Phase Transitions in Driven Informational Systems: A Two-Field Perspective on Learning Theory and Non-Equilibrium Chemistry

In three linesTheoretical paper proposing a unified framework for phase transitions in deep learning (grokking, emergent capabilities) and non-equilibrium chemistry. Introduces two gradient fields (entropy production rate and information quasi-potential) and two order parameters (adversarial breakdown threshold α†, self-referential coupling threshold κc) to describe driven informational systems.

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