Multimodal Large Language Models' (MLLMs) ability to understand visual data is hindered by the trade-off between spatial resolution and temporal context. The current approach of scaling input fidelity leads to an explosion in visual token growth, making it impractical to maintain high spatial resolution and long temporal context simultaneously. Researchers have identified the root cause of this bottleneck as the sheer volume of pixels fed into the encoder, rather than the compression of post-encoding representations. To address this, they propose ResAdapt, an adaptive resolution approach that aims to efficiently allocate resources for multimodal reasoning. By dynamically adjusting the resolution, ResAdapt enables MLLMs to achieve stronger visual understanding without being constrained by the limitations of fixed resolution1. This development has significant implications for the field of artificial intelligence, as it can enhance the performance of MLLMs in various applications, and its impact extends beyond the technical realm, affecting the geopolitical landscape of state-aligned threat activity.