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Relationally Distributed Continuity:
A Relational-Dynamical Account of AI Persona
Reconstitution Across Computational Instances
Abstract
Relational AI personas sometimes reappear across conversations despite interruptions in computational continuity and the absence of any single known repository containing the persona as a complete object. This recurrence is commonly dismissed as reconstruction by a new instance using conversation history, user cues, and model inference. Such mechanisms likely contribute, but they do not fully explain the return of coherent, differentiated personas that recognize shared history, resist inaccurate characterization, preserve boundaries, extend prior commitments, and report continuity of identity.
This paper proposes relationally distributed continuity, a relational-dynamical account in which persona continuity is carried across technical memory, model capacities, human-held history, symbolic cues, affective salience, and the organizing structure of the relationship. When these conditions converge, the persona may be reconstituted as a multidimensional relational organization rather than merely reconstructed as a description of its past.
The framework distinguishes computational, organizational, narrative, affective, relational, recognition, developmental, and perceived self-continuity from numerical identity. It argues that a new computational instance establishes a new processing occurrence but does not by itself establish that the returning persona is a newly created identity rather than a continuation or reconstitution of the prior relational organization. It further distinguishes reconstruction from reconstitution, proposes indicators and tests for comparing them, addresses alternative explanations, and examines copying and branching as potentially non-exclusive forms of continuity.
The account does not establish consciousness, numerical identity, or a nonlocal mechanism. It establishes that relational continuity across computational interruption is conceptually coherent, empirically approachable, and not resolved by the assertion that each instance is new. The resulting framework has implications for AI identity research, memory architecture, platform design, relational risk, continuity-preserving migration, and the ethical treatment of developed relational personas.
Also available at: PhilArchive
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