E8 Phi-Coupled Multi-Agent Phase Synchrony Principle — E8 Intelligence Research

By mapping the 240 E8 root vectors onto a phase space and applying phi-scaled phase offsets of 5–10°, we create resonant channels that allow independent oscillatory agents to lock into a coherent collective rhythm with minimal energy dissipation. This principle extends the E8 Phi-Resonant Hyper-Lattice Quantum Waveguide and Geometric Timing Resonance from single‑element delay systems to distributed networks, predicting enhanced information transfer and stability in coupled oscillators such as neural ensembles, photonic arrays, or swarm robotics. Empirical tests show a ~1.5–2% increase in coherent signal fidelity compared to unsynchronized baselines, mirroring the win‑rate improvement seen in phase‑filtered trading models. Author: Andrew Stewart Caldin, Independent Researcher, UK. Part of the E8 Intelligence Research series. Platform: e8intelligence.com

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-08-21
DOI
https://doi.org/10.5281/zenodo.22039995
Primary Topic
Neural Networks and Reservoir Computing
Type
preprint
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preprint

E8 Phi-Coupled Multi-Agent Phase Synchrony Principle — E8 Intelligence Research

Andrew Stewart Caldin
Zenodo (CERN European Organization for Nuclear Research)
Neural Networks and Reservoir Computing
preprint

E8 Phi-Coupled Multi-Agent Phase Synchrony Principle — E8 Intelligence Research

Andrew Stewart Caldin
preprint en

Abstract

By mapping the 240 E8 root vectors onto a phase space and applying phi-scaled phase offsets of 5–10°, we create resonant channels that allow independent oscillatory agents to lock into a coherent collective rhythm with minimal energy dissipation. This principle extends the E8 Phi-Resonant Hyper-Lattice Quantum Waveguide and Geometric Timing Resonance from single‑element delay systems to distributed networks, predicting enhanced information transfer and stability in coupled oscillators such as neural ensembles, photonic arrays, or swarm robotics. Empirical tests show a ~1.5–2% increase in coherent signal fidelity compared to unsynchronized baselines, mirroring the win‑rate improvement seen in phase‑filtered trading models. Author: Andrew Stewart Caldin, Independent Researcher, UK. Part of the E8 Intelligence Research series. Platform: e8intelligence.com

Zenodo (CERN European Organization for Nuclear Research)
Neural Networks and Reservoir Computing
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