Metric Rational:

Narrative cohesion refers to how well the elements of a story—its plot points, characters, themes, and transitions—stick together in a unified, coherent flow. In human storytelling, cohesive narratives avoid disjointed jumps in time or logic, ensure consistent character motivations, and maintain a clear thematic throughline. Even when a story contains flashbacks or multiple subplots, effective cohesion means the audience can still follow how each thread relates to the central storyline. Subtle cues—like repeated symbols or references—reinforce unity and help the audience make sense of evolving events.

For an AI or humanoid robot that processes or generates narratives, narrative cohesion focuses on detecting, preserving, or creating such structural and thematic unity. If the AI is generating a short story, it must ensure characters introduced at the beginning remain recognizable and relevant, avoid inconsistencies in the setting or timeline, and bring key plot lines to a satisfying resolution. For an agent analyzing a user’s account of an incident, it should interpret the user’s speech to confirm chronological order, continuity, and consistency of details. Without cohesion, the narrative feels fragmented—characters drop out without explanation, events seem to have no cause, or the central conflict remains unresolved.

Narrative cohesion relies on several combined aspects. Reference Consistency ensures characters and places remain distinguishable and referenced appropriately according to their original context, avoiding conflicting names or descriptions. Temporal Continuity maintains a clear timeline, even with jumps, using signposts (e.g., “Earlier that morning…”) to preserve cause-and-effect logic. Motivational Threads dictate that character actions stem logically from established goals, traits, or obstacles, with any sudden reversals credibly explained for logical character arc evolution. Thematic or Symbolic Throughlines use recurring imagery or issues (motifs) to unify the narrative across different parts, linking them to key messages or character growth. Finally, Structured Transitions provide clear bridging sentences or indicators between shifts in location, topic, or perspective, ensuring readers understand the connections.

Evaluating narrative cohesion for an AI includes observing how it handles the introduction and resolution of plot points. Does it resolve a conflict or question that was central? Does each paragraph or section clearly connect to the next, without leaving the audience puzzled about missing links? On an analysis side, if the AI ingests a user’s narrative, can it detect unaddressed details or contradictory plot lines that weaken cohesion?

Effective narrative cohesion lays a foundation for engaging storytelling, clarity in reporting events, or even structured organizational communication. An AI skilled in this domain can, for example, compose stories that captivate, generate coherent summary narratives from scattered logs, or flag where a user’s account might have a significant gap. By weaving plot elements together in a purposeful sequence, it matches the human penchant for stories that feel whole and logically structured.