Concurrent Image Understanding and Generation: Self-Correcting Coupled Markov Jump Processes
Human cognition does not separate understanding and generation. A teacher at a whiteboard speaks and draws $\textit{together}$, each modality reshapes the other. In this paper, we bring this coupled loop to artificial systems. Masked Diffusion Models (MDMs) are ideally suited to this task, yet existing samplers either decode text and image interleavedly or independently update them in parallel branches that share only previous-step history, but not the other modality's latest decisions $\textit{within}$ the same step; combined with MDMs' inability to remask, cross-modal contradictions are neither detected nor repaired. We introduce $\textbf{Self-Correcting Coupled Markov Jump Processes (S...
| Authors | Minh-Quan Le, Armand Comas, Alexandros Lattas et al. |
|---|---|
| Primary category | cs.LG |
| Published | 14 July 2026 |
| Last updated at source | 14 July 2026 |
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