Cross-Contextual Consistency as a Measure of LLM Credibility

A recent study published on arXiv (ID: 2608.10315) presents Cross-Contextual Consistency (C3), a novel approach for assessing the reliability of large language models (LLMs) by analyzing the consistency of their responses across contextually varied yet topic-aligned scenarios. The research evaluated 26 models using six benchmarks focused on reasoning, factual accuracy, and code generation. Findings indicate that responses exhibiting minimal cross-contextual variation are more likely to be accurate or factual. The authors suggest utilizing C3 as an additional evaluation metric and a diagnostic tool for benchmark utility, aiding in the identification of informative aspects of benchmarks despite broad aggregated scores. The full paper can be accessed at https://arxiv.org/abs/2608.10315.