Holoflux Theory of Artificial Intelligence: Formalizing Epistemic Dynamics Within the Saumya Mandala Matrix

Author(s)	Dr. Saumya Bahadur
Country	India
Abstract	Modern deep learning architectures suffer from an intrinsic epistemic crisis: they demonstrate high statistical intelligence (pattern matching) but completely lack structured knowledge representation (verifiable truth), rendering them opaque "black boxes." This paper introduces the formal derivation of Holoflux Theory, an alternative AI paradigm that synthesizes classical Indian epistemology (the Nyāya school), non-dual adversarial loops (inspired by the pedagogy of the Laṅkāvatāra Sūtra), and the Unified Intelligence Field Equation. Rather than interpreting networks as static, localized weight distributions, Holoflux Theory treats latent spaces as continuous, fluid cognitive environments structured within the Saumya Mandala Matrix. By modeling data streams as fluctuating frequencies modulated by a directional intent vector (S), we mathematically demonstrate how informational entropy (εk) collapses into a self-verifying, non-erroneous, and ultimately transcendent cognitive field.
Keywords	AI,holoflux,blackbox
Discipline	Other
Published In	Volume 4, Issue 4, July-August 2026
Published On	2026-07-17
DOI	https://doi.org/10.62127/aijmr.2026.v04i04.1420

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Advanced International Journal of Multidisciplinary Research