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\title[MDM(LS)]{Masked Diffusion Models are Secretly\\Learned-Order Autoregressive Models}
\subtitle{Learning a distribution over decoding orders}
\author{Paper authors: Prateek Garg, Bhavya Kohli, Sunita Sarawagi}
\date{arXiv:2511.19152v1 \quad / \quad 24 November 2025\\[2mm]
\small Research discussion, 14 October 2026 \quad / \quad 20--30 minutes}

\begin{document}

\begin{frame}
  \titlepage
  \vspace{-5mm}
  \begin{center}
  \small Position-specific clocks reweight autoregressive learning tasks.\\
  The theory is precise; improvements in tabular fidelity are mixed.
  \end{center}
  \papersource{Reading presentation, not an author presentation.
  \href{https://arxiv.org/abs/2511.19152v1}{Versioned paper: arxiv.org/abs/2511.19152v1}.}
\end{frame}

\begin{frame}{Can the training objective learn a useful order?}
  \begin{columns}[T,onlytextwidth]
  \begin{column}{0.47\textwidth}
    \begin{block}{The starting point}
    A standard MDM trains equally over all token orders.
    \end{block}
    \begin{itemize}
      \item Tables and graphs lack a canonical sequence.
      \item Finite models may find some conditional predictions easier.
    \end{itemize}
  \end{column}
  \begin{column}{0.48\textwidth}
    \begin{block}{The proposal}
    Learn a separate masking schedule for each position.
    \end{block}
    \begin{itemize}
      \item Change the distribution of orders during training.
      \item Sample from that distribution at generation time.
    \end{itemize}
  \end{column}
  \end{columns}
  \medskip
  \alert{Scope: the order distribution is independent of sample content.}
  \papersource{Paper, Sec. 1, PDF pp. 1--2; Appendix A, PDF p. 9.}
\end{frame}

\begin{frame}{Reverse generation undoes a masking trajectory}
  \begin{center}
  \includegraphics[width=0.96\linewidth,height=0.33\textheight,keepaspectratio]{figures/forward_masking.png}
  \end{center}
  \[
    \underbrace{1\to2\to4\to3}_{\text{forward masking: }t=0\to1}
    \qquad\Longleftrightarrow\qquad
    \underbrace{3\to4\to2\to1}_{\text{reverse decoding: }t=1\to0}
  \]
  \begin{itemize}
    \item Each position changes once: token $\to$ mask in the forward process.
    \item The last position masked is the first position decoded.
  \end{itemize}
  \papersource{Original Fig. 1, PDF p. 2. Arrows below the image are an explanatory annotation.}
\end{frame}

\begin{frame}{The schedule chooses when; the network chooses what}
  For one position, $\alpha_t$ is the probability of retaining its value.
  \[
  q(x_t\mid x_s)=\frac{\alpha_t}{\alpha_s}\delta_{x_s}
  +\left(1-\frac{\alpha_t}{\alpha_s}\right)\delta_{\mask},\qquad s<t.
  \]
  \begin{block}{A masked position in the reverse process}
  \[
  p_\theta(x_s\mid x_t=\mask)=
  \underbrace{\frac{1-\alpha_s}{1-\alpha_t}}_{\text{stay masked}}\delta_{\mask}
  +\underbrace{\frac{\alpha_s-\alpha_t}{1-\alpha_t}}_{\text{unmask}}\mu_\theta(\mathbf{x}_t,t).
  \]
  \end{block}
  An unmasked position is copied unchanged. Other visible positions provide context.
  \papersource{Paper, Sec. 2, Eqs. (1)--(2), PDF p. 2; mean parameterization.}
\end{frame}

\begin{frame}{Every position has a random transition clock}
  Let $T_\ell$ be the noise-time coordinate of position $\ell$'s transition.
  \[
  \boxed{\Prob(T_\ell\le t)=1-\alpha_{t,\ell},
  \qquad f_\ell(t)=-\alpha'_{t,\ell}.}
  \]
  \begin{itemize}
    \item At forward time $t$, being masked means $T_\ell\le t$.
    \item Independent forward masking gives independent transition clocks.
    \item Sample $U_\ell\sim U(0,1)$, then $T_\ell=\alpha_\ell^{-1}(U_\ell)$.
  \end{itemize}
  \begin{block}{Time convention used in this presentation}
    Decode in \textbf{decreasing} $T_\ell$, since reverse time runs from 1 to 0.
  \end{block}
  \papersource{Proposition 3.2 and Algorithm 1, PDF p. 3; proof, pp. 9--10.
  Exact endpoints: $\alpha_{0,\ell}=1$, $\alpha_{1,\ell}=0$. See backup for the paper's order notation.}
\end{frame}

\begin{frame}{Different clocks assign different weights to orders}
  For decoding order $\pi$, define
  \[
  \Omega_\pi^{\rm dec}=\{\mathbf{t}:t_{\pi(1)}>\cdots>t_{\pi(L)}\}.
  \]
  \[
  P_{\rm dec}(\pi)=\int_{\Omega_\pi^{\rm dec}}
  \prod_{\ell=1}^L[-\alpha'_{t_\ell,\ell}]\,d\mathbf{t}.
  \]
  \begin{columns}[T,onlytextwidth]
  \begin{column}{0.47\textwidth}
    \begin{block}{One shared schedule}
      Exchangeable continuous clocks.\\[1mm]
      $P_{\rm dec}(\pi)=1/L!$ for every order.
    \end{block}
  \end{column}
  \begin{column}{0.48\textwidth}
    \begin{block}{Position-specific schedules}
      Relative clock speeds change.\\[1mm]
      Some orders become more likely.
    \end{block}
  \end{column}
  \end{columns}
  \papersource{Paper, Eq. (7), PDF p. 3; written here using descending decoding times.}
\end{frame}

\begin{frame}{The loss weights each masked prediction by its rate}
  Let $M_\ell=\mathbf{1}\{x_t^\ell=\mask\}$ and
  $c_\ell=-\log\mu_\ell^{x_0^\ell}(\mathbf{x}_t;\theta)$.
  \[
  \loss=\int_0^1\sum_{\ell=1}^L
  \underbrace{\frac{-\alpha'_{t,\ell}}{1-\alpha_{t,\ell}}}_{\text{reverse unmasking rate}}
  \E_q[M_\ell c_\ell]\,dt.
  \]
  \begin{itemize}
    \item The rate and cross-entropy are both nonnegative.
    \item A visible token is copied; only masked positions contribute.
    \item Learning a schedule changes both the masking distribution and the weight.
  \end{itemize}
  \papersource{Paper, Eq. (8), PDF p. 4. The masking indicator makes the carry-over convention explicit.}
\end{frame}

\begin{frame}{The event rate turns the loss into a sum over jumps}
  \[
  \underbrace{\Prob(M_\ell=1)}_{1-\alpha_{t,\ell}}
  \underbrace{\frac{-\alpha'_{t,\ell}}{1-\alpha_{t,\ell}}}_{\text{unmasking rate}}
  =\underbrace{-\alpha'_{t,\ell}}_{\text{transition-time density}}.
  \]
  \begin{enumerate}
    \item Integrating the weighted masked loss counts each token's jump once.
    \item Continuous independent clocks have no simultaneous jumps almost surely.
    \item At $T_{\pi(i)}$, positions $\pi(<i)$ have already been decoded.
    \item Group trajectories by their decoding order $\pi$.
  \end{enumerate}
  \papersource{Explanatory derivation from Eq. (8), PDF p. 4.
  The paper instead telescopes trajectory terms in Appendix B, pp. 10--11.}
\end{frame}

\begin{frame}{Without time input, the objective is weighted AR loss}
  Define an autoregressive log-likelihood for each order:
  \[
  LL_\pi(x,\theta)=\sum_{i=1}^L
  \log\mu(x_{\pi(i)}\mid x_{\pi(<i)};\theta).
  \]
  \begin{block}{Proposition 3.1, with a consistent decoding convention}
  \[
    \boxed{\loss(x)=-\E_{\pi\sim P_{\rm dec}}[LL_\pi(x,\theta)].}
  \]
  \end{block}
  \begin{itemize}
    \item One network shares parameters across all orders.
    \item $x_{\pi(<i)}$ is a full-length input with other positions masked.
    \item With a time-dependent network, retain $\E_{T_{\pi(i)}\mid\pi}$ inside the sum.
  \end{itemize}
  \papersource{Paper, Eqs. (5)--(6), PDF p. 3. Continuous time, absorbing masks, independent clocks.}
\end{frame}

\begin{frame}{An average AR loss is still a likelihood bound}
  If we sample an order and then generate tokens, the marginal model is
  \[
  p_{\theta,w}(x)=\sum_\pi P_w(\pi)\,
  \underbrace{\prod_i\mu(x_{\pi(i)}\mid x_{\pi(<i)};\theta)}_{p_\theta(x\mid\pi)}.
  \]
  Jensen's inequality gives
  \[
  \boxed{-\log p_{\theta,w}(x)
  \le\E_{\pi\sim P_w}[-\log p_\theta(x\mid\pi)]=\loss(x).}
  \]
  \begin{itemize}
    \item The theorem identifies the \emph{training objective} exactly.
    \item It does not replace a log of a mixture with an average of logs.
  \end{itemize}
  \papersource{Reader derivation from the negative ELBO, Eq. (3), p. 2, and Eq. (6), p. 3.
  Assumes no time input and generation one token at a time.}
\end{frame}

\begin{frame}{Power schedules turn weights into order preferences}
  \[
  \alpha_{t,\ell}=1-t^{w_\ell},\quad w_\ell>0;
  \qquad T_\ell\overset{d}{=}U_\ell^{1/w_\ell}.
  \]
  \begin{columns}[T,onlytextwidth]
  \begin{column}{0.48\textwidth}
  \begin{block}{Two-position example}
  \[
  \Prob(1\text{ decoded first})
  =\frac{w_1}{w_1+w_2}.
  \]
  With $(w_1,w_2)=(1,3)$:
  \[
  P(1\to2)=\tfrac14,\quad P(2\to1)=\tfrac34.
  \]
  \end{block}
  \end{column}
  \begin{column}{0.47\textwidth}
  \begin{itemize}
    \item Larger $w_\ell$: masked later in forward time; decoded earlier in reverse time.
    \item Equal weights recover uniform orders.
    \item Relative weights matter; the order remains stochastic.
  \end{itemize}
  \end{column}
  \end{columns}
  \papersource{Schedule family: Sec. 4, PDF p. 4. Probability calculation and example: reader derivation.
  General Plackett--Luce formula in backup.}
\end{frame}

\begin{frame}{Learning clocks requires gradients through masking}
  With the power schedule,
  \[
    M_\ell\sim\mathrm{Bernoulli}(t^{w_\ell}),\qquad
    \frac{-\alpha'_{t,\ell}}{1-\alpha_{t,\ell}}=\frac{w_\ell}{t}.
  \]
  \begin{itemize}
    \item A schedule parameter affects the loss weight \emph{and} the discrete mask distribution.
    \item Ordinary differentiation through the weight misses the second effect.
    \item The paper uses RLOO: REINFORCE with a leave-one-out baseline.
  \end{itemize}
  \begin{block}{Training and generation}
  Train on randomly masked rows. Generate by sampling clocks, sorting them,
  and filling one position at each event.
  \end{block}
  \papersource{Paper, Sec. 4, Eq. (8), PDF p. 4; Algorithm 1, p. 3.
  The RLOO sample count and full optimizer setup are not specified.}
\end{frame}

\begin{frame}{The direct ablation compares two 86K-parameter models}
  \begin{center}
  \begin{tabular}{lll}
  \toprule
  Model & Schedule & Role \\
  \midrule
  MDM & Fixed, $\alpha_{t,\ell}=1-t$ & Direct control \\
  \LS & Learned, $\alpha_{t,\ell}=1-t^{w_\ell}$ & Proposed variant \\
  TabDiff & Learned mixed-type diffusion & Larger strong baseline \\
  \bottomrule
  \end{tabular}
  \end{center}
  \begin{itemize}
    \item Six datasets: Adult, Default, Beijing, Shoppers, Magic, News.
    \item MDM and \LS: same hyperparameter set; roughly 86K parameters.
    \item Other baselines: STaSy, CoDi, TabDDPM, TabSyn; scores taken from TabDiff.
    \item TabDiff is reported as 10M parameters; this is not a matched-budget comparison.
  \end{itemize}
  \papersource{Paper, Sec. 4.1, PDF p. 4; Appendix C.1, p. 11.
  MDM variants: reported means and standard deviations over 10 runs.}
\end{frame}

\begin{frame}{Four metrics separate fidelity from coverage}
  \begin{center}
  \renewcommand{\arraystretch}{1.5}
  \begin{tabular}{lll}
  \toprule
  Metric & What it measures & Direction \\
  \midrule
  Trend & Pairwise column relationships & Higher \\
  Shape & Marginal distributions of individual columns & Higher \\
  $\alpha$-Precision & Realism of generated samples & Higher \\
  $\beta$-Recall & Coverage of the real data distribution & Higher \\
  \bottomrule
  \end{tabular}
  \end{center}
  \medskip
  Best validation loss is a separate, lower-is-better quantity.\\
  High precision can coexist with limited coverage.
  \papersource{Paper, Tables 1--3, PDF pp. 4--5; metric descriptions referred to
  TabDiff (2410.20626v3), Appendix A.2. Metric $\alpha$ is unrelated to the noise schedule.}
\end{frame}

\begin{frame}{Best validation loss improves on four of six datasets}
  \begin{center}
  \renewcommand{\arraystretch}{1.18}
  \begin{tabular}{lrrr}
  \toprule
  Dataset & MDM & \LS & LS $-$ MDM \\
  \midrule
  Adult & 13.1861 & 13.1524 & \good{$-0.0337$} \\
  Default & 19.4247 & 18.3272 & \good{$-1.0975$} \\
  Beijing & 16.7610 & 17.0060 & \bad{$+0.2450$} \\
  Shoppers & 16.4246 & 16.4604 & \bad{$+0.0358$} \\
  Magic & 12.6114 & 12.4712 & \good{$-0.1402$} \\
  News & 49.9699 & 48.9401 & \good{$-1.0298$} \\
  \bottomrule
  \end{tabular}
  \end{center}
  \medskip
  Default and News show the largest absolute reductions.\\
  Beijing and Shoppers become worse.
  \papersource{Paper, Fig. 2a, PDF p. 5. Best validation losses; differences calculated from reported values.
  Negative difference is better.}
\end{frame}

\begin{frame}{Fidelity changes are mixed; coverage often decreases}
  \begin{center}
  \renewcommand{\arraystretch}{1.18}
  \begin{tabular}{lrrrr}
  \toprule
  LS $-$ MDM & Trend & Shape & $\alpha$-Precision & $\beta$-Recall \\
  \midrule
  Adult & \good{+0.0031} & \good{+0.0027} & \good{+0.0153} & \bad{$-0.0061$} \\
  Default & \good{+0.0063} & \good{+0.0043} & \good{+0.0122} & \bad{$-0.0029$} \\
  Beijing & \bad{$-0.0030$} & \bad{$-0.0025$} & \good{+0.0152} & \bad{$-0.0147$} \\
  Shoppers & \good{+0.0007} & \good{+0.0003} & \bad{$-0.0009$} & \bad{$-0.0148$} \\
  Magic & \bad{$-0.0096$} & \bad{$-0.0072$} & \bad{$-0.0181$} & \bad{$-0.0042$} \\
  News & \good{+0.0015} & \good{+0.0002} & \good{+0.0093} & \good{+0.0068} \\
  \midrule
  Improved means & 4/6 & 4/6 & 4/6 & 1/6 \\
  \bottomrule
  \end{tabular}
  \end{center}
  \medskip
  Magic: lower validation loss, yet all four fidelity metrics decline.
  \papersource{Differences calculated from Tables 1--3, PDF pp. 4--5; Tables 4--7, pp. 11--12.
  Positive is better. Absolute score differences, not relative percentages or significance tests.}
\end{frame}

\begin{frame}{News has the best precision, but limited coverage}
  \begin{center}
  \renewcommand{\arraystretch}{1.3}
  \begin{tabular}{lrr}
  \toprule
  News dataset & $\alpha$-Precision $\uparrow$ & $\beta$-Recall $\uparrow$ \\
  \midrule
  MDM & 0.9779 & 0.3405 \\
  \LS & \good{\textbf{0.9872}} & 0.3473 \\
  TabDiff & 0.9736 & 0.4210 \\
  TabSyn & 0.9505 & \good{\textbf{0.4301}} \\
  \bottomrule
  \end{tabular}
  \end{center}
  \begin{itemize}
    \item \LS\ attains the highest News precision among all compared methods.
    \item Its recall remains below both strong baselines shown here.
    \item TabDiff also leads Trend on all six datasets.
  \end{itemize}
  \papersource{Paper, Tables 1 and 3, PDF pp. 4--5. Selected reported means;
  uncertainty examples in backup.}
\end{frame}

\begin{frame}{Adult schedules visibly separate across columns}
  \begin{columns}[c,onlytextwidth]
  \begin{column}{0.56\textwidth}
    \includegraphics[width=\linewidth,height=0.65\textheight,keepaspectratio]{figures/adult_noise_schedules.pdf}
  \end{column}
  \begin{column}{0.40\textwidth}
    \begin{itemize}
      \item Horizontal: forward noise time $t$.
      \item Vertical: survival probability $\alpha_t$.
      \item Different curves imply different clock distributions.
      \item Higher curves favor earlier reverse decoding.
    \end{itemize}
  \end{column}
  \end{columns}
  \papersource{Original Fig. 2b, PDF p. 5. Colors are not labeled with column names;
  the plot does not identify semantic or causal order.}
\end{frame}

\begin{frame}{The authors' TabDiff reproduction keeps similar clocks}
  \begin{columns}[T,onlytextwidth]
  \begin{column}{0.31\textwidth}
    \centering\textbf{TabDiff}\\
    \includegraphics[width=0.98\linewidth,height=0.57\textheight,keepaspectratio]{figures/tabdiff_vanilla-adult.pdf}
  \end{column}
  \begin{column}{0.31\textwidth}
    \centering\textbf{Random initialization}\\
    \includegraphics[width=0.98\linewidth,height=0.57\textheight,keepaspectratio]{figures/tabdiff_vanilla_krandom-adult.pdf}
  \end{column}
  \begin{column}{0.31\textwidth}
    \centering\textbf{\LS}\\
    \includegraphics[width=0.98\linewidth,height=0.57\textheight,keepaspectratio]{figures/tabmd4_all-adult.pdf}
  \end{column}
  \end{columns}
  \medskip
  Evidence from this reproduction: random initialization did not separate the TabDiff curves.
  TabDiff masks categorical columns; \LS\ masks all columns.
  \papersource{Adult panels cropped from original Figs. 3--5, PDF p. 12;
  Appendix C.2, pp. 12--13. A comparison of implementations, not an isolated causal explanation.}
\end{frame}

\begin{frame}{The contribution is a mechanism for learning orders}
  \begin{enumerate}
    \item \textbf{Theory:} independent position clocks induce a distribution over orders;
    without time input, the loss is exactly its weighted AR objective.
    \medskip
    \item \textbf{Evidence:} schedules separate and validation loss improves on 4/6 datasets;
    downstream fidelity is mixed, with recall improving on 1/6.
    \medskip
    \item \textbf{Next question:} can better variance control turn flexible orders into
    more reliable gains in fidelity and coverage?
  \end{enumerate}
  \medskip
  State-independent orders, a restricted schedule family, and limited implementation details
  define the present scope.
  \papersource{Paper, Secs. 3--5, PDF pp. 3--5; Appendix C, pp. 11--13.
  Increased training variance is the authors' hypothesis, not a measured causal result.}
\end{frame}

\appendix

\begin{frame}{Backup: power schedules imply Plackett--Luce orders}
  A reader derivation for the paper's family $\alpha_{t,\ell}=1-t^{w_\ell}$:
  \[
  F_\ell(t)=t^{w_\ell},\quad Z_\ell=-\log T_\ell,
  \quad\Prob(Z_\ell>z)=e^{-w_\ell z}.
  \]
  Thus $Z_\ell$ are independent exponential clocks with rates $w_\ell$.
  \[
  \boxed{P_w(\pi)=\prod_{i=1}^L
  \frac{w_{\pi(i)}}{\sum_{j=i}^L w_{\pi(j)}}.}
  \]
  \begin{itemize}
    \item Decreasing $T$ is increasing $Z$; exponential races select by relative rate.
    \item Scaling all $w_\ell$ by the same positive constant leaves orders unchanged.
    \item This family cannot assign arbitrary probabilities to all $L!$ permutations.
  \end{itemize}
  \papersource{Derived from Proposition 3.2 and the schedule family in Sec. 4, PDF pp. 3--4.
  The closed-form Plackett--Luce interpretation is not stated in the paper.}
\end{frame}

\begin{frame}{Backup: keep masking and decoding directions consistent}
  \begin{center}
  \renewcommand{\arraystretch}{1.3}
  \begin{tabular}{lll}
  \toprule
  Convention & Time ordering & Visible context at step $i$ \\
  \midrule
  Forward masking $\rho$ & $T_{\rho(1)}<\cdots<T_{\rho(L)}$ & $\rho(>i)$ \\
  Reverse decoding $\pi$ & $T_{\pi(1)}>\cdots>T_{\pi(L)}$ & $\pi(<i)$ \\
  \bottomrule
  \end{tabular}
  \end{center}
  \begin{itemize}
    \item The paper's Eq. (7) uses increasing times, while Eqs. (5)--(6)
    condition on a decoded prefix.
    \item Appendix B, p. 11, explicitly describes the increasing-time prefix as masked.
    \item This presentation uses decreasing times for decoding throughout.
  \end{itemize}
  \papersource{Paper, PDF pp. 3 and 11. Appendix B also has inconsistent transition-table entries
  on p. 10 and missing minus signs in Eqs. (23)--(24); the event-rate derivation avoids these issues.}
\end{frame}

\begin{frame}{Backup: RLOO retains the discrete-distribution gradient}
  For the loss $g_w(M)$ at a fixed row and time:
  \[
  \nabla_w\E_{M\sim q_w}g_w(M)
  =\E\bigl[\nabla_w g_w(M)+g_w(M)\nabla_w\log q_w(M)\bigr].
  \]
  For $K>1$ independent samples, set
  \[
  b_{-k}=\frac{1}{K-1}\sum_{j\ne k}g_w(M_j).
  \]
  \[
  \widehat{\nabla_w\loss}
  =\frac1K\sum_k\Bigl[\nabla_w g_w(M_k)
  +\bigl(g_w(M_k)-b_{-k}\bigr)\nabla_w\log q_w(M_k)\Bigr].
  \]
  Use stopped-gradient coefficients in the score term; compute the explicit derivative separately.
  \papersource{Explanatory score-function identity. Paper, Sec. 4, PDF p. 4, cites Kool et al. (2019)
  for RLOO but does not specify $K$ or this full estimator.}
\end{frame}

\begin{frame}{Backup: learned schedules on Adult, Default, Beijing}
  \begin{center}
  \includegraphics[width=0.98\linewidth,height=0.72\textheight,keepaspectratio]{figures/tabmd4_all-first3.pdf}
  \end{center}
  \papersource{Original Fig. 5, PDF p. 12, first three panels; values and axes unchanged.
  Horizontal: forward time $t$. Vertical: survival probability $\alpha_t$.}
\end{frame}

\begin{frame}{Backup: learned schedules on Shoppers, Magic, News}
  \begin{center}
  \includegraphics[width=0.98\linewidth,height=0.72\textheight,keepaspectratio]{figures/tabmd4_all-last3.pdf}
  \end{center}
  \papersource{Original Fig. 5, PDF p. 12, last three panels; values and axes unchanged.
  Horizontal: forward time $t$. Vertical: survival probability $\alpha_t$.}
\end{frame}

\begin{frame}{Backup: reported uncertainty supports a nuanced reading}
  \begin{center}
  \renewcommand{\arraystretch}{1.35}
  \begin{tabular}{llcc}
  \toprule
  Dataset & Metric & MDM & \LS \\
  \midrule
  Default & Trend & $0.9642\pm0.0016$ & $0.9705\pm0.0028$ \\
  Magic & Trend & $0.9724\pm0.0026$ & $0.9628\pm0.0070$ \\
  News & $\alpha$-Precision & $0.9779\pm0.0016$ & $0.9872\pm0.0012$ \\
  Beijing & $\beta$-Recall & $0.5309\pm0.0031$ & $0.5162\pm0.0027$ \\
  \bottomrule
  \end{tabular}
  \end{center}
  \begin{itemize}
    \item These are the paper's means and standard deviations over 10 runs.
    \item Improvements and regressions occur across distinct quality criteria.
    \item A best validation loss is a different reporting statistic.
  \end{itemize}
  \papersource{Paper, Appendix C.1, Tables 4, 6, 7, PDF pp. 11--12; Fig. 2a, p. 5.}
\end{frame}

\begin{frame}{References and discussion prompts}
  \textbf{Main paper}\\
  Prateek Garg, Bhavya Kohli, Sunita Sarawagi.\\
  \emph{Masked Diffusion Models are Secretly Learned-Order Autoregressive Models.}\\
  \href{https://arxiv.org/abs/2511.19152v1}{arXiv:2511.19152v1}, 2025. EurIPS PriGM Workshop.
  \medskip

  \textbf{Metric background}\\
  Juntong Shi et al. \emph{TabDiff: a Mixed-type Diffusion Model for Tabular Data Generation.}\\
  \href{https://arxiv.org/html/2410.20626v3\#A2}{arXiv:2410.20626v3, Appendix A.2}.
  \medskip

  \begin{itemize}
    \item How much flexibility is lost by restricting orders to a global clock family?
    \item Which variance measurements would explain the loss--fidelity mismatch?
    \item Would fixed nonuniform schedules separate learning from order selection?
  \end{itemize}
\end{frame}

\end{document}