Deterministic Structural Layers for Stabilizing Probabilistic AI System – A Hybrid Semantic-Operational Control Architecture for Drift‑Resistant, Auditable and Reproducible AI Workflows

Abstract Large Language Models (LLMs) operate as probabilistic sequence predictors and therefore exhibit structural instability in long form interactions. Typical failure modes include semantic drift, context fragmentation, inconsistent reasoning, and missing auditability. These limitations restrict the applicability of LLMs in domains requiring reproducibility, transparency, and operational reliability. This paper introduces a deterministic–structural hybrid layer that wraps probabilistic AI systems with lightweight semantic topology and deterministic operational control. The architecture stabilizes context, enforces reproducibility, prevents drift, maintains workflow continuity, and generates audit ready traces. The proposed approach enables predictable and transparent AI assisted processes without modifying the underlying model.