Whitepaper

Information-Geometric and Topological Features of AI Behavioral Signatures

A paper describing the 14-module mathematical framework used to construct high-dimensional behavioral signatures from raw outputs.

15 Nov 2024Manuscriptvalidatedframeworkmathematicsfeature-extraction
CIJ Labs

Abstract

This paper describes the mathematical core of the ABIS feature-extraction approach. It explains why one framework is not enough and why multiple mathematical views are combined into a single behavioral signature.

What it establishes

  • Behavioral state is high-dimensional and structurally rich.
  • Multiple mathematical modules capture complementary aspects of behavior.
  • The feature pipeline is a designed measurement layer, not an arbitrary bundle of metrics.

Why it matters

This paper is the clearest bridge between the broader research claims and the underlying measurement philosophy. It explains why the library talks about signatures instead of simple score thresholds.