Phase Transitions in Deep Learning and Prebiotic Chemistry Unified by Two-Field Framework
A new theoretical paper on arXiv (2605.16325) outlines an extensive framework to grasp phase-transition events in deep learning and non-equilibrium chemical reaction networks. It explores deep learning concepts like grokking and emergent capabilities by using representational compression and information metrics. In the realm of non-equilibrium statistical physics, it highlights phase transitions in chemical networks tied to prebiotic selection, noting that these behaviors are hard to replicate with simple gradient models. The authors suggest viewing both fields as informational systems driven by two gradients: the entropy production rate, Sigma, and the information quasi-potential, Phi_I = -ln p*. They also introduce two order parameters to help unify the mathematical understanding of these emergent phenomena.
Key facts
- Paper arXiv:2605.16325 proposes a two-field framework for phase transitions in deep learning and non-equilibrium chemistry.
- Deep learning phenomena include grokking, emergent capabilities, and ontological reorganization.
- Non-equilibrium statistical physics has identified phase transitions in driven chemical reaction networks underlying prebiotic selection.
- The framework uses entropy production rate Sigma and information quasi-potential Phi_I = -ln p*.
- Two candidate order parameters are introduced: adversarial breakdown threshold alpha_dagger and a second parameter s.
- The goal is a common description for driven informational systems.
- Published as a cross-type announcement on arXiv.
- The paper connects learning theory and prebiotic chemistry through statistical physics.
Entities
Institutions
- arXiv