Structural Emergence of Intelligence (SEI) v2.0: A Unified Structural Theory Across Natural and Artificial Systems

This repository contains the full manuscript and reproducible figure set for: Structural Emergence of Intelligence (SEI) v2. 0 This work proposes a unified structural framework in which intelligence is not treated as a domain-specific phenomenon, but as a scale-invariant emergence governed by general structural constraints. The central result is a minimal and falsifiable condition: C · Γ · dSC/dt > Θ (E, S, T) where: - C represents effective structural density, - Γ represents fixation or stabilization, - dSC/dt represents persistence of structured organization over time, - Θ is a context-dependent threshold determined by environment, scale, and temporal regime. A key concept introduced in this work is the Structural Emergence Window (SEW), a bounded regime within which intelligence can stably arise. Outside this window, intelligence is suppressed due to: - insufficient structural density (under-structured systems), - instability or fragmentation (collapse of fixation), - overscaled collapse (loss of coherent persistence). The framework unifies natural systems (galactic and planetary environments) and artificial systems (machine learning models) under a common structural interpretation. It also explains why intelligence does not scale monotonically with size, providing a structural explanation for both emergence and failure. This repository includes: - the full manuscript (PDF), - all figures used in the paper, - a reproducible Python script for figure generation. The theory is designed to be minimal, testable, and applicable across domains including: - artificial intelligence scaling, - complex systems, - astrobiology and extraterrestrial intelligence search. This work aims to provide a structural foundation for understanding intelligence as a general emergent phenomenon rather than a domain-specific artifact.