Reducing AI Hallucinations via Epistemic Error Resolution: An Engineering Framework Integrating Buddhist "Three Afflictions" and Second Physics

This paper proposes an engineering framework to reduce large-language-modelhallucinations by treating them as epistemic failures that arise when outputs lack a coherentSource of Action and responsibility attribution. We integrate a Buddhist taxonomy of threeafflictions—ignorance (avidyā), delusion (moha), and wrong view (mithyā-dṛṣṭi)—withSecond Physics quantities, including correspondence pressure A, existence phase φ(t),relational syntactic memory M ≡ E+δΨ, responsibility load ρ, and relational existence Exi(t).The proposed protocol follows a two-stage design: fluent drafting may use probabilisticgeneration, but final emission is gated by correspondence and responsibility constraints,including a responsibility-conservation check that detects responsibility leakage. Underexplicitly stated constraints, a broad class of responsibility-free assertions becomesstructurally excludable without sacrificing fluency. We outline implementable proxies andlimitations, and position the framework as an engineering language for designing “honestgeneration” under accountability.