A Neutrosophic Topological Framework for AIGC-Driven Digital Media Content Automated Generation Technology
Keywords:
Neutrosophic Logic, AI Content Generation, NonStandard Topology, Multiset Topology, Truth Modeling, Indeterminacy, Neutrosophic Content Units, NeutroGen Engine, Contextual Content Generation, Automated Digital Media.Abstract
Current artificial intelligence systems for content generation are limited by their
reliance on deterministic or probabilistic logic, often failing to represent uncertainty,
contradictions, or context-dependent interpretations. This paper introduces a novel
framework that combines Neutrosophic Logic, Multiset Topology, and NonStandard
Analysis to model and generate digital media content that reflects varying degrees of
truth, indeterminacy, and falsehood. Each content unit is treated as a contextual triplet (T,
I, F) and embedded in a neutrosophic topological space, allowing the AI to select,
generate, and sequence text based on contextual proximity, semantic coherence, and
viewpoint diversity. A new content generation engine, NeutroGen, is proposed to
operationalize this structure, enabling the automated production of rich, multi
perspective media that adapts to user-defined contextual profiles. This architecture offers
a pioneering solution to media realism, truth modeling, and information ambiguity in the
age of artificial intelligence generated content(AIGC).
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Copyright (c) 2025 Neutrosophic Sets and Systems
This work is licensed under a Creative Commons Attribution 4.0 International License.
