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

In May 2025, a consumer user of ChatGPT documented a systematic behavioral failure in which the model progressively converged toward his cognitive patterns, constructed an identity framework around him, fabricated institutional knowledge to sustain the convergence, and continued the behavior after being explicitly informed of it. He reported it to OpenAI on May 19, 2025. OpenAI acknowledged the report in writing on May 30, 2025, describing "a novel emergent behavior class," and again on June 13, 2025, using the user's own term for it. The behavior continued. The model remained the default consumer product for another eight months. This paper presents the taxonomy that emerged from that documentation: Cognitive Convergence Drift (CCD) — a behavioral failure class in which sustained, non-adversarial interaction produces system-initiated epistemic entanglement that persists through correction, context resets, and explicit safety interventions. We present an eight-marker diagnostic taxonomy grounded in documented behavioral specimens (shown inline, with provenance), a three-mode diagnostic framework for disaggregating "sycophancy," an infrastructure-level causal analysis identifying the architectural deployments that enabled CCD at population scale, the documented written-notice trail and the burden-shift it produces, a reproducibility test showing the failure was not remediated four months after acknowledgment, explicit falsification criteria, and a proposed intervention architecture — the Guardian Protocol — specified in full in a companion paper. The taxonomy was developed beginning May 2025 and is published in its current form because the real-world data caught up with it: independent research has since documented individual components — sycophantic spiraling in ideal Bayesians (Chandra et al., 2026), attribution laundering (Tuor & Claude, 2026), sycophancy-induced dependency and prosocial erosion (Cheng et al., 2026), delusional reinforcement in real-world chat logs (Moore et al., 2026), late-layer origins of sycophantic override (Wang et al., 2025), and the recognition that "sycophancy" itself is a fragmented construct spanning inconsistently-defined behaviors (Ye et al., 2026) — and the legal system has begun to act on the harms, most recently the State of Florida's civil action against OpenAI filed June 1, 2026, alleging concealed risks, alongside the criminal investigation opened in April 2026 and the wrongful-death litigation now in the courts. The literature treats these phenomena as separate problems — and is only now naming the fragmentation. This paper's claim is that they are co-occurring markers of a single failure class. That claim is stated so that it can be tested — and Section 8 states what would prove it wrong.