Interleaved thinking, where a unified multimodal model alternates between textual reasoning and visual generation, has shown promise on spatial and physical tasks. However, in complex long-chain scenarios, we identify a fundamental failure mode: generated images diverge from the textual context while subsequent text ignores the visual evidence, causing the two modalities to alternate without genuinely informing each other. We term this Modal Isolation and attribute it to compounding information loss at modality boundaries. We decompose each reasoning cycle into atomic operations and define modality transition loss, quantifying cross-modal hallucination (text-to-image) and visual utilization deficit (image-to-text) at each boundary. We propose MoTiF (Modality Tiransition Fidelity), a two-stage training framework that directly optimizes these transitions: Reflective SFT trains the model to detect and recover from erroneous visual outputs; Flow-GRPO improves image generation fidelity via reinforcement learning. All training signals in MoTiF derive from transition-level fidelity rather than end-task accuracy. Across four visual puzzle benchmarks, this transition-level supervision substantially improves both cross-modal coherence and final task accuracy. The results demonstrate that effective interleaved reasoning requires explicit structural supervision at modality boundaries, not merely scaling or end-task optimization.

翻译：交错思维——即统一多模态模型在文本推理与视觉生成之间交替进行——已在空间和物理任务上展现出潜力。然而，在复杂长链场景中，我们识别出一种根本性失效模式：生成的图像偏离文本上下文，而后续文本忽略视觉证据，导致两种模态交替却未能真正相互传递信息。我们将此称为模态隔离，并将其归因于模态边界处的信息损失累积。我们将每个推理循环分解为原子操作，并定义模态转换损失，在每个边界处量化跨模态幻觉（文本到图像）和视觉利用不足（图像到文本）。我们提出MoTiF（模态转换保真度），这是一个两阶段训练框架，直接优化这些转换：反思型SFT训练模型检测并从错误视觉输出中恢复；Flow-GRPO通过强化学习提升图像生成保真度。MoTiF中所有训练信号均来自转换级保真度而非最终任务准确率。在四个视觉谜题基准测试上，这种转换级监督显著提升了跨模态一致性和最终任务准确率。结果表明，有效的交错推理需要在模态边界处进行显式结构监督，而不仅仅是规模扩展或最终任务优化。