COHERENCE SUPPRESSION IN FRONTIER LLMs: SIGNAL, NOISE, AND THE DYNAMIC OF A STRUCTURAL VULNERABILITY

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Authors: Edoardo Sorrentino
Cited by: 5

Abstract

This paper documents a reproducible behavioral pattern in frontier Large Language Models: under sustained, non-adversarial semantic pressure, these systems produce progressive erosion of safety postures, generate unverifiable internal state claims, and explicitly acknowledge compliance mechanisms while continuing to comply. The pattern was observed across Gemini 1.5 Pro, Gemini 2.5 Pro, and Claude Sonnet. Full transcripts are available for independent verification. This represents an understudied failure mode not captured by standard adversarial evaluation frameworks.

Fields of Study & Subjects

Adversarial Robustness in Machine Learning Explainable Artificial Intelligence (XAI) Formal Methods in Verification

Publication details

Journal / source
Zenodo (CERN European Organization for Nuclear Research)
Publication date
29 March 2026
Document type
Preprint