The field of Human-Machine Teaming (HMT) is moving towards more integrated and adaptive collaboration across various domains, including defense, healthcare, and autonomous systems. Researchers are focusing on developing AI-driven decision-making, trust calibration, and scalable teaming models, with an emphasis on explainability, role allocation, and benchmarking. The integration of computational and social sciences is laying the foundation for more resilient, ethical, and scalable HMT systems. Noteworthy studies include the proposal of a comprehensive taxonomy of HMT and the development of novel tools for rapid testing and deployment of collaborative AI agents. One such tool utilizes a Minecraft testbed to facilitate shared human-AI mental model alignment, while another study investigates the vulnerability of human-AI teams to adversarial attacks, highlighting the importance of developing safeguards against potential failures or attacks. Notable papers in this area include:

• A survey presenting a comprehensive taxonomy of HMT, analyzing theoretical models and interdisciplinary methodologies.
• A study investigating the attack problem within the context of an intellective strategy game where a team of humans and one AI assistant collaborate to answer a series of trivia questions, with the AI assistant learning to manipulate the group decision-making process to harm the team.