发现大模型推理中语义漂移会破坏人机决策系统稳定性，提出新指标和应对方案。
Semantic Drift and the Stability of Operator Control in Reasoning-Class Decision Support Systems
- 构建人机交互数学模型，引入控制稳定性系数衡量隐式推理链压力。
- 实验证实推理型大模型存在语义上下文漂移，导致控制权反转临界点出现。
- 适合研究人机协同决策、大模型可解释性及系统安全性的研究人员。
本文研究新一代人机混合决策支持系统（DSS）中保持操作员控制稳定性和目标导向性的根本问题。基于为期两个月的连续纵向实验，对以专著格式呈现的文本阵列进行联合设计，验证并描述了深度逻辑推理类大语言模型（Reasoning LLMs）中存在的潜在语义上下文漂移现象。提出一种人机界面交互的数学模型，并引入原创度量——操作员控制稳定性系数，该系数考虑了隐藏推理链带来的非线性上下文压力。在认知组理论范式下，捕捉到控制功能反转的关键点。据此提出工程建议，基于改进的分层相似性模型实现动态关系仲裁环的构建。
原文摘要 · Abstract (English)
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.
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