提出数学冲突框架，显式建模数据与上下文的结构差异。
A Mathematical Conflict Framework for Contextual Data Modulation
- 将冲突定义为局部、方向性、上下文敏感的数学量
- 统一整合权重、尺度行为与输出映射的抽象算子结构
- 适用于多种问题，适合研究数据偏差与上下文建模者
本研究提出一种基于算子的广义数学冲突框架，显式表征原始数据与上下文数据之间的结构差异。该结构将冲突视为局部、方向性且上下文敏感的量，通过统一的抽象算子整合权重、尺度行为和输出映射等组件。框架不依赖于特定学习算法或优化方法，可适应不同类别的问题。与现有方法通常将冲突视为优化过程中的隐含副作用不同，该框架将其作为独立的、算子化的、组件级的数学对象进行建模。
原文摘要 · Abstract (English)
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.
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