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

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.