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\title[Jeva paper reading]{Jeva: System One Models from First Principles}
\subtitle{A native architecture for typed decisions}
\author{Paper by Qi Li and Xinchao Wang\\National University of Singapore}
\date{\href{https://arxiv.org/abs/2610.18773v2}{arXiv:2610.18773v2} \quad\textbar\quad Paper revised 18 Oct 2026\\Reading presentation: 28 Oct 2026}

\begin{document}

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

\begin{frame}{Takeaway: separate reusable options from set-dependent judgment}
  \begin{block}{The paper's central move}
    Encode each option independently; refine the current request against the \emph{whole} option set; separately estimate whether any option is valid.
  \end{block}
  \begin{itemize}
    \item This yields an explicit null outcome, permutation-equivariant probabilities, variable-size choice, and candidate-side caching.
    \item Reported evidence: 98.66\% null AUROC, zero flips in tested permutations, and 27.98$\times$ lower ratio of median latencies for fully reused novel MLDR sets.
  \end{itemize}
  \vspace{1mm}
  {\small Publication: \href{https://liqiiiii.github.io/}{not found as of 28 Oct 2026; author lists a 2026 preprint}.\par
  Official code: \href{https://github.com/LiQiiiii/Jeva}{LiQiiiii/Jeva}.}
  \papersource{Paper, Abstract and Secs. 3--4, PDF pp. 1, 4--9.}
\end{frame}

\begin{frame}{A decision problem has four structural requirements}
  \begin{tabular}{@{}p{0.26\linewidth}p{0.66\linewidth}@{}}
    \toprule
    Requirement & What a model should do \\
    \midrule
    Null-aware & Distinguish no valid option from uncertainty among valid options. \\
    Order-stable & Reordering alternatives should only reorder their probabilities. \\
    Option-scalable & More candidates should not require a new label vocabulary. \\
    Cacheable & Reuse computation for recurring options across new requests and sets. \\
    \bottomrule
  \end{tabular}
  \vspace{4mm}
  Jointly serializing request and options ties reusable content to each request and gives unordered options incidental text positions.
  \papersource{Paper, Sec. 1, PDF pp. 2--3; Fig. 1, PDF p. 1.}
\end{frame}

\begin{frame}{Jeva updates the request; its option memory stays fixed}
  \centering
  \begin{tikzpicture}[>=stealth,thick]
    \node[draw,rounded corners,fill=AcademicBlue!6,minimum width=2.2cm,minimum height=1.05cm,align=center] (req) at (0,1.45) {Request\\$(t,\iota,x)$};
    \node[draw,rounded corners,fill=AcademicBlue!6,minimum width=2.2cm,minimum height=1.05cm,align=center] (encx) at (3.0,1.45) {Shared text\\encoder};
    \node[draw,rounded corners,fill=AcademicTeal!10,minimum width=2.2cm,minimum height=1.05cm,align=center] (head) at (6.0,1.45) {Decision\\block};
    \node[draw,rounded corners,fill=AcademicBlue!6,minimum width=2.2cm,minimum height=1.05cm,align=center] (out) at (9.0,1.45) {Choice / Noul\\ / Score};
    \node[draw,rounded corners,fill=AcademicBlue!6,minimum width=2.2cm,minimum height=1.05cm,align=center] (opts) at (0,-0.65) {Options\\$o_1,\ldots,o_K$};
    \node[draw,rounded corners,fill=AcademicBlue!6,minimum width=2.2cm,minimum height=1.05cm,align=center] (enco) at (3.0,-0.65) {Shared text\\encoder};
    \node[draw,rounded corners,fill=AcademicTeal!10,minimum width=2.2cm,minimum height=1.05cm,align=center] (mem) at (6.0,-0.65) {Option memory\\$U$ and $K/V$};
    \draw[->] (req) -- (encx);
    \draw[->] (encx) -- node[above]{\small $z_0$} (head);
    \draw[->] (head) -- node[above]{\small $z_L$} (out);
    \draw[->] (opts) -- (enco);
    \draw[->] (enco) -- (mem);
    \draw[->] (mem) -- node[right]{\small read} (head);
  \end{tikzpicture}
  \par\vspace{5mm}
  \begin{itemize}
    \item Option memory is request-independent and may be cached.
    \item Only the request state is refined against the current candidate set.
  \end{itemize}
  \papersource{Teaching schematic redrawn from Paper, Fig. 1, PDF p. 1; Sec. 3, PDF pp. 4--6.}
\end{frame}

\begin{frame}{Independent encoding makes each option reusable}
  \[
    z_0=P_xE_\theta(T_x(t,\iota,x)),\qquad
    u_i=\operatorname{nrm}\bigl(P_oE_\theta(T_o(t,o_i))\bigr),
    \quad z_0,u_i\in\mathbb R^d.
  \]
  \begin{itemize}
    \item $x$: state; $\iota$: question; $t$: decision type; $o_i$: candidate text.
    \item $T_x$ includes type, question, state. $T_o$ includes only type and one option.
    \item The shared backbone pools the last non-padding token; the option projection is $\ell_2$ normalized.
  \end{itemize}
  \begin{block}{Cache identity}
    The same typed option has the same encoder input under fixed weights, tokenizer, template, and length policy.
  \end{block}
  \papersource{Paper, Eq. (1), Sec. 3.1, PDF p. 4; Appendix C, PDF pp. 16--18.}
\end{frame}

\begin{frame}{Independent candidate scores cannot reverse a retained pair}
  \[
    q_i(\mathcal O)=\frac{e^{g(x,\iota,o_i)/\tau}}{\sum_{j\in\mathcal O}e^{g(x,\iota,o_j)/\tau}},
    \qquad
    \log\frac{q_A(\mathcal O)}{q_B(\mathcal O)}
    =\frac{g(x,\iota,A)-g(x,\iota,B)}{\tau}.
  \]
  \vspace{3mm}
  \begin{itemize}
    \item Adding a third option changes $q_A$ and $q_B$, but their odds ratio does not change.
    \item Jeva keeps option vectors reusable while letting the \emph{request state} depend on the current set.
  \end{itemize}
  \papersource{Paper, Eq. (2), Sec. 3.1, PDF p. 4.}
\end{frame}

\begin{frame}{A small mixer collects evidence from the whole set}
  \[
    a_{\ell i}^{h}=\operatorname{softmax}_i\!\left(
      \frac{(q_\ell^h)^\top k_{\ell i}^{h}}{\tau_\ell}\right),
    \qquad
    m_\ell=W_\ell^O\operatorname{Concat}_{h}
       \left(\sum_i a_{\ell i}^{h}v_{\ell i}^{h}\right).
  \]
  \[
    \widetilde z_\ell=z_\ell+\alpha_\ell m_\ell,
    \qquad z_{\ell+1}=\widetilde z_\ell+
      L^{-1/2}F_\ell(\operatorname{RMSNorm}(\widetilde z_\ell)),
    \quad \alpha_\ell=\frac{\tau_\ell\sigma(\gamma_\ell)}{\sqrt L}.
  \]
  \begin{itemize}
    \item Keys and values come from unchanged options; only $z_\ell$ evolves.
    \item $a_{\ell i}^{h}$ allocates \emph{evidence}; it is not the final choice probability.
  \end{itemize}
  \papersource{Paper, Eqs. (3)--(4), Sec. 3.2, PDF pp. 4--5; Appendix A.1, PDF p. 11.}
\end{frame}

\begin{frame}{Even one block can reverse a pair's preference}
  \begin{columns}[T,onlytextwidth]
    \begin{column}{0.53\textwidth}
      \centering
      \includegraphics[width=\linewidth,height=0.64\textheight,keepaspectratio]{figures/fig-03-set-context.pdf}
    \end{column}
    \begin{column}{0.45\textwidth}
      \begin{itemize}
        \item Keep request, $A$, and $B$ fixed; replace only $C_+$ by $C_-$.
        \item The refined state moves in opposite directions.
        \item The authors construct $s_A-s_B=\pm24\alpha w/25\approx\pm0.00559$; $A$ and $B$ win in different sets.
      \end{itemize}
    \end{column}
  \end{columns}
  \papersource{Paper, Proposition 1 and Eq. (5), PDF p. 5; Fig. 3, PDF p. 3. Existence construction.}
\end{frame}

\begin{frame}{Competition and answer availability are different probabilities}
  \[
    s_i=z_L^\top u_i,\qquad
    q_i=\operatorname{softmax}_i(s/\tau_{\rm dec}),
  \]
  \[
    \rho=\sigma\!\left(\frac{\max_i s_i-b_{\rm cov}}{\tau_{\rm cov}}\right),
    \qquad p_\bot=1-\rho,\qquad p_i=\rho q_i.
  \]
  \begin{itemize}
    \item $q_i$: preference \emph{conditional on some valid option existing}.
    \item $\rho$: estimated probability that at least one candidate is acceptable.
    \item Open-set Choice uses $(p_\bot,p_1,\ldots,p_K)$; closed-set Choice uses $q$.
  \end{itemize}
  \papersource{Paper, Eq. (6), Sec. 3.3, PDF p. 5.}
\end{frame}

\begin{frame}{The coverage gate avoids a forced candidate-count effect}
  \begin{columns}[T,onlytextwidth]
    \begin{column}{0.49\textwidth}
      \centering
      \includegraphics[width=\linewidth,height=0.62\textheight,keepaspectratio]{figures/fig-04-coverage.pdf}
    \end{column}
    \begin{column}{0.49\textwidth}
      \begin{itemize}
        \item Shift all scores: $q$ stays fixed, $\rho$ changes.
        \item At \emph{fixed final scores}, adding a below-max option keeps $\rho$ fixed.
        \item Flat softmax with a null logit instead gives
      \end{itemize}
      \[
        p_\bot^{\rm flat}=\sigma\!\left(
           \frac{b_0-s}{\tau}-\log K\right).
      \]
    \end{column}
  \end{columns}
  \papersource{Paper, Proposition 2 and Eq. (7), PDF pp. 5--6; Fig. 4, PDF p. 4. Set refinement can still alter scores.}
\end{frame}

\begin{frame}{One head supports Choice, Noul, and Score}
  \begin{tabular}{@{}p{0.18\linewidth}p{0.72\linewidth}@{}}
    \toprule
    Type & Readout \\
    \midrule
    Choice & Closed set: select from $q$; open set: include $p_\bot$ and select from $p$. \\
    Noul & Return $q_{i^+}$, probability of the positive Boolean candidate. \\
    Score & Return $\sum_i q_i v_i$, expected value in the supplied rubric units. \\
    \bottomrule
  \end{tabular}
  \vspace{3mm}
  \[
    \ell_{\rm open}=\operatorname{BCEWithLogits}(\operatorname{logit}\rho,c_y)
       -\sum_i y_i\log q_i,\qquad c_y=\sum_i y_i.
  \]
  All-null examples train coverage without imposing a candidate ranking.
  \papersource{Paper, Eqs. (6), (9), PDF pp. 5--7; Appendix C examples, PDF pp. 16--17. Noul is not $p_\bot$.}
\end{frame}

\begin{frame}{Symmetry is structural; caching reuses candidate computation}
  \begin{columns}[T,onlytextwidth]
    \begin{column}{0.46\textwidth}
      \centering
      \includegraphics[width=\linewidth,height=0.65\textheight,keepaspectratio]{figures/fig-05-option-reuse.pdf}
    \end{column}
    \begin{column}{0.52\textwidth}
      \begin{itemize}
        \item Reordering complete candidate entries reindexes the attention sum; state and coverage stay fixed, candidate probabilities permute.
        \item A cached entry contains $u_i$ and each layer's key/value projections.
        \item New request: state encoding and decision readout still run.
      \end{itemize}
    \end{column}
  \end{columns}
  \papersource{Paper, Proposition 3, Eq. (8), PDF p. 6; Fig. 5, PDF p. 4. Exact arithmetic, fixed weights.}
\end{frame}

\begin{frame}{Prepared memory gives linear-in-$K$ decision work}
  \[
    \mathcal M_t(o_i)=\left(u_i,\{k_{\ell i}^{h},v_{\ell i}^{h}\}_{\ell,h}\right),
  \]
  \[
    \text{decision cost}=O\bigl(L(d^2+Kd+df)\bigr),
    \qquad \text{bank storage}=O\bigl(M(L+1)d\bigr).
  \]
  \begin{itemize}
    \item $K$: candidates in one request; $M$: options in the stored bank; $f$: FFN width.
    \item New options still need text encoding and key/value projection.
    \item Hybrid cache hits individual options even when the assembled set is new.
  \end{itemize}
  \begin{block}{Jeva-4B configuration}
    Qwen3.5-4B backbone; $d=768$, $L=1$, $H=8$, $f=3072$; rank-64 LoRA plus 13.37M projection/head parameters.
  \end{block}
  \papersource{Paper, Secs. 3.4--3.5, PDF pp. 6--7; Appendix B.6--B.7, PDF p. 15.}
\end{frame}

\begin{frame}{Evaluation separates quality, structure, and latency}
  \begin{tabular}{@{}p{0.30\linewidth}p{0.59\linewidth}@{}}
    \toprule
    Question & Protocol / metric \\
    \midrule
    General accuracy & Typed Decisions (2,000); BANKING77/MASSIVE ($K=8,16$); Mind2Web (three $K=16$ slices). \\
    Null detection & 3,080 BANKING77 requests at $K=16$; 588 answer-absent; AUROC and TPR at near-5\% FPR. \\
    Order stability & 600 Choice items; original, reverse, up to three seeded orders; align candidate identities. \\
    Large sets & 500 ToolRet requests in six disjoint $K=32\ldots2048$ cohorts; support, accuracy, p50 latency. \\
    Reuse & Off, exact-set, hybrid; MLDR novel sets and shared banks; priming separately timed. \\
    \bottomrule
  \end{tabular}
  \papersource{Paper, Sec. 4, PDF pp. 7--9; Appendix C, PDF pp. 15--18. Open-set baseline adapters differ by interface.}
\end{frame}

\begin{frame}{Jeva leads seven of eight general-accuracy slices}
  \begin{tabular}{@{}lrrr@{}}
    \toprule
    Slice & Jeva-4B & Best other & Difference \\
    \midrule
    Typed Decisions & 78.30 & 78.70 (Intern-4B) & $-0.40$ \\
    BANKING77, $K=16$ & 91.62 & 82.50 (Jev) & $+9.12$ \\
    MASSIVE, $K=16$ & 90.22 & 81.61 (Jev) & $+8.61$ \\
    Mind2Web task & 79.99 & 66.26 (Jev) & $+13.73$ \\
    Mind2Web website & 62.25 & 59.97 (Jev) & $+2.28$ \\
    Mind2Web domain & 66.23 & 62.47 (Jev) & $+3.76$ \\
    \bottomrule
  \end{tabular}
  \par\vspace{3mm}
  {\small Accuracy is first-argmax \%, including null when applicable; differences are percentage points. Comparators here have full slice support.}
  \papersource{Paper, Table 1, PDF p. 5; Sec. 4.1, PDF p. 7.}
\end{frame}

\begin{frame}{Typed fidelity is strong, but not every metric is best}
  \begin{tabular}{@{}lrrr@{}}
    \toprule
    Model & Noul Brier $\downarrow$ & Score MAE $\downarrow$ & Score RMSE $\downarrow$ \\
    \midrule
    Jeva-4B & \textbf{0.0184} & \textbf{0.2315} & 0.3222 \\
    InternDecision-4B & 0.0340 & 0.2488 & \textbf{0.3208} \\
    Jev-1.13.0 & 0.0342 & 0.3744 & 0.4795 \\
    \bottomrule
  \end{tabular}
  \vspace{4mm}
  \begin{itemize}
    \item Noul: 600 items with soft probability targets; Score: 800 items.
    \item Score errors use the original rubric's numeric units.
    \item Brier and RMSE are different metrics; compare models within a column.
  \end{itemize}
  \papersource{Paper, Table 2, PDF p. 6; Fig. 6(a), PDF p. 7.}
\end{frame}

\begin{frame}{A separate coverage gate detects missing answers}
  \begin{columns}[T,onlytextwidth]
    \begin{column}{0.49\textwidth}
      \centering
      \includegraphics[width=\linewidth,height=0.68\textheight,keepaspectratio]{figures/fig-06-null-panel.pdf}
    \end{column}
    \begin{column}{0.49\textwidth}
      \begin{itemize}
        \item BANKING77, $K=16$: 3,080 matched requests; 588 have no valid option.
        \item Null AUROC: Jeva 98.66\%; KEV-BF16 93.61\%; Intern-4B 84.27\%; JevK5 79.44\%.
        \item Jeva detects 93.88\% of absent answers at 5.02\% FPR.
      \end{itemize}
    \end{column}
  \end{columns}
  \papersource{Paper, Sec. 4.2, PDF pp. 7--8; cropped from Fig. 6(b), PDF p. 7.}
\end{frame}

\begin{frame}{Observed Choice decisions do not flip under tested orders}
  \begin{columns}[T,onlytextwidth]
    \begin{column}{0.49\textwidth}
      \centering
      \includegraphics[width=\linewidth,height=0.68\textheight,keepaspectratio]{figures/fig-06-order-panel.pdf}
    \end{column}
    \begin{column}{0.49\textwidth}
      \begin{itemize}
        \item 600 Choice items, original + reverse + up to three seeded permutations.
        \item Jeva: 0\% flips, mean TV $3.17\!\times\!10^{-8}$, 76.67\% all-order accuracy.
        \item Jev: 17.00\% flips, 66.67\% all-order accuracy; Intern-4B: 22.67\%, 64.50\%.
      \end{itemize}
    \end{column}
  \end{columns}
  \papersource{Paper, Table 2, PDF p. 6; Sec. 4.1 and cropped Fig. 6(c), PDF pp. 7--8.}
\end{frame}

\begin{frame}{Large sets remain supported; batching cuts encoder latency}
  \begin{columns}[T,onlytextwidth]
    \begin{column}{0.51\textwidth}
      \centering
      \includegraphics[width=\linewidth,height=0.68\textheight,keepaspectratio]{figures/fig-08-accuracy-latency.pdf}
    \end{column}
    \begin{column}{0.47\textwidth}
      \begin{itemize}
        \item ToolRet: 500 requests in six \emph{disjoint-query} cohorts; added negatives include weak or unjudged items. Jeva covers all.
        \item Aggregate accuracy: 78.40\% vs recursive JevK5's 64.00\%. At $K=2048$: 69.88\% vs 67.47\%.
        \item At $K=2048$, p50 is 12.040 s vs about 28 s; length-aware batching within Jeva changes 94.459 s to 12.040 s (7.85$\times$).
      \end{itemize}
    \end{column}
  \end{columns}
  \papersource{Paper, Sec. 4.3 and cropped Fig. 8(a,b), PDF p. 8. Batching: 256 rows / 131,072 padded tokens.}
\end{frame}

\begin{frame}{Hybrid reuse helps even when no whole set repeats}
  \begin{columns}[T,onlytextwidth]
    \begin{column}{0.51\textwidth}
      \centering
      \includegraphics[width=\linewidth,height=0.68\textheight,keepaspectratio]{figures/fig-09-speedup-latency.pdf}
    \end{column}
    \begin{column}{0.47\textwidth}
      \begin{itemize}
        \item Novel MLDR sets have \emph{zero} set-cache hits; priming is timed separately. With full option reuse, p50 goes from 2,102.0 to 75.1 ms: 27.98$\times$ ratio of medians.
        \item At about 50\% option reuse: 1.95$\times$. Exact-set cache stays near 2.1 s on novel sets.
        \item Shared Typed bank: 93.1 to 46.7 ms with hybrid reuse; 99.2\% candidate rows avoid encoding.
      \end{itemize}
    \end{column}
  \end{columns}
  \papersource{Paper, Sec. 4.4 and cropped Fig. 9(a,b), PDF pp. 8--9. Priming is timed separately; no answers are cached.}
\end{frame}

\begin{frame}{What the paper establishes, and what to carry forward}
  \begin{enumerate}
    \item Independent option features can still support set-dependent choice when a small state reads the full set.
    \item Coverage, competition, symmetry, and cacheability have explicit computational roles; the experiments test each one.
    \item Reuse gains concern candidate-side work under fixed weights and measured cache conditions. Request encoding and decision still run.
  \end{enumerate}
  \vspace{3mm}
  {\small Interpretation: cross-model gains also reflect differing training and evaluation adapters; the paper does not isolate every architecture component with a common-backbone removal study.}
  \papersource{Paper, Secs. 3--5, PDF pp. 4--9; Appendix B.5--B.7, PDF pp. 14--15.}
\end{frame}

\begin{frame}{Paper and release links}
  \begin{itemize}
    \item Qi Li and Xinchao Wang. \href{https://arxiv.org/abs/2610.18773v2}{\emph{Jeva: System One Models from First Principles}}, arXiv:2610.18773v2 (18 Oct 2026).
    \item \href{https://github.com/LiQiiiii/Jeva}{Official implementation and documentation: LiQiiiii/Jeva}.
    \item \href{https://huggingface.co/LIQIIIII/Jeva-4B}{Released Jeva-4B model}.
    \item \href{https://liqiiiii.github.io/}{Author publication page}: lists Jeva among 2026 preprints (checked 28 Oct 2026).
  \end{itemize}
  \vspace{4mm}
  Publication status at lookup: no formal venue found; the author lists this work as a preprint.
\end{frame}

\appendix

\begin{frame}{Backup: the same queries across growing candidate sets}
  \begin{columns}[T,onlytextwidth]
    \begin{column}{0.48\textwidth}
      \centering
      \includegraphics[width=\linewidth,height=0.68\textheight,keepaspectratio]{figures/fig-07-all-size.pdf}
    \end{column}
    \begin{column}{0.50\textwidth}
      \begin{itemize}
        \item 83 fixed queries; nested sets grow from 32 to 2,048 candidates.
        \item Correct at \emph{every} size: Jeva 58/83 (69.88\%); JevK5 48/83 (57.83\%); CLM 7/83 (8.43\%).
        \item This 69.88\% uses a different denominator from the $K=2048$ ToolRet cohort.
      \end{itemize}
    \end{column}
  \end{columns}
  \papersource{Paper, Fig. 7, PDF p. 7; Sec. 4.3, PDF p. 8.}
\end{frame}

\begin{frame}{Backup: open-set evaluation and numerical limits}
  \begin{itemize}
    \item Open-set baseline adaptation: JevK5/CLM receive a literal or descriptive None option; Jev/InternDecision/KEV use an auxiliary coverage Noul question.
    \item Exact symmetry and cache substitution assume fixed weights, deterministic exact arithmetic, and full candidate metadata permutation.
    \item Implementation: no decisions change across cache conditions, but maximum absolute probability difference is 0.00736.
    \item Novel-set sample count differs across manuscript locations: 50 matched queries in Sec. 4.4; 46 plotted per reuse level in Fig. 9(a) caption and Appendix C.4.
  \end{itemize}
  \papersource{Paper, Sec. 4, PDF pp. 7--9; Appendix B.5 and C.4--C.6, PDF pp. 14, 16--18.}
\end{frame}

\end{document}