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Genomic Convergence Verification: A Multi-Agent Framework for Hallucination Detection in Retrieval-Augmented Generation. Overcoming Gödel's Limitation Through Multi-System Convergence.
2026-08-23

We present Genomic Convergence Verification (GCV), a novel theoretical framework and practical implementation for detecting hallucinations in Retrieval-Augmented Generation (RAG) systems. The framework addresses a fundamental limitation: any single verification system is bounded by Gödel's incompleteness theorems, meaning there will always exist statements whose truth value cannot be determined within that system. Our approach deploys multiple independent verification agents and identifies their absolute convergence points—termed the genomic code of truth—where all agents unanimously agree with high confidence. We prove that as the number of independent verification systems increases, the probability of a statement being undecidable across all systems asymptotically approaches zero. The implementation comprises three specialized verification agents (fact checker, contradiction detector, completeness evaluator), a differential convergence equation for modeling confidence evolution, an apogee detection algorithm for finding optimal convergence points, and a genomic code extraction mechanism for identifying absolute truth patterns. The framework is modular, extensible, and available as open-source software. It represents a step toward trustworthy AI systems where verification is not bounded by the mathematical limitations of any single approach.

Ссылка для цитирования:

Тишков В. В. 2026. Genomic Convergence Verification: A Multi-Agent Framework for Hallucination Detection in Retrieval-Augmented Generation. Overcoming Gödel's Limitation Through Multi-System Convergence. PREPRINTS.RU. https://doi.org/10.24108/preprints-3116223

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