The Hidden Evolution of Disguised Visual Context inside the VLM

The paper presents a comparative analysis of integration architectures for visual tokens in Visual Language Models (VLMs), specifically examining in-context prompting versus layer-wise injection. It evaluates these paradigms under consistent training conditions across various benchmarks, revealing that the method of integration significantly influences the transformation of visual tokens into meaningful representations, affecting the model's performance on tasks. This work highlights the importance of architectural choices in optimizing visual feature utilization and alignment with language, emphasizing that attention mechanisms alone do not guarantee effective performance without high-quality visual representations at each layer.