Reflective AI: A Conceptual Framework for Second-Order Reflection and Temporal Self-Understanding

This paper proposes Reflective AI: an architecture in which an intelligent agent can examine not only its recorded prior reasoning contexts, but also the history of its subsequent interpretations of those contexts. The conceptual starting point is the difficulty of establishing subjective self-awareness in another system. The continuing sense of human identity across interruptions in conscious experience suggests a narrower, functionally investigable question: how can a system connect what it once knew, believed and anticipated with what later occurred? The architecture retains selective, immutable records of the information, assumptions, goals, forecasts and documented reasoning links relevant to decisions; it does not attempt to capture every machine computation. Multiple later interpretations are stored separately, and second-order reflection evaluates whether current interpretations and the criteria for selecting evidence should change. Reflection is event-triggered and directed towards identifying missing knowledge and improving future decisions, not endless introspection. This paper situates the proposal within existing work on autobiographical memory, prospective simulation, metacognition and reflective agents, and outlines a feasible comparative evaluation without claiming that the system would possess subjective consciousness.