用高维向量编码Lisp，实现可解释的结构化计算。
Hey Pentti, We Did (More of) It!: A Vector-Symbolic Lisp With Residue Arithmetic
- 用频域全息向量表示法扩展Lisp，支持残差超维度计算。
- 在高维空间中编码图灵完备语法，提升神经网络表达能力。
- 适合研究可解释智能体与结构化知识表征的学者。
通过频域全息还原表示（FHRR），我们将向量符号架构（VSA）扩展至Lisp 1.5，引入基于残差超维度计算（RHC）的算术操作原语。在高维向量空间中编码图灵完备语法，增强了神经网络状态的表达力，使网络状态能够包含任意结构化的表示，并具备内在可解释性。我们探讨了该编码在机器学习任务中的潜在应用，强调结构化表示的重要性，以及设计对表示结构敏感的神经网络对于构建更通用智能体的关键意义。
原文摘要 · Abstract (English)
Using Frequency-domain Holographic Reduced Representations (FHRRs), we extend a Vector-Symbolic Architecture (VSA) encoding of Lisp 1.5 with primitives for arithmetic operations using Residue Hyperdimensional Computing (RHC). Encoding a Turing-complete syntax over a high-dimensional vector space increases the expressivity of neural network states, enabling network states to contain arbitrarily structured representations that are inherently interpretable. We discuss the potential applications of the VSA encoding in machine learning tasks, as well as the importance of encoding structured representations and designing neural networks whose behavior is sensitive to the structure of their representations in virtue of attaining more general intelligent agents than exist at present.
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