Recursive learning is closest to what machine learning literature calls continual or online learning. The distinction is that AI systems in dynamic environments should treat their reference model as something that evolves with the environment rather than something set once and periodically refreshed, with each calibration cycle informing the next. Yet most enterprise deployments still rely on periodic baseline updates. A recursive system instead treats its current understanding of “normal” as provisional, evaluating changes against performance, experience, and risk. Healthy outcomes adjust exp
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