Where AGI Confuses Change: A Recurrence–Structure Diagnostic for Autonomous Self-Improvement Claims in Artificial Intelligence

Artificial General Intelligence (AGI) discourse frequently interprets capability growth as evidence of structural change in machine intelligence. However, many claims of “recursive self-improvement” fail to explicitly declare the recurrence conditions under which such structural change could be evaluated.

This paper introduces a recurrence–structure diagnostic grounded in the KOGNETIK framework. The method separates state variation (Δx) from rule-class mutation (ΔS) under declared recurrence conditions and enforces Rule–State Separation (RSSA) as a structural admissibility gate.

The protocol requires explicit declaration of:

• recurrence operator (R),

• structure object defined as an equivalence class of rule-objects (S := [g]~),

• rule–state separation conditions (RSSA),

• structural regime output (Ψ).

Applying this diagnostic reveals that many AGI self-improvement narratives resolve either into state variation within a fixed rule class (Ψ = 0) or into structural undecidability when recurrence and structure are not formally declared.

The contribution of this paper is methodological rather than evaluative: it provides a reproducible audit protocol that research laboratories can use to test structural claims about AI systems under explicit recurrence conditions.

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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.

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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.