Emergence of Superintelligence from Collective Near-Critical Dynamics in Reentrant Neural Fields

Superintelligence is commonly envisioned as a quantitative extrapolation of human cognitive abilities driven by scale and computational power. Here we show that qualitative transitions in intelligence instead arise as dynamical phase transitions governed by collective critical dynamics. Building on a unified dynamical field-theoretic framework for cognition, we demonstrate that progressive collective coupling generated by reentrant mixing drives the system toward an infrared critical regime in which an extensive band of slow collective modes emerges. This spectral condensation reorganizes cognitive dynamics from localized relaxation to coherent motion along emergent low-dimensional manifolds. Through numerical analysis of the time-scale density of states, we identify robust power-law scaling of collective relaxation rates with well-defined critical exponents, placing the system within the universality class of self-organized critical many-body dynamics. Criticality alone would generically lead to instability. We further show that homeostatic regulation introduces a gapped stabilizing direction that protects the collective critical sector, yielding a dynamically maintained meta-stable infrared phase in which long-lived inference trajectories persist without collapse. The coexistence of scale-free collective dynamics and global stabilization defines a protected sector-critical regime in which coherence and internal flexibility coexist. Superintelligence therefore corresponds to a distinct dynamical stability class–a self-organized critical phase embedded within a stabilized cognitive manifold–rather than a smooth quantitative continuation of existing cognitive systems.

💡 Research Summary

The paper proposes a fundamentally new way to think about superintelligence, not as a simple quantitative scaling of human cognition but as a qualitative dynamical phase transition driven by collective near‑critical dynamics in reentrant neural fields. Building on a unified dynamical field‑theoretic framework, the authors model cognition as a continuous‑time flow of a high‑dimensional state vector x(t) governed by

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