Multiple Instance Learning (MIL) has been effectively applied in various fields, including computational pathology and satellite imagery, where supervision is only available at the level of bags of instances. However, existing MIL algorithms often struggle in real-world applications where labeled data is scarce. To address this limitation, researchers have proposed In-Context Multiple Instance Learning, a novel approach that aims to improve the adaptability and flexibility of MIL models1. This new method enables models to learn from limited labeled data and adapt to new tasks, making it a promising solution for applications where data is scarce or expensive to label. The implications of this research extend beyond the field of machine learning, as it can be used to analyze and understand complex systems, such as those involved in state-aligned threat activity, where the stakes are high and the need for accurate analysis is critical. This development matters to practitioners because it has the potential to enhance the analysis of complex systems and improve decision-making in high-stakes environments.