Concurrent Image Understanding and Generation: Self-Correcting Coupled Markov Jump Processes
- Published
- Source
- arXiv
- Paper number
- 655
- Field
- Machine Learning
- arXiv ID
- 2607.13188
Key points
- It generates text and images simultaneously in a single decoding process, with each side verifying the other's decisions.
- It proposes CO2Jump, a training-free sampler that outperforms prior methods on image editing and visual reasoning tasks.
- It adds a remasking mechanism so that tokens once fixed can be hidden again when evidence changes, allowing the model to correct contradictions on its own.
- It builds and plans to release three large multimodal datasets: JEdit-1M, JMaze-200K, and JNono-200K.
- Performance improves steadily as the number of denoising steps increases, showing that the benefits of cross-modal coupling accumulate.
Paper links
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