The Computational Primitives of Adaptation
Abstract
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.
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BibTeX
@misc{page2026computational,
title = {{The Computational Primitives of Adaptation}},
author = {Jonathan W. Page},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.11989},
eprint = {2609.11989},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.11989},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - The Computational Primitives of Adaptation
AU - Jonathan W. Page
PY - 2026
DA - 2026/09/08
PB - arXiv
DO - 10.48550/arxiv.2609.11989
UR - https://arxiv.org/abs/2609.11989
AB - 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.
ER -