What question did this study set out to answer?

This study aims to develop intelligent monitoring models for information systems altered by large language models (LLMs) using genetic optimisation techniques.

June 17, 2026Open Access

Adaptive Synthesis of Intelligent Monitoring Models for LLM-modified Information Systems: A Genetic Optimisation Approach

VLVasyl LyashkevychLviv University

Key Points

This study aims to develop intelligent monitoring models for information systems altered by large language models (LLMs) using genetic optimisation techniques.
Proposed a multi-objective, property-driven genetic approach with tensor chromosome representation.
Utilized migration from Oracle to PostgreSQL as a controlled case study to examine monitoring configurations.
Analyzed the impacts of architectural changes on maintaining functional equivalence during monitoring property drift.
Demonstrated that functional equivalence is maintained while monitoring-related properties shift.
Supported adaptive architecture-aware monitoring over traditional static settings, enhancing system efficiency.

Abstract

Abstract This study investigates adaptive synthesis of intelligent monitoring (IM) for applications modified by large language models (LLMs). A multi-objective, property-driven genetic approach with a tensor of chromosome representation is proposed to generate monitoring configurations under architectural changes. Using migration from Oracle to PostgreSQL as a controlled case, the study shows that functional equivalence can be maintained while monitoring-related properties drift, supporting adaptive archi tecture-aware monitoring over static settings.

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