Deterministic Detection of Large Language Model Behavioral Drift Using Information Theoretic Text Signatures

We present a deterministic approach to detecting behavioral drift in large language models (LLMs) that requires no training data, no baseline distributions, and no machine learning based monitoring. Our system, ABIS (Adaptive Behavioral Intelligence System), applies a 14 module mathematical pipeline to convert raw LLM outputs into 1,544-dimensional behavioral vectors. We validate this approach in three stages: independent replication of GPT-4 behavioral drift findings (L2 drift magnitude 3.56, Cohen's d 33.00); an XGBoost classifier trained on 112,360 LLM conversation windows from 29 models achieving per-model AUCs of 0.66-1.00 (GPT-4: 0.958); and zero-shot cross domain transfer at 0.81 AUC. Feature ablation identifies Energy Distance (f1536) as the dominant cross domain signal. These findings suggest that LLM behavioral drift possesses a deterministic mathematical signature measurable without recursive reliance on machine learning.