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

arXiv: https://arxiv.org/abs/2607.09790 · HTML: https://mlsanity.com/paper/2607.09790/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning.
- Builds on 9 of its 23 references that are in the map.
- First posted on arXiv 8 Jul 2026.

## Placed in

- LLM reasoning / [Reasoning benchmarks and evaluation](https://mlsanity.com/lr/reasoning-benchmarks-and-evaluation/)

## Builds on (most cited first, 9 of 9)

- [Challenges and Applications of Large Language Models](https://mlsanity.com/paper/2307.10169/) (2023)
- [A Survey of Process Reward Models: From Outcome Signals to Process Supervisions for Large Language Models](https://mlsanity.com/paper/2510.08049/) (2025)
- [Drift No More? Context Equilibria in Multi-Turn LLM Interactions](https://mlsanity.com/paper/2510.07777/) (2025)
- [FocusLLM: Precise Understanding of Long Context by Dynamic Condensing](https://mlsanity.com/paper/2408.11745/) (2024)
- [The Hidden Signal of Verifier Strictness: Controlling and Improving Step-Wise Verification via Selective Latent Steering](https://mlsanity.com/paper/2605.20745/) (2026)
- [Reasoning Shift: How Context Silently Shortens LLM Reasoning](https://mlsanity.com/paper/2604.01161/) (2026)
- [Process Reward Models Meet Planning: Generating Precise and Scalable Datasets for Step-Level Rewards](https://mlsanity.com/paper/2604.17957/) (2026)
- [Process Supervision via Verbal Critique Improves Reasoning in Large Language Models](https://mlsanity.com/paper/2604.21611/) (2026)
- [Verifiable Counterfactual Supervision for Process Reward Models](https://mlsanity.com/paper/2605.02395/) (2026)

## Abstract (arXiv)

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.