The field of autonomous systems and decision-making is rapidly advancing, with a focus on developing more efficient, fair, and scalable methods for complex tasks. Recent research has explored the use of iterative exchange frameworks for multi-UAV cooperative path planning, semantic-aware graph-assisted stitching for offline temporal logic planning, and novel MDP decomposition frameworks for scalable UAV mission planning. These approaches aim to balance competing objectives, such as efficiency and fairness, and to enable real-time decision-making in complex and uncertain environments. Noteworthy papers in this area include: A Novel MDP Decomposition Framework for Scalable UAV Mission Planning in Complex and Uncertain Environments, which presents a two-stage decomposition strategy for solving large-scale Markov Decision Processes. SAGAS: Semantic-Aware Graph-Assisted Stitching for Offline Temporal Logic Planning, which proposes a novel framework combining graph-assisted trajectory stitching with automata-guided planning. Automating the Refinement of Reinforcement Learning Specifications, which introduces a framework for improving coarse-grained logical specifications via an exploration-guided strategy.