Algorithmic Temperature Induced by Adopted Regular Universal Turing Machine
Abstract
We prove that an effective temperature naturally emerges from the algorithmic structure of a regular universal Turing machine (UTM), without introducing any external physical parameter. In particular, the redundancy growth of the machine's wrapper language induces a Boltzmann--like exponential weighting over program lengths, yielding a canonical ensemble interpretation of algorithmic probability. This establishes a formal bridge between algorithmic information theory and statistical mechanics, in which the adopted UTM determines the intrinsic ``algorithmic temperature.'' We further show that this temperature approaches its maximal limit under the universal mixture (Solomonoff distribution), and discuss its epistemic meaning as the resolution level of an observer.
Cite
@article{arxiv.2510.11737,
title = {Algorithmic Temperature Induced by Adopted Regular Universal Turing Machine},
author = {Kentaro Imafuku},
journal= {arXiv preprint arXiv:2510.11737},
year = {2025}
}
Comments
12 pages, 2 figures. This version includes minor textual and conceptual clarifications to improve precision and avoid potential ambiguity