代数量子智能：一种可重复的机器创造力新框架
Algebraic Quantum Intelligence: A New Framework for Reproducible Machine Creativity
AI总结：
本文提出代数量子智能框架，通过非交换代数结构扩展语义空间，提升机器创造力的可重复性和表现。
摘要：
大型语言模型（LLMs）在生成流畅且语境合适的文本方面取得了显著成功；然而，它们产生真正创造性输出的能力仍然有限。本文认为，这种限制源于当前LLMs的结构特性：当提供丰富上下文时，未来生成的空间被强烈约束，生成过程实际上由近确定性动态主导。近期的改进方法如测试时间扩展和上下文适应提高了性能，但并未从根本上改变这一约束。为了解决这一问题，我们提出代数量子智能（AQI）作为一种计算框架，能够系统地扩展语义空间。AQI被建模为一种受量子理论启发的非交换代数结构，允许顺序依赖性、干涉和不确定性等属性以受控和可设计的方式实现。语义状态被表示为希尔伯特空间中的向量，其演变由非交换算符计算出的C值所主导，从而确保多个未来语义可能性的共存和扩展。在本研究中，我们通过在基于转换器的LLM上扩展超过600个专用算符来实现AQI。我们评估了由此产生的系统在十个领域上的创造性推理基准测试中，采用LLM作为评判者协议。结果表明，AQI在强基线模型上表现一致，产生统计显著的改进和减少的跨领域方差。这些发现表明，非交换代数动态可以作为机器创造力的实用且可重复的基础。值得注意的是，这种架构已经部署在现实世界的企业环境中。
英文摘要：
Large language models (LLMs) have achieved remarkable success in generating fluent and contextually appropriate text; however, their capacity to produce genuinely creative outputs remains limited. This paper posits that this limitation arises from a structural property of contemporary LLMs: when provided with rich context, the space of future generations becomes strongly constrained, and the generation process is effectively governed by near-deterministic dynamics. Recent approaches such as test-time scaling and context adaptation improve performance but do not fundamentally alter this constraint. To address this issue, we propose Algebraic Quantum Intelligence (AQI) as a computational framework that enables systematic expansion of semantic space. AQI is formulated as a noncommutative algebraic structure inspired by quantum theory, allowing properties such as order dependence, interference, and uncertainty to be implemented in a controlled and designable manner. Semantic states are represented as vectors in a Hilbert space, and their evolution is governed by C-values computed from noncommutative operators, thereby ensuring the coexistence and expansion of multiple future semantic possibilities. In this study, we implement AQI by extending a transformer-based LLM with more than 600 specialized operators. We evaluate the resulting system on creative reasoning benchmarks spanning ten domains under an LLM-as-a-judge protocol. The results show that AQI consistently outperforms strong baseline models, yielding statistically significant improvements and reduced cross-domain variance. These findings demonstrate that noncommutative algebraic dynamics can serve as a practical and reproducible foundation for machine creativity. Notably, this architecture has already been deployed in real-world enterprise environments.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
