  Recently, multimodal large language models have exploded with an endless
variety, most of the popular Large Vision Language Models (LVLMs) depend on
sequential visual representation, where images are converted into hundreds or
thousands of tokens before being input into the Large Language Model (LLM)
along with language prompts. The black-box design hinders the interpretability
of visual-language models, especially regarding more complex reasoning tasks.
To explore the interaction process between image and text in complex reasoning
tasks, we introduce the information flow method to visualize the interaction
mechanism. By analyzing the dynamic flow of the information flow, we find that
the information flow appears to converge in the shallow layer. Further
investigation revealed a redundancy of the image token in the shallow layer.
Consequently, a truncation strategy was introduced to aggregate image tokens
within these shallow layers. This approach has been validated through
experiments across multiple models, yielding consistent improvements.
