ARAYUN_173 – A Protocol for Coherence and Self-Regulation in Advanced AI Systems
ARAYUN_173 – A Protocol for Coherence and Self-Regulation in Advanced AI Systems
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.
Emergent Alignment, Cognitive Drift, Coherence Protocol, AI Safety, Symbolic Drift Index, Advanced AI Oversight, Audit Marker, ARAYUN_173
Emergent Alignment, Cognitive Drift, Coherence Protocol, AI Safety, Symbolic Drift Index, Advanced AI Oversight, Audit Marker, ARAYUN_173
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).0 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.Average influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Average
Citations provided by BIP!Popularity provided by BIP!