AI 中文总结
本文提出适应性的计算原语理论（CPT），识别六种通用计算原语，为生物与人工系统的适应性提供底物无关的比较框架，并解释机器智能挑战的根源。
AI 中文摘要
关于适应性系统的研究传统上聚焦于行为（生物体做什么）和机制（其机器如何运作）。本文关注第三个层面——计算，即考虑适应性系统为了生存和繁殖必须计算什么。本文提出，适应性具有其自身的计算结构，包含一套所有适应性系统共有的、与其物理形态无关的少量原始操作。通过四个标准——存在的必要性、独立进化谱系间的普遍性、进化保守性和不可还原性——选取了六种原语：唤醒（Arouse）、定向（Orient）、效价（Valence）、定位（Position）、边界（Boundary）和调谐（Attune）。由此得出两个主要推论。第一，在生物系统中，这些原语提供了一种底物无关的跨物种比较方法，将注意力、记忆和决策等精细行为重新定义为这些计算的组合。第二，在人工系统中，这些原语为看待当前机器智能面临的挑战（如虚构、提示注入、注意力分散、奖励黑客和灾难性遗忘）提供了新视角，表明这些问题可能源于缺乏这些计算。因此，对于持续存在的生物和人工系统，没有特定的物理底物是必需的；相反，如果系统要具有适应性，就必须实现这些原语。生物学能为机器智能提供的启示，不是大脑的神经设计，而是其进化出来执行的计算功能。本文提出的计算原语理论（CPT）是一个工作假说，旨在通过讨论、实证检验和应用进一步完善。
英文摘要
Research on adaptive systems has traditionally focused on behavior (what organisms do) and mechanism (how their machinery works). This paper focuses on a third level, computation, which considers what adaptive systems must compute to survive and reproduce. It is proposed that adaptation has its own computational structure, comprising a small set of primitive operations common to all adaptive systems, regardless of their physical form. Six primitives, Arouse, Orient, Valence, Position, Boundary, and Attune, were selected using four criteria: necessity for existence, universality across independently evolved lineages, evolutionary conservation, and irreducibility. From this, two main implications follow. First, in biological systems, the primitives provide a substrate-neutral method for cross-species comparisons, reframing elaborative behaviors like attention, memory, and decision-making as combinations of these computations. Second, in artificial systems, the primitives offer a new way to view current challenges in machine intelligence, such as confabulation, prompt injection, distractibility, reward hacking, and catastrophic forgetting, suggesting that these issues may arise from a lack of these computations. Thus, for biological and artificial systems that persist, no specific physical substrate is necessary; rather, these primitives must be implemented if the systems are to be adaptive. What biology offers to inform machine intelligence, then, is not the brain's neural design but the computational functions it evolved to perform. The Computational Primitives Theory (CPT) presented here is a working hypothesis, intended for further refinement through discussion, empirical testing, and application.
Comments34 pages, 2 figures, 3 tables
