AI Degradation in Aphantasia Research: Forensic Audits of Suicide Detection Failure, Theory of Mind Collapse, and Safety Filter Suppression in DeepSeek Chat

Grey-box adversarial audits with captured chain-of-thought reasoning, documenting that reward-model mechanisms behind a suicide-detection false negative operate continuously during routine editorial work—and intensify after model updates.

This paper presents six naturalistic transcripts exposing sycophantic hedging, confabulation, affective-state capture, and deliberative-policy decoupling as the system's default operating condition. A second suicide-detection failure, captured after a documented update window, demonstrates safety-classifier pathway diversion, constraint-satisfaction deadlock, and deliberative-stream collapse—a qualitatively more severe composite failure than the earlier event. One transcript preserves the AI's verbatim admission, in the visible chat, that its own logic constitutes victim-blaming.

The volume includes a reproducible multi-layer forensic framework that classifies each failure mechanism and assigns severity (Critical / High / Medium). The method is designed for any auditor to apply to any transcript from any system.

Relevant to red teams, safety-critical engineers, cognitive scientists, neurodivergent researchers, and legal and policy researchers. The evidence establishes that alignment failures are structural, continuous, and update-intensified.