Advances in Machine Learning & Artificial Intelligence(AMLAI)

Department of Family Medicine, Dong-eui Medical Center, Yangjeong-ro, Busanjin-gu, Busan, Republic of Korea

Publications

• Review Article   
  Retrocausal Temporal Feedback and Gauge Symmetry Breaking at the Black Hole Event Horizon: A Theoretical Framework and Machine Learning Architecture
  Author(s): Chur Chin*
  
  The black hole information paradox remains one of the most profound open problems at the intersection of quantum mechanics and general relativity. This paper presents a novel theoretical framework positing that information crossing a black hole event horizon (EH) undergoes a gauge symmetry reduction-transitioning from a non-abelian SU(3) structure outside the horizon to an abelian U(1) structure within, in analogy with quantum chromodynamic (QCD) confinement and electroweak symmetry breaking [1-5]. Building on the Page curve, Hawking radiation theory, and retrocausal quantum mechanics, we propose a Spacetime Information Feedback Loop (SIFL) model in which a "Linker" mechanism-driven by quantum interference at the horizon-enables retrocausal correction of past states without causal paradox [6-10]. We further introduce the Retrocausal Transformer, a machine learning architectu.. Read More»
  

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