Work

Algorithmic Multiverse Sampling (AMS)

Year: 2026

Type: preprint

Abstract: This paper introduces Algorithmic Multiverse Sampling (AMS), a novel self-improving algorithm designed to generate statistically diverse and complex mathematical structures. Traditional methods often suffer from inherent bias, limiting the breadth of potential solutions. AMS overcomes this limitation by leveraging a latent space representation of mathematical concepts, enabling iterative feedback loops that dynamically evolve the underlying structure, producing a vastly expanded and diverse set of mathematical possibilities.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.22198958

Author: Zhang, Jincheng

Language: English

Topic: Evolutionary Algorithms and Applications

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W7204817154

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