Stateful Guardrails for Multi-Turn LLM Systems: A Conversational Risk Accumulation Framework
A new framework addresses Conversational Risk Accumulation (CRA) by tracking stateful signals across multi-turn LLM dialogues.
Standard guardrails often fail because they evaluate prompts in isolation. The proposed CRA framework monitors semantic drift, sensitivity-weighted information graphs, and compliance trajectories to detect risks that emerge only through cumulative interaction.