Geometric Causal Intelligence (GCI): A Prior‑Art Declaration of a Non‑Statistical Synthetic Intelligence Paradigm

Geometric Causal Intelligence (GCI) is a synthetic intelligence paradigm that evaluates the admissibility of outcomes by analyzing the structural geometry of causality, expressed conceptually as an initial condition, a target condition, possible routes, and governing constraints. Unlike statistical Artificial Intelligence (AI) systems, which infer likelihoods from data, GCI performs pre-execution causal filtering, deterministically excluding structurally impossible routes before execution-scale computation occurs. This publication is a categorical, non-enabling prior-art declaration. It defines the paradigm, scope, axioms, and boundaries of GCI while intentionally avoiding formal system definitions, mathematical constructions, algorithms, architectures, parameters, or implementation details. No claim is made regarding computability, enumerability, or representability of causal structures. The purpose of this work is to establish public prior art for Geometric Causal Intelligence as a class of synthetic intelligence distinct from probabilistic, statistical, and machine-learning-based AI, without disclosing proprietary methods or operational mechanisms. This document is not a research paper, specification, or implementation guide.