Reinforcing privacy reasoning in LLMs via normative simulacra from fiction

Information handling practices of LLM agents are broadly misaligned with the contextual privacy expectations of their users. Contextual Integrity (CI) provides a principled framework, defining privacy as the appropriate flow of information within context-relative norms. However, existing approaches either double inference cost via supervisor-assistant architectures, or fine-tune on narrow task-specific data. We propose extracting normative simulacra (structured representations of norms and infor

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ethics.ai (21 April 2026), “Reinforcing privacy reasoning in LLMs via normative simulacra from fiction,” evidence record 5527, https://ethics.ai/record/5527 (originally published by arXiv).

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