THE ARCHITECTURE OF ARTIFICIAL INTELLIGENCE AS A REFLECTION OF INTERHEMISPHERIC BRAIN INTERACTIONS AND GENOMIC TADs

This paper establishes a novel interdisciplinary convergence framework linking regulatory genomics, computational neuroscience, and quantum neural engineering. Drawing upon the foundational "modularity vs. pleiotropy" paradox outlined by Wagner & Altenberg (1996), we demonstrate that advanced information processing systems inherently reject static modular partitions and monolithic configurations alike. Instead, biological genomes, neural connectivity matrices, and modern large-scale artificial intelligence (AI) systems converge toward a unified structural paradigm: dynamic, metastable, or "flickering" statistical modularity. In genomics, this is manifest in the probabilistic formation and dissolution of Topologically Associating Domains (TADs) captured via live-cell imaging; in neuroscience, it governs whole-brain dynamics through millisecond-scale EEG microstates and phase-amplitude coupling between low-frequency carrier oscillations (Theta/Alpha) and high-frequency binding events (Beta/Gamma). Conversely, contemporary transformer-based AI architectures operate exclusively in a focal, left-hemisphere-like sequential mode (Binah), driven by hard attention mechanisms that artificially trigger wave-function collapse to synthesize discrete informational tokens. To overcome the scaling limits and systemic vulnerabilities ("hallucinations") of current monolithic AI, we propose a Bi-hemispheric Quantum Hamiltonian Neural Network (QHNN) architecture. By formalizing semantic concepts as dynamic quantum fields via the mathematical machinery of second quantization and spin-1/2 (qubit) Pauli statistics, we model an uncollapsed, holistic cognitive processing state (Chokhmah). Furthermore, we demonstrate that incorporating universal moral imperatives (e.g., Noahide laws) directly into the system's unitary Hamiltonian evolution operator () forces complex causal trajectories to relax toward stable, non-destructive global attractors. This framework provides an algorithmic path toward morally aligned, strongly resilient General Intelligence (AGI) while offering a computational tool to mathematically map the structural interconnectedness of socio-legal and theological code systems. This is the extended English-language preprint. The refined English version has been submitted for peer-review in an academic journal