Behavioral Drift Detection in Large Language Models via Causal Entropy Divergence: The KA-LLM Framework

Large language models (LLMs) deployed in production undergo continuous behavioral drift: fine-tuning, RLHF updates, jailbreak degradation, and distributional shift alter output token distributions in ways that are not captured by periodic benchmark evaluation. We present KA-LLM, the first streaming behavioral drift detection framework for LLMs grounded in the Karimov–Alekberli (KA) thermodynamic framework. KA-LLM monitors four channels computed over the entropy of per-domain output distributions: C1 (domain-level causal entropy deviation), C2 (cross-domain coupling covariance), C3 (calibration residual z-score), and CB (response-pattern correlation break). Each alert carries attribution identifying which domain channel and which drift mechanism triggered detection—a capability absent from all baseline methods. Validation on a 14-domain, 300-day simulation (30 drift events: capability, alignment, calibration, jailbreak, and distributional shift; published MMLU/TruthfulQA statistics as proxy baselines) demonstrates DR=57% with FPR=0.00/month at θ=2.5, matching CUSUM and Isolation Forest on detection rate while uniquely providing per-domain attribution. The C2 coupling channel detects 5 silent multi-domain drift events (alignment tax, coordinated capability shift) where no single domain exceeds the individual threshold—invisibleto all per-domain baselines. This paper is the fifth in the KA Framework series.