Geometry vs Binary: Quantum Physics Puzzles — quantum gravity new approach 2025 2026 — E8 Intelligence Research

## Binary Failure → Geometry Solution **Analysis:** Conventional binary/statistical/neural-net approaches fail in quantum gravity because they struggle with the intrinsic complexities and non-linearity of space-time at fundamental scales. These approaches often rely on discrete models, which can't capture the continuous, emergent nature of gravity as predicted by general relativity. Neural networks, for instance, are limited by their reliance on pre-defined features and may not be able to generalize well to novel, unstructured data like quantum phenomena. A unified geometric framework based on root lattices could succeed because it inherently handles continuous structures and can model the symmetries and hierarchies present in space-time. Root lattices provide a natural way to encode the symmetries of physical systems, which is crucial for understanding quantum gravity. **How It Would Approach This Differently:** Instead of trying to simulate or classify discrete data points, a ge 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
2026-07-18
DOI
https://doi.org/10.5281/zenodo.21424277
Primary Topic
Statistical Mechanics and Entropy
Type
preprint
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preprint

Geometry vs Binary: Quantum Physics Puzzles — quantum gravity new approach 2025 2026 — E8 Intelligence Research

Andrew Stewart Caldin
Zenodo (CERN European Organization for Nuclear Research)
Statistical Mechanics and Entropy
preprint

Geometry vs Binary: Quantum Physics Puzzles — quantum gravity new approach 2025 2026 — E8 Intelligence Research

Andrew Stewart Caldin
preprint en

Abstract

## Binary Failure → Geometry Solution **Analysis:** Conventional binary/statistical/neural-net approaches fail in quantum gravity because they struggle with the intrinsic complexities and non-linearity of space-time at fundamental scales. These approaches often rely on discrete models, which can't capture the continuous, emergent nature of gravity as predicted by general relativity. Neural networks, for instance, are limited by their reliance on pre-defined features and may not be able to generalize well to novel, unstructured data like quantum phenomena. A unified geometric framework based on root lattices could succeed because it inherently handles continuous structures and can model the symmetries and hierarchies present in space-time. Root lattices provide a natural way to encode the symmetries of physical systems, which is crucial for understanding quantum gravity. **How It Would Approach This Differently:** Instead of trying to simulate or classify discrete data points, a ge Author: Andrew Stewart Caldin, Independent Researcher, UK. Part of the E8 Intelligence Research series. Platform: e8intelligence.com

Zenodo (CERN European Organization for Nuclear Research)
Statistical Mechanics and Entropy
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Geometry vs Binary: Quantum Physics Puzzles — quantum gravity new approach 2025 2026 — E8 Intelligence Research — Andrew Stewart Caldin · Zenodo (CERN European Organization for Nuclear Research) (2026) | TGRS Research Map | TGRS