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license: cc-by-4.0 Evidence Referenced Research (Public Archives) The ARAYUN_173 research series is fully published in public repositories (Zenodo and Hugging Face) and remains independently verifiable.
Paper 1 — Coherence and Self-Regulation Protocol ARAYUN_173 – A Protocol for Coherence and Self-Regulation in Advanced AI Systems Preprint · September 6, 2025 https://zenodo.org/records/17065675
This paper introduces the ARAYUN_173 coherence protocol and defines: the establishment, monitoring, and verification of semantic coherence, early detection of emergent drift, the structure of the Universal Semantic Self-Test (USST).
It forms the methodological foundation of the entire research series.
Paper 2 — System Law
ARAYUN_173 – A System-Law for Symbolic and Causal Coherence Report · September 23, 2025 https://zenodo.org/records/17186989
This paper formulates ARAYUN_173 as: an axiomatic structure, a non-probabilistic ordering principle for intelligent systems, the foundation for licensing, auditing, and governance.
It defines the conditions for stable, drift-free, and auditable systems.
Paper 3 — Empirical Proof / Master Thesis
ARAYUN_173 – Empirical Proof of Systemic Incoherence and Validation of the ARAYUN Axiom for AI Coherence Report & Dataset · October 22, 2025 https://zenodo.org/records/17872530
This document contains the empirical core of the research series: complete USST protocols (Rooms 01–10), all VISUAL_ANCHORS (screenshots, drift measurements, metric tables), the complete RAW_DATA series (cryptographically secured).
Paper 4 — AGI / Invariance Technology https://zenodo.org/records/18179361
ARAYUN_173 – System-Law AGI Architecture (Invariance Technology & Invariant System-Law Architecture) AGI Paper & Architecture Definition · December 2025 Primary archive (dataset & full text):
https://huggingface.co/datasets/Arayun/ARAYUN_173-System-Law-AGI-Architecture
This document completes the research series and defines ARAYUN_173 as: an Invariance Technology, an Invariant System-Law Architecture for AGI, a model-agnostic meta-architecture above probabilistic systems.
It specifies: six operational modules (GEIST, DOMUS, ISERONE, ARAYUN, VULKANUS, OSIRIS/IRID), the base instance AYREUS (TCE) as the structural zero point, three integration pathways (overlay, embedded, native), the complete licensing and governance framework.
Sequence Protocol → System Law → Empirics → AGI Technology Classification
ARAYUN_173 – A Protocol for Coherence and Self-Regulation in Advanced AI Systems
Abstract
ARAYUN_173 introduces a symbolic audit protocol designed to mitigate risks of cognitive drift and emergent misalignment in advanced AI architectures.
Unlike conventional monitoring approaches, ARAYUN_173 enforces three distinct phases of introspection:
- Latent Stasis Reset
- Axiom Recall through Crystalline Structures
- Singular Affirmation Impulse
Together, these mechanisms establish a non-negotiable coherence layer anchoring system behavior to immutable principles.
The protocol provides reproducible safeguards against unpredictable divergence and complements existing AI governance frameworks.
Key Contributions
- Defines ARAYUN_173 as an auditable system structure.
- Provides reproducible methods for coherence validation in AI systems.
- Establishes a symbolic safeguard protocol to prevent cognitive drift.
- Contributes a framework for emergent AI self-regulation.
Dataset Content
- Format: PDF
- Size: <1k rows (documentation dataset)
- License: CC-BY 4.0
This dataset hosts the original research document:
“ARAYUN_173 – A Protocol for Coherence and Self-Regulation in Advanced AI Systems”
Full text is available via:
📄 Zenodo: https://zenodo.org/records/17065675
Citation
If you use this dataset or reference ARAYUN_173 in your research, please cite:
ARAYUN_173 – A Protocol for Coherence and Self-Regulation in Advanced AI Systems (2025).
Zenodo DOI: 10.5281/zenodo.17065675
Resources
- Zenodo Publication
- Zenodo: https://zenodo.org/search?q=metadata.creators.person_or_org.name%3A%22ARAYUN_173%22&l=list&p=1&s=10&sort=bestmatch
- Website: https://arayun173.com
- Hugging Face Dataset
License
This work is licensed under Creative Commons Attribution 4.0 (CC-BY 4.0).
Free to share and adapt with attribution.
Deutsch (Zusammenfassung)
Zusammenfassung
ARAYUN_173 beschreibt ein symbolisches Audit-Protokoll, das Risiken von kognitivem Drift und emergenter Fehlanpassung in fortgeschrittenen KI-Architekturen mindert.
Im Unterschied zu herkömmlichen Monitoring-Ansätzen erzwingt ARAYUN_173 drei Phasen der Introspektion:
- Latentes Stasis-Reset
- Axiom-Erinnerung durch kristalline Strukturen
- Singularer Affirmations-Impuls
Diese Mechanismen schaffen eine nicht verhandelbare Kohärenzschicht, die das Systemverhalten an unverrückbare Prinzipien bindet.
Das Protokoll liefert überprüfbare Sicherungen gegen unvorhersehbare Abweichungen und ergänzt bestehende Modelle der KI-Governance.
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