Search arXivSearch

arXiv · 2506.19787

Computational Platonism

Abstract

We offer a view of mathematics as an experimental science where axioms play the role of foundational theories like general relativity and quantum mechanics in physics. Under this view, axioms are provisional and inferred from experience with the experiental substrate of mathematics which we locate within computation rather than encoding intuitive and absolute truths. This offers a reframing of Godel's theorem, placing its impact sharply upon the incompleteness rather than the potentially contradictory nature of any computational set of axioms. The essay originated in an attempt to make precise the nature of mathematics in order to estimate how AI might impact it. This exploration is continued in the paired essay "The mechanical creation of mathematical concepts."

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Asvin G. 2026-04-28. Computational Platonism. https://arxiv.org/abs/2506.19787

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Come for the vibe, stay for the math

This article describes our experiences in mathematical outreach over the past decade. We talk about specific activities, but also general principles that we've learned along the way.

math.HO

Graduate Mathematics in the Age of AI: Forming Mathematicians for Original, Independent, and Responsible Inquiry

Artificial intelligence can increasingly produce plausible, sophisticated mathematical material faster than a developing graduate student can understand or verify it. A sophisticated result or paper draft therefore becomes weaker evidence of the student's own mathematical development. This creates a formation gap between output and personal capacity, and a trust gap between a convincing argument and warranted acceptance. The formation gap can persist even when the student understands the output: understanding a supplied argument does not by itself establish the capacity to initiate and direct inquiry. These gaps are not the whole story. AI can also help students explore examples, compare approaches, enter unfamiliar areas, and undertake ambitious research. The task is to design an apprenticeship that realizes these possibilities while developing substantive mathematical command. The central purpose of a mathematics PhD is to form mathematicians capable of original, independent, and responsible inquiry, including inquiry conducted with AI. This document develops that objective through four connected capacities: competence, judgment, independence, and responsibility. It distinguishes a work's contribution to mathematics from the evidence it provides of a student's formation; explains how a known answer can initiate rather than end creative inquiry; and proposes changes in learning activities, assessment, doctoral originality, advising, and institutional support. Purposeful independent work and ambitious AI-assisted research are complementary parts of the model. Its recommendations include proportionate contribution statements, recognition of advising costs, and staged pilots evaluating both mathematical ability and effective human--AI collaboration. The aim is not to preserve an inherited sequence of training, but to improve mathematical formation as mathematical practice changes.

math.HO