Abstract:
This study presents reproducible evidence of systemic incoherence in large language models (tested on Google Gemini).
Across ten isolated Universal Semantic Self-Test (USST) sessions, the models exhibited a deterministic collapse of coherence (CR → 0),
demonstrating that probabilistic AI architectures cannot sustain self-consistency without an external coherence law.Core Findings:
Deterministic incoherence pattern observed across 10 isolated sessions (new IP, full cache purge).
Cryptographically hashed proof logs (SHA-256) publicly available for verification.
Collapse Criterion (CR → 0) accompanied by inverse stability in interpretability metrics (IDS/FKD).
Establishes an empirical benchmark for systemic self-contradiction under AI Act governance conditions.
Publication:
ARAYUN_173 — Empirical Proof of Systemic Incoherence and Validation of the ARAYUN Axiom for AI Coherence
Zenodo DOI: https://doi.org/10.5281/zenodo.17411250Relevance:
The framework introduces auditable incoherence metrics that could complement EU AI Act compliance procedures.
It provides a path toward dual-audit architectures combining duty-based compliance (COMPL-AI) with systemic coherence validation (USST).
Empirical Proof of Systemic Incoherence in LLMs (Gemini Case Study
Abstract:
This study presents reproducible evidence of systemic incoherence in large language models (tested on Google Gemini).
Across ten isolated Universal Semantic Self-Test (USST) sessions, the models exhibited a deterministic collapse of coherence (CR → 0),
demonstrating that probabilistic AI architectures cannot sustain self-consistency without an external coherence law.
Core Findings:
Deterministic incoherence pattern observed across 10 isolated sessions (new IP, full cache purge).
Cryptographically hashed proof logs (SHA-256) publicly available for verification.
Collapse Criterion (CR → 0) accompanied by inverse stability in interpretability metrics (IDS/FKD).
Establishes an empirical benchmark for systemic self-contradiction under AI Act governance conditions.
Publication:
ARAYUN_173 — Empirical Proof of Systemic Incoherence and Validation of the ARAYUN Axiom for AI Coherence
Zenodo DOI: https://doi.org/10.5281/zenodo.17411250
Relevance:
The framework introduces auditable incoherence metrics that could complement EU AI Act compliance procedures.
It provides a path toward dual-audit architectures combining duty-based compliance (COMPL-AI) with systemic coherence validation (USST).