Imagine teaching AI to see and understand the world like we do. It's a complex task, involving bridging the gap between visual information and language. Multimodal Large Language Models (MLLMs) are making strides in this area, but they face a critical challenge: efficiently connecting what they "see" with what they "say." Traditional methods often compress visual data, losing crucial details in the process. This is where DeCo comes in. Researchers have discovered that these models sometimes perform a "double abstraction" of visual information, leading to inefficiencies and errors. Think of it like trying to understand a picture by looking at a blurry thumbnail, and then summarizing it based on someone else's vague description. DeCo, short for "Decoupling Token Compression from Semantic Abstraction," offers a smarter approach. Instead of abstracting visual concepts before sending them to the language model, DeCo simplifies the process. It compresses the visual data at a lower level, preserving more of the original image's richness. Then, it lets the powerful language model do what it does best: extract meaning and generate accurate descriptions. The results are impressive. DeCo outperforms existing methods in tasks like visual localization and open-ended visual question answering. It's like giving the AI a clearer lens and a sharper mind, allowing it to understand images with greater accuracy and detail. This breakthrough has significant implications for the future of multimodal AI. By improving the efficiency and effectiveness of vision-language models, DeCo paves the way for more sophisticated applications, from advanced image search to more intuitive human-computer interaction. While challenges remain, especially with very high compression ratios, DeCo represents a significant step forward in our quest to build AI that truly understands the visual world.