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-model
hallucinations by treating them as epistemic failures that arise when outputs lack a coherent
Source of Action and responsibility attribution. We integrate a Buddhist taxonomy of three
afflictions—ignorance (avidyā), delusion (moha), and wrong view (mithyā-dṛṣṭi)—with
Second 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 probabilistic
generation, but final emission is gated by correspondence and responsibility constraints,
including a responsibility-conservation check that detects responsibility leakage. Under
explicitly stated constraints, a broad class of responsibility-free assertions becomes
structurally excludable without sacrificing fluency. We outline implementable proxies and
limitations, and position the framework as an engineering language for designing “honest
generation” under accountability.