Breaking the Chains of Probability: Neutrosophic Logic as a New Framework for Epistemic Uncertainty in Large Language Models
arXiv:2605.24053v1 Announce Type: cross Abstract: Large Language Models (LLMs) are predominantly governed by probabilistic frameworks in which the sum of outcome probabilities is constrained to unity. This architectural limitation, often imposed by Softmax layers, leads to a collapse of uncertainty that makes it difficult to differentiate between epistemic uncertainty, paradox, and vagueness. We present an empirical investigation of the application of Neutrosophic Logic, a framework that treats Truth (T), Indeterminacy (I), and Falsity (F) as three independent dimensions, to model epistemic st
The increasing sophistication and widespread deployment of LLMs highlight fundamental limitations in their current probabilistic understanding of uncertainty, making an alternative framework timely.
This research suggests a potential paradigm shift in how LLMs manage uncertainty, moving beyond probabilistic constraints to improve their ability to distinguish nuanced epistemic states, which is critical for robust autonomous systems.
The adoption of Neutrosophic Logic could lead to LLMs that are more transparent about what they 'don't know,' differentiating between true uncertainty, paradox, and vagueness, rather than collapsing all into a single probability.
- · AI researchers
- · Developers of safety-critical AI
- · Developers of advanced AI agents
- · LLMs reliant solely on probabilistic uncertainty frameworks
Improved reliability and explainability of AI models through a more nuanced handling of uncertainty.
Accelerated development of AI agents capable of more sophisticated reasoning under ambiguous conditions.
Enhanced trust in AI systems leading to broader adoption in complex decision-making processes, potentially impacting regulatory frameworks.
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Read at arXiv cs.CL