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This paper introduces a spectral shape-based metric using Heavy-Tailed Self-Regularization theory to characterize, compare, and manage large language models. The approach is data-free, computationally efficient, and scale-invariant, enabling model lineage tracing, unsupervised clustering, and performance quantification across diverse model collections.