A Mathematical Conflict Framework for Contextual Data Modulation
📰 ArXiv cs.AI
arXiv:2606.02381v1 Announce Type: new 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 optimizati
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Title: A Mathematical Conflict Framework for Contextual Data Modulation
Abstract:
arXiv:2606.02381v1 Announce Type: new 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 optimizati
Abstract:
arXiv:2606.02381v1 Announce Type: new 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 optimizati
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