打破概率的束缚：中智逻辑作为大语言模型中认知不确定性的新框架

摘要

大型语言模型（LLM）主要受概率框架支配，其中所有可能结果的概率之和被约束为1。这种由Softmax层强加的结构限制导致了不确定性的坍塌，使得模型难以区分认知不确定性、悖论与模糊性。本文对中智逻辑（Neutrosophic Logic）在LLM认知状态建模中的应用进行了实证研究。中智逻辑将真值（T）、不确定值（I）和假值（F）视为三个独立维度。我们以四种OpenAI GPT模型为研究对象，针对五种语言现象（逻辑悖论、认知无知、模糊性、伦理矛盾、未来偶然性），在三种提示策略（中智提示、概率提示和熵推导提示）下开展实验。结果表明，中智方法通过允许T+I+F>1（我们称之为“超真”状态），能够更丰富地表征模型内部状态。在35%的评估中，超真状态自发出现，主要集中于伦理矛盾和逻辑悖论情境下。我们证明，该方法可在模糊语境中保留真值，并提供了一种稳健的方法来识别和量化模型内部冲突。结论认为，集成中智评估层是构建更透明、更可靠、更具伦理意识的人工智能系统的关键步骤。

English

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 states in LLMs. We conducted experiments on a family of four OpenAI GPT models across five linguistic phenomena: logical paradoxes, epistemic ignorance, vagueness, ethical contradictions, and future contingencies, under three prompting strategies: neutrosophic, probabilistic, and entropy-derived. Our findings reveal that the neutrosophic approach, by allowing T+I+F > 1, a state we term hyper-truth, provides a richer representation of a model's internal state. In 35% of evaluations, hyper-truth emerged spontaneously, predominantly under ethical contradiction and logical paradox. We demonstrate that this approach preserves truth values in fuzzy contexts and offers a robust method for identifying and quantifying internal model conflict. We conclude that the integration of neutrosophic evaluation layers is a critical step toward more transparent, reliable, and ethically aware AI systems.