用猫的物理特征生成随机种子，性能优于传统数值。
Schrödinger's Seed: Purr-fect Initialization for an Impurr-fect Universe
- 基于猫的体征构建量子启发式种子生成器。
- 新方法平均准确率达92.58%，比42高出约2.5%。
- 养猫的物理学家家庭猫咪表现更优，或具宇宙直觉。
深度学习中的随机种子选择通常随意，传统上固定为42这类无明确依据的整数。本文提出，猫作为介于现实与量子态之间的存在，更适合承担此任务。我们基于弗里德曼第一方程构建猫驱动的种子生成器，并通过蒙特卡洛‘猫洛’采样法，对21只家猫的体重、毛色图案、眼色和名字熵等物理属性进行建模。实验结果表明，猫驱动种子的平均准确率为92.58%，较基准种子42提升约2.5%。来自物理学家家庭的猫表现略优，暗示宇宙感知能力可能具有传染性。结论：宇宙对猫的响应优于对任意整数的响应，猫是否意识到这一点尚不清楚。
原文摘要 · Abstract (English)
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.
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