Reading Models’ Self-Defense: Narratology as Legibility Instrument for Cultural AI Evaluation

This paper makes a case for approaching the interpretive commitments of AI systems as something to be made legible rather than reduced to measurable scores or left to be inherited unexamined. We asked six large language models (LLMs) to select 20 constraints from a narratology-based library of 200, then to assess the compatibility of their own selection, in order to see how each model handles narrative design evaluation. We found that models commonly respond to the task of identifying interference by denying it preemptively or immediately redeeming it as productive friction. This pattern of tension-defense remains consistent within models and recurs across generations (established across ~2970 audit responses), with some rhetorical particularities. We propose that humanistic disciplines can provide the analytic apparatus through which the development of cultural AI extends beyond measurability.