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This paper advances theoretical foundations in algorithmic information theory by establishing a geometric coding theorem that links symmetry group actions to Kolmogorov complexity. By framing these relationships through the lens of algorithmic probability, it provides rigorous mathematical underpinnings relevant to understanding the limits and structures of AI learning algorithms.
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CAS I: A Geometric Coding Theorem

arXiv2 min read

arXiv:2607.13796v2 Announce Type: replace-cross Abstract: This paper establishes a direct analogue of the classical Coding Theorem in the setting of symmetry groups. We consider computable bijections on the set of binary strings and define the symmetry prior of a string x as the probability that a randomly chosen symmetry from a given group G has x as its unique fixed point. We show that for any fix-retractable symmetry group G, a group admitting a computable section that selects an isolating symmetry for every string, the symmetry prior is a universal lower semi-computable semi-measure. In this case, the Geometric Coding Theorem holds. This result is a coding-theoretic restatement of a theorem of Trejo, Kreinovich and Longpr\'e, who showed that the complexity of describing a string by a symmetry with that string as its unique fixed point equals its Kolmogorov complexity. Our contribution is to recast it in terms of algorithmic probability and to treat the symmetry group as a parameter, identifying fix-retractability as exactly the condition under which symmetry complexity collapses onto Kolmogorov complexity.

Abstract:This paper establishes a direct analogue of the classical Coding Theorem in the setting of symmetry groups. We consider computable bijections on the set of binary strings and define the symmetry prior of a string x as the probability that a randomly chosen symmetry from a given group G has x as its unique fixed point. We show that for any fix-retractable symmetry group G, a group admitting a computable section that selects an isolating symmetry for every string, the symmetry prior is a universal lower semi-computable semi-measure. In this case, the Geometric Coding Theorem holds. This result is a coding-theoretic restatement of a theorem of Trejo, Kreinovich and Longpré, who showed that the complexity of describing a string by a symmetry with that string as its unique fixed point equals its Kolmogorov complexity. Our contribution is to recast it in terms of algorithmic probability and to treat the symmetry group as a parameter, identifying fix-retractability as exactly the condition under which symmetry complexity collapses onto Kolmogorov complexity.

Information Theory (cs.IT); Artificial Intelligence (cs.AI); Category Theory (math.CT); Group Theory (math.GR)

arXiv:2607.13796 [cs.IT]

(or arXiv:2607.13796v2 [cs.IT] for this version)

https://doi.org/10.48550/arXiv.2607.13796 Focus to learn more arXiv-issued DOI via DataCite

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Full text under the publisher's open licence · 269 words

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