AI终于学会边看边画，像人一样边想边改
现在的AI要么只会看图（理解），要么只会画图（生成），但人不是这样——老师边讲边画，手和嘴互相影响。这篇论文让AI同时做两件事：生成图像时，文字描述也在同步调整，而且每一步都互相看对方的最新进展。关键创新是“自纠正跳跃”：如果图像和文字出现矛盾（比如画了猫但文字说狗），AI会立刻撤回之前的决定重新来。在迷宫解题、数独填图等任务上，效果比分开做或轮流做好得多。它不是你明天能用上的，但方向很对：AI不再是一个模块干完传给下一个，而是像人一样边想边改。
📄 原文摘要(英文)
Human cognition does not separate understanding and generation. A teacher at a whiteboard speaks and draws 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 within the same step; combined with MDMs' inability to remask, cross-modal contradictions are neither detected nor repaired. We introduce Self-Correcting Coupled Markov Jump Processes (SC-CMJP), a framework in which one modality's transition rates are functionals of the other modality's confidence score, as weighted by cross-modal attention. Furthermore, a remasking jump retracts commitments the moment cross-modal evidence turns against them. In conjunction with SC-CMJP, we introduce CO_2Jump (Self-text{CO}rrecting text{CO}upled text{Jump}), a novel training-free single-pass sampler for joint multimodal geneneration. For training and evaluation purposes, we have created and will release three large-scale joint multimodal generation corpora: JEdit-1M, JMaze-200K, JNono-200K, with matching in- and out-of-distribution benchmarks. CO_2Jump achieves best joint performance for image understanding and editing as well as visual reasoning (maze and nonogram solving). The performance of the sampler scales monotonically with the number of denoising steps, evidence that the benefits of cross-modal coupling compound across the trajectory. Project page: https://coupled-jump.github.io