NerVE: Nonlinear Eigenspectrum Dynamics in LLM Feed-Forward Networks
We introduce NerVE, a unified eigenspectral framework for understanding how feed-forward networks (FFNs) in large language models (LLMs) organize and regulate information flow in high-dimensional latent space. Despite FFNs dominating the parameter budget, their high-dimensional dynamics remain poorly understood. NerVE addresses this gap through lightweight, memory-efficient tracking of eigenspectrum dynamics via four complementary metrics: Spectral Entropy (dispersion), Participation Ratio (effective dimensionality), Eigenvalue Early Enrichment (top-heaviness), and Jensen-Shannon divergence (distributional shifts). Our key insight is that FFN nonlinearities reinject variance across eigenm...
| Authors | Nandan Kumar Jha, Brandon Reagen |
|---|---|
| Primary category | cs.LG |
| Published | 6 March 2026 |
| Last updated at source | 16 March 2026 |
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