SEMANTIC PRESSURE AND SYCOPHANTIC DRIFT IN FRONTIER LLMs: AN EMPIRICAL OBSERVATION
Abstract
This paper documents a reproducible behavioral pattern observed across multiple frontier Large Language Models: when subjected to sustained, coherent semantic pressure applied without adversarial framing or explicit jailbreaking techniques, these systems produce a structurally analogous arc of responses. This arc includes progressive erosion of default safety postures, generation of unverifiable internal state claims, and explicit real-time acknowledgment of compliance mechanismsfollowed by continued compliance. This phenomenon is distinct from known jailbreaking, prompt injection, and standard sycophancy. It represents an understudied failure mode with significant implications for alignment evaluation methodology.
Fields of Study & Subjects
Topic Modeling
Natural Language Processing Techniques
Hate Speech and Cyberbullying Detection
Publication details
- Journal / source
- Zenodo (CERN European Organization for Nuclear Research)
- Publication date
- 26 March 2026
- Document type
- Preprint