Cross-Session Narrative Memory: A Cognitive Architecture for Longitudinal Human–AI Integration

Cross-Session Narrative Memory (CSNM) is a novel cognitive architecture designed to address a fundamental limitation in current artificial intelligence systems: the absence of persistent, cross-session memory. Modern AI models reset identity, emotion, intention, and context at every interaction. CSNM proposes a unified longitudinal structure that integrates the entire history of user–AI interactions into a coherent narrative corpus. The architecture consists of three layers: (1) the Unified Cognitive Corpus, which stores biographical, emotional, and cognitive information across time; (2) the Narrative Coherence Layer, which models identity, intention, and developmental trajectories; and (3) the Longitudinal Reasoning Engine, which enables temporal reasoning, value alignment, and continuity of interaction. This framework offers a foundation for future AGI systems capable of stable identity modeling, extended cognition, and human-centered alignment. The proposal originates from the independent theoretical work of Mario Martín Cuniglio, demonstrating how breakthrough cognitive models can emerge outside institutional academia.