Existing vision language models (VLMs), including GPT-4 and DALL.E, often struggle to preserve logic, object identity, and style in multimodal image-text generation. This limitation significantly hinders the generalization capability of VLMs in complex image-text input-output scenarios. To address this issue, we propose IUT-Plug, a module grounded in an Image Understanding Tree (IUT), which enhances existing interleaved VLMs through explicit structured reasoning, thereby mitigating context drift in logic, entity identity, and style. The proposed framework operates in two stages. (1) A dynamic IUT-Plug extraction module parses visual scenes into hierarchical symbolic structures. (2) A coordinated narrative-flow and image synthesis mechanism ensures cross-modal consistency. To evaluate our approach, we construct a novel benchmark based on 3,000 real human-generated question-answer pairs over fine-tuned large models, introducing a dynamic evaluation protocol for quantifying context drift in interleaved VLMs. Experimental results demonstrate that IUT-Plug not only improves accuracy on established benchmarks but also effectively alleviates the three critical forms of context drift across diverse multimodal question answering (QA) scenarios.

翻译：现有的视觉语言模型（包括GPT-4和DALL.E）在多模态图文生成任务中，往往难以保持逻辑连贯性、对象一致性与风格统一性。这一局限严重制约了视觉语言模型在复杂图文输入-输出场景中的泛化能力。为解决该问题，本文提出IUT-Plug——一种基于图像理解树（IUT）构建的增强模块，通过显式的结构化推理机制提升现有交错式视觉语言模型的性能，从而有效缓解逻辑、实体一致性与风格三个维度的上下文漂移问题。该框架包含两个核心阶段：（1）动态IUT-Plug提取模块将视觉场景解析为层次化符号结构；（2）协同的叙事流与图像合成机制确保跨模态一致性。为评估本方法，我们基于3000组真实人工生成的微调大模型问答对构建了新型评测基准，并提出动态评估协议以量化交错式视觉语言模型中的上下文漂移。实验结果表明，IUT-Plug不仅在现有基准测试中提升了准确性，还能在多模态问答场景中有效缓解三类关键性上下文漂移问题。