A Random Matrix Theory method identifies growing Correlation Traps in neural network weight spectra during an 'anti-grokking' overfitting phase, and applies the same diagnostic to some foundation LLMs.
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A tunable microscopic model of network liquids with a liquid-liquid phase transition, analyzed via RFOT theory, predicts nanonucleation near the glass transition and links thermodynamic and kinetic anomalies when matched to water-like conditions.
Enrico Fermi's legacy from his Varenna lectures has shaped milestones in laser spectroscopy, Bose-Einstein condensation, and quantum information science.
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Detecting overfitting in Neural Networks during long-horizon grokking using Random Matrix Theory
A Random Matrix Theory method identifies growing Correlation Traps in neural network weight spectra during an 'anti-grokking' overfitting phase, and applies the same diagnostic to some foundation LLMs.