Imagine trying to understand a complex image with blurry vision. That’s the challenge many multimodal AI models face when processing high-resolution images. These models, designed to understand both text and images, often struggle with the sheer amount of visual data present in high-res pictures. Traditional methods either compress the image, losing crucial details, or strain computational resources by processing an overwhelming sequence of visual tokens. Researchers have introduced an innovative approach called DeepStack, a surprisingly simple yet powerful method for enhancing the visual capabilities of Large MultiModal Models (LMMs). Instead of feeding all visual data into the model at once, DeepStack strategically distributes it across different layers of the model, like building a detailed understanding layer by layer. This method avoids the computational bottleneck of traditional approaches, allowing LMMs to process multiple times more visual tokens without significant cost increases. DeepStack enables models to process the equivalent of multiple image crops simultaneously, enriching their understanding of the image's intricate details. This innovation leads to significant performance improvements on tasks requiring fine-grained visual understanding, such as document analysis, text-based visual question answering (VQA), and even video comprehension. For example, DeepStack brought notable gains on benchmarks like TextVQA, DocVQA, and InfoVQA, demonstrating its effectiveness in scenarios where high-resolution visual processing is crucial. While DeepStack demonstrates great promise, challenges remain. The method currently uses a fixed strategy for distributing the visual data, which may not be optimal for all scenarios. Future research may explore more adaptive methods of visual token distribution, personalizing the process for different image types and tasks. DeepStack offers an exciting glimpse into the future of multimodal AI. By enabling models to process a greater wealth of visual detail without significantly increasing computational overhead, DeepStack paves the way for more advanced and intuitive applications, from sophisticated document understanding to highly responsive video analysis, enriching the interactions between humans and AI.