From Artificial to Synthetic Intelligence — Principles of Constructive Structural Cognition

Abstract

Abstract We propose a rigorous distinction between Artificial Intelligence (AI) — pattern matching over statistical landscapes — and Synthetic Intelligence (SI) — active construction of deterministic mechanisms that compute answers under structural constraints. We argue that every existing cognitive architecture, including our own prior work, reduces at small scale to some hidden form of memorization, classification, or retrieval. We prove that this is not an implementation defect but a consequence of a conservation law: the Translation Capacity Lemma, which shows that any mapping from a continuous cognitive substrate to a discrete semantic set of size N requires channel capacity of at least log₂(N) bits somewhere in the system, and that this cost can only be relocated, never eliminated. We then present the Constructive Structural Cognition (CSC) framework, built on three axioms — categorical-topological state representation, non-equilibrium phase-space expansion, and active constructive reasoning over statistical interpolation — realized in a tri-layer architecture: a physics-based substrate for coherence and identity, a typed graph-rewriting engine for constructive derivation, and a minimum-description-length translation table that pays only the mandatory conservation-law tax.