Researchers from Georgia Tech and MIT proposed MASCOT, a two-stage training framework for multi-agent socio-collaborative companion systems. MASCOT first aligns each agent with a role-specific behavior and then trains a Director Agent to select the next speaker and response strategy as the conversation evolves. The paper reports stronger role consistency, group-interaction scores, and human preference results than compared baselines on Empathetic Dialogues and QMSum.