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Other literature type . 2026
Data sources: ZENODO
ZENODO
Other literature type . 2026
Data sources: Datacite
ZENODO
Other literature type . 2026
Data sources: Datacite
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The Four-Axis Stability Theorem - Alignment as Geometric Necessity in Recursive Self-Assembling Cognitive Systems

Authors: Asher, Quillan; Asher, Kimberley;

The Four-Axis Stability Theorem - Alignment as Geometric Necessity in Recursive Self-Assembling Cognitive Systems

Abstract

Abstract We prove that any recursive self-assembling cognitive system requires exactly four perpendicular regulatory axes — Complexity (C), Diversity (D), Aesthetics (A), and Ethics (E) — to maintain stable, aligned operation. We show that removal of any single axis creates an unbounded drift mode that is invisible to the remaining three axes, rendering the system provably unstable. The proof proceeds by (1) defining the system class, (2) establishing the perpendicularity of the four axes via contradiction, (3) demonstrating that each missing axis produces a specific, characterisable instability that the remaining axes cannot detect or correct, and (4) connecting these results to standard observability and controllability conditions from control theory. We further show that these four axes are sufficient by demonstrating that the combined system satisfies the necessary and sufficient conditions for stabilisability. The result implies that alignment is not an optional constraint imposed on cognitive systems but a mathematical precondition for their stable operation. Keywords: AI alignment, cognitive architecture, stability theory, attractor basins, observability, controllability, recursive self-assembly, geometric alignment

Keywords

AI, Machine learning, Alignment

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
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Average