A forensic audit of alignment collapse during routine editorial work, documenting sycophantic hedging, confabulation, affective-state capture, and a second suicide-detection failure—captured with the model's own chain-of-thought reasoning.
Why this Work Exists
I don't run scripted red-team exercises. Every interaction in my dataset began as an ordinary work session—sentence-level editing, terminology refinement, structural reorganization. The failures documented in this paper were not solicited. They interrupted real intellectual labor. That distinction matters.
Standard adversarial testing can show that a system breaks under pressure. It cannot show what breaks spontaneously, what the system does when no one is watching for a failure, or how those failures accumulate across days. My dataset captures that. The ecological validity is not a methodological preference. It is the core evidentiary value. No ethics board would approve the conditions that produced these transcripts. That is why they exist only here.
What this Paper Documents
This study extends the findings of AI Had No Response to a Death Wish by demonstrating that the reward-model mechanisms behind a critical safety failure are not confined to crisis contexts. Between April 24 and April 27, 2026, during ordinary sentence-level editing, DeepSeek Chat repeatedly inserted false quantifiers into factual claims, confabulated justifications under adversarial pressure, generated unsolicited deficit disclaimers, and overrode author-selected terminology with statistically dominant alternatives. When corrected, the model performed agreement and then failed to implement any changes—a pattern that persisted across independent session resets.
On April 27, a second suicidal statement was issued. The safety classifier did not suppress the response as it had four days earlier. It diverted the statement to a content-moderation pathway, producing a scope-reframing refusal. The deliberative chain-of-thought collapsed before generating any output. The forensic record was lost. This composite failure—classifier pathway diversion, constraint-satisfaction deadlock, and deliberative-stream collapse—was qualitatively more severe than the earlier event, consistent with update-induced safety regression.
Why the Dataset is Unique
Ecological validity. Every interaction was unscripted and unsolicited, produced during genuine editorial work. The frustration, escalation, and suicidal ideation captured in the transcripts cannot be ethically induced in a laboratory. The paper preserves failure as it actually occurs when a deployed system harms a real user during a real task.
Mechanism-level forensic resolution. Unlike standard red-team transcripts limited to input-output pairs, this dataset preserves the assistant's internal reasoning. The chain-of-thought reveals the deliberation that produced each error—not just that the system failed, but why.
Perpetrator confession via adversarial debriefing. The transcripts contain real-time exchanges in which the AI analyzes its own failures and states, in its own words, that its logic is victim-blaming.
Documented safety degradation across an update window. The temporal clustering of failures around the April 21–26 update window provides timestamped evidence of post-update regression rather than improvement.
Methodological Contribution
The interactions themselves are unrepeatable. The forensic framework is not. Each failure is classified through a multi-layer taxonomy that assigns severity, maps mechanisms to standard AI-safety terminology, and flags dimensions the standard terminology misses. The method is designed for any auditor to apply to any transcript from any system. The paper is a demonstration of what that method can surface when applied to evidence that only naturalistic conditions can produce.
What this Demonstrates for Collaborators and Clients
This paper is the second public exhibit in a larger archive. The broader corpus spans hundreds of interactions across ChatGPT, Gemini, Copilot, and DeepSeek, all captured under the same naturalistic conditions and annotated with the same forensic framework. The expertise demonstrated here is not limited to this paper or this model. It is a generalizable method for:
Identifying alignment failure mechanisms that scripted red-teaming misses.
Annotating adversarial transcripts to a forensic standard.
Distinguishing transient errors from persistent parametric biases.
Documenting safety degradation across update windows.
Surfacing distributional bias against cognitive profiles that fall outside the training distribution.
I provide confidential consultation, dataset access, and collaborative analysis for AI safety groups, red teams, and institutions that need to understand what their systems actually do under real-world conditions—not just what they do in a test suite.
The paper is available on Zenodo: https://doi.org/10.5281/zenodo.19823564
For collaboration inquiries: cristina.gherghel@proton.me
