A Hidden Bridge Between Phase Transitions and Machine Learning
Year: 2026
Type: article
Abstract: Two recent results, one from statistical physics and one from machine learning, look completelyunrelated at rst glance. The rst reports that pushing a magnet through its phase transitiontakes more energy than it should and the extra energy follows a curious mathematical law.The second observes that very large neural networks behave strangely near a critical thresholdwhere the number of parameters equals the number of data points. The accompanying paperargues that these two phenomena are, secretly, the same mathematical event: a system beingdragged through a tipping point faster than it can keep up. This summary explains what thatmeans, why it matters, and what new things we can now predictincluding a precise predictionabout the timing of grokking in articial neural networks.
Author: Sepulveda-Jimenez, Alfredo
Language: English
Subfield: Condensed Matter Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7162198483
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