Emergence via Phase Transitions: Mechanism Landscapes and Universal Convergence Across Complex Systems
arXiv:2606.07563v1 Announce Type: new Abstract: Across machine learning, biology, and physics, independently evolving systems often converge toward strikingly similar high-level structures despite radically different microscopic details. Grokking circuits converge across random seeds, evolutionary lineages rediscover similar metabolic solutions, and renormalization flows approach common fixed points. We propose the Hierarchical Emergence Framework (HEF) as a candidate universality framework for such convergence phenomena. HEF models emergence as a phase transition in a mechanism landscape cons
This paper introduces a theoretical framework seeking to explain prevalent convergence phenomena across diverse complex systems, reflecting a growing need for universal laws in AI and other fields.
A universal framework for emergence could accelerate AI development and understanding, facilitate cross-disciplinary insights, and potentially lay groundwork for more robust and generalizable AI systems.
Our understanding of how complex systems, particularly AI, achieve similar outcomes despite varied initial conditions, could become more formalized and predictive.
- · AI researchers
- · Complex systems scientists
- · Theoretical physics
- · Fragmented disciplinary approaches
The Hierarchical Emergence Framework (HEF) provides a new conceptual tool for explaining 'grokking' and other convergence phenomena in AI.
This improved theoretical understanding could lead to the design of more efficient and generalizable AI architectures.
A unified theory of emergence might unlock entirely new classes of algorithms or enable truly autonomous AI capable of principled self-improvement.
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Read at arXiv cs.LG