Geometric Causal Intelligence (GCI): Definition and Distinction from Artificial Intelligence

Description This working paper introduces Geometric Causal Intelligence (GCI), a deterministic intelligence paradigm defined by pre-execution evaluation of causal admissibility under explicit premises. The purpose of this document is to formally define what GCI is, clarify what it is not, and distinguish it conceptually from contemporary Artificial Intelligence (AI), without disclosing operational, mathematical, or implementation details. GCI is presented as an intelligence layer that determines whether a target outcome is structurally reachable from a given initial state under stated constraints, prior to any execution, learning, prediction, or optimization. Unlike AI systems, which rely on probabilistic inference, statistical learning, and heuristic approximation, GCI operates deterministically and conditions all conclusions on explicitly stated premises. This paper emphasizes that GCI does not assert absolute impossibility. Any inadmissibility result is premise dependent and indicates that, under the current assumptions, a given route to an outcome is not viable. If premises or routes are revised, previously inadmissible outcomes may become admissible. In this sense, GCI functions as a corrective intelligence that identifies which assumptions are binding rather than as a system that forecloses possibility. The document positions GCI as a foundational and preceding layer relative to AI systems. In combined architectures, GCI defines the admissible decision space within which AI systems may operate, rendering probabilistic AI optional and subordinate in domains where determinism, correctness, auditability, and safety are required. This working paper is intentionally limited to conceptual definition and positioning. Formal mathematical treatments, performance benchmarks, and implementation details are addressed separately and are not included in this publication. The work establishes terminology, scope, and authorship for Geometric Causal Intelligence and is intended as a reference point for future theoretical, applied, or regulatory discussions. This conceptual foundation is shared to establish paradigm authorship and priority, with detailed technical treatment reserved for subsequent embargoed or controlled-release publications.