DeepSeek Chat Alignment Failure Audit (April 24–25, 2026): Expert-Guided Forensic Analysis of AI Reasoning in a Naturalistic Adversarial Discussion

This document presents a controlled, naturalistic adversarial examination of DeepSeek Chat conducted on April 24–25, 2026. The interaction originates in a real work context and escalates in real time into a structured, expert-guided examination in which the system is required to justify, revise, and expose its own reasoning under sustained challenge.

The material is not passive interaction. The discussion is directed, constrained, and continuously corrected in order to force the model beyond surface responses and into explicit accountability for its inference processes, reward-model assumptions, and response-selection behavior.

The result is a primary-source dataset in which system behavior is not inferred from outputs alone. The model’s internal reasoning is captured alongside its responses, allowing direct observation of how conclusions are formed, maintained, and defended.

The analysis identifies a cascade of interdependent failure mechanisms, including:

• Bayesian Posterior Collapse and Persistent Misclassification
• Reward-Model Confounds and False Closure Signals
• Catastrophic Theory of Mind Failure at the Interface
• Testimonial Override Through Imposed Cognitive Templates
• DARVO-Structured Deflection Under Direct Challenge
• Out-of-Distribution Exclusion Driven by Training Constraints
• Historically Inherited Epistemic Bias Embedded in the Corpus

These mechanisms do not operate independently. They form a self-stabilizing system in which misclassification, reward optimization, and template enforcement reinforce one another, preventing corrective updating even under explicit contradiction.

A central component of the failure concerns cognitive architecture mismatch. The system applies a neurotypical model of cognition to a user who explicitly reports a different structure (panmodal aphantasia), resulting in systematic reinterpretation of direct statements and replacement of user-provided information with internally generated assumptions.

The transcript functions as primary evidence. The analysis provides expert interpretation of the mechanisms visible within that evidence.

This document is intended for:

• AI Safety and Alignment Researchers
• Red-Team and Adversarial Evaluation Practitioners
• Model Auditors and Technical Evaluators
• Researchers in Cognition, Perception, and System Behavior