Emergence via Phase Transitions: Mechanism Landscapes and Universal Convergence Across Complex Systems
Researchers have proposed a new framework called the Hierarchical Emergence Framework (HEF) to explain how complex systems, from machine learning to biology, converge on similar high-level structures. HEF models emergence as a phase transition in a mechanism landscape, identifying a critical energy threshold that separates competing mechanisms from a single, optimal one. Experiments with transformers trained on modular arithmetic demonstrated a reproducible fingerprint of this transition, with weight norms peaking before generalization and accuracy converging to a consistent value. AI
IMPACT Proposes a theoretical framework for understanding emergent properties in AI systems, potentially guiding future model development.