This theoretical framework addresses hallucination in large language models, suggesting improved accuracy in AI outputs.
This paper formalizes the Landry AGI Model, a theoretical framework that addresses the hallucination problem in Large Language Models (LLMs) through the architectural separation of deterministic fact processing from probabilistic narrative generation.
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Marie Seshat Landry (2026) studied this question.
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