Narrative Frequency Coding™ (NFC): A Structural–Theoretical Description of Narrative Coherence in Artificial Language Systems

Despite substantial advances in semantic modeling, probabilistic optimization, and context handling, contemporary AI-based language systems continue to exhibit recurring phenomena such as narrative drift, transitional instability, coherence breakdowns, and inconsistent long-range trajectories. In the existing literature, these phenomena are predominantly addressed through data-centric, probabilistic, or control-oriented approaches, while narrative coherence is often implicitly treated as an emergent byproduct of semantic or statistical optimization.

This paper proposes an alternative theoretical perspective. It argues that a subset of the observed instabilities can be traced to a description level that has not yet been explicitly formalized: narrative form as an autonomous structural order beyond meaning, truth, or function. To this end, the paper introduces Narrative Frequency Coding™ (NFC) as a purely structural-theoretical ordering model.

NFC does not describe narrative coherence in terms of semantic content or technical mechanisms, but as a qualitative organization of frequency within formal sequence movement. Narrative processes are conceptualized as structured movement trajectories within a sequence field, manifesting as stable, unstable, or fragmented frequency profiles. These profiles are explicitly non-operative, non-neurobiological, and non-functional; they serve exclusively the formal description of narrative structural trajectories.

The paper positions NFC neither as a model of artificial intelligence nor as an algorithm, optimization approach, or control instrument. Instead, it introduces a model-agnostic ordering level on which narrative stability, transitions, and drift can be described comparatively, independent of specific architectures. NFC thus functions as a theoretical reference framework that renders explicit a previously unconsolidated description level within AI language research, without anticipating technical implementations.