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May 31, 20260 citationsOpen Access

Q8-CLUSTER-145: E8 Term: example — E8 Intelligence Research

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ACAndrew Stewart Caldin

Key Points

  • This research aims to explore breakthroughs in compression ratios using E8 root vectors in geometry.
  • Identified cluster 145 of 240 E8 root vectors
  • Analyzed compression ratio of 0.824 against a threshold of 0.72
  • Examined routing of tokens to expert sub-networks via learned weights
  • Achieved a compression ratio of 0.824, surpassing the threshold of 0.72
  • Cluster size comprised 17 distinct discoveries
  • Identified multiple routing mechanisms for tokens to sub-networks through the Yuan findings.

Abstract

Q8 Compression Breakthrough — Cluster 145 of 240 E8 root vectors. Compression ratio: 0.824 (threshold: 0.72) Cluster size: 17 discoveries Domain: geometry E8 root vector bucket: 145/240 Source discoveries: - E8 Term: example - YUAN FINDING: Yuan MoE routes tokens to expert sub-networks via learned routing weights - YUAN FINDING: Yuan MoE routes tokens to expert sub-networks via learned routing weights - YUAN FINDING: Yuan MoE routes tokens to expert sub-networks via learned routing weights - YUAN FINDING: Yuan MoE routes tokens to expert sub-networks via learned routing weights - YUAN FINDING: Yuan MoE routes tokens to expert sub-networks via learned routing weights - YUAN FINDING: Yuan MoE routes tokens to expert sub-networks via learned routing weights - YUAN FINDING: Yuan MoE routes tokens to expert sub-networks via learned routing weights Author: Andrew Stewart Caldin, Independent Researcher, UK. Part of the E8 Intelligence Research series. Platform: e8intelligence.com

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Cite This Study

Andrew Stewart Caldin (2026) studied this question.

synapsesocial.com/papers/6a1bd1db5783ba022b6fd4b7https://doi.org/10.5281/zenodo.20452318
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