Geometry vs Binary: Unsolved Mathematics — unsolved problems in geometric topology knot invar — E8 Intelligence Research

Abstract

## Binary Failure → Geometry Solution **Analysis of Findings:** The search results point to a fundamental failure mode in conventional approaches to knot theory: **the inability to compute or approximate knot invariants (like the Jones polynomial, Khovanov homology, or unknotting number) using neural networks or brute-force statistical methods.** Knot invariants are inherently topological—they depend on global, non-local properties of the embedding of a circle in 3-space. Neural networks, which rely on local feature extraction and statistical correlations, fail because: - **Binary/statistical failure:** A neural network trained on crossing diagrams cannot generalize to knots with high crossing numbers or exotic presentations. The unknotting problem (determining if a knot is trivial) is known to be in NP, and no statistical model can reliably solve it—there are "hard" knots where the unknotting number requires deep, non-local geometric reasoning. Brute force enumeration of Reidemeist Author: Andrew Stewart Caldin, Independent Researcher, UK. Part of the E8 Intelligence Research series. Platform: e8intelligence.com