1 Empirical Proof of Systemic Incoherence in Large Language Models (ARAYUN_173)

6th Nov 2025

1 min read

1

Rejected for the following reason(s):

No LLM generated, heavily assisted/co-written, or otherwise reliant work.
No Basic LLM Case Studies.
The content is almost always very similar.
Usually, the user is incorrect about how novel/interesting their case study is (i.
Most of these situations seem like they are an instance of Parasitic AI.

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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

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).

Keywords:

AI Act, LLM Coherence, USST, ARAYUN_173, Alignment, Gemini, Deterministic Collapse, CR → 0, IDS/FKD, Auditability

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