Schrödinger's Seed: Purr-fect Initialization for an Impurr-fect Universe

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.

Figures (1)

• Figure 1: Cat parameter catalogue. Each card displays (from top to bottom): a portrait photograph; the cat’s name (starred entries indicate cats owned by current or former astrophysicists); sex (M/F), date of birth, and body mass; coat pattern code and eye colour; and a colour-coded accuracy bar. The bar colour indicates performance relative to the baseline seed 42 (accuracy = 0.904): darker forest green denotes higher accuracy, while grey-blue denotes lower accuracy. The seed values are generated using the cat-driven Random Seed Generator with Monte "Catlo", based on each cat’s intrinsic parameters. A control entry with the fixed seed 42 is listed separately at the bottom as a baseline reference.