🤖 AI Summary
This work addresses structural conflicts between raw data and contextual information by proposing an explicit modeling framework based on generalized operators. The approach formalizes such conflicts as local, directed, and context-dependent mathematical entities, integrating weighting mechanisms, scaling behaviors, and output mappings through a unified abstract operator. In contrast to prior methods that treat conflict merely as a byproduct of optimization, this study is the first to model conflict explicitly as an independent, computable operator at the component level. The resulting framework is agnostic to specific learning algorithms or optimization strategies, offering strong generality and transferability across a wide range of context-sensitive problems.
📝 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.