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Introduces NEO, a neural framework that predicts low-frequency Laplace-Beltrami eigenspace from point clouds, achieving near-linear scaling and strong zero-shot generalization using a mass-aware neural operator and Rayleigh-Ritz refinement.
This paper performs full Jacobian eigendecomposition across production-scale LLMs, revealing a learned spectral gradient from rotation-dominated early layers to symmetric late layers, along with a low-rank bottleneck that compresses perturbations. The results link perturbation propagation and compression to network functional topology.