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 · dS_C/dt > Θ(E, S, T) where:- C represents effective structural density,- Γ represents fixation or stabilization,- dS_C/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.