Structural Emergence of Intelligence (SEI) v2.3: From Structural Conditions to Dynamical and Falsifiable Predictions

This work presents SEI v2. 3 (Structural Emergence of Intelligence), a unified and minimal framework describing how intelligence emerges across natural and artificial systems. The central claim is that intelligence does not arise from scale or complexity alone, but only when structural organization exceeds a context-dependent threshold. The core condition is defined as: C · Γ · (dSC/dt) > Θ (E, S, T) where: - C: effective structural density- Γ: fixation / stabilization- dSC/dt: persistence of structured organization- Θ (E, S, T): context-dependent emergence threshold SEI v2. 3 extends earlier versions by introducing: - nonlinear scaling behavior of intelligence emergence, - dual-regime threshold structure, - effective ratio representation R (E), - temporal evolution of emergence thresholds, - cross-scale structural relevance mapping, - and explicit falsifiable predictions. The framework predicts that intelligence emergence follows a non-monotonic performance curve with a bounded optimal regime, rather than increasing indefinitely with size. This prediction is directly testable across domains, including artificial intelligence systems, biological networks, and social systems. If intelligence is found to scale monotonically without a bounded optimum, the framework is falsified. This work provides a unified structural language linking: - physical systems, - biological systems, - and artificial intelligence, offering a minimal and testable alternative to scale-based interpretations of intelligence. All figures are fully reproducible using the provided Python script. This work provides a clear and directly testable pathway toward empirical validation of intelligence emergence.