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