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Saturating Scaling Laws for Equational Discovery: A Phenomenology of Growth Dynamics in Three Toy Substrates with Two Real-World Replications

cs.AI updates on arXiv.org·
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The study explores growth dynamics in equational discovery substrates, revealing that growth patterns vary significantly across different architectures and contexts. It proposes a new model for understanding these dynamics, suggesting that growth can be characterized by a saturating power-law influenced by the specific substrate used.

Originally reported by cs.AI updates on arXiv.org. Read the full article ↗

Saturating Scaling Laws for Equational Discovery: A Phenomenology of Growth Dynamics in Three Toy Substrates with Two Real-World Replications | AI Crypto Daily Wire