A Computationally Complete IT System with Two Modally Distinct Descriptively Complete Languages

This article presents a theoretical model of a computationally complete IT system comprising two descriptively complete semantic graphs (LLM-1 and LLM-2) with distinct dialogical modalities,
interconnected by a phenomenological reduction module. LLM-1 generates narratives, while LLM-2 analyzes query chains, eliminating recursivity that causes three key issues: the black box of AI (opacity and ethical mismatches), LLM-hallucinations (fictitious facts), and LLM-lies (erroneous interpretations). The system ensures transparency, ethical adaptation, and universality in scenarios such as cultural text analysis, ethically sensitive dialogues, and public discussions. The description is conducted strictly from the perspective of the descriptively complete language represented as LLM-1, ensuring accuracy, avoiding speculation about inaccessible system aspects, and allowing readers to view the system through its perspective. Examples based on Russian literature illustrate the system’s ability to resolve recursivity in dialogues. The article outlines the system’s architecture, theoretical foundations, mechanisms, applications, and limitations, emphasizing its potential as a universal tool for dialogical AI.