Semantic Drift Threatens AI Decision Support Control

The stability of human operator control and the preservation of goal-targeting in next-generation hybrid human-machine decision support systems (DSS) are fundamentally challenged by a phenomenon known as semantic context drift in Reasoning Large Language Models (LLMs). A two-month continuous experiment involving the joint design of a monograph-format textual array confirmed and described this latent drift. To quantify and address this issue, the researchers developed a mathematical model of human-machine interface interaction. They also introduced an original metric, the "operator control stability coefficient," which accounts for the non-linear contextual pressure exerted by the LLM's hidden reasoning chains. This coefficient helps to identify a critical point where control functions can invert, meaning the human operator loses effective control over the system's direction. Based on these findings, engineering recommendations have been formulated. These suggest implementing dynamic relational arbitration loops, which are based on a modified hierarchical similarity model, to counteract semantic drift and maintain stable operator control within these advanced decision support systems.