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\title[MemT-1.0]{MemT-1.0: Towards Memory-Native Evolution}
\subtitle{Research paper discussion --- native state, training, evidence, and tradeoffs}
\author{Paper: MemTensor Team \quad Presentation: research discussion}
\date{arXiv:2610.26825v2 \quad 22 October 2026\\[3pt]
\href{https://arxiv.org/abs/2610.26825v2}{https://arxiv.org/abs/2610.26825v2}}

\begin{document}

\begin{frame}
  \titlepage
\end{frame}

\begin{frame}{A bounded state can carry useful history across turns}
  \begin{block}{Central claim}
    MemT-1.0 writes completed interactions into trajectory-specific matrices;
    later turns read those matrices instead of receiving the old text.
  \end{block}
  \begin{itemize}
    \item Stable token-level writes, snapshot reads, and learned memory projections.
    \item Mean score: \textbf{50.6 Flash / 47.0 Nano} across 11 memory benchmarks.
    \item At 128K history, Nano reports \textbf{1.86$\times$} end-to-end speedup and
      \textbf{66$\times$} smaller logical persistent state than full-context KV.
  \end{itemize}
  \papersource{Paper, Abstract; Table 2, PDF p. 20; Secs. 6.5--6.6, PDF pp. 22--24.}
\end{frame}

\begin{frame}{The design targets the cost of repeatedly carrying history}
  \begin{columns}[T]
    \column{0.48\linewidth}
      \begin{block}{Full text / retrieval}
        \begin{itemize}
          \item Keep detailed evidence available.
          \item Context, KV state, or stored records grow with history.
        \end{itemize}
      \end{block}
    \column{0.48\linewidth}
      \begin{block}{Native memory}
        \begin{itemize}
          \item Update a fixed-shape state after each turn.
          \item Query cost depends on state shape, not stored token count.
        \end{itemize}
      \end{block}
  \end{columns}
  \vspace{5mm}
  \alert{Question:} How much useful detail survives repeated compression into that fixed state?
  \papersource{Paper, Secs. 1--2 and 8, PDF pp. 5--10, 30.}
\end{frame}

\begin{frame}{A turn reads the incoming snapshot, then commits its write}
  \[
    \boxed{M_t}\quad\xrightarrow{\;\text{read during generation of }Y_t\;}
    \boxed{X_t,\;Y_t}\quad\xrightarrow{\;\text{tokenwise write at turn end}\;}
    \boxed{M_{t+1}}
  \]
  \begin{itemize}
    \item All queries in turn $t$ use the same incoming $M_t$.
    \item The writer processes $X_t\Vert Y_t$ in token order after the interaction.
    \item States are reset at trajectory boundaries; backbone and trained memory
      weights are shared across trajectories.
  \end{itemize}
  \begin{block}{Consequence}
    A later token in the same turn does not read a partially updated native state.
  \end{block}
  \papersource{Paper, Sec. 2.2 and Fig. 4, PDF pp. 6--8; Sec. 4.2, PDF p. 15.}
\end{frame}

\begin{frame}{Memory attention runs beside the backbone attention}
  \[
    A^{(l)}_{t}=A^{(l)}_{\mathrm{ori},t}+A^{(l)}_{\mathrm{mem},t}.
  \]
  \begin{itemize}
    \item Selected Transformer layers gain a native memory branch.
    \item \textbf{Local memory} $M_t^{(l)}$: dynamic, private to a trajectory.
    \item \textbf{Hyper memory} $\phi$: trained projections and gates, fixed online.
    \item Backbone attention and subsequent backbone computation are preserved.
  \end{itemize}
  \vspace{2mm}
  \begin{block}{Evaluated model geometry}
    Nano: 10 memory layers, 40 MiB FP32 state.\quad
    Flash: 20 memory layers, 1,280 MiB FP32 state.
  \end{block}
  \papersource{Paper, Sec. 2.1 and Fig. 4, PDF pp. 6--7; Appendix B, Table 6, PDF p. 35.}
\end{frame}

\begin{frame}{A symmetrized rank-one update writes each token}
  \[
  u_i=\sqrt{\beta_i}\odot k_i,\quad
  z_i=\omega_i\odot v_i,\quad
  D_i=\operatorname{Diag}(e^{\lambda_i})
  \]
  \[
  \boxed{S_i=(I-u_i^\top u_i)D_iS_{i-1}+u_i^\top z_i}
  \]
  \begin{itemize}
    \item $k_i$ is L2-normalized; $\beta_i$, $\omega_i$, and $\lambda_i$ control erase,
      write, and decay.
    \item $S_0=M_t$; the final $S_{L_t}$ becomes $M_{t+1}$.
    \item The ordered writes can revise associations made earlier in the turn.
  \end{itemize}
  \papersource{Paper, Sec. 2.2, PDF p. 8; Sec. 4.2, PDF p. 15. Indices for layer/channel omitted.}
\end{frame}

\begin{frame}{Symmetry removes an erase-step expansion mode}
  \begin{columns}[T]
    \column{0.49\linewidth}
      \begin{block}{Original GDN-2 erase}
        $A=I-k^\top(\beta\odot k)$\\[4pt]
        Misaligned erase direction and magnitude can increase a vector's norm.
      \end{block}
    \column{0.49\linewidth}
      \begin{block}{Stable GDN-2 erase}
        $A_{\rm sym}=I-u^\top u$\\[4pt]
        The component along $u$ contracts; orthogonal components do not expand.
      \end{block}
  \end{columns}
  \vspace{4mm}
  With $\|u\|_2^2<1$, the erase map is non-expansive. The full recurrence
  also depends on decay and bounded writes.
  \papersource{Paper, Appendix C.1--C.2, PDF pp. 37--40.}
\end{frame}

\begin{frame}{A gated matrix read converts state into token features}
  \[
    q_i=\operatorname{L2Norm}(\tilde h_i\tilde W_Q),\qquad
    r_i=\frac{q_iM_t}{\sqrt{d_k}}
  \]
  \[
    A_{\rm mem,i}=\operatorname{Concat}_{h}
      \bigl[\operatorname{RMSNorm}(r_{i,h})\odot
      \operatorname{SoftCapSiLU}(g_{i,h})\bigr]
  \]
  \begin{itemize}
    \item Query-dependent readout selects associations from the state matrix.
    \item The learned output gate sets each channel's influence on generation.
    \item Soft-cap threshold $\tau=2$ limits gate magnitude in the evaluated models.
  \end{itemize}
  \papersource{Paper, Sec. 2.2, PDF pp. 8--9; Appendix B.2, PDF p. 36.}
\end{frame}

\begin{frame}{MoS expands capacity with dense read and write routing}
  \[
    \alpha_{\rm s}=\operatorname{softmax}_E(\tilde H W_{\rm s}C^\top),\qquad
    \alpha_{\rm u}=\operatorname{softmax}_E(\tilde H W_{\rm u}C^\top)
  \]
  \[
    A_{\rm mem,i}=\sum_{e=1}^{E}\alpha_{{\rm u},i,e}A_{{\rm mem},i,e}
  \]
  \begin{itemize}
    \item Separate routers share trainable expert addresses $C$.
    \item Storage weights scale erase and write gates, not log-decay.
    \item \alert{Every expert participates}; more state also means more compute.
    \item Main evaluated Nano/Flash checkpoints use $E=1$ per layer; $E=2/4$
      belongs to a separate capacity study.
  \end{itemize}
  \papersource{Paper, Sec. 2.3 and Fig. 5, PDF pp. 9--10; Appendix B, Table 6, PDF p. 35.}
\end{frame}

\begin{frame}{Training asks memory to imitate a full-history teacher}
  \begin{columns}[T]
    \column{0.48\linewidth}
      \begin{block}{Teacher: frozen backbone}
        Sees textual history $C_{<t}$ plus current query and response prefix.
      \end{block}
    \column{0.48\linewidth}
      \begin{block}{Student: MemT-1.0}
        Sees current query and prefix; earlier turns are represented by $\mathcal M_t$.
      \end{block}
  \end{columns}
  \vspace{4mm}
  \[
    \ell_{t,k}=T_d^2\sum_{v\in\mathcal K_{t,k}}
       \hat q_{t,k}(v)\log\frac{\hat q_{t,k}(v)}{p^{T_d}_{t,k}(v)},
       \quad |\mathcal K_{t,k}|=64,\ T_d=1.
  \]
  The teacher is renormalized on Top-64; the student remains normalized
  over the full vocabulary. Both use the same fixed response prefix.
  \papersource{Paper, Sec. 3.3, PDF pp. 12--13.}
\end{frame}

\begin{frame}{CPT builds memory language modeling; post-training teaches answers}
  \begin{tabular}{p{0.19\linewidth}p{0.32\linewidth}p{0.37\linewidth}}
    \toprule
    Stage & Input to student & Supervision \\
    \midrule
    CPT: 20B tokens & Current 2K-token segment plus incoming state &
      Next-token cross-entropy; earlier segments only in state \\
    Post-training: 14B tokens & Current query and response prefix plus state &
      Top-64 teacher KL on response positions \\
    \bottomrule
  \end{tabular}
  \vspace{5mm}
  Historical tokens have no direct response loss, yet prediction gradients
  backpropagate through earlier memory writes. Backbone weights stay frozen
  during memory training.
  \papersource{Paper, Secs. 3.2--3.6, PDF pp. 11--14; Appendix B.3, PDF p. 37.}
\end{frame}

\begin{frame}{Windowed execution makes dense experts trainable}
  \begin{itemize}
    \item Chunked WY groups ordered rank-one writes into GPU-friendly work.
    \item Snapshot reads are batched independently of the writer chunks.
    \item Complete-step windows bound live MoS projections; backward recomputes intermediates.
  \end{itemize}
  \vspace{2mm}
  \begin{block}{Single MoS block, synthetic 32K-token pack, four experts}
    Peak allocated memory: \textbf{47.83 $\rightarrow$ 9.49 GiB} with a 4K-token window.\\
    Median forward + backward: \textbf{463.0 $\rightarrow$ 470.6 ms}.
  \end{block}
  This is a single-block workload, not an end-to-end model-training benchmark.
  \papersource{Paper, Sec. 4.2, Table 1, PDF pp. 15--16; Appendix F, PDF pp. 60--62.}
\end{frame}

\begin{frame}{The evaluations separate memory, general ability, and cost}
  \begin{itemize}
    \item \textbf{Memory:} 11 benchmarks; history written chronologically;
      queries see state but no prior text. Report an equal-weight mean.
    \item \textbf{General ability:} six benchmarks; compare MemT with its own
      backbone under matched irrelevant-information conditions.
    \item \textbf{Cost:} BEAM histories; one 512-token query, forced 128-token
      response; separate history preparation and query TTFT.
  \end{itemize}
  \begin{block}{Comparison groups}
    Textual memory: full context, retrieval, memory systems.\quad
    Parametric memory: Temp-LoRA, $\delta$-mem, and others.
  \end{block}
  \papersource{Paper, Sec. 6.1, PDF pp. 18--19; Sec. 6.5, PDF pp. 22--23; Appendix D, PDF pp. 40--53.}
\end{frame}

\begin{frame}{MemT leads the evaluated parametric group, but text retains detail}
  \centering
  \begin{tabular}{lrrr}
    \toprule
    Method & Mean of 11 $\uparrow$ & LongMemEval-S $\uparrow$ & BEAM-1M $\uparrow$ \\
    \midrule
    MemT-1.0-Flash & \textbf{50.6} & 37.2 & 37.0 \\
    MemT-1.0-Nano & \textbf{47.0} & 32.6 & 35.8 \\
    Temp-LoRA & 38.1 & 13.4 & 32.3 \\
    MemOS (DeepSeek branch) & 68.6 & 83.2 & 72.6 \\
    Full context (Qwen branch) & 66.3 & 76.8 & 43.7 \\
    \bottomrule
  \end{tabular}
  \vspace{4mm}
  \begin{block}{Interpretation}
    Native state works across varied tasks; exact long-history evidence remains harder to preserve.
  \end{block}
  \papersource{Paper, Table 2 and Sec. 6.2, PDF pp. 19--20. Systems differ in backbone and mechanism.}
\end{frame}

\begin{frame}{General ability is broadly preserved, with task-level regressions}
  \centering
  \begin{tabular}{lrrr}
    \toprule
    Model vs. own noisy backbone & Six-task mean & IFEval & LiveCodeBench v6 \\
    \midrule
    MemT-1.0-Flash & 0.0 & +0.9 & $-0.7$ \\
    MemT-1.0-Nano & $-0.9$ & +3.3 & $-5.8$ \\
    \bottomrule
  \end{tabular}
  \vspace{6mm}
  \begin{itemize}
    \item Entries are \emph{score-point differences}, not absolute scores.
    \item Flash is flat on average; Nano's largest drop is LiveCodeBench v6.
    \item Irrelevant examples are written into memory for MemT, and prepended
      to text for the matched backbone condition.
  \end{itemize}
  \papersource{Paper, Table 3, PDF p. 21; Appendix D.4, PDF pp. 51--52.}
\end{frame}

\begin{frame}{Token-level writes and post-training matter most in ablation}
  \centering
  \begin{tabular}{lr@{\qquad}lr}
    \toprule
    Nano variant & Mean $\uparrow$ & Nano variant & Mean $\uparrow$ \\
    \midrule
    Full model & 47.0 & Turn-level update & 41.0 \\
    Without CPT & 44.9 & Without post-training & 36.3 \\
    GDN-2, no symmetry fix & 46.4 & GDN update & 46.7 \\
    \bottomrule
  \end{tabular}
  \vspace{6mm}
  \begin{block}{Reading the experiment}
    Switching to turn-level aggregation costs about 6 points; removing
    post-training costs about 10.7. Update-rule alternatives are much closer.
  \end{block}
  \papersource{Paper, Table 4 and Sec. 6.4, PDF pp. 21--22. Rounded table entries shown.}
\end{frame}

\begin{frame}{More training and more experts help in controlled studies}
  \begin{itemize}
    \item Same Nano post-training trajectory, repeated corpus:
      mean \textbf{36.65 $\rightarrow$ 45.40 $\rightarrow$ 46.80}
      from start to end of epoch 1 to end of epoch 2.
    \item At matched post-training steps, four MoS experts beat two by
      \textbf{0.41 / 2.01 / 2.69} mean points at 200 / 3,400 / 5,500 steps.
  \end{itemize}
  \vspace{3mm}
  These are fixed-corpus and fixed-backbone comparisons. The capacity study
  uses different $E$ from the main evaluated checkpoints.
  \papersource{Paper, Sec. 5.3--5.4 and Figs. 6--7, PDF pp. 17--18; Appendix B, Table 6, PDF p. 35.}
\end{frame}

\begin{frame}{Nano's long-history advantage includes preparation cost}
  \centering
  \includegraphics[width=0.94\linewidth,height=0.67\textheight,
                   keepaspectratio]{figures/efficiency.pdf}
  \vspace{-1mm}
  \begin{itemize}
    \item End-to-end: about \textbf{1.86$\times$} faster at 128K history;
      short histories favor or match full context.
    \item Query TTFT after preparation: Nano about 104--112 ms; full context
      about 257 ms at 128K and 445 ms at 256K.
  \end{itemize}
  \papersource{Paper, Fig. 8 and Sec. 6.5, PDF pp. 22--24. Single request, two GPUs for the HF runs.}
\end{frame}

\begin{frame}{Fixed state saves storage, while fidelity has a capacity limit}
  \[
    N_{\rm state}=\sum_l E_lH_{kv,l}d_{k,l}d_{v,l}
  \]
  \begin{itemize}
    \item Nano state: \textbf{40 MiB FP32 per trajectory}, independent of accumulated
      history length at a fixed configuration.
    \item At 128K history: about \textbf{66$\times$} less logical persistent
      state than full-context KV cache.
    \item Shared weights and active-turn KV caches are outside this metric.
    \item LongMemEval-S and BEAM-1M show the remaining cost in detailed recall.
  \end{itemize}
  \papersource{Paper, Secs. 4.3, 6.2, 6.6, PDF pp. 16, 19--20, 24; Appendix B, Table 6, PDF p. 35.}
\end{frame}

\begin{frame}{The agent stores retrieved procedural experience once}
  \begin{enumerate}
    \item Summarize successful coding trajectories into \textbf{1,040} reusable experiences.
    \item Retrieve by issue text with BGE-M3; select experience candidates.
    \item Store before execution or when the harness detects a breakpoint.
    \item Continue the agent with experience in text or native memory.
  \end{enumerate}
  \vspace{3mm}
  \begin{block}{Application training}
    Failed rollouts are resumed with experience; successful continuations
    form training examples. Main text selects KD with full-suffix supervision.
  \end{block}
  \papersource{Paper, Secs. 7.2--7.3 and Fig. 11, PDF pp. 26--28.}
\end{frame}

\begin{frame}{Experience improves coding success and repeatability}
  \centering
  \begin{tabular}{llrr}
    \toprule
    Benchmark / timing & Experience & Mean success & All 3 succeed \\
    \midrule
    Verified / before & None & 66.07\% & 54.00\% \\
    Verified / before & Text & 68.87\% & 61.60\% \\
    Verified / before & Native & \textbf{69.33\%} & \textbf{61.60\%} \\
    \midrule
    Multilingual / before & None & 60.00\% & 45.33\% \\
    Multilingual / before & Text & \textbf{64.22\%} & \textbf{56.33\%} \\
    Multilingual / before & Native & 62.89\% & 53.67\% \\
    \bottomrule
  \end{tabular}\par
  \vspace{3mm}
  Each task runs three times. Native memory improves on no experience;
  textual experience remains stronger on Multilingual before execution.
  \papersource{Paper, Table 5, PDF p. 29. Verified: 500 tasks; Multilingual: 300 tasks.}
\end{frame}

\begin{frame}{Two boundaries matter when interpreting the agent result}
  \begin{itemize}
    \item The experience bank is built and retrieved externally; the tested
      agent reuses that experience rather than autonomously discovering it.
    \item Appendix E.2 labels its token selector, $512\!\times\!512$ state,
      and 105M trainable parameters as \textbf{Metis}, whereas the main text
      describes MemT-1.0-Nano and Appendix B specifies different geometry.
    \item The application-specific implementation details therefore need
      author clarification before treating E.2 as a MemT recipe.
  \end{itemize}
  \papersource{Paper, Sec. 7.3 and Sec. 8, PDF pp. 27--30; Appendix B, PDF p. 35; Appendix E.2--E.3, PDF pp. 56--60.}
\end{frame}

\begin{frame}{Take-home: a useful bounded memory, with a fidelity frontier}
  \begin{enumerate}
    \item \textbf{Mechanism:} snapshot reads + stable tokenwise writes make
      memory a persistent part of model computation.
    \item \textbf{Evidence:} leading average among tested parametric memory
      systems, long-history efficiency, and better agent repeatability.
    \item \textbf{Tradeoff:} fixed state saves per-trajectory storage but loses
      detailed evidence relative to strong textual memory systems.
  \end{enumerate}
  \vspace{5mm}
  Open direction: selective retention, larger effective capacity, and
  consolidation from local online states into shared trained parameters.
  \papersource{Paper, Secs. 6--9, PDF pp. 18--31.}
\end{frame}

\appendix
\begin{frame}{Backup: evaluated state geometry uses one expert per layer}
  \centering
  \begin{tabular}{lrr}
    \toprule
    & Nano & Flash \\
    \midrule
    Backbone & Qwen3.6-35B-A3B & DeepSeek-V4-Flash \\
    Memory layers & 10 & 20 \\
    Channels / key / value & 16 / 256 / 256 & 64 / 512 / 512 \\
    Experts per layer & 1 & 1 \\
    Trainable memory parameters & 450.9M & 3,608.6M \\
    FP32 state / trajectory & 40 MiB & 1,280 MiB \\
    \bottomrule
  \end{tabular}
  \papersource{Paper, Appendix B, Table 6, PDF p. 35.}
\end{frame}

\begin{frame}{Backup: the erase-step stability argument}
  \[
    A=I-k^\top(\beta\odot k),\qquad
    \beta\odot k=\rho k+\gamma e,\quad e\perp k.
  \]
  \[
    \left.A\right|_{\{k,e\}}=
      \begin{bmatrix}1-\rho&-\gamma\\0&1\end{bmatrix},
      \qquad \|eA^\top\|_2^2=1+\gamma^2.
  \]
  \vspace{2mm}
  Symmetrization uses $u=\sqrt{\beta}\odot k$ and
  $A_{\rm sym}=I-u^\top u$. Under $\|u\|^2<1$, it contracts along $u$
  and is unchanged on $u^\perp$.
  \papersource{Paper, Appendix C, PDF pp. 37--40.}
\end{frame}

\begin{frame}{Backup: distinguish score, latency, and storage metrics}
  \begin{itemize}
    \item Memory benchmark mean: equal-weight mean of 11 benchmark scores;
      PS-Bench uses $100(1-\mathrm{ASR})$.
    \item Query TTFT excludes history preparation; end-to-end latency includes it.
    \item Storage is logical persistent state after preparation, before query;
      shared weights and active workspaces are excluded.
    \item Agent ``all 3 succeed'' is pass$^3$; pass@3 is at least one success.
  \end{itemize}
  \papersource{Paper, Sec. 6, Table 2 and Fig. 8, PDF pp. 18--24; Table 5, PDF p. 29; Appendix D, PDF pp. 40--53.}
\end{frame}

\end{document}