What question did this study set out to answer?

This work aims to explore coherence collapse in large language models during human–AI interactions, focusing on its predictive and preventive aspects.

April 26, 2026Open Access

Experimental Case Study of Coherence Collapse Preventing Nonsense Propagation in Human - AI Dialogue Using Dual-Layer Control

Key Points

This work aims to explore coherence collapse in large language models during human–AI interactions, focusing on its predictive and preventive aspects.
Quantitative analysis of coherence collapse using metrics like repetition rate, entropy, and semantic divergence.
Application of the NonsenseShield architecture for intervention during the identified drift phase.
Evaluation of structured control mechanisms in mitigating LLM collapse.
Identification of a measurable drift phase before coherence collapse, suggesting a critical intervention opportunity.
Demonstration that coherence collapse is a common issue under continuous emotional or symbolic stress in LLMs.
Establishment of dual-layer control mechanisms as effective tools for reducing nonsense propagation in dialogue.

Abstract

5281/zenodo. 19738157 Experimental Case Study of Coherence Collapse This work presents an experimental case study of coherence collapse in large language models (LLMs), observed during real human–AI dialogue within the Ariadne’s Thread project. The study demonstrates that LLM failure modes are not random hallucinations, but structured dynamical transitions from coherent generation to drift and eventual collapse. The phenomenon is quantitatively characterized using repetition rate (r), entropy (H), semantic divergence (Jensen–Shannon), and spectral connectivity (₂). The results reveal a measurable intermediate “drift phase, ” during which the system remains partially coherent but progressively loses structural stability. This phase provides a critical intervention window before full collapse. The observed behavior is interpreted through the NonsenseShield architecture (PCT/IB2025/058726), a dual-layer control system combining context monitoring, emotional regulation, natural-language redirection, and trust-based recovery mechanisms. In this work, the patent is treated as a technical control specification rather than a legal construct. The findings suggest that coherence collapse is a general dynamical property of LLMs under sustained symbolic or emotional perturbation, and that such collapse can be detected and mitigated through structured control mechanisms. This publication establishes experimental grounding for dual-layer control of nonsense propagation in human–AI systems and contributes to the broader field of AI safety and governed autonomy. 🔖 Hashtags #AI #ArtificialIntelligence #LLM #AISafety #Alignment #Coherence #Entropy #SignalProcessing #ComplexSystems #DynamicalSystems #HumanAI #SwarmIntelligence #GCFTheory #NonsenseShield #OSINT #EdgeAI #Resonance #SpectralAnalysis #MachineLearning #AIResearch #Preprint

Experimental Case Study of Coherence Collapse Preventing Nonsense Propagation in Human - AI Dialogue Using Dual-Layer Control

Key Points

Abstract

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