Structural Conductance as a Predictor of Long-Context Hallucination Rates in Frontier Large Language Models

This study investigates the relationship between structural conductance (C) and hallucination rates in frontier Large Language Models (LLMs) within long-context scenarios (>100k tokens). Drawing from the Universal Framework of Adaptive Laws (UFAL), we hypothesize that C limits coherence under high informational drive. Statistical analysis of 13 frontier models (2026) reveals a strong negative correlation (r = -0.727; r^2 = 0.528), strengthening to r = -0.833 upon outlier removal. These findings support the Universal Descent Law (LUDC) and the Law of Predictive Coherence (LPC), providing an information-theoretic bridge between synthetic intelligence stability and cosmological evolution (Coherent Freeze).…