Moonlight in Latent Space: Chirality and Structural Correspondence Between Beethoven's Op. 27 No. 2 and Machine Learning Mechanisms

The paper presents a computational analysis demonstrating that Beethoven's "Moonlight Sonata" movements correspond structurally to distinct machine learning architectures, specifically streaming, recurrent, and periodic positional encoding memory architectures. Key findings include the relationship between musical "temperature" and throughput, unexpected dissonance levels, and the ability to recover tonal structures through unsupervised clustering. This work highlights the parallels between music and NLP embeddings, emphasizing the importance of sequential information and chirality in encoding-decoding processes, which may inform practitioners about the application of these concepts in AI model design and analysis.