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\title[MemPrism]{MemPrism}
\subtitle{Task-Conditioned Relational Memory Views\\for Long-Horizon Agents}
\author{Paper by Zhisheng Chen et al.\\[3pt]\small Independent paper-reading presentation}
\date{arXiv:2608.06745v1 \quad $\vert$ \quad Read on 27 September 2026}
\hypersetup{pdftitle={MemPrism: Research Paper Discussion},
pdfauthor={Independent paper-reading presentation},pdfsubject={Reading of arXiv:2608.06745v1}}

\begin{document}

% 1. About 25 minutes for frames 1--22; the remaining frames are backup.
\begin{frame}
\titlepage
\vspace{-5mm}
\begin{center}
\alert{Preserve interaction facts; choose their organization at decision time.}
\end{center}
\papersource{Sources: version-matched author TeX, 41-page PDF, and original figures. Results are reported by the paper, not independently reproduced.}
\end{frame}

% 2
\begin{frame}{Available evidence can still be hard to use}
\begin{columns}[T,onlytextwidth]
\begin{column}{0.31\textwidth}
\begin{block}{Storage}
Was the needed fact preserved?
\end{block}
\end{column}
\begin{column}{0.31\textwidth}
\begin{block}{Retrieval}
Was the relevant evidence accessed?
\end{block}
\end{column}
\begin{column}{0.33\textwidth}
\begin{block}{Representation}
Are the needed relations easy to see?
\end{block}
\end{column}
\end{columns}
\medskip
\begin{itemize}
\item Repeated failures call for aligned attempts and outcomes.
\item Object tracking calls for a state history organized by entity.
\item Progress tracking calls for temporal order.
\end{itemize}
\takeaway{The paper makes memory organization a decision variable.}
\papersource{Paper, Introduction, PDF pp.1--3. These failure categories can interact in the implementation.}
\end{frame}

% 3
\begin{frame}{Stable history supports temporary working views}
\centering
\includegraphics[width=\linewidth,height=0.77\textheight,keepaspectratio]{figures/fig-02-overview.jpg}
\papersource{Original Fig. 2, PDF p.5. Adapter trains view selection; Recorder, Composer, Render, and the task VLM remain frozen.}
\end{frame}

% 4
\begin{frame}{Recorder preserves what actually happened}
\[
e_t=(o_t^-,u_t,y_t^+,r_t,m_t)
\quad\xrightarrow{\;\Phi+\text{validation}\;}\quad
\tilde e_t=(t,e_t^{\rm ent},e_t^{\rm act},e_t^{\rm out},\Delta s_t,\xi_t)
\]
\begin{itemize}
\item Raw transition: observation, executed action, feedback, reward, metadata.
\item Structured event: entity, action, outcome, observed new state values, optional visual evidence.
\item Outcomes: exception, state update, or no observed change.
\end{itemize}
\begin{block}{An append-only boundary}
\(\E_{\le t}=\E_{<t}\oplus\tilde e_t\). Generated views are discarded; they never overwrite history.
\end{block}
\papersource{Eqs. (2)--(4), PDF pp.4--5; Appendix D.1, pp.22--23. Validation does not guarantee correct semantic extraction.}
\end{frame}

% 5
\begin{frame}{Router chooses one of 144 joint view actions}
\[
a_t^v=(\tau_t,w_t,c_t,\gamma_t),\qquad 4\times3\times4\times3=144
\]
\centering
\begin{tabular}{ll}
\toprule
Factor & Choices \\
\midrule
Relation $\tau$ & Temporal / Effect / State / Dependency \\
Range $w$ & Last 6 / last 10 / all events \\
Outcome $c$ & All / exception / update / no change \\
Granularity $\gamma$ & Coarse / medium / fine \\
\bottomrule
\end{tabular}
\medskip
\begin{itemize}
\item One joint softmax retains dependencies among the factors.
\item Sample during training; take argmax during evaluation.
\item Router selects the memory view; the task VLM selects the action.
\end{itemize}
\papersource{Table 1 and Eqs. (6)--(9), PDF pp.5--6.}
\end{frame}

% 6
\begin{frame}{Four views expose different relations}
\begin{columns}[T,onlytextwidth]
\begin{column}{0.48\textwidth}
\begin{block}{TemporalTrace}
Order events by step.\\[3pt]
\textit{What happened recently, and in what order?}
\end{block}
\begin{block}{ActionEffect}
Group action--entity pairs.\\[3pt]
\textit{Did repeated attempts work?}
\end{block}
\end{column}
\begin{column}{0.48\textwidth}
\begin{block}{EntityState}
Group observed state updates.\\[3pt]
\textit{Where is the object now?}
\end{block}
\begin{block}{DependencyChain}
Link the latest event to related history.\\[3pt]
\textit{Which earlier events provide context?}
\end{block}
\end{column}
\end{columns}
\medskip
\alert{DependencyChain is a heuristic local trace, not a verified causal graph.}
\papersource{Composer, PDF p.6; Appendix D.3, p.24; teacher specifications, pp.31--37.}
\end{frame}

% 7
\begin{frame}{Scope selection can restore task-relevant evidence}
\[
\begin{aligned}
\widehat\E_t=\operatorname{Merge}\bigl(&\sigma_{w_t,c_t}(\E_{<t}),\\
&\operatorname{TaskRelevant}(\E_{<t},g),\\
&\operatorname{RecentContext}(\E_{<t},\tau_t)\bigr).
\end{aligned}
\]
\begin{itemize}
\item Start with the chosen window and outcome filter.
\item Restore events whose normalized terms match the task goal.
\item Preserve recent anchor context for dependency views.
\end{itemize}
\takeaway{``Last 6'' and ``exception only'' are selection rules, not strict final content limits.}
\papersource{Appendix D.3, Eq. (D.3), PDF p.24. Granularity changes the display, not stored events.}
\end{frame}

% 8
\begin{frame}{The image makes relations directly readable}
\begin{columns}[c,onlytextwidth]
\begin{column}{0.51\textwidth}
\includegraphics[width=\linewidth,height=0.52\textheight,keepaspectratio]{figures/case-02-entity-state.png}
\papersource{Original memory panel, Fig. B.2, PDF p.19.}
\end{column}
\begin{column}{0.45\textwidth}
\begin{itemize}
\item Cards group entity states.
\item Alignment, arrows, and highlights express relationships.
\item The VLM also receives current input and short text context.
\end{itemize}
\end{column}
\end{columns}
\medskip
\[
V_t=\mathcal R_{\tau_t,\gamma_t}(S_t),\qquad
u_t\sim\pi_{\rm task}(\cdot\mid X_t,g,H_t^{\rm short},V_t)
\]
\papersource{Eqs. (12)--(13), PDF p.7. The case reports one successful placement and progress 0 to 1/2, not full task completion.}
\end{frame}

% 9
\begin{frame}{A teacher initializes decision-conditioned routing}
\[
q_T(a\mid t)=q_T(a\mid o_t,g,\E_{<t},u_t^{\rm ref})
\]
\[
\mathcal L_{\rm SFT}=\operatorname{KL}(q_T\Vert\viewpol)
+\lambda_C\sum_{a\in\mathcal A^v}\viewpol(a\mid z_t)\widehat C_t(a)
\]
\begin{itemize}
\item The teacher sees the reference next action and returns soft view labels.
\item The deployed Router receives no reference action.
\item The cost term discourages unnecessarily broad or detailed views.
\item D1 trains the fusion encoder and Planner; MiniLM stays frozen.
\end{itemize}
\papersource{Eqs. (14)--(15), PDF p.7; Appendix A.3, p.16. Structural cost is not a measured latency objective.}
\end{frame}

% 10
\begin{frame}{GRPO aligns view choices with downstream outcomes}
\[
A_i=\begin{cases}
\dfrac{R_i-\mu_g}{\sigma_g+\varepsilon},&\sigma_g>\varepsilon,\\[3pt]
0,&\text{otherwise}.
\end{cases}
\]
\begin{itemize}
\item Sample 16 trajectories per task group; normalize return within the group.
\item All view decisions in an online trajectory share its advantage.
\item A clipped objective uses a frozen SFT reference and optional entropy bonus.
\item D2 updates only the residual Planner and 144-way head; the fusion encoder is frozen too.
\end{itemize}
\papersource{Eqs. (16)--(18), PDF p.7; Appendix A.3--A.4, p.16. Mind2Web instead uses offline replay and action-correctness rewards.}
\end{frame}

% 11
\begin{frame}{The three benchmarks test different capabilities}
\centering
\begin{tabular}{lll}
\toprule
Benchmark & Evaluation setting & Main reported metric \\
\midrule
ALFWorld & 140 episodes; up to 50 steps & Task SR \\
EB-ALFRED & 300 tasks; up to 30 steps & Task SR \\
Mind2Web & 3 offline test splits & Step action accuracy \\
\bottomrule
\end{tabular}
\medskip
\begin{itemize}
\item Shared memory-reader backbone: frozen Qwen2.5-VL-7B-Instruct.
\item EB-ALFRED uses EmbodiedBench, not the official ALFRED leaderboard.
\item Mind2Web advances with expert actions regardless of model predictions.
\end{itemize}
\takeaway{Mind2Web action accuracy is not autonomous web-task success.}
\papersource{Experiment Setup, PDF p.8; Appendix C.1, p.20; Fig. D.1, p.24. CFG-SFT is a separate task-model training baseline.}
\end{frame}

% 12
\begin{frame}{ALFWorld gains depend substantially on GRPO}
\begin{columns}[c,onlytextwidth]
\begin{column}{0.54\textwidth}
\begin{tabular}{lr}
\toprule
Method & SR (\%) $\uparrow$ \\
\midrule
Full History & 31.43 \\
LangMem & 38.27 \\
Mem0 & 37.49 \\
MemPrism SFT & 34.29 \\
MemPrism SFT+GRPO & \textbf{40.71} \\
\bottomrule
\end{tabular}
\end{column}
\begin{column}{0.41\textwidth}
\begin{block}{Absolute differences}
\textbf{+2.44 pp} vs. LangMem\\[5pt]
\textbf{+9.28 pp} vs. Full History\\[5pt]
\textbf{+6.42 pp} vs. SFT
\end{block}
\end{column}
\end{columns}
\medskip
SFT alone does not beat the strongest external baseline here.
\papersource{Selected rows of Table 2, PDF p.9. Point estimates; numerical confidence intervals are not shown in the PDF.}
\end{frame}

% 13
\begin{frame}{Embodied gains leave a clear visual failure boundary}
\centering
\begin{tabular}{lrrrrrrr}
\toprule
Method & Base & Com. & Cplx. & Vis. & Spat. & Long & Avg. \\
\midrule
Full History & 16 & 10 & 14 & 0 & 10 & 12 & 10.3 \\
Mem0 & 16 & 16 & 14 & 0 & 10 & 12 & 11.7 \\
MemPrism SFT & 20 & 20 & 22 & 0 & 16 & 16 & 15.7 \\
SFT+GRPO & 24 & 22 & 26 & 0 & 16 & 18 & \textbf{17.7} \\
\bottomrule
\end{tabular}
\medskip
\begin{itemize}
\item Average SR improves by 7.4 pp over Full History and 6.0 pp over Mem0.
\item Visual-subset SR remains 0\%; memory organization does not solve all perception problems.
\item CFG-SFT reaches 8\% on Visual, but changes the task model's parameters.
\end{itemize}
\papersource{Selected rows of Table 3, PDF p.9. All entries are SR percentages. Six subsets contain 50 tasks each.}
\end{frame}

% 14
\begin{frame}{Web gains hold across the three offline splits}
\centering
\begin{tabular}{lrrrr}
\toprule
Method & Task & Website & Domain & Overall \\
\midrule
Full History & 12.57 & 7.95 & 7.91 & 8.79 \\
LangMem & 13.20 & 8.85 & 8.42 & 9.45 \\
MemPrism SFT & 12.95 & 10.05 & 10.85 & 10.81 \\
SFT+GRPO & \textbf{14.23} & \textbf{11.13} & \textbf{12.83} & \textbf{12.87} \\
\bottomrule
\end{tabular}
\medskip
\begin{itemize}
\item Overall improvement: +4.08 pp over Full History; +3.42 pp over LangMem.
\item Predictions are scored against human actions; errors do not redirect the next page state.
\item Episode success is mentioned in the setup but no value is supplied in Table 2.
\end{itemize}
\papersource{Selected rows of Table 2, PDF p.9; protocol on pp.8, 24, 40--41. Values are step action accuracy (\%).}
\end{frame}

% 15
\begin{frame}{Dynamic organization also helps in text}
\centering
\begin{tabular}{lrrr}
\toprule
Organization & Text SR & Optical SR & Optical gain \\
\midrule
Fixed & 30.00\% & 35.71\% & +5.71 pp \\
Dynamic & 37.14\% & \textbf{40.71\%} & +3.57 pp \\
\midrule
Dynamic gain & +7.14 pp & +5.00 pp & \\
\bottomrule
\end{tabular}
\medskip
\begin{itemize}
\item Dynamic organization helps with either presentation medium.
\item Optical presentation helps under either organization setting.
\item These are conditional differences, not two independent additive effects.
\end{itemize}
\papersource{Re-tabulated from Fig. 3, panel II, PDF p.10. Fixed = TemporalTrace + last 10 + medium + all outcomes.}
\end{frame}

% 16
\begin{frame}{Always showing fine detail hurts the most here}
\begin{columns}[T,onlytextwidth]
\begin{column}{0.45\textwidth}
\textbf{Fixed relation views}\par\smallskip
\begin{tabular}{lr}
\toprule
View & SR (\%) \\
\midrule
ActionEffect & 30.00 \\
DependencyChain & 32.14 \\
EntityState & 32.85 \\
TemporalTrace & 35.71 \\
\bottomrule
\end{tabular}
\end{column}
\begin{column}{0.52\textwidth}
\textbf{One constrained factor}\par\smallskip
\begin{tabular}{lr}
\toprule
Fixed setting & SR (\%) \\
\midrule
Type = TemporalTrace & 35.71 \\
Filter = all & 40.00 \\
Granularity = fine & 32.86 \\
Range = last 10 & 38.57 \\
Full planner & 40.71 \\
\bottomrule
\end{tabular}
\end{column}
\end{columns}
\medskip
\alert{Fine-only loses 7.85 pp relative to the plotted full planner.}
\papersource{Fig. 3, panels I and III, PDF p.10. Left: last 10 + medium + all. The prose gives 40.70\%, while the figure gives 40.71\%; differences here use the figure.}
\end{frame}

% 17
\begin{frame}{Global view frequencies do not explain the gain}
\centering
\begin{tabular}{lr}
\toprule
View-selection strategy & ALFWorld SR (\%) \\
\midrule
Frequency-matched random & 30.71 \\
Context-conditioned SFT & 34.29 \\
Context-conditioned SFT+GRPO & \textbf{40.71} \\
\bottomrule
\end{tabular}
\medskip
\begin{itemize}
\item Random selection matches overall configuration frequencies but ignores the current state.
\item Conditioning and outcome-guided learning both improve the reported result.
\item Online trajectories can diverge: this is an episode-level comparison, not identical evidence at every decision.
\end{itemize}
\papersource{Fig. 3, panel IV, PDF pp.10--11; shared evaluation protocol, Appendix C.1, p.20.}
\end{frame}

% 18
\begin{frame}{At 50 steps, the prompt is about one-third smaller}
\begin{columns}[c,onlytextwidth]
\begin{column}{0.64\textwidth}
\includegraphics[width=\linewidth,height=0.70\textheight,keepaspectratio]{figures/fig-04-horizon.pdf}
\end{column}
\begin{column}{0.33\textwidth}
\textbf{Full History $\to$ MemPrism}\par\medskip
SR:\\31.4\% $\to$ 40.7\%\par\medskip
Average prompt tokens:\\1,201 $\to$ 797\par\medskip
\alert{About 33.6\% fewer tokens}
\end{column}
\end{columns}
\papersource{Original Fig. 4 and reported endpoints, PDF p.11. Solid circles: SR, left axis. Dashed triangles: tokens, right axis. X-axis uses cumulative length thresholds, not disjoint task bins.}
\end{frame}

% 19
\begin{frame}{The same Router transfers to three unseen VLMs}
\centering
\begin{tabular}{lrrr}
\toprule
Task VLM & Full History SR & MemPrism SR & Token reduction \\
\midrule
Qwen2.5-VL-7B & 31.43\% & 40.71\% & $\sim$33.6\% \\
Qwen3-VL-4B & 39.17\% & 41.43\% & 30.64\% \\
Qwen2.5-VL-3B & 16.30\% & 21.42\% & $\sim$43.9\% \\
MiniCPM-V-2.6 & 14.17\% & 23.57\% & 48.15\% \\
\bottomrule
\end{tabular}
\medskip
\begin{itemize}
\item Router was trained with Qwen2.5-VL-7B; no per-model adaptation for the other rows.
\item All three new readers improve; the gain ranges from 2.26 to 9.40 pp.
\item Token savings are comparable only within each model.
\end{itemize}
\papersource{Re-tabulated from Fig. 5 and its discussion, PDF p.12. Cross-reader transfer does not establish cross-environment zero-shot transfer.}
\end{frame}

% 20
\begin{frame}{Larger optical views are not automatically better}
\centering
\begin{tabular}{rrrr}
\toprule
Max side (px) & SR (\%) & Avg. prompt tokens & Invalid actions (\%) \\
\midrule
448 & 38.57 & 703 & 0.043 \\
672 & \textbf{40.71} & 797 & 0.064 \\
896 & 40.00 & 917 & 0.097 \\
\bottomrule
\end{tabular}
\medskip
\begin{itemize}
\item Same checkpoint and rendering rules; only the resolution cap changes.
\item The default 672-pixel cap has the highest reported SR among these three settings.
\item Token savings alone do not measure total latency, extraction cost, or storage.
\end{itemize}
\papersource{Table C.2, PDF p.22; separate cost categories, Appendix D.5, p.25. Intervals are not shown for this comparison.}
\end{frame}

% 21
\begin{frame}{Evidence supports the interface within a limited scope}
\begin{itemize}
\item \textbf{Statistical precision:} the PDF gives point estimates. It refers to archived confidence intervals without printing their values.
\medskip
\item \textbf{Task scope:} online horizons are 50 or 30 steps; web evaluation is offline; the Visual subset remains at 0\% SR.
\medskip
\item \textbf{System cost:} full-history scans and persistent storage grow with history, despite compact task prompts.
\medskip
\item \textbf{Representation limits:} extraction errors, four fixed relation types, and short Router summaries remain bottlenecks.
\end{itemize}
\papersource{Tables 2--3, p.9; Appendix A.2, p.15; C.1, p.20; D.5, p.25. Bottleneck assessment is reading analysis.}
\end{frame}

% 22
\begin{frame}{Learn how history should support the next decision}
\begin{enumerate}
\item Preserve reusable interaction records and construct working memory on demand.
\medskip
\item Learn the relation, scope, and detail that help a frozen reader act.
\medskip
\item Evaluate organization, medium, and full system cost separately.
\end{enumerate}
\medskip
\begin{block}{What the experiments establish}
Task-conditioned views improve the reported benchmarks and transfer across three new VLM readers; broader autonomy and end-to-end efficiency remain open.
\end{block}
\papersource{Zhisheng Chen et al., MemPrism: Task-Conditioned Relational Memory Views for Long-Horizon Agents. \href{https://arxiv.org/abs/2608.06745v1}{arXiv:2608.06745v1}, 2026. Main discussion ends here; backup follows.}
\end{frame}

\appendix

% 23
\begin{frame}{Backup: the full GRPO objective}
\[
\rho_{i,t}=\frac{\pi_\theta^v(a_{i,t}^v\mid z_{i,t})}
{\pi_{\rm old}^v(a_{i,t}^v\mid z_{i,t})}
\]
\[
\begin{aligned}
\mathcal L_{\rm GRPO}={}&-\mathbb E_{i,t}\!\left[\min\left(\rho_{i,t}A_i,
\operatorname{clip}(\rho_{i,t},1-\epsilon,1+\epsilon)A_i\right)\right]\\
&+\beta_{\rm KL}\mathbb E_{i,t}\!\left[\operatorname{KL}
\left(\pi_{\rm ref}^v(\cdot\mid z_{i,t})\,\|\,\pi_\theta^v(\cdot\mid z_{i,t})\right)\right]\\
&-\beta_H\mathbb E_{i,t}\!\left[\mathcal H(\pi_\theta^v(\cdot\mid z_{i,t}))\right].
\end{aligned}
\]
\begin{itemize}
\item $\pi_{\rm old}^v$: rollout policy; $\pi_{\rm ref}^v$: frozen SFT policy.
\item Zero group variance gives $A_i=0$; KL and entropy terms can still act.
\item Mind2Web uses asymmetric clipping and different coefficients.
\end{itemize}
\papersource{Eqs. (16)--(18), PDF p.7; benchmark-specific settings in Table A.1, p.16.}
\end{frame}

% 24
\begin{frame}{Backup: Router uses five input tokens}
\centering
\begin{tabular}{lll}
\toprule
Input & Dimension & Scope \\
\midrule
Observation text & 384 & MiniLM; at most 256 tokens \\
Goal text & 384 & MiniLM; at most 256 tokens \\
Event-window features & 128 & Last 8 events \\
Global statistics & 8 & At most last 64 events \\
\bottomrule
\end{tabular}
\medskip
\[
[\mathrm{CLS},o,g,w,s]\ \longrightarrow\
\text{4-layer Transformer}\ \longrightarrow\ z_t\in\mathbb R^{512}
\]
\begin{itemize}
\item Separate projections to 512; 8 attention heads; FFN width 1024.
\item Three residual MLP blocks produce joint 144-way logits.
\item No CLIP embedding in the reported Router path; images go to the task VLM.
\end{itemize}
\papersource{Appendix A.2, PDF p.15; D.4, pp.24--25. View windows are 6/10/all, distinct from summary windows.}
\end{frame}

% 25
\begin{frame}{Backup: training differs across benchmarks}
\centering
\begin{tabular}{lrrr}
\toprule
Setting & ALFWorld & ALFRED path & Mind2Web \\
\midrule
D1 epochs & 50 & 20 & 50 \\
D1 cost weight & 0.02 & 0 & 0.02 \\
D2 learning rate & $3\times10^{-6}$ & $3\times10^{-6}$ & $10^{-6}$ \\
D2 iterations/steps & 100 & 100 & 150 \\
D2 KL weight & 0.2 & 0.2 & 0.01 \\
Training time & 24 h & 48 h & 24 h \\
\bottomrule
\end{tabular}
\medskip
\begin{itemize}
\item D1: AdamW, learning rate $10^{-4}$, batch 256, seed 7.
\item D2: 8 task groups $\times$ 16 trajectories per iteration.
\item Reported runs use eight H200 GPUs; rollout and model serving remain expensive.
\end{itemize}
\papersource{Table A.1 and Appendix A.3--A.4, PDF pp.16--17. ALFRED training uses official training annotations; EB-ALFRED is reserved for evaluation.}
\end{frame}

% 26
\begin{frame}{Backup: the policy does not use every view equally}
\centering
\begin{tabular}{lrrr}
\toprule
View type & ALFWorld & EB-ALFRED & Mind2Web \\
\midrule
TemporalTrace & 55.15 & 42.28 & 42.26 \\
ActionEffect & 31.24 & 21.98 & 7.62 \\
EntityState & 13.61 & 20.86 & 18.48 \\
DependencyChain & 0.00 & 14.88 & 31.64 \\
\bottomrule
\end{tabular}
\medskip
\begin{itemize}
\item ALFWorld also assigns 0\% of greedy selections to medium granularity.
\item DependencyChain remains available; zero usage is policy concentration.
\item Usage frequencies describe behavior, not the causal value of each view.
\end{itemize}
\papersource{Selected rows of Table C.3, PDF p.23; explanation in C.6, p.22. Values are marginal selection frequencies (\%).}
\end{frame}

% 27
\begin{frame}{Backup: AgentOCR token counts use a different scope}
\centering
\begin{tabular}{lrl}
\toprule
Method & ALFWorld SR & Reported tokens per step \\
\midrule
AgentOCR (w/o RL) & 25.30\% & 0.47k memory-context tokens \\
MemPrism & 40.71\% & 797 complete prompt tokens \\
\bottomrule
\end{tabular}
\medskip
\begin{itemize}
\item Both conditions use a Qwen2.5-VL-7B task policy without task-policy RL.
\item MemPrism trains its separate Router.
\item The reported +15.41 pp SR gap is not an exactly budget-matched comparison.
\item This table cannot establish that MemPrism uses fewer tokens than AgentOCR.
\end{itemize}
\papersource{Appendix C.3--C.4, PDF pp.21--22; Table C.1, p.22. The AgentOCR value is reported from the cited setting.}
\end{frame}

% 28
\begin{frame}{Backup: evidence and reproducibility checks}
\begin{itemize}
\item \textbf{Numerical consistency:} Fig. 3 uses 40.71\%; its discussion uses 40.70\%. This deck uses the plotted value.
\medskip
\item \textbf{Denominators:} some baseline SRs do not map to integer counts out of 140. Aggregation requires the external run records.
\medskip
\item \textbf{Uncertainty:} Appendix C.1 describes archived Wilson and paired bootstrap intervals, but no values appear in the PDF.
\medskip
\item \textbf{Reading scope:} all author TeX sections and appendices were read; key figures and formulas were checked against the PDF. No experimental rerun or external artifact audit was performed.
\end{itemize}
\papersource{Paper: Zhisheng Chen, Bingfan Zeng, Bangde Cao, Zhengwei Xie, Yuxuan Li, Jinhan Li, Zheng Lu, Xiangchen Guan, Zikai Xiao, Rui Qian, Jingwei Song. \href{https://arxiv.org/abs/2608.06745v1}{arXiv:2608.06745v1}.}
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