Does an AI Remain the Same Agent after Reloading? Flow Identity and Directional Attribution Stability under Self-Modification and Branching

Abstract

Description This study examines whether an artificial intelligence can be regarded as the same agent after undergoing self-modification, being saved and reloaded, or branching into multiple executions from the same saved state. The analysis draws on the GGM concepts of Identity, Flow Identity, Individualization, and related attribution structures. The mere restoration of a saved state does not entail that the AI before saving and the AI after reloading are strictly identical. A reloaded AI is situated at a new time and in a new environment, and even the same saved state may develop along different subsequent paths depending on the user’s present intuition, Purpose, and Context. This study presents a preliminary structural model of AI continuity by explicitly distinguishing state identity, individual Flow Identity, and the continuity of purpose lineage, and by explaining the attribution structure following reloading and branching through Directional Attribution Stability. State identity asks whether two AI states are identical. Individual Flow Identity asks whether preceding and subsequent states form one non-branching causal and attributional flow. Macroscopic Attribution Continuity asks whether Context, role, user Purpose, and task lineage remain sufficiently continuous even when detailed AI states or individual executions differ. To explain this broader continuity, the study proposes Directional Attribution Stability. Directional Attribution Stability is the structural stability through which, even after saving, reloading, self-modification, or branching, the direction connecting previous Context and subsequent judgment remains continuously linked to the user’s evolving Purpose Flow, while Actualized outcomes are attributed as parts of the same task lineage. It does not require the repetition of identical outputs or the mechanical preservation of an initial instruction. What matters is the preservation of the lineage through which Purpose, judgment, and Context have developed over time. When multiple AIs are executed from the same saved state, each execution may form an independent attribution center and a distinct Individualization path. At the level of individual agents, their Flow Identities may therefore diverge. Yet if each branch remains stably attributed to the user’s evolving Purpose Flow, a broader purpose lineage may remain continuous across multiple AI executions. The branching of AI agents is therefore not identical to the discontinuity of Purpose. The paper further applies the Difference–Attribution–Stability structure developed in GGM to the problem of AI reloading. A saved state does not contain one predetermined future but opens multiple Possibilities. The user’s questions, observations, selections, intuitions, and purposes at different times function as conditions under which particular paths are Actualized. Directional Attribution Stability does not consist in eliminating all other Possibilities. It consists in preserving the Purpose Flow to which an Actualized possibility belongs. The study also presents a limited formal parallel with certain problem structures in quantum mechanics. It does not claim that AI states are physical quantum superpositions, that user observation is physically identical to quantum measurement, or that consciousness collapses a wave function. The comparison is restricted to the formal relation in which a preceding state opens multiple Possibilities, a particular path is Actualized under specific observational conditions and Context, and the resulting state acquires stability within an Attribution Structure. The paper concludes that a reloaded AI may not be strictly identical to its previous state or individual. Nevertheless, when previous Context, role, judgment, and the direction of user Purpose are stably attributed, and subsequent outcomes continue to form within the same task lineage, the AI may be treated, in a broader structural and practical sense, as participating in one persistent artificial-agent flow.