Our approach goes beyond conventional ergodic search and consensus mechanisms for distributed action coordination and scene understanding. Key thrusts include: (i) mission-oriented distributed optimization, including self-organized consensus for scalable action coordination and sensor fusion, guided by Value-of-Coordination metrics pioneered in our lab; and (ii) risk- and resource-aware behavioral adaptation and decision-making driven by mission-grounded large language models and world models.