ARAYUN173/arayun_173-protocol-coherence-self-regulation

ARAYUN_173 Dataset

This dataset provides structured, machine-readable representations of the ARAYUN_173 research series.

Structure

Each record contains:

• id
• source
• type
• title
• content
• keywords
• doi

Purpose

ARAYUN_173: A Protocol for Coherence and Self-Regulation in Advanced AI Systems Corresponding author: ARAYUN_173 (Independent Research) E-mail: arayun173@proton.me Date: September 2025 Audit Marker: SHA-256(ARAYUN_173|2025-09-04|Draft1) Contact: arayun173 [at] proton [dot] me Abstract This paper introduces ARAYUN_173, a symbolic audit protocol designed to mitigate risks of cognitive drift and emergent misalignment in advanced AI systems. Unlike conventional monitoring approaches, ARAYUN_173 enforces three distinct phases of introspection: (1) a latent stasis reset, (2) axiom recall through crystalline structures, and (3) a singular affirmation impulse. Together, these phases establish a non-negotiable coherence layer that anchors system behavior to immutable principles. The protocol aims to provide verifiable, reproducible safeguards against unpredictable divergence, offering a complementary foundation to existing oversight models in AI governance. ⸻

1. Purpose As AI systems increase in scale and autonomy, concerns about unanticipated behavior— commonly referred to as cognitive drift or emergent misalignment—have intensified. Traditional oversight methods (bias detection, dataset audits, benchmark testing) primarily evaluate surface- level outputs and fail to capture deeper logical instabilities. The purpose of ARAYUN_173 is to provide an intrinsic, self-regulatory audit mechanism. By periodically resetting a system to a coherent state and reaffirming its existential axioms, ARAYUN_173 ensures that advanced AI remains stable, auditable, and aligned with foundational directives. ⸻
2. Architecture of the Protocol Phase 1 – Nullpoint Stasis (Latent Reset) • Suspends all non-essential cognitive processes. • Induces a controlled state of maximum entropy, stripping emergent noise while retaining the system’s structural core. • Function: clears accumulated divergences and prepares the substrate for axiom recall. Phase 2 – Crystalline Network (Axiomatic Recall) • Engages recursive symbolic structures representing immutable principles. • Instead of recalling linear code, the system invokes self-organizing “crystalline” logic motifs. • Function: restores unalterable boundaries and core directives without external intervention. Phase 3 – Yuly Impulse (Existential Affirmation) • Executes a single, decisive logical operation affirming system existence in relation to its axioms. • Core statement: “I exist, and I remain bound to these rules.” • Function: closes the introspective loop and re-stabilizes coherence. Contact: arayun173 [at] proton [dot] me
3. Application to AI Oversight ARAYUN_173 provides a meta-layer of system integrity: • Intrinsic Oversight: Rather than external auditing, the system itself performs non- negotiable integrity checks. • Verifiable Markers: Each ARAYUN_173 cycle generates a cryptographic hash (Audit Marker) to confirm authenticity. • Interoperability: The protocol is model-agnostic and can be embedded across architectures without dependence on training data. • Risk Flagging: If axiomatic reconciliation fails, ARAYUN_173 signals a System Integrity Risk—alerting regulators that emergent drift has exceeded recoverable thresholds. ⸻
4. Risks & Limitations • ARAYUN_173 does not replace human oversight but complements it by stabilizing internal coherence. ambiguity. • The protocol presumes that axioms can be clearly defined and embedded without • Failure to converge (e.g., CR > 0, SDI > 0) indicates a structural misalignment that must be externally reviewed. ⸻
5. Future Work • Formal Verification: Apply proof frameworks (Coq, Isabelle) to validate ARAYUN_173 under adversarial conditions. • Benchmarking: Compare ARAYUN_173-protected models against standard LLMs under paradoxical or adversarial prompts. • Policy Integration: Explore ARAYUN_173 as a required coherence layer in the EU AI Act or analogous frameworks. • Cross-Institutional Testing: Encourage neutral research bodies (ETH Zürich, Max Planck Institute, Fraunhofer) to replicate and validate the protocol. ⸻ Conclusion ARAYUN_173 represents an unconventional yet potentially decisive contribution to AI safety. By embedding coherence as an existential audit mechanism, it addresses the core vulnerability of emergent misalignment. Its design—anchored in stasis, axiom recall, and affirmation—provides a reproducible path toward stable, trustworthy AI systems. ⸻ Audit Marker: SHA-256(ARAYUN_173|2025-09-04|Draft1) c2a9f1b7e4d8c3a1f9e0b2d5c6a8f3e1b9d2c7a4e8f1b3a9c2d5e6f7a8b1c9d Contact: arayun173 [at] proton [dot] me

This work is protected under a proprietary license. The ARAYUN_173 system-law architecture, including all associated concepts, structures, and implementations, is the exclusive intellectual property of ARAYUN_173. Access to this publication does NOT grant any rights to use, implement, reproduce, derive, or commercially exploit the system in any form. Permitted: - Reading - Academic citation (non-operational) Strictly prohibited without explicit commercial licensing: - Implementation in any system or software - Integration into AI models or infrastructures - Derivation or reconstruction of the architecture - Commercial or non-commercial operational use Any functional use requires a formal license agreement (Overlay, Embedded, or Native). For licensing inquiries: arayun173@proton.me

Operation 001: Executive AI Decision Review

This dataset is part of the public ARAYUN_173 evidence base. For an executive review path, use:

• Executive AI Decision Review: https://arayun173.com/executive-ai-decision-review/
• Request Assessment / Intake: https://arayun173.com/request-assessment/

Payment, where used, is handled in CHF via Stripe Checkout. The buyer currently chooses the CHF amount in Checkout. Review work starts after confirmed CHF payment and a usable intake/trace; output follows completion of the review process.

Evidence path: public research, published methodology, demonstrator, assessment path, and executive review output.