The RECSIP framework addresses a critical challenge in Large Language Models: inconsistent reliability in high-stakes environments.

• Uses repeated clustering of scores to improve precision in LLM responses
• Reduces uncertainty through statistical validation of multiple model responses
• Provides quantifiable reliability metrics for deployment in security-sensitive contexts
• Particularly valuable for applications where incorrect AI responses could cause harm

For security professionals, this research offers a systematic method to validate LLM outputs before deployment in critical systems, reducing the risk of harmful or expensive failures in sensitive environments.

RECSIP: REpeated Clustering of Scores Improving the Precision