What question did this study set out to answer?

The research aims to examine how structural conductance affects hallucination rates in long-context applications of large language models.

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February 14, 2026Open Access

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

Key Points

The research aims to examine how structural conductance affects hallucination rates in long-context applications of large language models.
Analyzed the structural conductance of 13 frontier large language models from 2026.
Utilized statistical correlation methods to assess the relationship between conductance and hallucination rates.
Applied the Universal Framework of Adaptive Laws to interpret findings.
Found a strong negative correlation between structural conductance and hallucination rates (r = -0.727).
Correlation strengthened to r = -0.833 after removing outliers.
Results support the Universal Descent Law and the Law of Predictive Coherence.

Abstract

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; r² = 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).

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

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Abstract

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