Narrative Framing Drives LLM Agent Behavior More Than Persona Prompts, Study Finds
A new preprint demonstrates that the narrative context of a task has a much greater influence on large language model (LLM) agent behavior than the assigned persona. Analyzing 1,890 sessions across three models and ten personas, the authors find that narrative priors account for 5-31 times more behavioral variance than persona, with this effect consistent across models and often linked to lower task success. The study also shows that removing anchor words from persona descriptions reduces cross-narrative behavioral consistency by 95%.
Why it matters: Understanding that narrative framing, not just persona, shapes LLM behavior is crucial for designing more robust and predictable AI agents.
Full story at: arXiv Computation and Language ↗