What question did this study set out to answer?

The research aims to enhance the inference architecture of large language models by applying thermodynamic principles to address common issues like hallucination and context collapse.

April 7, 2026Open Access

ENTRO-AI: Entropy-Resistant Inference Architecture for Large Language Models & Neural Computing Systems

Key Points

The research aims to enhance the inference architecture of large language models by applying thermodynamic principles to address common issues like hallucination and context collapse.
Applied a thermodynamic framework to inference architecture of LLMs and neural networks.
Derived entropy scaling exponents for transformer-based LLMs, convolutional networks, and neuromorphic systems.
Introduced the Entropy-Driven Throttling (EDT) controller for real-time entropic monitoring.
Conducted 1,247 controlled inference stress tests to validate findings.
Achieved 91.4% prediction accuracy for collapse events with a mean lead time of 34.7 seconds.
Reduced hallucination occurrences by 67.3% under supercritical load conditions.

Abstract

ENTRO-AI (E-LAB-02) is the second project of the EntropyLab research program. It applies the thermodynamic framework established in ENTROPIA (E-LAB-01) to the inference architecture of large language models and deep neural networks, modeling hallucination, context collapse, and inference degradation as thermodynamic phase transitions governed by the Dissipation Coefficient Ψ. The work derives architecture-specific entropy scaling exponents for transformer-based LLMs (n ≈ 1.63), convolutional neural networks (n ≈ 1.74), and neuromorphic substrates (n ≈ 1.42), and introduces the Entropy-Driven Throttling (EDT) controller integrated into the Ψ-Dashboard microservice for real-time entropic monitoring at 10-millisecond resolution. Validated across 1,247 controlled inference stress tests with 91.4% collapse prediction accuracy (mean lead time 34.7 ± 9.3 s) and 67.3% hallucination reduction under supercritical load conditions.

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