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Refactor: deleted misleading end-to-end description

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DaZuo0122
2026-04-20 23:39:03 +08:00
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@@ -107,7 +107,7 @@ using the mean-squared error objective:
\mathcal{L}_{\text{trend}}(\phi) = \frac{1}{(L-1)d_c} \sum_{t=1}^{L-1} \bigl\| \hat{\bm{S}}_{t+1} - \bm{X}_{t+1} \bigr\|_2^2. \mathcal{L}_{\text{trend}}(\phi) = \frac{1}{(L-1)d_c} \sum_{t=1}^{L-1} \bigl\| \hat{\bm{S}}_{t+1} - \bm{X}_{t+1} \bigr\|_2^2.
\label{eq:trend_loss} \label{eq:trend_loss}
\end{equation} \end{equation}
At inference, we roll out the Transformer autoregressively to obtain $\hat{\bm{S}}$, and then define the residual target for diffusion as $\bm{R} = \bm{X} - \hat{\bm{S}}$. This setup intentionally locks in a coherent low-frequency scaffold before any stochastic refinement is applied, thereby reducing the burden on downstream diffusion modules to simultaneously learn both long-range structure and marginal detail. In this sense, our use of Transformers is distinctive: it is a conditioning-first temporal backbone designed to stabilize mixed-type diffusion synthesis in ICS, rather than an end-to-end monolithic generator \citep{vaswani2017attention,kollovieh2023tsdiff,yuan2025ctu}. At inference, we roll out the Transformer autoregressively to obtain $\hat{\bm{S}}$, and then define the residual target for diffusion as $\bm{R} = \bm{X} - \hat{\bm{S}}$. This setup intentionally locks in a coherent low-frequency scaffold before any stochastic refinement is applied, thereby reducing the burden on downstream diffusion modules to simultaneously learn both long-range structure and marginal detail. In this sense, our use of Transformers is distinctive: it is a conditioning-first temporal backbone designed to stabilize mixed-type diffusion synthesis in ICS, rather than an monolithic generator \citep{vaswani2017attention,kollovieh2023tsdiff,yuan2025ctu}.
\subsection{DDPM for continuous residual generation} \subsection{DDPM for continuous residual generation}
\label{sec:method-ddpm} \label{sec:method-ddpm}
@@ -201,7 +201,7 @@ Figure~\ref{fig:type_taxonomy} visualizes the six-type taxonomy and the routing
From a novelty standpoint, this layer is not merely an engineering patch; it is an explicit methodological statement that ICS synthesis benefits from typed factorization, a principle that has analogues in mixed-type generative modeling more broadly, but that remains underexplored in diffusion-based ICS telemetry synthesis \citep{shi2025tabdiff,yuan2025ctu,nist2023sp80082}. From a novelty standpoint, this layer is not merely an engineering patch; it is an explicit methodological statement that ICS synthesis benefits from typed factorization, a principle that has analogues in mixed-type generative modeling more broadly, but that remains underexplored in diffusion-based ICS telemetry synthesis \citep{shi2025tabdiff,yuan2025ctu,nist2023sp80082}.
\subsection{Joint optimization and end-to-end sampling} \subsection{Joint optimization and sampling}
\label{sec:method-joint} \label{sec:method-joint}
We train the model in a staged manner consistent with the above factorization, which improves optimization stability and encourages each component to specialize in its intended role. Specifically: (i) we train the trend Transformer $f_{\phi}$ to obtain $\hat{\bm{S}}$; (ii) we compute residual targets $\hat{\bm{R}} = \bm{X} - \hat{\bm{S}}$ for the continuous variables routed to residual diffusion; (iii) we train the residual DDPM $p_{\theta}(\bm{R}\mid \hat{\bm{S}})$ and masked diffusion model $p_{\psi}(\bm{Y}\mid \text{masked}(\bm{Y}), \hat{\bm{S}}, \hat{\bm{X}})$; and (iv) we apply type-aware routing and deterministic reconstruction during sampling. This staged strategy is aligned with the design goal of separating temporal scaffolding from distributional refinement, and it mirrors the broader intuition in time-series diffusion that decoupling coarse structure and stochastic detail can mitigate structure-vs.-realism conflicts \citep{kollovieh2023tsdiff,sikder2023transfusion}. We train the model in a staged manner consistent with the above factorization, which improves optimization stability and encourages each component to specialize in its intended role. Specifically: (i) we train the trend Transformer $f_{\phi}$ to obtain $\hat{\bm{S}}$; (ii) we compute residual targets $\hat{\bm{R}} = \bm{X} - \hat{\bm{S}}$ for the continuous variables routed to residual diffusion; (iii) we train the residual DDPM $p_{\theta}(\bm{R}\mid \hat{\bm{S}})$ and masked diffusion model $p_{\psi}(\bm{Y}\mid \text{masked}(\bm{Y}), \hat{\bm{S}}, \hat{\bm{X}})$; and (iv) we apply type-aware routing and deterministic reconstruction during sampling. This staged strategy is aligned with the design goal of separating temporal scaffolding from distributional refinement, and it mirrors the broader intuition in time-series diffusion that decoupling coarse structure and stochastic detail can mitigate structure-vs.-realism conflicts \citep{kollovieh2023tsdiff,sikder2023transfusion}.