Schr\"odinger's Seed: Purr-fect Initialization for an Impurr-fect Universe

TL;DR

This paper introduces a novel cat-inspired seed generator for deep learning, inspired by quantum mechanics and the Friedmann equation, demonstrating improved accuracy over traditional seeds.

Contribution

It presents a unique, cat-driven seed generation method based on physical properties and quantum-inspired models, outperforming conventional fixed seeds.

Findings

Cat-driven seeds achieve 92.58% accuracy, surpassing the baseline of 42.

Cats from astrophysicist households perform slightly better, indicating possible cosmic influence.

The universe responds better to cats than to arbitrary integers.

Abstract

Context. Random seed selection in deep learning is often arbitrary -- conventionally fixed to values such as 42, a number with no known feline endorsement. Aims. We propose that cats, as liminal beings with a historically ambiguous relationship to quantum mechanics, are better suited to this task than random integers. Methods. We construct a cat-driven seed generator inspired by the first Friedmann equation, and test it by mapping 21 domestic cats' physical properties -- mass, coat pattern, eye colour, and name entropy -- via a Monte ``Catlo'' sampling procedure. Results. Cat-driven seeds achieve a mean accuracy of 92.58%, outperforming the baseline seed of 42 by $\sim$2.5%. Cats from astrophysicist households perform marginally better, suggesting cosmic insight may be contagious. Conclusions. The Universe responds better to cats than to arbitrary integers. Whether cats are aware of this remains unknown.