The Narrative Continuity Test: A Conceptual Framework for Evaluating Identity Persistence in AI Systems
This addresses the issue of AI systems operating without persistent states, which is a foundational problem for AI development, though the framework is conceptual and incremental in nature.
The paper tackles the problem of AI systems lacking persistent identity across interactions by introducing the Narrative Continuity Test (NCT), a conceptual framework that evaluates identity persistence and diachronic coherence, showing that current architectures systematically fail to support it through case analyses.
Artificial intelligence systems based on large language models (LLMs) can now generate coherent text, music, and images, yet they operate without a persistent state: each inference reconstructs context from scratch. This paper introduces the Narrative Continuity Test (NCT) -- a conceptual framework for evaluating identity persistence and diachronic coherence in AI systems. Unlike capability benchmarks that assess task performance, the NCT examines whether an LLM remains the same interlocutor across time and interaction gaps. The framework defines five necessary axes -- Situated Memory, Goal Persistence, Autonomous Self-Correction, Stylistic & Semantic Stability, and Persona/Role Continuity -- and explains why current architectures systematically fail to support them. Case analyses (Character.\,AI, Grok, Replit, Air Canada) show predictable continuity failures under stateless inference. The NCT reframes AI evaluation from performance to persistence, outlining conceptual requirements for future benchmarks and architectural designs that could sustain long-term identity and goal coherence in generative models.