A Mathematical Conflict Framework for Contextual Data Modulation
2026-06-01 • Artificial Intelligence
Artificial IntelligenceMachine Learning
AI summaryⓘ
The authors introduce a new math framework to better understand and describe differences between raw data and related background information. Their approach treats these differences, or conflicts, as specific, local, and context-aware quantities instead of hidden side effects. This framework uses abstract operators to handle factors like weighting and scaling in a flexible way. It is designed to fit various problems without depending on one fixed method or algorithm.
operator theorydata conflictcontext sensitivitymathematical frameworklocalityweightingscale behavioroptimizationdata representation
Authors
Hakan Emre Kartal
Abstract
In this study, a generalized operator-based mathematical conflict framework is presented to explicitly represent structural discrepancies between raw data and contextual data. The proposed structure treats conflict as a local, directional, and context-sensitive quantity, integrating components such as weighting, scale behavior, and output mapping under a unified abstract operator. Without being reduced to a specific learning algorithm or optimization method, the framework is defined as a general structure adaptable to different classes of problems. While existing approaches typically treat conflict merely as an implicit side effect embedded within the optimization process, the proposed framework considers conflict as an independent, operator-based, and component-level mathematical object.