AI RESEARCH

Narrative Landscape: Mapping Narrative Dispositions Across LLMs

arXiv CS.AI

ArXi:2605.08742v1 Announce Type: cross This study proposes a quantitative framework for profiling LLM dispositions as stable, model-specific regularities in output under repeated, controlled elicitation. Using a structured narrative constraint-selection task administered across six frontier models and three instruction types, we operationalize disposition through two dimensions: "consistency", measured as cross-replication selection overlap via Jaccard similarity, and "diversity", measured as dispersion across options via the inverse Simpson index. We further.