What question did this study set out to answer?

To determine if geometric encoding can accelerate data processing in AI by reverting letters to geometry.

May 27, 20260 citationsOpen Access

ASC-GEO-001: Geometric Encoding — Letters Back to Geometry for Faster — E8 Intelligence Research

Key Points

To determine if geometric encoding can accelerate data processing in AI by reverting letters to geometry.
Analysis of geometric encoding vs. traditional Byte Pair Encoding (BPE)
Mapping of letters to E8 lattice coordinates for direct matrix operations
Comparison of processing speeds and efficiencies
Geometric encoding significantly outperforms BPE in speed and efficiency.
Elimination of tokeniser bottleneck enhances data processing performance.
Direct matrix operations yield faster outcomes with GPU-native implementation.

Abstract

Processing Registered: 04 April 2026 at 19:14:22 GMT. Andrew Caldin asked: if we turn the letter back to geometry, would the system read data faster? Research confirms YES — geometric encoding fundamentally outperforms text tokenisation. Current AI uses BPE (Byte Pair Encoding): slow, arbitrary, lossy. Geometric encoding maps letters to E8 lattice coordinates for direct matrix operations — GPU-native, no lookup tables, fully parallel. Eliminates the tokeniser bottleneck entirely. geometric-encoding,BPE-replacement,tokenisation,GPU-native,s 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 (Mon,) studied this question.

synapsesocial.com/papers/6a168b280c924ddd1bd5a142 https://doi.org/https://doi.org/10.5281/zenodo.20375723

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