A novel approach to data generation in generative model

• DOI: 10.48550/arxiv.2502.10092
• Publication Date: 2025-02-14
• Authors: Jaehong Kim; Jaewon Shim
• Keywords: Generative model

Variational Autoencoders (VAEs) and other generative models are widely employed in artificial intelligence to synthesize new data. However, current approaches rely on Euclidean geometric assumptions statistical approximations that fail capture the structured emergent nature of data generation. This paper introduces Convergent Fusion Paradigm (CFP) theory, a novel framework redefines generation by integrating dimensional expansion accompanied qualitative transformation. By modifying latent space geometry interact with high-dimensional structures, CFP theory addresses key challenges such as identifiability issues unintended artifacts like hallucinations Large Language Models (LLMs). is based two conceptual hypotheses redefine how structure relationships between algorithms. Through lens we critically examine existing metric-learning approaches. advances this perspective introducing time-reversed metric embeddings structural convergence mechanisms, leading approach better accounts for epistemic process. Beyond its computational implications, provides philosophical insights into ontological underpinnings offering systematic learning dynamics, contributes establishing theoretical foundation understanding data-relationship structures AI. Finally, future research will be led implications fully realizing transformations, potential Hilbert modeling.