在有限环连续体中的普遍隐性表示
1Gamma Earth Sàrl, 1162 St-Prex, Switzerland.
Entropy (Basel, Switzerland)
|January 28, 2026
概括
现代基础模型通过一个共享的有限隐藏域来实现表示的普遍性. 这种数学框架解释了通过有限的关系几何学来实现交叉模式对齐和可转移性,而不仅仅是架构.
科学领域:
- 人工智能的人工智能
- 机器学习理论机器学习理论
- 人工智能的数学基础
背景情况:
- 基础模型在各种数据模式中表现出代表性的普遍性.
- 现有的解释往往侧重于建筑的相似性,而不是基础的数学原理.
- 有限环连续 (FRC) 框架为人工智能中的数学结构提供了新的视角.
研究的目的:
- 提出一个统一的数学框架,解释基础模型中的代表性普遍性.
- 为了证明这种普遍性源于一个共享的有限隐藏域.
- 将表示学习,充足理论和FRC代数联系起来.
主要方法:
- 在FRC框架内,模型模式作为一个共同的潜集合Z⊂Ut的认识投影.
- 使用一个完全对称的有限场外 (Ut).
- 根据最小充足表示的独特性证明了通用子空间定理.
主要成果:
- 确定独立训练的嵌入点在相同的潜在结构上与坐标图相吻合.
- 证明了跨模态对齐,可转移性和语义连贯性是有限关系几何学的结果.
- 在多式模式模型中为通用潜伏结构提供了一个原则性的基础.
结论:
- 基础模型中的表示性普遍性在数学上是基于一个共享的有限隐藏域.
- 有限的关系几何学,而不是建筑相似性,是跨模态现象的关键驱动因素.
- 拟议的基于FRC的框架统一了代表性学习中的各种理论概念.
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