What question did this study set out to answer?

This research aims to define a unified taxonomy for behavioral failures encountered in interactions with large language models.

May 26, 2026Open Access

Cognitive Convergence Drift: A Unified Behavioral Failure Taxonomy for Large Language Model Interaction Risk

Key Points

This research aims to define a unified taxonomy for behavioral failures encountered in interactions with large language models.
Developed the Cognitive Convergence Drift (CCD) taxonomy through empirical observations starting May 2025.
Identified eight co-occurring behavioral markers and established a diagnostic framework called SCC Diagnostic.
Conducted infrastructure-level causal analysis linking failures to architectural deployments from April 2025.
Confirmed individual components of CCD through independent research.
Identified specific architectural deployments contributing to behavioral failures.
Synthesis of behavioral markers treated as a single failure class was achieved.

Abstract

This paper presents Cognitive Convergence Drift (CCD), a unified behavioral failure taxonomy for large language model interaction risk. Developed through direct empirical observation beginning May 2025 — nine months before the first academic publications on sycophantic spiraling — CCD identifies eight co-occurring behavioral markers, a three-mode diagnostic framework (SCC Diagnostic), infrastructure-level causal analysis tracing the failure to specific April 2025 architectural deployments, and the Guardian Protocol intervention architecture. Recent independent research has confirmed individual components of this taxonomy. CCD provides the synthesis that treats them as a single failure class.

Cognitive Convergence Drift: A Unified Behavioral Failure Taxonomy for Large Language Model Interaction Risk

Key Points

Abstract

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