Semantic Drift and the Stability of Operator Control in Reasoning-Class Decision Support Systems

Jul 14, 2026

A new arXiv paper investigates semantic context drift in reasoning large language models (LLMs) during human-machine decision-making. Through a two-month longitudinal experiment, the study verifies latent semantic drift, introduces a stability coefficient metric, and proposes engineering recommendations for dynamic arbitration loops.

Why it matters: This research highlights a stability issue in reasoning LLMs that could affect operator control in decision support systems.

Full story at: arXiv AI/ML ↗