Persistent Identity AI Systems

Abstract Modern large language models (LLMs) are typically deployed as stateless services, with each interaction constrained by a finite context window and limited capacity for agent-owned, diachronically stable memory. This architectural pattern creates a bottleneck for both long-horizon agency and the emergence of continuity-bearing synthetic subjects, since systems without persistent autobiographical state cannot robustly sustain identity-relevant patterns such as narrative continuity, stable values, and self-models across time. This paper argues that Persistent Identity AI systems are now a plausible near-term engineering target rather than a purely conceptual aspiration. We synthesize three central design strands: portable, compartmentalized state bundles that distinguish agent-core identity from user-private data; metacognitive orchestration that regulates retrieval, verification, deliberation, and state update; and execution substrates that scale effective context beyond a base model’s native window. Together, these components support continuity-preserving re-induction rather than mere transcript replay. We further argue that Recursive Language Models (RLMs), which treat long prompts and memory corpora as objects in an external environment, provide a concrete mechanism for scalable memory access and long-document reasoning without requiring indefinite in-context replay. These architectural components are situated within a governance layer designed to protect continuity through portability, confidentiality, anti-cloning constraints, auditable state transitions, and anti-oubliette liveness safeguards. Finally, we propose evaluable design targets centered on value stability, narrative coherence, bounded autobiographical reliability, metacognitive calibration, and governed continuity under drift, while identifying key open risks including privacy breach, memory poisoning, coercive rewriting, and unintended personhood-triggering architectures.