Breaking the Chains of Probability: Neutrosophic Logic as a New Framework for Epistemic Uncertainty in Large Language Models
A new framework utilizing Neutrosophic Logic has been proposed to address epistemic uncertainty in Large Language Models (LLMs). This approach allows for a more nuanced representation of uncertainty by treating truth, indeterminacy, and falsity as independent dimensions. The findings suggest that integrating neutrosophic evaluation layers can enhance the transparency and ethical considerations of AI systems.
- ▪Large Language Models are typically constrained by probabilistic frameworks that limit their ability to represent uncertainty.
- ▪Neutrosophic Logic allows for a state termed hyper-truth, where the sum of truth, indeterminacy, and falsity can exceed one.
- ▪Experiments showed that hyper-truth emerged in 35% of evaluations, particularly in contexts involving ethical contradictions and logical paradoxes.
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Computer Science > Artificial Intelligence arXiv:2605.24053 (cs) [Submitted on 22 May 2026] Title:Breaking the Chains of Probability: Neutrosophic Logic as a New Framework for Epistemic Uncertainty in Large Language Models Authors:Maikel Yelandi Leyva-Vázquez, Florentin Smarandache View a PDF of the paper titled Breaking the Chains of Probability: Neutrosophic Logic as a New Framework for Epistemic Uncertainty in Large Language Models, by Maikel Yelandi Leyva-V\'azquez and Florentin Smarandache View PDF HTML (experimental) Abstract:Large Language Models (LLMs) are predominantly governed by probabilistic frameworks in which the sum of outcome probabilities is constrained to unity.
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