. Search: EM; Research: RACE; SQL: execution accuracy; Code: resolved rate.}
\end{frame}

% 15
\begin{frame}{Online evaluation scores a task before writing memory}
\begin{enumerate}
\item Start with the initial memory bank and frozen model parameters/codebooks.
\item Retrieve support, execute the incoming task, and score its answer.
\item Give the trajectory to MemEvolver; apply its update before the next task.
\item After each block, evaluate a fixed, disjoint probe set with read-only access.
\end{enumerate}
\begin{block}{Information boundary}
Held-out reference answers are used for scoring and are excluded from the update prompt.
\end{block}
Content-based baselines rebuild their indices from the current payloads at each checkpoint.
\papersource{Paper, Sec. 4.3, PDF pp. 7--8; Appendix E.3, p. 27.}
\end{frame}

% 16
\begin{frame}{GenMem benefits most from the reported evolution stream}
\centering
\begin{tabular}{lccc}
\toprule
Method & Hit@5, $t=0\to9$ & Task ACC, $t=0\to9$ & ACC change\\
\midrule
Qwen3-Embedding & $2.89\to2.52$ & $28.53\to27.98$ & $-0.55$\\
TF-IDF & $12.52\to13.36$ & $30.02\to31.08$ & $+1.06$\\
SkillRouter & $10.28\to9.72$ & $28.74\to28.95$ & $+0.21$\\
\textbf{GenMem} & $\mathbf{13.15\to17.63}$ & $\mathbf{30.56\to36.20}$ & $\mathbf{+5.64}$\\
\bottomrule
\end{tabular}
\vspace{5mm}
\begin{itemize}
\item GenMem Candidate Hit: $20.25\%\to25.58\%$.
\item Gains are not monotonic: checkpoints 2, 5, and 8 dip.
\item TF-IDF exceeds GenMem on Hit@5 and task ACC at checkpoints 2 and 5.
\end{itemize}
\papersource{Paper, Table 4, PDF p. 9. Values are percentages; changes are percentage points.}
\end{frame}

% 17
\begin{frame}{Evolution retains many slots and substantially edits others}
\centering
\includegraphics[width=.98\linewidth,height=.60\textheight,keepaspectratio]{figures/fig-03-cd.pdf}
\vspace{2mm}
\begin{itemize}
\item Left: targeted slots split into insert, content-changing revise, and retention.
\item Right: payload edit distances remain substantial across update blocks.
\end{itemize}
\papersource{Paper, Fig. 3(c,d), PDF p. 9. Original panels, cropped with axes and legends preserved.}
\end{frame}

% 18
\begin{frame}{SID tokens carry useful task information on GPQA}
\begin{columns}[T,onlytextwidth]
\column{.51\textwidth}
\includegraphics[width=\linewidth,height=.68\textheight,keepaspectratio]{figures/fig-04-gpqa.pdf}
\column{.46\textwidth}
\begin{itemize}
\item Raw query: \textbf{48.32\%}.
\item Full SID, no payload: \textbf{54.06--54.66\%}.
\item Rewrite: 55.65--56.91\%.
\item Retrieved text: 56.09--56.63\%.
\end{itemize}
\medskip
Rewrite is better only at $k=10$.\par\medskip
\alert{Unseen SID-combination generalization remains untested.}
\end{columns}
\papersource{Paper, Fig. 4 and Sec. 5, PDF p. 9; Appendix G.5, pp. 37--38. Beam sizes: 1, 5, 10. Both text conditions read the bank.}
\end{frame}

% 19
\begin{frame}{Evolution, policy learning, and search all contribute}
\centering\small
\begin{tabular}{lrrr}
\toprule
Variant & Accuracy & Token F1 & ACC drop\\
\midrule
Full GenMem & \textbf{49.5} & \textbf{52.1} & ---\\
Frozen bank & 46.5 & 46.8 & 3.0\\
TF-IDF + evolution & 40.5 & 43.4 & 9.0\\
Embedding + evolution & 37.0 & 41.2 & 12.5\\
No memory access & 41.0 & 45.2 & 8.5\\
No memory-policy RL & 38.5 & 42.6 & 11.0\\
Greedy SID decoding & 42.0 & 45.7 & 7.5\\
\bottomrule
\end{tabular}\par\vspace{4mm}
\normalsize
\textbf{Protocol:} 200 samples, three complete evolution rounds.\par
Frozen bank isolates the benefit of updates within this setup.\par
Embedding + evolution falling below no memory illustrates harmful retrieval.
\papersource{Paper, Table 23, PDF p. 37. Two mislabeled table rows are described here by their actual retrieval replacements.}
\end{frame}

% 20
\begin{frame}{Mid-training lowers exact SID retrieval in the appendix}
\centering\small
\begin{tabular}{lrrr}
\toprule
Configuration & Mean candidates & R@50 & HR@5\\
\midrule
PT, four levels & 49.57 & 20.25 & 13.15\\
MT, four levels, full beam & 33.72 & 4.90 & 3.10\\
MT, four levels, SID-only beam & 50.00 & 6.80 & 3.75\\
MT, four levels, MemEvolver & 50.00 & 5.31 & 1.36\\
\bottomrule
\end{tabular}
\normalsize\vspace{4mm}
\begin{itemize}
\item PT: alignment pretraining. MT: memory-operation mid-training.
\item Learning interaction traces does not automatically preserve address accuracy.
\item The appendix lacks a matched final-RL retrieval row that closes this gap.
\end{itemize}
\papersource{Paper, Appendix G.2 and Table 18, PDF pp. 30--31. MemRetriever: 2,000 examples; MemEvolver: 735. Samples are drawn from training examples.}
\end{frame}

% 21
\begin{frame}{Three evidence gaps limit the strongest claims}
\begin{enumerate}
\item \textbf{Domain separation.} Several QA datasets labeled OOD also supply seed-bank experiences. Exact splits and overlap checks remain unspecified.
\medskip
\item \textbf{Attribution.} Main tables compare systems with incompletely documented per-row budgets and provenance. Stable IDs are also possible in vector stores.
\medskip
\item \textbf{Symbolic generalization.} Prefix depth helps GPQA, but fixed semantic axes and unseen-combination transfer need additional controls.
\end{enumerate}
\papersource{Paper, Table 6, p. 25; Appendices E.1 and F.1, pp. 26, 28; B.2, p. 16; G.5, pp. 37--38. Interpretation synthesized for this reading.}
\end{frame}

% 22
\begin{frame}{The reusable idea is a stable learned memory interface}
\begin{block}{Design contribution}
Generate compact structured addresses for both retrieval and update targeting; keep experience editable behind those addresses.
\end{block}
\begin{itemize}
\item The full trained system reports strong interactive and QA results.
\item Address invariance is guaranteed by the update rule; payload usefulness still depends on learned curation.
\item The next decisive evidence is a matched comparison with controlled data splits, checkpoints, and stable-address retrieval baselines.
\end{itemize}
\papersource{Jiang et al., GenMem: Generative Symbolic Memory for Self-Evolving Harness, arXiv:2609.34633v1.\\
\url{https://arxiv.org/abs/2609.34633v1} \quad End of main presentation; backup slides follow.}
\end{frame}

\appendix

% 23
\begin{frame}{Backup: four levels trade quantization error for vocabulary}
\centering\small
\begin{tabular}{lrrrr}
\toprule
Codebook & Capacity & SID vocab & Loss ($10^{-4}$) & Leaf use (\%)\\
\midrule
$48\times16\times8\times8$ & 49,152 & 80 & 3.48 & 62.22\\
$128\times24\times16$ & 49,152 & 168 & 3.31 & 64.25\\
$96\times24\times24$ & 55,296 & 144 & 3.34 & 56.71\\
\bottomrule
\end{tabular}
\normalsize\vspace{4mm}
\begin{itemize}
\item Selected four-level setting uses fewer special tokens.
\item It does not minimize reconstruction loss among these alternatives.
\item The prefix-prompt ablation uses a separate three-level setting: loss improves while leaf occupancy slightly falls.
\end{itemize}
\papersource{Paper, Tables 12 and 15, PDF pp. 29--30. Weighted setting, $w=0.5$.}
\end{frame}

% 24
\begin{frame}{Backup: four-level alignment loss is lower late in training}
\centering
\includegraphics[width=.79\linewidth,height=.66\textheight,keepaspectratio]{figures/fig-07-alignment-loss.pdf}
\par\smallskip
Red begins above blue and crosses around step 1,100.\par
Depth, capacity, and per-level vocabulary all change together.
\papersource{Paper, Fig. 7, PDF p. 34. Original figure. Global batch 48; 500-step moving average.}
\end{frame}

% 25
\begin{frame}{Backup: prefix matches do not imply a correct full address}
\centering
\begin{tabular}{lrrrr}
\toprule
MT policy & $L_1$ & $L_{1:2}$ & $L_{1:3}$ & $L_{1:4}$\\
\midrule
MemRetriever, SID-only & 40.90 & 13.00 & 6.30 & 3.75\\
MemEvolver & 40.41 & 11.29 & 3.27 & 1.36\\
\bottomrule
\end{tabular}
\vspace{5mm}
\begin{itemize}
\item Cumulative Top-5 hit after reranking: one candidate must match the whole prefix.
\item Independent level accuracy can count a match even when all other levels are wrong.
\item Beam width 5 to 20 raises Recall@5 from 7.93\% to 8.53\%, with reported decoding latency from 121.0 to 406.1 ms.
\end{itemize}
\papersource{Paper, Tables 19 and 21, PDF pp. 32--33. Latency refers to the SID decoding experiment.}
\end{frame}

% 26
\begin{frame}{Backup: GRPO optimizes group-relative memory utility}
\[
\begin{aligned}
\mathcal L=-\mathbb E\Biggl[\frac1G\sum_{i=1}^{G}\frac1{|u_i|}
\sum_{t=1}^{|u_i|}\Bigl\{
&\min\bigl(\rho_{i,t}\hat A_i,\\[-1mm]
&\operatorname{clip}(\rho_{i,t},1-\epsilon,1+\epsilon)\hat A_i\bigr)
-\beta D_{\rm KL}^{i,t}\Bigr\}\Biggr].
\end{aligned}
\]
\begin{itemize}
\item $u_i$: sampled output; $G$: number of outputs per context.
\item $\rho_{i,t}$: token probability ratio against the rollout policy.
\item $\hat A_i$: group-normalized reward; $\epsilon$: clipping threshold.
\item KL regularization anchors the updated policy to a reference.
\end{itemize}
Proxy/audit advantages are normalized separately; complete channel weighting and reward scaling are not resolved in the manuscript.
\papersource{Paper, Eq. (9), PDF p. 5; Appendix C.3, pp. 21--22.}
\end{frame}

% 27
\begin{frame}{Backup: reported settings need checkpoint-level reconciliation}
\centering\small
\begin{tabular}{lll}
\toprule
Setting & Main text, pp. 6--7 & Appendix F.2, p. 28\\
\midrule
Alignment learning rate & $1\times10^{-5}$ & $2\times10^{-5}$\\
Memory GRPO steps & 130 & 200\\
\bottomrule
\end{tabular}
\normalsize\vspace{5mm}
\begin{itemize}
\item Appendix also lists GRPO LR $3\times10^{-5}$, batch 256, group size 8, and KL coefficient 0.04.
\item Main text includes $R_{\rm SID}$ explicitly; some appendix reward decompositions omit it.
\item Reported 16,416 final summaries do not match the approximately 30.6K denominator implied by several bank statistics.
\end{itemize}
\papersource{Paper, Sec. 4.1; Tables 8--9 and 11; Appendix C.3, pp. 21--22. The implied denominator is a calculation in this reading.}
\end{frame}

% 28
\begin{frame}{Backup: correct task outcomes can yield overgeneralized memory}
\begin{block}{The payment case in Appendix H}
The answer is correct, but the inserted memory says to interpret ``double'' or ``half'' using the immediately preceding payment.
\end{block}
\begin{itemize}
\item That rule fails when the question refers to an earlier payment or the initial amount.
\item A fixed SID preserves access to the rule even if the rule becomes too broad.
\item This motivates revision checks that preserve applicability conditions and test previously served queries.
\end{itemize}
\papersource{Paper, Appendix H, PDF p. 41. Failure analysis and proposed check are this presentation's interpretation.}
\end{frame}

% 29
\begin{frame}{Backup: source and reading materials}
\textbf{Xinke Jiang, Tao Feng, Weixuan Xu, Zhixin Zhang, Zhibang Yang, Wentao Zhang, Runchuan Zhu, Xu Chu, Junfeng Zhao, Yasha Wang.}
\medskip

\emph{GenMem: Generative Symbolic Memory for Self-Evolving Harness.}
\medskip

arXiv:2609.34633v1, submitted September 28, 2026.\par
\url{https://arxiv.org/abs/2609.34633v1}
\vspace{5mm}
\begin{itemize}
\item Companion Chinese analysis: \texttt{reading-notes.zh.md}.
\item Editable deck: \texttt{slides.en.tex}; build: \texttt{build\_slides.py}.
\item Original paper figures retain their source identifiers and PDF page locators.
\end{itemize}
\end{frame}

\end{document}