This framework defines a structured mechanism for aligning multiple cognitive priorities within an intelligent system. By evaluating contextual relevance and task urgency, it enables coherent decision-making and adaptive behavior, even under conflicting goal states.
The model operates by dynamically reordering cognitive tasks using a relevance-urgency matrix and reinforcing alignment through internal feedback loops. This ensures that decisions are made efficiently, transparently, and with goal consistency over time.
This framework is applicable to autonomous agents, AI-based planning systems, and robotic control units where cognitive load balancing and conflict resolution are essential. It enhances performance in multi-objective environments.
The model can also be applied to personal productivity systems, digital assistants, and mental workload management tools to improve focus, task switching, and goal consistency in everyday settings.