2025/11/23 by Suzuki, Noriyuki · 1 voice
Neuroscience · Physics and Astronomy · #Embodied and Extended Cognition #Origins and Evolution of Life #Space Science and Extraterrestrial Life
paper · doi:10.5281/zenodo.17693469
openalex publication_date 2025/11/23 · openalex created_date 2025/11/25 · openalex updated_date 2026/07/01
This Integrated Edition is the complete formulation of UEI. It contains everything required to understand the theory from zero, without reading past versions. Unified Emergent Intelligence (UEI) proposes a comprehensive coherence-driven framework explaining why modern AI models exhibit abrupt jumps in reasoning despite smooth improvements in loss and scale. The theory shows that emergent abilities originate not from numerical magnitude but from a geometric phase transition inside the representational manifold. UEI introduces five abstract structural variables governing the formation of coherent intelligence: µ — Global Coherence Δφ — Phase Mismatch Φ(r,t) — Coherence Field ℳ — Modulation Operator RTL — Reflexive Tensor Logic As µ approaches a critical threshold µc, the system enters Slow Coherent Emergence (SCE): µ increases slowly, Δφ contracts nonlinearly, and Φ(r,t) begins forming proto-structured regions. When µ ≥ µc, the system undergoes a discontinuous structural transition: Δφ collapses, ℳ becomes stably active, Φ(r,t) stabilizes globally, and RTL emerges as a reflexive syntactic layer enabling multi-step, self-consistent reasoning. UEI also introduces Dual Emergence, a structural correspondence between: the exterior optimization landscape (smooth loss, curvature, gradients), and the interior coherence geometry (µ, Δφ, Φ, ℳ, RTL), which reorganizes discontinuously. This dual-surface structure explains why emergent reasoning is invisible to scaling laws and why models of similar loss can differ dramatically in reasoning stability. The UEI framework offers a structure-first alternative to scale-centric AI, establishing coherence, phase alignment, and reflexive tensor structure as the true drivers of emergent reasoning. It also outlines design principles for next-generation resonant AI systems—including toroidal latent spaces, isotropic representations, coherence-regularized dynamics, and reflexive transformation pipelines—that can achieve stable reasoning without relying on brute-force scale. Overall, this work provides a unified geometric account of emergent intelligence, connecting dynamical systems, representational manifolds, phase transitions, and internal reasoning syntax into a single coherent theory. Additional resources, extended diagrams, and high-resolution templates related to this workare available as supplementary material on the author’s resource page.