NEXUS: A Future-Oriented Decision Framework for Autonomous Systems

Abstract Modern autonomous systems in the contemporary world typically optimize immediate actions based on current states, which often results in short-term decision behavior and suboptimal performance in complex and uncertain environments. This paper presents NEXUS, a future-oriented decision framework that reformulates decision-making as an optimization over multiple possible future trajectories rather than individual actions. The proposed framework generates probabilistic future scenarios using ensemble world models, evaluates them using multi-objective criteria including performance, safety, and uncertainty, and selects actions that lead to the most desirable future outcome. The methodology integrates model-based reinforcement learning, probabilistic modeling, and uncertainty-aware trajectory optimization to enable proactive risk mitigation and long-term decision stability. Experimental evaluation in simulated robotic control and cybersecurity environments demonstrates improved performance, reduced safety violations, and enhanced robustness compared to conventional reinforcement learning approaches. The results establish future-centric optimization as an effective paradigm for autonomous decision-making in dynamic systems. Keywords: Future trajectory optimization, autonomous systems, reinforcement learning, future trajectory optimization, multi-objective optimization, decision-making, world models