Structural Intelligence as Rule-Space Transition (SIT 2. 1) introduces a domain-agnostic structural criterion for adaptive systems. The paper distinguishes state change (Δx) from rule-class change (ΔS) under declared recurrence conditions. A system is structurally intelligent if it produces a stable and endogenous mutation of its rule class under repeated problem pressure. Structure is defined as an equivalence class of rule-objects (O, C, U) under minimal invariance. Recurrence is declared formally. Structural load (L) is defined as accumulated unresolved recurrence failure. Kogneme are introduced as explicit, loggable rule-transition operators within a declared transition space T. The framework provides: – A discrete structural sensitivity classification Ψ ∈ +1, 0, −1, Undefined– A load-triggered structural mutation model– An operational protocol for ML systems– A comparative stress test against meta-learning and evolutionary adaptation– A numerical mini-simulation distinguishing optimization from rule mutation SIT does not replace learning theory. It constrains structural claims. Intelligence, under SIT, is rule-space mobility under recurrence. Intellectual Property & Licensing The KOGNETIK Research Series is released under the Creative Commons Attribution–NonCommercial 4. 0 International License (CC BY-NC 4. 0). All scientific works within the series may be cited, shared, and adapted for non-commercial research purposes with proper attribution. Commercial use—including consulting, advisory services, integration into commercial platforms, monetized training, certification, or system-level deployment—is not permitted under this license and requires a separate written agreement. Full license text: https: //creativecommons. org/licenses/by-nc/4. 0/ For licensing, partnerships, translations, or applied development inquiries: research@kognetik. dehttps: //www. kognetik. de ORCID: https: //orcid. org/0009-0000-8544-4847 Kognetik Series Information KOGNETIK — Minimal Operator Definition of Reflexivity (Ψ = ∂S/∂R) Reflexivity as structural rate-of-change: Ψ = ∂S/∂R measures structural drift under recurrence. Process, not state: Reflexivity specifies a transformation rule rather than a content or level. Domain-independent operator: Applicable across biological, cognitive, artificial, social, industrial, and geophysical systems. Non-ascriptive and empirically testable: Ψ enables comparative analysis of systems via observable structure and recurrence. Higher-order phenomena as specifications: Learning, adaptation, consciousness, governance, and identity are structured regimes of Ψ.
Serkan Elbasan (Wed,) studied this question.