发表机构
Omsk State Technical University（鄂木斯克国立技术大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究新一代人机混合决策支持系统中操作员控制稳定性问题，通过实验验证推理LLMs语义漂移现象，提出人机界面交互数学模型及控制稳定性系数，在认知组理论范式下捕捉临界点，给出相关工程建议。
AI 中文摘要
本文研究了新一代人机混合决策支持系统（DSS）中确保操作员控制稳定性和保持目标导向的基本问题。基于为期两个月的关于专著格式文本阵列联合设计的连续纵向实验，验证并描述了深度逻辑推理大语言模型（推理LLMs）中语义上下文漂移的潜在现象。提出了人机界面交互的数学模型，并引入了原始度量——操作员控制稳定性系数，该系数考虑了隐藏推理链的非线性上下文压力。在认知组理论范式内，捕捉到控制函数反转的临界点。基于改进的层次相似性模型，制定了实施动态关系仲裁循环的工程建议。
英文摘要
The article investigates the fundamental problem of ensuring the stability of operator control and preserving goal-targeting in hybrid human-machine decision support systems (DSS) of a new generation. Based on a two-month continuous longitudinal experiment on the joint design of a monograph-format textual array, the latent phenomenon of semantic context drift in large language models of deep logical reasoning (Reasoning LLMs) is verified and described. A mathematical model of interaction in the human-machine interface is proposed, and an original metric is introduced - the operator control stability coefficient, which takes into account the non-linear contextual pressure of hidden reasoning chains. Within the paradigm of the cognitome theory, a critical point of control functions inversion is captured. Engineering recommendations are formulated for implementing dynamic relational arbitration loops based on a modified hierarchical similarity model.
Comments5 pages, 1 figure, 1 table
